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Difference between revisions of "Dana Ullman"
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He is a regular contributor to The Huffington Post.<ref>http://www.huffingtonpost.com/news/dana-ullman/</ref>
==Bibliography==
*''Everybody's Guide to Homeopathic Medicines'' (with Stephen Cummings, MD), 1984, 2004. ISBN 0-87477-843-3.
*''Discovering Homeopathy: Medicine for the 21st Century'', 1991. ISBN 1-55643-108-2
*''Homeopathic Medicines for Children and Infants'', 1992. ISBN 0-87477-692-9
*''The Consumer's Guide to Homeopathy'', 1995. ISBN 0-87477-813-1
*''The Steps to Healing: Wisdom from the Sages, the Rosemarys, & the Times'', 1999. ISBN 1-56170-657-4.
*''Essential Homeopathy'', 2002. ISBN 1-57731-206-6
*''The One-Minute (or so) Healer'', 2004. ISBN 1-55643-494-4
*''The Homeopathic Revolution: Why Famous People and Cultural Heroes Choose Homeopathy'', October, 2007. ISBN 1-55643-671-8
===Other writings===
Ullman's letters and writings have appeared in ''The Western Journal of Medicine'', ''Social Policy'', ''Utne Reader'', ''The Futurist'', ''The Arthritis Foundation's Guide to Alternative Medicine'', Atlanta: Arthritis Foundation, (J. Horstman) 1999 and ''The Reader's Digest Family Guide to Natural Medicine''.<ref> http://www.homeopathic.com/main/bio_dana.jsp |title=Homeopathic Educational Services - Dana Ullman, M.P.H. |accessdate=2008-01-26</ref>
*[http://www.homeopathic.com/ Dana Ullman's Web site]
Gregory Dana Ullman (born December 22, 1951) is an American author, publisher, journalist, and proponent in the field of homeopathy.
Ullman received his Master of Public Health (MPH) from the University of California at Berkeley, and has since taught homeopathy and integrative health care.[1] Ullman served as an instructor in homeopathy at the University of California at San Francisco, and as member of the Advisory Council of the Alternative Medicine Center at Columbia University's College of Physicians and Surgeons.[2] In previous years he has been the chairperson for the National Center for Homeopathy's Annual Conference, and has been consulted by Harvard Medical School's Center to Assess Alternative Therapy for Chronic Illness.[3] He is also a regular speaker at universities, medical schools, pharmacy schools, and hospitals.[3]
He is a regular contributor to The Huffington Post.[4]
1.1 Other writings
Everybody's Guide to Homeopathic Medicines (with Stephen Cummings, MD), 1984, 2004. ISBN 0-87477-843-3.
Discovering Homeopathy: Medicine for the 21st Century, 1991. ISBN 1-55643-108-2
Homeopathic Medicines for Children and Infants, 1992. ISBN 0-87477-692-9
The Consumer's Guide to Homeopathy, 1995. ISBN 0-87477-813-1
The Steps to Healing: Wisdom from the Sages, the Rosemarys, & the Times, 1999. ISBN 1-56170-657-4.
Essential Homeopathy, 2002. ISBN 1-57731-206-6
The One-Minute (or so) Healer, 2004. ISBN 1-55643-494-4
The Homeopathic Revolution: Why Famous People and Cultural Heroes Choose Homeopathy, October, 2007. ISBN 1-55643-671-8
Ullman's letters and writings have appeared in The Western Journal of Medicine, Social Policy, Utne Reader, The Futurist, The Arthritis Foundation's Guide to Alternative Medicine, Atlanta: Arthritis Foundation, (J. Horstman) 1999 and The Reader's Digest Family Guide to Natural Medicine.[5]
↑ http://www.alumni.berkeley.edu/Alumni/Cal_Monthly/February_1999/QA_with_Dana_Ullman.asp | accessdate = 2008-01-22
↑ Dana Ullman, - Healthworld Online - HealthWorld Online, HealthWorld Online - Natural health and Alternative medicine information, products and services.. Retrieved on 2008-01-25.
↑ 3.0 3.1 Penguin Group USA. About Dana Ullman. Retrieved on 2008-01-24.
↑ http://www.huffingtonpost.com/news/dana-ullman/
↑ http://www.homeopathic.com/main/bio_dana.jsp |title=Homeopathic Educational Services - Dana Ullman, M.P.H. |accessdate=2008-01-26
Dana Ullman's Web site
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US Automotive News
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Home › Blog › Auto › Aston Martin's architect on how to make technology beautiful – The Verge
Aston Martin's architect on how to make technology beautiful – The Verge
Posted: Sunday, February 05, 2017
Marek Reichman is the architect of Aston Martin's modern day form language, the basis for what is arguably the world's most beautiful car brand. The 104-year old company has a legacy for vehicles that have sinuous bodies and enduring cool, and has long been a particular point of British pride. Look no further than the romance of James Bond's original DB5 in the 1964 film Goldfinger. When Aston Martin celebrated its centenary in 2013, thousands gathered on the green lawns of Kensington Gardens in central London to fawn at 550 historical and posh cars including elegant coachbuilt Zagatos and the iconic Bond car collection, in total worth well over a $1 billion dollars.
It might be surprising then that a company so steeped in its heritage would be enthusiastic about how technology is impacting its design plans for its second century.
I sat down with Reichman, Design Director and Chief Creative Officer, in Carmel-by-the-Sea, California, to talk about how new technology has made car design a more exciting gig in the last few years. Reichman joined Aston Martin in 2005 after serving as a designer at Ford Motor Company. He also had a hand in the Rolls-Royce Phantom and the Range Rover MKIII in past designer roles. Aston Martin has introduced the V12 Vanquish Volante, a DBX Concept, and the Vulcan under his watch.
Tall and lean, with spiky red hair, Reichman has the enthusiasm of an earnest professor — he also teaches car design at the Royal College of Art. Reichman spends between much of his time in Gaydon in northern England, where Aston Martin is based, and in many ways represents the way Aston Martin is hedging its fine pedigree with edgy advances into sports cars of the future.
Its lineup of new products starts with the DB11, which is now on the streets and its next feat, a highly technical collaboration with Red Bull, AM-RB 001. "This is the car that sets the tone for the next century in terms of technology and our competitive nature. The one thing we've never been recognized for in the past is being the outright fastest," Reichman said.
Aston Martin AM-RB001
Photo by Vlad Savov / The Verge
How do you make technology tasteful in a beautiful car? What do you put inside?
If there's anything Aston Martin lacked in the past people might argue that it's technology. Now, we might add that we have the best technology in the world. It's Daimler-based electrical architecture. It's your Intel chip. It's the connectivity. It's the driver control units. It's the safety units in the cars. It's how the car talks to the outside world. It's how you become autonomous and that's embedded in our cars.
Technology allows you more freedom. It's not constrained. In the past you were constrained with a screen because you had to put 12 buttons inside of the the car, and initially there was no screen. But now these devices will be so much more with voice recognition, recognizing retina, noticing your movements, and gesture control. All of those things are giving design more freedom in terms of beauty of the object. We make beautiful cars and it's allowing the cars to have more beauty, to be unconstrained by technology.
No one quite knows how the introduction of autonomous cars will unfold. You kind of have to plan for every scenario. How do you do that?
The whole idea of autonomy doesn't mean it has to look it from the outside of the car. Potentially it will look different on the inside of the car, because you'll have more free time in the inside of the car. Maybe it's more like an airline lounge or your living room, but for us the object is something of desire. Customers want desirability even if its autonomous or a drone. That's part of the ingredients to feed a designer. What a designer is really good at is finding an application for technology or finding the technology to get the idea into production. For example, designers will google "hyper bendable material", and find that there's one funky college in Princeton that makes nanostructures. Designers will find a way to make something happen, the more you give them an impossible task, the better the solutions.
I think the car industry is incredibly innovative. Silicon Alley might argue, but when they think about developing cars they are hiring car people. Other than airplanes that are made in smaller numbers, there isn't a more complex product that exists. The car is so complex, when you add connectivity and software and hardware, it becomes a hyper computer that can do so many different things. You've got to make it repeatedly with quality and it's got to save your life. If you launch something and your software isn't fully developed you destroy your business.
Do you have a relationship with different tech companies?
One of the reasons we partnered with Red Bull Technologies and we're now doing a car together, is that they have material science technology, aerodynamics and Adrian Newey is probably the world's greatest aerodynamics engineer stroke designer. He's won more F1 championships than anyone else. Through material science it's through big data and how it drives and how it feels. It's connectivity. We have a huge relationship with Dow on the chemical side of things. Dow is important because they make materials, they make chemicals, they make bonding solutions. Making something lighter and stiffer means you can be more efficient. Daimler, the reason they have 5 percent shareholding in Aston Martin, is that we have an open door to their future technologies. Daimler, just in terms of electric and architecture that sits in DB11 is probably 2 billion euros worth of investment. They have engines, future technology, autonomous technology, and camera technology. And Bosch engineering who are software developers. It's not heavy engineering in terms of mechanical parts, but it's really important in terms of stability and activity and electrification and electric propulsion. Because we're small and independent it means you can pick and choose as well.
Is it getting more difficult to focus on form when so much of design is about integrating technology?
No, because the form has to support the function. No other Aston Martin has done it. Aluminium became the choice over steel, and they responded by making a thinner steel structure. And then carbon fiber and composites came in the game. Everything becomes lighter, easier to produce. Technology moves on and the cost base moves down. Carbon fiber composites will do the same. All the costs are coming down. The first autonomous cars used four camera units and they were hundreds of thousands of dollars. They are about $800 now and once they come down to $200 mark, you can have all those cameras in any car. As technology improves and becomes much more mainstream, you always find a way to make it more cost efficient. That's why the industry is so exciting right now. Who would have thought the car industry would be interested in Silicon Valley right now, but because of Google, because of Apple, because of Microsoft, because of Uber that's happening. Change is great to inspire great design.
Do you have to go to Silicon Valley to get that inspiration?
The UK used to have only one region the old automotive world and that was the Midlands. But now we have north of England, the south, and different technology and different supply bases. The UK is renowned for hi fidelity and some of the best high end micro chips. The Formula One and aerospace industries are feeding into our world. Marine technology has been building carbon fiber boats long before F1 technology.
Are you designing with autonomy in mind?
We are not testing. How much do we employ it? Should an Aston Martin be autonomous? We have to design and innovate for a future we're not quite sure of. We'll be a fast adopter, but we're not going to innovate in that way. Our innovation is in car control and driver control in terms of feeling and feedback. It's about making sports car more effective, efficient, capable, comfortable. We'll be a fast follower with something like an autonomous car, something that has 360 adaptive radar. The core of what we are is to make handbuilt luxury sports cars that are highly competitive. You don't necessarily want a sports car to be autonomous. It might someday be important, but right now it's more about the language of design, the technology that's there and with DB11, it's a grand touring sports cars. It's about the driver being alert and being relaxed and comfort on a longer journey. The more relaxed and comfortable you are the more you can enjoy the journey.
Photography by Julia LaPalme / The Verge
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Buffett's Berkshire to buy Dominion Energy gas assets for $4 billion
06 July, 2020 • News In English
Berkshire Hathaway Inc said its energy unit will buy Dominion Energy Inc's (D.N) natural gas transmission and storage network for $4 billion, helping billionaire Chairman Warren Buffett reduce his conglomerate's cash pile while letting Dominion focus on utilities operations.
The transaction announced on Sunday includes more than 7,700 miles (12,390 km) of natural gas transmission lines and 900 billion cubic feet of gas storage.
Berkshire Hathaway Energy is buying Dominion Energy Transmission, Questar Pipeline, Carolina Gas Transmission, 50% of the Iroquois Gas Transmission System, and 25% of the Cove Point liquefied natural gas facility in Maryland.
Dominion will retain 50% of Cove Point. Brookfield Asset Management Inc owns 25%. The Berkshire unit will also assume $5.7 billion of debt, giving the transaction a $9.7 billion enterprise value. It expects a fourth-quarter closing, pending regulatory approvals.
"We are very proud to be adding such a great portfolio of natural gas assets to our already strong energy business," Buffett said in a statement.
Dominion expects up to 90% of future operating earnings to come from utilities.
The company and Duke Energy Inc (DUK.N) separately announced on Sunday they would abandon their $8 billion Atlantic Coast Pipeline, running under the Appalachian Trail and through West Virginia, Virginia and North Carolina.
Dominion and Duke cited delays and uncertain costs, despite a favorable U.S. Supreme Court decision last month.
Berkshire controls 91.1% of Berkshire Hathaway Energy, which owns the MidAmerican Energy, NV Energy and PacifiCorp utilities, natural gas pipelines, wind power assets and electricity businesses in Britain and Canada.
The unit accounted for 12% of Berkshire's $23.97 billion of operating profit in 2019. Its chairman, Greg Abel, is also a Berkshire vice chairman.
ჩვენ გირჩევთ
Jeff Bezos is so rich he just set a new record
WIZZ AIR ANNOUNCES A NEW BASE IN LVIV
ბაფეტი $4 მლრდ-ად Dominion Energy-ს აქტივებს ყიდულობს
LinkedIn-მა დაასახელა 10 პროფესია, რომელთაც არ სჭირდება უმაღლესი განათლება


The previous big announcement from the Geomajas project dates back to August 2010. The project has not been standing still in the mean time. Quite the contrary. Let's breakdown the evolution of the project since.
Add extra level of indirection for pipelines and make pipeline steps check whether they should do something.
Update to Spring framework 3.0.5.
Add additional tranform() methods in GeoService.
There is a mechanism to limit the transformable area for CRS transformations, thus preventing transformation errors.
Provide a configuration point for any (custom) map widget.
Handle offset/limit properties in pipeline GetFeaturesEachStep.
Add service which returns the dispatcher URL.
Provide defaults on layer style configurations.
SearchByLocationCommand now also accepts a filter.
Fixed a security problem where the security context could be initialized with the wrong token.
Many bugfixes and improved test coverage.
The full change list can be found in JIRA.
It is recommended that you review the migration notes when upgrading.
Official release note is here.
Provide a way for properly caching (and compressing) SmartGWT library.
Allow embedding Geomajas map widget for integration with other web framework.
Upgrade to GWT 2.1.1, SmartGWT 2.4.
Support multiple map listeners (=passive or non-invasive map controller).
Add possibility to change layer order in the map model.
Add possibility to easily change opacity on raster layers.
The 1.8.0 version has been released.
Add showcases for recent improvements like geocoder, layer reodering, changing opacity.
Change application context structure to be more readable (recommended way).
You can check out the showcase online.
Some change to simplify the archetype.
More details in the getting started guide.
Version 1.8.0 of the OpenStreetMap layer was already release quite a while ago. The most important change there was simplified configuration, improved documentation and easy support of renderings different from the Mapnik default.
Already announced for some time, but a lot of work has been done on both the caching and rasterizing plug-ins. They will probably released in a weeks but the snapshot versions are already usable.
Four new plug-ins have been accepted for addition to the project and work is ongoing on the implementation. These contain additional widgets or extensions for the GWT face. There is a search and filter plug-in, a feature info plug-in, a reports plug-in and an advanced views plug-in.
A REST face started at the FOSS4G code sprint. This allows Geomajas maps to be surfaces using GeoJSON and can for example be used to use OpenLayers for accessing a Geomajas back-end. The read-only access is working but more work is needed to handle updates.
Lots of improvements have been made in the other plug-ins, including the static security, hibernate, Google plug-ins.
The docbook generation changed to use the docbkx plug-in and the style was improved.
We have a new continuous integration server, making our ci builds more reliable, and allowing selenium tests to run as part of the ci build,http://ci.geomajas.org/hudson/.
The source code repository is now fully browsable (including all versions, diffs etc), http://ci.geomajas.org/svn/.
For quality management, we have installed sonar, http://ci.geomajas.org/sonar/.


A good chicken tractor should be portable, simple, and secure. Through many years of experimenting with various homemade chicken designs, we never settled on a setup that fulfilled these needs to our satisfaction. Eventually, we chose to purchase a manufactured "Egg Cart'n" unit, after researching various options, and we have been very pleased with it overall.
Our "classic" unit is easier to move and maintain than anything we'd built, and we felt it was more secure. However, this spring a predator (likely a young fox) exploited a weakness in the design and slaughtered the coop's residents.
After realizing what had happened, we found a simple way to close the loophole and help restore our trust in this otherwise excellent and convenient product. Here's the problem and our solution.
The Egg Cart'n Classic has aluminum framing and mesh, so the unit is light enough to be moved by one person but still impenetrable to predators from the sides. The bottom is open so that birds have access to fresh ground. However, the sides can be tunneled under by a digging predator such as a fox or mink. The design attempts to solve this problem by housing the birds at night in an upper level, closed off by lifting a ramp. This ramp is the weak point — there are several minor flaws in the design which create opportunities for predator access.
When we found our slaughtered chickens, all were in the upper "secure" area of the coop, the doors locked and the ramp raised. However, an impressive tunnel had been dug under the aluminum siding, so something had clearly dug into the lower area, then squeezed past the ramp into the upper area, reversing the process when all the chickens were dead.
We often use this chicken tractor in conjunction with an electric net fence, as the electric acts as an additional barrier to ground-based predators, day and night. Use of chicken nets also allow us to give the birds daytime access to an area larger than the footprint of their coop. Occasionally, though, nets are inconvenient or power fails, and we knew we needed to find a way to increase the nighttime security of the coop so that the birds would be as safe as possible even without secondary electric protection.
1. Fill the gaps around the ramp. At the back and inner side of the ramp, even when raised, there is still a significant slot through which a predator can squeeze. To fill this gap, we cut pieces of scrap wood and screwed them together (above and below the deck), holding them in place to block the gap.
Inner deck and ramp, showing wood filler closing gaps around ramp.
2. Secure the ramp's deck. The ramp has an aluminum frame with a rubber mesh deck set into it. We hadn't realized, or considered, that this deck was simply held in place by gravity, not secured to anything. It can easily be pushed up from below, creating a significant gap for a predator to crawl through. So we wove lengths of wire around the deck frame, through the deck itself, holding it securely in place.
Closeup of wire wrapped around ramp frame to hold deck in place.
One major strength of the Egg Cart'n design is its modular construction — it's easy to assemble or disassemble. These changes would have been difficult if we'd had to crawl into the coop to make them, but instead we simply removed the roof and lifted out the main deck. Once we'd made the alterations, we simply put everything back; the whole process probably took half an hour.
Predators such as mink or foxes can squeeze through a smaller space than would seem possible; it's worth checking your chicken accommodations for any gap or weakness that can be filled or secured. Even a professionally manufactured, high-quality shed like the Egg Cart'n contained a fatal flaw that we discovered the hard way, but the fix is quick and easy. We're happy to have this flaw fixed, as we're very fond of this product overall.


text:
\section{Related work}

\paragraph{Text classification.}

Neural network-based methods have become increasingly popular for text classification \cite{Socher:2011emnlp,Wang:2012acl}. Recent work has used neural networks for text classification either at a sentence \cite{Kim:2014iclr,Hill:2016iclr} or full document \cite{Le:2014icml,Baker:2016coling,Joulin:2016arxiv} level. Many core NLP tasks are essentially text classification, from tweets \cite{Sriram:2010sigir} to reviews to spam. Even though there has been extensive work on feature engineering for text classification \cite{Chen:2009esa}, modern approaches often make use of word embeddings \cite{Mikolov:2013nips} or sentence representations \cite{Kiros:2015nips} learned from a large corpus in an unsupervised fashion.

\paragraph{Fusion strategies.}

Multi-modal fusion, or the integration of input from various modalities, is an important topic in the field of multimedia analysis \cite{Wu:2004acm,Atrey:2010ms}. The question of fusion has been explored in a variety of tasks, from audio-visual speech recognition \cite{Potamianos:2003ieee} to multi-sensor management \cite{Zhao:2003csur} and face recognition \cite{Xiong:2002if}. Much of this research has focused on the combination of two or more continuous modalities. Here, we are specifically interested in the fusion of discrete textual input with another, continuous, modality.

\paragraph{Multi-modal NLP.}

The usage of non-textual information in natural language processing \cite{Mooney:2008aaai} has become increasingly popular. On the one hand, there has been a lot of interest in cross-modal applications, such as image annotation \cite{Weston:2011ijcai}, image captioning \cite{Bernardi:2016jair}, mapping images to text or vice versa \cite{Frome:2013nips,Socher:2013nips,Lazaridou:2014acl} and visual question answering \cite{Antol:2015iccv,Fukui:2016arxiv}. On the other hand, multi-modal fusion has been extensively explored in the context of grounded representation learning for lexical semantics \cite{Bruni:2014jair,Kiela:2014emnlp,Lazaridou:2015iclr}. While much of this work has focused on vision \cite{Baroni:2016llc}, other perceptual modalities modalities \cite{Lopopolo:2015iwcs,Kiela:2015emnlp,Kiela:2015acl} have also been explored, as well as robotics \cite{Mei:2016aaai}, videos \cite{Regneri:2013tacl} and games \cite{Branavan:2012jair,Narasimhan:2015emnlp}. This work is similar in spirit to \cite{Bruni:2014jair}, in that we explore fusion techniques. However, similarly to \cite{Lazaridou:2015iclr}, we learn how to integrate the multi-modal inputs, and use transferred representations as in \cite{Kiela:2014emnlp}.

\paragraph{Multi-modal deep learning.}

Our work relates to previous work on integrating information from multiple modalities in neural networks \cite{Ngiam:2011icml,Srivastava:2012nips,Kiros:2014icml}. Here, we enhance a  well-known neural network architecture for efficient text classification \cite{Joulin:2016arxiv} with the ability to include continuous information, and explore methods for combining multi-modal features. The works of \cite{Arevalo:2017iclr} and \cite{Fukui:2016arxiv}, explore complex gating mechanisms and compact bilinear pooling as multi-modal fusion methods. In order to obtain visual representations, we transfer continuous features from neural networks trained on other tasks (in this case ImageNet), as has been shown to work well for a wide variety of tasks \cite{Oquab:2014cvpr,Razavian:2014cvpr}.

\begin{table*}[ht]
	\centering
	\begin{tabular}{l|rr|rr|rr}
		\toprule
		Dataset & \#Train & \#Words & \#Valid & \#Words & \#Test & \#Words\\\midrule

		Food101 & 58,131 & 98,365,392 & 6,452 & 10,893,597 & 21,519 & 36,955,182\\
		MM-IMDB & 15,552 & 2,564,734 & 2608 & 425,863 & 7799 & 1,266,681\\

		FlickrTag & 70,243,104 & 1,134,118,808 & 656,687 & 10,100,945 & 621,444 & 9,913,566\\
		FlickrTag-1 & 7,166,110 & 92,651,036 & 48,048 & 682,663 & 48,471 & 672,900\\
		\bottomrule
	\end{tabular}
	\caption{\label{table:evaluation-numbers}Evaluation datasets with their quantitative properties.}
\end{table*}

\section{Evaluation}
\label{sec:evaluations}

Surprisingly, there are not many large-scale multi-modal classification datasets available. We evaluate on three datasets that are large enough to examine accuracy/speed trade-offs in a meaningful way. Two of our datasets (Food101 and MM-IMDB) are medium-sized; while the third dataset (FlickrTag) is very large by today's standards. The quantitative properties of the respective datasets are shown in Table \ref{table:evaluation-numbers} and they are described in more detail in what follows.

\subsection{Food101}

The UPMC Food101 dataset \cite{Wang:2015icme} contains web pages with textual recipe descriptions for 101 food labels automatically retrieved online. Each page was matched with a single image, where the images were obtained by querying Google Image Search for the given category. Examples of food labels are \emph{Filet Mignon}, \emph{Pad Thai}, \emph{Breakfast Burrito} and \emph{Spaghetti Bolognese}. The web pages were processed with html2text\footnote{https://pypi.python.org/pypi/html2text} to obtain the raw text.

\subsection{MM-IMDB}

The recently introduced MM-IMDB dataset \cite{Arevalo:2017iclr} contains movie plot outlines and movie posters. The objective is to classify the movie by genre. This is a multi-label prediction problem, i.e., one movie can have multiple genres. The dataset was specifically introduced to address the lack of multi-modal classification datasets.

\subsection{FlickrTag and FlickrTag-1}

We use the FlickrTag dataset based on the massive YFCC100M Flickr dataset of \cite{Thomee:2016acm} that was used in \cite{Joulin:2016arxiv}. The dataset consists of Flickr photographs together (in most, but not all cases) with short user-provided captions. The objective is to predict the user-provided tags that belong to the photograph. This is a very large-scale dataset, so we perform the multi-modal fusion operator and speed-versus-accuracy studies on a subset (specifically, the first shard, which corresponds to one-tenth of the full dataset) for those studies, which we denote FlickrTag-1. We show that the inclusion of discretized features yields classification accuracy improvements with respect to text on the whole dataset.

\section{Approach}

As a starting point, we take the highly efficient text classification approach of FastText \cite{Joulin:2016arxiv}. To ensure a fair comparison, we enhance that model with the capability to handle continuous or discretized features. Specifically, we use 2048-dimensional continuous features that were obtained by transferring the pre-softmax layer of a 152-layer ResNet \cite{He:2016cvpr} trained on the ImageNet classification task. In the case of the large-scale FlickrTag datasets, we use ResNet-34 features (of 512 dimensions). It has been shown that convolutional network features can be transferred successfully to a variety of tasks \cite{Razavian:2014cvpr} and we take the same approach here. We explore a variety of models and experiment with discretization.

The scenario of multi-modal classification certainly admits, or even invites, highly sophisticated models. In our case, however, we also have to take into account efficiency, so we want to focus on models that are simple and efficient enough to handle large-scale datasets, while obtaining improved performance over our baselines. We experiment with a comprehensive set of models, listed below in increasing order of complexity.

In all cases, given a set of N documents, the objective is to minimize the negative log likelihood over the classes:

\begin{equation}
	- \frac{1}{N} \sum^N_{n=1} \log (softmax(o(x_n), y_n)),
\end{equation}

\noindent where $o$ is the network's output, $x_n$ is the multi-modal input and $y_n$ is the label.

\subsection{Baselines}

\paragraph{Text} The first baseline consists of FastText \cite{Joulin:2016arxiv}, a library for highly efficient word representation learning and sentence classification. FastText is trained asynchronously on multiple CPUs using stochastic gradient descent and a learning rate that linearly decays with the amount of words. It yields competitive performance with more sophisticated text classification approaches, while being much more efficient. That is, we ignore the visual signal altogether and only use textual information, i.e.,

\[o(x_n) = W U x^t_n,\]

\noindent where $W$ and $U$ are weight matrices and $x^t_n$ is the normalized bag of textual features representation.

\paragraph{Continuous}

The second baseline consists of training a classifier only on top of the transferred ResNet features \cite{He:2016cvpr}. That is, we ignore the textual information and only use the visual input, i.e.,

\[ o(x_n) = W V x^v_n,\]

\noindent where $W$ and $V$ are weight matrices and $x^v_n$ consists of the ResNet features, normalized to unit length.

\subsection{Continuous Multi-Modal Models}

\paragraph{Additive} We combine the information from both modalities using component-wise addition, i.e.,

\[o(x_n) = W (U x^t_n + V x^v_n).\]

\paragraph{Max-pooling} We combine the information from both modalities using the component-wise maximum, i.e.,

\[o(x_n) = W \max(U x^t_n, V x^v_n).\]

\paragraph{Gated} We allow one modality to ``gate'' or ``attend'' over the other modality, via a sigmoid non-linearity, i.e.,

\[o(x_n) = W (\sigma(U x^t_n) * V x^v_n),\]

or alternatively,

\[o(x_n) = W (U x^t_n * \sigma(V x^v_n)).\]

\noindent One can think of this approach as performing attention from one modality over the other. It is a conceptually similar simplification of multi-modal gated units, introduced in \cite{Arevalo:2017iclr}. The modality to be gated is a hyperparameter (see below).

\paragraph{Bilinear} Finally, to fully capture any associations between the two different modalities, we examine a bilinear model, i.e.,

\[o(x_n) = W (U x^t_n \otimes V x^v_n).\]

\noindent This approach can be thought of as a simpler version of the more complex multi-modal bilinear pooling introduced by \cite{Fukui:2016arxiv}. We also experiment with a method where we introduce a gating non-linearity into the bilinear model, which we call \textbf{Bilinear-Gated}.

\begin{table*}[t]
	\centering

	\begin{tabular}{lllll}
		\toprule
		& \textbf{Model} & \textbf{Food101} & \textbf{MM-IMDB} & \textbf{FlickrTag-1}\\
		\midrule
		\multirow{3}{*}{Previous work} & \cite{Wang:2015icme} & 85.1 & --- & ---\\
		& \cite{Arevalo:2017iclr}-GMU & --- & \textbf{63.0} & ---\\
		& \cite{Arevalo:2017iclr}-AVG & --- & 61.5 & ---\\
		\midrule
		\multirow{2}{*}{Baselines} & FastText & 88.0 $\pm$ 0.1 & 58.8 $\pm$ 0.1 & 23.0 $\pm$ 0.0\\
		& Continuous & 56.7 $\pm$ 0.2 & 49.3 $\pm$ 0.0 & 12.4 $\pm$ 0.0\\
		\midrule
		\multirow{5}{*}{Continuous} & Additive & 90.4 $\pm$ 0.1 & 61.0 $\pm$ 0.0 & 26.8 $\pm$ 0.0\\
		& Max-pooling & 90.5 $\pm$ 0.1 & 62.2 $\pm$ 0.1 & 26.9 $\pm$ 0.0\\
		& Gated & 90.1 $\pm$ 0.1 & 61.8 $\pm$ 0.1 & 27.7 $\pm$ 0.0\\
		& Bilinear & 88.1 $\pm$ 0.3 & 61.5 $\pm$ 0.1 & 27.8 $\pm$ 0.0\\
		& Bilinear-gated & \textbf{90.8 $\pm$ 0.1} & \textbf{62.3 $\pm$ 0.2} & \textbf{28.6 $\pm$ 0.0}\\\midrule
		\multirow{2}{*}{Discretized} & PQ (n=4, k=256) & 89.5 $\pm$ 0.1  &  60.5 $\pm$ 0.1  &  25.6 $\pm$ 0.1\\

		& RSPQ (n=4, k=256, r=4) & 89.8 $\pm$ 0.0  &  60.7 $\pm$ 0.1  &  26.1 $\pm$ 0.1\\
		\bottomrule
	\end{tabular}
	\caption{\label{table:results}Accuracy (averaged over 5 runs) of continuous and discretized multi-modal models, compared to baselines.}
\end{table*}

\subsection{Discretized Multi-Modal Models}

A downside of continuous models is that they require an expensive matrix-vector multiplication $Vx^v_i$ and storing large matrices of floating point numbers requires a lot of space. While the ResNet features used in these experiments consist of a relatively small number of components, these can easily run into the tens of thousands: consider e.g. combinations of SIFT and Fisher vectors used in state-of-the-art computer vision applications \cite{Perronnin:2015cvpr}. Hence, we experiment with discretizing the continuous features, where we convert the continuous features to a discrete sequence of tokens, which can be treated as if they are special tokens, which we normalize separately, and used in the standard FastText setup. This is a simple, computationally less intensive solution. Discretized features also obviously require less storage.

In particular, we investigate product quantization (PQ) \cite{Jegou:2011pami}, where we divide the continuous vector into subvectors of equal size, and then perform k-means clustering on each of the subvectors. For each image, we subsequently determine the closest centroid for each of its subwords, which is combined with the subvector index in order to obtain a discretized vector. For example, a $100$-dim continuous vector $x^v_i$ may be divided into ten $10$-dimensional subvectors $s_i$. Let $N(s_i)$ denote the index of the nearest centroid for $s_i$. The discretized representation of $v$ is then given as $\langle (1, N(s_1)), (2, N(s_2)), .., (10, N(s_{10}))\rangle$. We include these tokens in the text and treat them as if they were special tokens, in the standard fastText model, i.e.,

\[o(x_n) = W (U x^t_n + \alpha U x^d_n).\]

\noindent where $x^d_n$ are the discretized features and $\alpha$ is a reweighting hyperparameter. We normalize $x^t_n$ and $x^d_n$ independently. As we can see, the discretized models are closely related to the additive model, except that they use the same weight matrix $U$ with the discretized features used as ``words'' in the text.

While PQ is great for compressing information into a discretized sequence, it does impose hard boundaries on subvectors, which means that overlapping semantic content that is shared between subvectors may be lost. Hence, we introduce a novel quantization method, called random sample product quantization (RSPQ), in order to maintain (at least some) overlapping semantic information. In RSPQ, the process is the same as in PQ, except we perform PQ over $r$ repetitions of random permutations of $x^v_i$. In both cases, we treat the discretized features as if they are reweighted special tokens included in the textual data and run standard fastText.

\subsection{Model complexity}

There are various trade-offs at stake between these models. The additive, max-pooling and gated models are simplest and result in a hidden layer of the same size as with the normal FastText. The computational complexity of the linear classifier is thus $O(H K)$, where $K$ is the number of classes and $H$ is the size of $Ux^t$ and $Vx^v$. The max-pooling and gating models are slightly more complicated than the additive one, requiring an extra operation. For the bilinear model the complexity amounts to $O(H^2 K)$. Thus, the bilinear model is by far the most expensive to compute. The additive model has the benefit that it does not strictly require a continuous input at all times.

\subsection{Hyperparameters and training}

In all experiments, the model is tuned on the validation set. We tried the following hyperparameters: a learning rate in~$\{ 0.1, 0.25, 0.5, 1.0, 2.0 \}$, a number of epochs in~$\{5, 10, 20\}$, a reweighting parameter in~$\{0.01, 0.02, 0.05, 0.1, 0.2, 0.5\}$ and an embedding dimensionality of either $20$ or $100$. These hyperparameters were sweeped using grid search and we used a softmax loss. For other hyperparameters, such as the number of threads in the parallel optimization and the minimum word count, we fixed their values to standard values in FastText (4 threads, minimum count of 1, respectively), since we found that these did not impact classification accuracy. In the case of the gated and bilinear-gated models, the modality used to serve as a gate over the other modality is treated as a hyperparameter as well.

\section{Results}

The results of the comparison may be found in Table \ref{table:results}. We compare the continuous and discretized multi-modal models against the text-only FastText model and to the continuous features-only model. We also include results from \cite{Wang:2015icme} on Food-101, where they used TF-IDF features for text and a deep convolutional neural network features for images, as well as results from \cite{Arevalo:2017iclr} for Gated Multimodal Units (GMU) and their AVG\_Probs model. GMUs are a substantially more complicated model architecture than any of our relatively simple fusion methods, so this study is a good test of their capability. We note that in the case of Food101, our methods work considerably better than previously reported results. For MM-IMDB, the continuous multi-modal models perform very close to the GMU model and outperform the AVG\_Probs method, while being simpler and computationally more efficient.

We observe that multi-modal models always outperform standard FastText and the continuous-only approach, disregarding the particular type of fusion. This shows that the inclusion of multi-modal information (at least in these types of classification tasks) always helps and that making use of multi-modal information, where available, will lead to increased performance. FastText outperforms the continuous-only method on all datasets, which indicates that text plays a big role in these tasks, and that it is relatively more important than the visual information.

If we examine the continuous multi-modal models, we see that the bilinear-gated model is the clear winner: it outperforms all other methods on all three tasks. It is however also the most complicated model, and as a result is less efficient. We found that placing the gating non-linearity on the text led to the best performance on Food101 and MM-IMDB, while placing it on the visual modality led to the best performance on FlickrTag. It is interesting to observe that the more complicated gated model, as well as the non-gated bilinear model, do not necessarily outperform the simpler additive and max-pooling models. In fact, the performance of these much simpler models is not too far removed from the best scores. The take-away message appears to be: if you care more about accuracy, use the bilinear method with gating; if you care more about speed, use a simple model like the additive or max-pooling one, which have the additional potential benefit that they do not necessarily require the presence of continuous information if none is available.

\begin{table}[t]
	\centering
	\begin{tabular}{lr}
		\toprule
		\textbf{Model} & \textbf{Train time (FlickrTag-1)}\\
		\midrule
		FastText    &  0h01m\\\midrule
		Additive      &  0h39m\\
		Max-pooling & 0h39m\\
		Gated     &  0h40m\\
		Bilinear &  1h04m\\
		Bilinear-Gated &  1h06m\\\midrule
		PQ    &  0h01m\\
		RSPQ    &  0h02m\\
		\bottomrule
	\end{tabular}
	\caption{\label{table:results-train-time}Training time on FlickrTag-1}
\end{table}

\begin{table}[t]
	\centering
	\begin{tabular}{lr}
		\toprule
		\textbf{Model} & \textbf{FlickrTag}\\
		\midrule
		FastText & 36.7\\\midrule
		PQ (n=4, k=256) & 38.9\\
		RSPQ (n=4, k=256, r=4) & 39.4\\
		\bottomrule
	\end{tabular}
	\caption{\label{table:results-flickrtag}Performance on full FlickrTag.}
\end{table}

\subsection{Speed}

We can draw inspiration from the fact that the additive model performed reasonably well: if speed is essential---if necessary at the expense of some accuracy---the discretized models are an obvious choice to further simplify and speed up the model. Even though they outperform standard FastText by a large margin, as shown in Table \ref{table:results}, they only come with a minor performance impact. Table \ref{table:results-train-time} shows the training times for the various models on the FlickrTag-1 dataset: while the bilinear models take around one hour to train (recall that this constitutes only the first shard of the full dataset); the discretized methods, similarly to FastText, only take around one minute. If we scale up to the full FlickrTag dataset, Table \ref{table:results-flickrtag} shows that the discretized models substantially outperform standard FastText. An increase of 2.7\% in accuracy, as seen from FastText to RSPQ, represents having an additional $16778$ test set documents correctly classified using that model, which is a non-negligible amount.

\begin{table}[t]
	\centering
	\begin{tabular}{lll}
		\toprule
		\textbf{$\_\_q\_\_0\_253$} & \textbf{$\_\_q\_\_0\_13$} & \textbf{$\_\_q\_\_1\_253$}\\
		\midrule
donuts 0.987 & cr\`eme 0.933 & oishii 0.905\\
doughnuts 0.981 & ramekins 0.928 & shoga 0.885\\
donut 0.980 & brulee 0.925 & tenkasu 0.884\\
doughnut 0.979 & br\^ul\'ee 0.916 & octopus 0.883\\
donuts? 0.939 & custards 0.916 & aonori 0.881\\
		\bottomrule
	\end{tabular}
	\caption{\label{table:results-interpretability}Examples of nearest neighbors for quantized features in Food101.}
\end{table}

\subsection{Interpretability}

An interesting side effect of the discretized multi-modal methods is that they allow us to examine the nearest neighbors of the quantized features: if a particular feature corresponds to something that looks like a donut, for example, then its embedding should be close to words related to \emph{donut}. Indeed, as Table \ref{table:results-interpretability} shows, we can find clearly identifiable clusters, e.g. for donuts, cr\`eme br\^ul\'ee and certain types of Japanese food. Interpretability is an important but often overlooked aspect of classification models: we show that a simple and efficient method, that outperforms text-only methods by a large margin, yields the additional benefit that it allows for the interpretation of the visual features that a classifier picks up on---something that is difficult to achieve with standard convolutional features.

\section{Conclusion \& Outlook}

The internet is becoming increasingly multi-modal, which makes the task of multi-modal classification ever more pertinent. In order to be able to handle large quantities of data, we need efficient models for large-scale multi-modal classification. In this work, we examined these two questions together. First, we compared various multi-modal fusion methods and found a bilinear-gated model to achieve the highest accuracy, while the simpler additive and max-pooling models yielded reasonably high accuracy at higher speed. Second, we showed that the model can be speeded up even further by introducing discretized multi-modal features. Lastly, we showed that this method yields the additional benefit of interpretability, where we can examine what the multi-modal model picks up on when making its classification decision. We hope that this work can serve as a useful baseline for further work in multi-modal classification.

\section{Acknowledgments}

We thank the reviewers for their comments and our colleagues at FAIR for their feedback and support.

redpajama_set_name: RedPajamaArXiv


text:
La Francia partecipò alle XVII Olimpiadi, svoltesi a Roma dal 25 agosto all'11 settembre 1960, con una delegazione di 238 atleti impegnati in diciannove discipline per un totale di 120 competizioni. Portabandiera alla cerimonia di apertura fu il quarantenne cavaliere Pierre Jonquères d'Oriola, alla sua terza Olimpiade.

A dispetto dell'elevato numero di atleti partecipanti, la squadra francese conquistò solo cinque medaglie: due d'argento e tre di bronzo. Questa fu l'unica Olimpiade estiva in cui la Francia non conquistò nessuna medaglia d'oro, con la sola eccezione di , dove peraltro fu presente un solo atleta francese.

Medagliere

Per discipline

Medaglie

Risultati

Pallacanestro

Pallanuoto

Altri progetti

Collegamenti esterni

1960
Francia

redpajama_set_name: RedPajamaWikipedia


text:
MB Photography | Projects
Ephemeral Pools
Captured over a period of 3 years in a small patch of forestry near my home, Ephemeral Pools is an exploration of transient views in the landscape.
Most people's perception of the landscape is one of permanence; views which change little over time, reinforced by the fact that so many of them are returned to and photographed again and again. At first glance the landscape around us is fairly static, but the more you look the more changes you notice. It is this continual change that I am drawn to - whether man made or natural.
There are many long- and medium-term effects on the landscape; such as natural growth, death and decay; man-made development and intervention; changes in the seasons; and daily (or even more frequent) changes in the weather. However, my view of the landscape in this series is changing on a much faster time scale, affected not only by the changing seasons and weather, but also by the levels and often fast movement of the water in the pools which are the subject of these images. Often the views are very transient, both due to the nature of the subject matter and also how I have used it to view the forest within which these images are made. Occasionally the views are so fleeting that they are only present in a single frame and I may not have even registered them fully at the time of capture.
Over the life of the project, as I spent more time in the forest and became more familiar with my surroundings, the nature of my photographic approach changed. I moved away from the literal and the images evolved to become more abstract.
It is the unpredictability, fleeting existence and potential for abstraction that has attracted me to this place. Capturing moments in the forest that only exist for a very short period of time, and cannot be reproduced, is perhaps my reaction to a genre of photography in which many strive to reproduce views that have already been recorded by other photographers hundreds of times.
The images in this series are both physical and metaphorical reflections: all are captured in these ephemeral pools of water reflecting the surrounding forest flora, but they also reflect my own ideas, thoughts and feelings at the time they were made. They are perhaps much less about the subject being photographed and more about my own experiences and emotions.
In addition to the still images in this series I have also produced a number of video works, with original music composed by Zillah Myers.
Coins Not Accepted Here
The humble, traditionally red, telephone box has been a part of our landscape for just over 80 years now, and yet its days are perhaps numbered. In the last year BT, who owns most of the public payphones in the UK, have put forward plans to decommission a vast number of them. In Wales alone the plans are for 1500 boxes to be removed – almost half of all the payphones in Wales.
It is true that with the advent of better mobile connections there is little need for public telephones in most parts of the country, yet in rural locations such as the Brecon Beacons this is still often the only way a traveller can make contact with the outside world as the mobile coverage is still poor or non-existent in such areas. Despite this they are still rarely used and will no doubt soon be removed or adopted by local communities to be used for other purposes.
For anyone over the age of 30 the payphone will undoubtedly be part of their childhood memories, and so perhaps out of a sense of nostalgia, but also to ensure that their last days are documented, I have visited and photographed the remaining payphones that fall inside the Brecon Beacons National Park. During the few months of this project at least two of the boxes I photographed were removed, and I plan to return to each of these sites in a few years' time to photograph the changed landscape.
Reconnecting the Canal
Construction of the Glamorganshire Canal began in 1790 to link the iron works of Merthyr Tydfil with the port of Cardiff. Today much of the route is below the A470 dual carriageway, built in the 1970s. The Glamorganshire Canal has been reduced to just a few disjointed stretches of water, divided by roads and new construction.
This collaberation between myself and Rob Hudson seeks to reconnect those stretches of water along the canal through photography. Using a single roll of film exposed separately by both photographers at different locations along the canal to reveal serendipitious images as a result of the double exposures. You can follow the project on twitter @ReconnectingThe.
Maen Llia 2015
Maen Llia is a large, roughly hexagonal lump of old red sandstone which sits on moorland at the top of the valley between Fan Llia and Fan Nedd, near the source of the river Llia in the Brecon Beacons National Park. It is just a few miles from my house (or perhaps I should say "a stone's throw"!) and a location which I have visited many times over the past 15 years. Like many such megaliths its age and original purpose are unknown, although it is thought to date from the Bronze Age.
Legend has it that the stone goes down to the river, the Afon Llia, to drink on occasion. This tale seems likely to be an allusion to the fact that its shadow is cast across the moorland as far as the river when the sun is low in the evening sky. I am not the type of person who believes in standing stones having any magical power, purpose or being sited on energy lines, however I am interested in why it was erected here in the first place and by whom, and these people may well have held such shared myths about the stone. Its position in the valley and visibility as you drive up the valley does lend credence to the stone being a route marker, but the effort involved in moving this stone into the upright position would seem disproportionate to the value of erecting it for this purpose alone. Its shape is also distinctive enough to make one believe that those who sited it here thought it a bit more special than simply a "signpost", and some sources also claim there to be tooling marks on the stone indicating that it was not naturally this shape. Of course we will never know the true purpose of the stone, but it remains a steadfast landmark in the landscape while only the weather and light changes around it.
By photographing the stone throughout the year using different techniques I have tried to capture the varying conditions which the stone experiences, and represent it in the landscape in a way that reflects my mood and the weather at the time. I hope some of these images also reflect the stone's permanance in this wild landscape.
The images here represent an edited selection of my visits to the stone. See a video presentation of the images on Vimeo. To see more images from my visits see the twitter hashtag #maenllia2015.
Travels in a Strange Land
I have visited a strange and beautiful place, a land containing creatures and locations that are fantastical but somehow familiar. A negative world. A landscape unknown. So close, but as yet undiscovered.
While some people may think that most of their local landscapes have been discovered and photographed ad-nauseum, I have sought out the landscapes of my imagination. However, these are not photoshop creations but real places. Illuminating the dark spaces beneath rocks and trees by viewing them in negative I have unearthed a landscape previously unknown to me. What you see in them is only limited by your imagination (read more).
Images from this series have been exhibited at Littleman Coffee in Cardiff and Tilt & Shift Gallery in Llanrwst. If you are interested in exhibiting any images from this series please contact me. Individual prints can be purchased by browsing the images below. Buy the book of the series HERE.
Gower Seascapes
Originally taking inspiration from Rob Carter's Travelling Still series of images, these Gower Seascapes utilise camera movement to blur the image whilst retaining enough detail to recognise the subject matter. Combining abstract simplicity with the natural beauty of the South Wales Gower coastline.
I have adopted a very low-tech approach to producing these images, using intentional camera movement (ICM) panning the camera by hand with no tripod support. This results in sometimes unpredictable results and I often have to capture many test images before I find one that works. Captured between 2008 and 2014, this series is now available as a book.
Survivors of Carnau Gwynion
The lone tree may be a shallow photographic cliché, but the reasons for its existence run much deeper and are often more disturbing.
When visitors to the Brecon Beacons are told that the English translation of Fforest Fawr is Great Forest they might well be surprised. With the exception of the modern areas of forestry monoculture there are few trees to be found on the upland slopes of this part of the National Park. Centuries of deforestation by man to provide building materials, cooking and heating fuel, and for quicklime production removed almost all the trees from this landscape. The more recent grazing by domesticated livestock has ensured that the land remains tree free. As George Monbiot says, "Sheep have reduced most of our uplands to bowling greens with contours".
This series documents the remaining trees on Carnau Gwynion in Ystradfellte. Primarily of the hawthorn family, many of these trees now cling to life by a thread soon to die from old age and storm damage. Continual grazing ensures that very few new trees will grow, and those that do will have a tortured existence clinging to the limestone pavement while new growth is continually nibbled away by sheep and rabbits. They appear in these images mostly as lone trees, not just out of photographic choice, but because they are significantly spaced out to make it challenging to include more than one at a time (although I have in some cases). Each of them is a reminder of the effect man has on the environment and how managed our supposedly "wild" places are.
Images from this series have been exhibited at Littleman Coffee in Cardiff and No 18 Cafe in Crickhowell. If you are interested in exhibiting any images from this series please contact me. Individual prints can be purchased by browsing the images below. Buy the book of the series on Blurb.
The ubiquitous farm gate, old and new, in various states of use and disrepair. All from my home village of Ystradfellte. A small typology that interests me because of the small (or sometimes large) differences in essentially the same piece of farm hardware.
Winter Roads
An abstract study of frozen puddles found on mountain roads and paths in the Brecon Beacons. I marvel at the infinite variation in patterns that freezing water can generate. Short lived they may be, but they contain beautiful details and a hidden world waiting to be discovered right beneath our feet. Grouped in series based on their location and the different weather & temperature conditions they have been created by.
As a landscape photographer I am fascinated by clouds, both for their weather giving properties and the infinite variety in shapes, patterns and textures they add to the sky. For some time now I have been photographing the sky on its own, with no added landscape features, whenever I visit a location. Most of the time these images never make it further than the memory card, and sometimes they don't stay there long! However, occasionally a cloud is sufficiently interesting or beautiful that the image gets processed further and then filed.
Abandoned Shopping Trolleys
An homage to thoughtless and lazy people. A lot of these are just a short walk from where they belong and you wonder why people couldn't make it that far, others are beyond salvage. An ongoing project which I hope will become harder and harder to add to as people become more determined to get their £1 coin back!
Into The Darkness
I've driven past this forest every day for over a decade without giving it a second glance, but then one day I wondered what it would be like inside. At first I only explored the fringes of the forest, apprehensive of the seemingly impenetrable darkness within which there surely could be nothing of interest. But eventually I plucked up the courage to step inside, alone.
I soon discovered that darkness was not absolute, light penetrated the edges of the forest and deeper in it filtered through the tall canopy. There were noises too. The wind causing the trees to creak and groan, sticks falling, animals hidden from view quietly complaining about their intruder. And in the periods of silence, the sound of my own footsteps and my own breathing, perhaps more unnerving than anything the forest could offer.
Despite the uniformity of the regularly planted trees there were variations, there was life, and there was death. Like those who chose to watch horror films for an adrenaline rush I found I wanted to find its darkest centre and be scared. (buy the book here)
Dancing Trees
An ongoing study of woodland and individual trees (captured in infrared) that take on more human forms and appear to be dancing - alone, or with one another. The more I look at these images of trees the more I seem to see in them.
This interpretation is very personal and I appreciate that you may not see them the same way, but I hope you can enjoy the images on some level.
From the Edge of Darkness
Are you looking into the depths of the forest, or trapped inside the woods looking out? I like the way negatives play with our perception of location and transform the dark interior of a forestry plantation into a ghostly but sometimes enticing light. But even once you know that these images are negative your mind is telling you that the view is normal, and in many of these images the manipulation of light is less obvious that you might expect.
Like my previous exploration of a forestry plantation I hope to convince you that it is not all predictable, monotonous, regular planting. There are variations in planting patterns and fallen or broken trees, individually and in groups, make each forest view unique.
While this series may continue for some time the source material is not unlimited. The requirements for these images are specific and the final processing is important to set the mood of the location. While I continue to find new locations I hope that you will follow my exploration of these places that many shun or even despise.
Unseen Beacons
When out in the landscape it is easy to overlook the mundane and the ugly and just focus on the pretty view, but these are all part of the environment in which we live and often tell a more interesting story than the ubiquitous oversaturated sunset.
In this ongoing project I am trying to capture views of the Brecon Beacons that are edited out of most landscape imagery, and show more of the everyday life that goes on in the National Park and some of its history.


text:
Mahnoor Ahmed- PhD
Mahnoor (Noor) Ahmed is a first-year Ph.D. student in the Biomedical Informatics and Data Science program. Her research interests lie at the intersection of digital health and clinical informatics. Noor holds a B.A. in neuroscience from Vanderbilt University, and a M.Eng. from Duke University in biomedical engineering. She joins Johns Hopkins University with several years of experience in health care operations and digital health policy.
Akintunde Akinseye – AHSI Online
Tunde is a Survey Statistician at the National Center for Health Statistics. He works on the National Hospital Ambulatory Medical Care Survey which is a probability survey that assesses ambulatory care utilization at non-federal hospitals in the United States. He has a Bachelor's in Biochemistry from Babcock University (Nigeria), an MSPH in Microbiology and Emerging Infectious Diseases from GWU Milken Institute School of Public Health, and a Certificate in Spatial Analysis in Public Health from the JHU Bloomberg School of Public Health. He is interested in data analytics and infectious diseases as they pertain to ambulatory care and public health.
Victoria Alms- AHSI Online
Victoria Alms currently lives in Baltimore, Maryland. She works for Johns Hopkins University School of Medicine in molecular diagnostics and collaborate very closely with physicians within the medical field. She was born and raised in Maryland, but moved away to Georgia for college to pursue a degree in Genetics. She has three sisters that she loves dearly, and a dog. Victoria is fluent in Spanish, She loves to read, and loves music. Victoria has played the piano since four years old, and learned to read sheet music before she could read in school. She taught herself to play the guitar at twelve years old. Victoria is excited to begin this new chapter in her career in applied sciences informatics and data science, and looks forward to what the coming years have to offer.
Emir Amaro- MS 2 Yr Research
Emir Amaro lives in Serang, Indonesia. For the last 2 years, Emir has worked as an emergency physician at RSUD Banten. He also manages his clinic in Serang. Previously, he attended medical school at Universitas Sriwijaya. Emir is interested in data interoperability, clinical informatics, and AI applicability in cardiology. Outside work, Emir enjoys playing music, creating videos, playing games, and reading books.
Waddah Arafat- AHSI- Online
Born and raised in Damascus, Syria; Waddah completed medical school at Damascus University Faculty of Medicine and completed a residency in Internal Medicine at Indiana University. Waddah received advanced training through a Hematology‐Oncology fellowship at the University of Wisconsin School of Medicine at Madison. During his time in Madison, he would drive to Epic Electronic Health Record (EHR) Headquarters to complete Physician Builder Courses (Basic and Advanced) and earned certification in both and took the Advanced Analytics course. This (in addition to prior passion about IT) was the introduction to clinical informatics. He started his career at UTSW as an Assistant Professor of Genitourinary Medical Oncology treating kidney, bladder, testicular and prostate cancer. Upon his arrival, Waddah joined the UTSW health system's CMIO team as a deputy CMIO representing the Cancer Center and later was appointed as the Director of the newly established Cancer Clinical Informatics office at SCCC.
Emily Ashby- AHSI Online
Emily grew up in Northern Virginia and moved to San Francisco in 2014 after graduating from Virginia Tech, where she studied Dietetics and Exercise Science. For the last 5 years, she's worked at Salesforce, developing and managing strategic technology partnerships within the Healthcare and Life Sciences industry. She's a certified yoga instructor and an avid skier who loves trying new restaurants and fitness classes, cooking vegetarian dishes, live music, time on the water and long weekend hikes. Emily has raised 50k for the Michael J. Fox Foundation for Parkinson's disease and hopes to continue that fundraising effort into the future.
Haibin Bai- MS 2 Yr Research
Haibin Bai is a graduate student of the Health Informatics Research Program at the Johns Hopkins University School of Medicine. He received his Master degree in Health Policy and Management from Seoul National University. His research interests are Public Health Informatics and Digital Health.
Anas Belouali- PhD
Anas is an experienced Health Data Scientist with a background in software and data engineering and a demonstrated history of leading and implementing data science projects in healthcare. Prior to joining the PhD program in Health Sciences Informatics at JHU, Anas earned two master's degrees in computer engineering and systems medicine, and has over 10 years of professional and research experience. He worked as a research faculty at the Innovation Center for Biomedical Informatics (ICBI) at Georgetown University, where he led research efforts in clinical informatics and health IT, and taught in the Master's program in Health Informatics & Data Science. As a lead data scientist for Georgetown University Medical center, he designed and built clinical research data warehouses and developed predictive models for disease risks, outcomes, and adverse events using EHR, omics, and behavioral data. His work has contributed to publications and proceedings at national level conferences.
Samir Bhalodia- AHSI Online
Dr. Bhalodia received his Bachelor of Science (BS) in Biochemistry (Magna cum laude) from Temple University in Philadelphia, Pennsylvania and Doctor of Medicine (MD) from American University of the Caribbean in St. Maarten. After graduation, he completed his Internal Medicine residency at MedStar Good Samaritan and Union Memorial Hospitals in June 2017. He continued as a Hospitalist upon completion of his residency and took up the role of a Unit Director at Good Samaritan Hospital. Dr. Bhalodia is currently pursuing a Master of Science in Applied Health Sciences Informatics at Johns Hopkins School of Medicine in Baltimore, Maryland. His interest in informatics grew when he experienced his hospitals' transition to an Electronic Health Record (EHR) system. Dr. Bhalodia aspires to play a key informatics role in healthcare settings by improving patient quality, safety and health information exchange with the knowledge and skills he learns from his Applied Health Sciences Informatics Masters program.
Cindy Cai- AHSI Online
Cindy X. Cai, M.D. is an Assistant Professor at the Wilmer Eye Institute and specializes in the medical and surgical management of retinal diseases. She obtained her M.D. from Columbia University and completed ophthalmology residency at the Wilmer Eye Institute before finishing a vitreoretinal surgery fellowship at the Duke Eye Center. Dr. Cai is currently pursuing a Master of Science in Applied Health Sciences Informatics. Her research lies at the intersection of ophthalmology, public health, and informatics and is aimed at developing novel strategies that incorporate social determinants of health for the prevention of vision loss from diabetic retinopathy.
Michael Cook- AHSI Online
Michael Cook is a Sr Programmer Analyst with the CCDA at Johns Hopkins University. In his role, he helps to provision clinical data to research teams. He has a background in Information Systems and statistics. He currently resides in Baltimore, MD.
Xiyu Ding- PhD
Xiyu grew up in Jiangsu, China and graduated from Nanjing University, China with a B.S. in Biology. However, she was not totally satisfied with studying biological problems via pure experimental approaches. She then went to Harvard and completed a M.S. in Computational Biology, where she was able to systematically learn statistical and computational methods with their applications in health-related areas. During her master study, she witnessed the rapid growth of EHR system and how it brought burgeoning opportunities to biomedical research. At Johns Hopkins University, she wants to study and contribute to the technology that drives the development of EHRs system, or more broadly, the digitization of healthcare.
Chen Dun- PhD
Chen Dun is a senior data analyst that has conducted interventional and epidemiologic studies in implementation science and clinical outcome research. She holds a MHS from the Johns Hopkins Bloomberg School of Public Health concentrating on quantitative research methods and social epidemiology. Her project interests include clinical analytics, public health informatics, and design/deploy innovative interventions to promote healthcare systems. Chen aims to enhance health service delivery and access and improve patient quality and safety. She aspires to use advanced health IT to identify inappropriate medical practices and develop evidence-based, data-driven interventions to optimize provider practices and enhance patient experience.
Andrea Fourquet- AHSI Online
Andrea Fourquet is an Informatics consultant that has been working in the informatics standards field for over five years. Her main project interests have to do with integrating Emergency Medical Services and electronic health records for a more efficient and informed patient care. Andrea is a board member for the National EMS Information Systems (NEMSIS) working to further reduce the gap between emergency patient records and healthcare records. She has participated in the development of CDC and international standards utilizing Fast Healthcare Information Request (FHIR) and CDA data structures. She has supported the updating and maintenance of the CDC funded HIMSS Immunization Integration Program test plan, and configuration using the NIST Test Case Authoring and Management Tool (TCAMT). Andrea has supported interoperability demonstrations at the HIMSS Interoperability Showcase and Public Health Informatics Conference as well as supporting standards testing bodies with ONC certification testing.
Franky Franky- AHSI Online
Franky is a Senior Pharmacist at the Institute of Mental Health, Singapore. He is currently involved in managing the current and implementing the future hospital electronic medical record and pharmacy systems. He is interested in healthcare data analytics, pharmacy informatics education, and medication safety. Franky received his Bachelor of Pharmacy (Honours) from Monash University, Australia and Master of Social and Health Care (Digital Health) from Savonia University of Applied Sciences, Finland. He is board certified in geriatric pharmacy and credentialed as PMI Professional in Business Analysis.
Davera Gabriel- PBCCI
Davera Gabriel, R.N. is a Senior Research Terminologist in the Division of Health Sciences Informatics at Johns Hopkins University. Davera has participated in data standards development, led national multi-site observational data harmonization efforts and implementation of data standards supporting clinical, translational and population health research for nearly 3 decades. She currently serves as a member of the Health Level Seven Terminology Authority and Health Information Management System Society Interoperability & Health Information Exchange Committee, as well as an active participant in the American Medical Informatics Association. As a Nurse-Informatician, Davera enjoys applying her standards implementation experience in multidisciplinary teams representing diverse clinical and technical backgrounds that achieve creative data solutions resulting in knowledge discovery and improvements in personal and population health.
Yuan Gao- MS 2 Yr Research
M.S. in Health Sciences Informatics, Johns Hopkins University School of Medicine
Nadia Haque- PBCCI
Nadia Haque is a pharmacist, currently working as a Biomarker Testing Account Executive at Genentech in the area of precision oncology. Prior to joining Genentech, she served as Senior Content Management Consultant in Pharmacogenomics with Wolters Kluwer, and a strategic consultant to the Henry Ford Medical Group CEO. She has served as the Precision Medicine Director at Henry Ford Health System where she worked on numerous informatics projects, and has held pharmacy management and research positions at Henry Ford Health System in Detroit. Nadia holds a Doctorate of Pharmacy degree from Wayne State University, and a Masters in Health Services Administration from the University of Michigan. She has published multiple times in the peer reviewed literature, and is currently on the Board of Directors for the Michigan Society of Health System Pharmacists. She is a Board Certified Pharmacotherapy Specialist, and a Fellow of the American Society of Health System Pharmacists.
Ting He – PhD
Ting He is currently a research associate at Columbia University.
Ting He received her Bachelor's degree in Bioinformatics from Dalian University of Technology, China, and was a visiting student at the University of Calgary, Canada. Her prior research work was in analyzing cancer genome data and frequent itemset mining. Having seen many old Chinese people without the medical background to understand their physical examination reports, she led a team to create an Android application to solve this problem during her sophomore year.
Her goal is to find the intersection between informatics, technology and healthcare, and to improve the unfair distribution of health resources. Her research interests lie in health system management, mHealth, and eHealth.
Stephanie Hong- AHSI Online
Stephanie hails from Baltimore by way of sweet tea (Nashville) and maple syrup (Cambridge, New Haven, and NYC). She currently works in Dr. Chute's lab as a Sr. Healthcare Informatics Analyst, Biomedical Informatics and Data Science Section, Department of Medicine, Johns Hopkins School of Medicine. Most recently, she has been deeply involved in data ingestion and harmonization effort for the National Covid19 Cohort Collaborative (N3C) Project. N3C is the largest COVID19 cohort repository to date with 19.2 billion rows of data, versioned data repository available for research. She also works in CRISP project where she built the data pipeline to harmonize the state of Maryland COVID registry data in to OMOP CDM. CRSIP is the State Designated Health Information Exchange (HIE) for Maryland. Previously, she curated EHR data to build research data warehouse repository for secondary use of clinical data at Vanderbilt University. She received BS in Electrical Engineering from MIT. She has been inducted into the Fellows of AMIA(FAMIA) this year.
Nauman Islam — AHSI Online
Nauman is an international business leader with experience in Singapore, the United Arab Emirates and the USA. He has more than 17 years experience in various roles with increasing responsibility, such as P&L management, sales, marketing and operations. He has worked with Fortune 500 companies such as Roper Industries, GE Oil & Gas, Cameron and Ingersoll Rand. He is founding member and current Chairman of the Board at Innovatel Telepsychiatry founded in 2014 with now over 30 psychiatrists practicing in over 9 states. Nauman completed his MBA from the University of Texas at Austin in July 2010. He also holds Bachelor Engineering degree in Chemical Engineering from McMaster University in 1997.
Joshua Jorvina- AHSI Online
Joshua Jorvina is an RN transplant from the Philippines. He finished his BSN from Far Eastern University with honors (Cum Laude). He currently works as a Chemo Infusion RN at Memorial Sloan Kettering Cancer Center in NYC and plans to transfer to their nursing informatics or health informatics department in the future.
Robert Kirchoff- AHSI Online
Dr. Robert Kirchoff is physician and researcher at Mayo Clinic, and Assistant Professor of Medicine at Mayo Clinic Alix School of Medicine. He holds board certifications in Internal Medicine and Addiction Medicine. Dr. Kirchoff's research focus is on inpatient management of withdrawal in medically complex patients.
Carly Lara- AHSI Online
Carly is a vascular sonographer from Austin, TX and the second in her household to attend JHU for an advanced degree. Carly put herself through ultrasound school as a single mom and was thrilled to obtain an Associate's degree to begin working. However, Carly knew she wasn't done with her education and decided to pursue a Bachelor's degree in diagnostic imaging. It was during this time that she discovered the fascinating field of informatics and data science's expanding role in healthcare technology. She is motivated by the shortcomings in current PACs systems and the opportunity to be a part of the solutions proposed by imaging informaticians. Carly hopes to use the knowledge gained at JHU to be a contribution to the sonographic field and to the medical community as a whole.
Star Liu- MS 2 Yr Research
Named after the Lone Star State, Star Liu grew up in Shanghai, China. Star graduated from Emory University with a B.S. in Quantitative Sciences (concentration in Biology). He has been very interested in the healthcare and public health informatics sphere and believes this niche is up for big changes and innovations. Thus, Star wanted to equip himself with all the necessary skillset to succeed. In his free time, he enjoys working out, playing basketball, hiking, and cooking among many other things. Finally, he is the biggest fan of Chipotle.
Junjie Luo- PhD
Junjie Luo began the BIDS Ph.D. program at the School of Medicine in Fall 2022. Before this, he was an Information System Ph.D. student in Rh.Smith Business School of UMD from 2020. Junjie received M.S. in Data Science and B.B.A. in Applied Economics from The Chinese University of Hong Kong, Shenzhen. His primary research interests include healthcare, artificial intelligence, and behavioral economics, especially the interaction between human decisions (from both providers and patients) and AI adoption in healthcare contexts.
Jessica Malenfant — AHSI Online
Jessica is a Senior Health Informatics Analyst in the Dept. of Population Medicine at Harvard Medical School & Harvard Pilgrim Health Care Institute in Boston, MA. She provides informatics and software development leadership and expertise to support socio-technical platforms used in large-scale research and public health surveillance initiatives. Prior experience includes working as a surveillance epidemiologist at the MA Dept. of Public Health and implementing Electronic Health Records-based tools to help radiologists meet quality measures. She focuses on using public health experience to interpret and apply data, standards, & technologies to projects. Jessica holds an MPH from the Boston University School of Public Health and Certificate in Clinical Informatics from the Johns Hopkins School of Medicine.
Siddhi Mathur- AHSI Online
Siddhi Mathur is an Obstetrician/Gynecologist and Canadian Surgical Fellow in Advanced Pelvic Surgery at Mount Sinai hospital in Toronto. Siddhi is a student in the Online Applied Health Science Informatics program. Siddhi's interests and previous work include research into surgical outcomes, curriculum development, surgical coaching, resident teaching and building research capacity.
Niru Murali- AHSI Online
Niru Murali graduated from Ohio State in 2018, excited to pursue a career in public health. Her curiosity about health informatics led her to Epic Systems, where she worked as a senior implementation manager for two years. She currently works as an enterprise implementation manager at Updox, a health tech startup in Ohio. She's committed to improving healthcare access and delivery.
Niru is excited to meet professionals of diverse backgrounds. Eventually, she would like to adopt another dog and pursue a career in research. Specifically, she's interested in how biases in system design and deployment affect health disparities.
When Niru isn't working, she enjoys exploring social epidemiology with the Bloomberg School of Public Health, watching Peloton hype videos, gardening, and travelling to the country's best dog parks with her supermutt puppy.
Kanthi Narra- PBBCI
Kanthi Narra is a geriatric physician in Middle Tennessee in the Nashville area. Kanthi is passionate about clinical quality and data analytics; and enjoys spending time with family, reading books and walking on trails.
Clara Ngoh- AHSI Online
Clara Ngoh graduated Edinburgh University, UK, with a Bachelor in Medicine and Surgery, before going on to receive her Masters in Medicine from the National University Singapore. She is a fellow of the Royal College of Physicians UK, and a fellow of the Academy Medicine Singapore, as a specialist in Renal Medicine. It is her belief that healthcare informatics and big data in the realm of precision preventative medicine, is what ultimately will save many lives in Singapore and globally from developing and suffering devastating effects of kidney failure. In spare time, she enjoys baking, and is an accomplished pianist.
Monica Ngov- AHSI Online
Monica Ngov is a Virginia native (Go Hoos!), Brooklyn-based, Product Manager with over eight years of experience in user-centered product design. Currently, she works on the personal planning software for financial advisors, but she draws inspiration from all industries. It is her belief that cross-pollination is one of the most original paths to innovation. In the future, she aspires to create intuitive, thoughtful, data-driven applications for experienced, subject matter experts in the healthcare and healthtech industry, using the skills and frameworks from the product and design world. She is also interested in the effect of technologies, such as machine learning and blockchain, and increased data interoperability on provider and patient journeys.
Alfred Ouyang- AHSI online
Alfred Ouyang is a cybersecurity professional with extensive experience in advising and assisting federal government agencies on the acquisition of health information technology (HIT) systems for their complex healthcare delivery ecosystems. He is a continuous learner. Alfred has received graduate degrees from Johns Hopkins University Whiting School of Engineering and University of Pittsburgh Swanson School of Engineering. He is a Certified Professional in Health Information & Management Systems (CPHIMS), a Health Care Information Security & Privacy Practitioner (HCISPP), and a Certified Information Systems Security Professional (CISSP).
Natasha Palamuttam – MS 2 Year Research
Natasha Palamuttam graduated with a Bachelor's degree in Biomedical Engineering from University of California Irvine. As someone that has seen the medical device side of the healthcare industry, she wants to help bridge the gap between the physicians using the tools and the engineers creating the tools. She believes that by pursuing a Masters in Health Sciences Informatics that she can learn how to process the data from these medical devices to provide meaningful metrics for healthcare professionals to use.
Jen Wooyeon Park- PhD
Jen Wooyeon Park has professional experience in healthcare data science, having worked at Memorial Sloan Kettering Cancer Center and at Blink Health, a cloud pharmacy startup company. She managed various types of data such as claims, prescriptions, pharmacy operation software logs, lab results to solve problems in quality and safety, operation efficiency, data quality, and medication adherence. She is also interested in improving data standardization and cost efficiency through better system designs and data science methods.
Jennifer Peet- AHSI Online
Jennifer Peet is Head of Health Informatics at Avellino Labs, a global genetics and precision medicine company. Prior to Avellino, she worked for UC Irvine Health for over eight years where she designed and managed destination medicine patient programs. Jennifer holds a PhD in Sociology from the University of Edinburgh, and recently completed the MS in Population Health Management from Johns Hopkins University, Bloomberg School of Public Health. Her professional career also includes 10 years working in the legal field and lecturing in higher education. Jennifer has lived and worked in Brazil and the UK.
Kanimozhi Raja- MS 2 Yr Research
Kanimozhi Raja's firm belief is healthcare innovations are to enhance the quality of everyone's life. As a healthcare futurist, her vision is to develop or be part of a team that develops next-generation healthcare technologies, devices, and gadgets to change healthcare delivery and disrupt the industry. She is incredibly passionate about research in the areas of data-driven smart care. Her primary focus is to create Precise Data-Driven Digital Care (P3DC) protocols and build a P3DC delivery Platform that can take life-saving healthcare technologies beyond socio-economic and geographical barriers. Her primary objective at Johns Hopkins is to embark on extensive research by teaming up with eminent medical futurists and improve evidence-based practice. During her previous job at HCI Corp, she was part of the team that developed Telth, a next generation blockchain Technology called Embedded Logistics Tunnel for Health care, a private permissioned hybrid blockchain built by utilizing root cloud architecture. Telth ecosystem can use P3DC Protocols for Digital Health Care delivery. A Unique care protocol for precise data-driven care (P3DC) uses TWBAN technology for Remote Patient monitoring/management. Raja's research focus is developing, testing, and commercializing P3DC protocols, further building versatile WBAN gadgets, especially biochip and bio-chip hubs for RPM. She wishes to be part of a social movement that builds a healthy society that chases the ailments before it comes, not to chase treatment. Enhance the quality of life through person-centric affordable primary and preventive care through seamless monitoring with greater affordability through technology-driven superior Smart care. Her other areas of interest are AI in Healthcare and big data. At Johns Hopkins, Raja is hoping to gain exposure to the next-generation disruptive technologies in healthcare.
Molly Redman- AHSI Online
Molly Redman is a first year graduate student pursuing a master's in the Applied Health Sciences Informatics program. She grew up in a small town located in Northern Arizona and graduated from Arizona State University with honors in May 2021. Her degree was in biomedical informatics, with a minor in mathematics. Her undergraduate thesis project surrounded evaluating contraceptive decision support systems for young women. Overall, her academic interests include data analytics and visualization, health management, and public health. In her free time, she enjoys spending quality time with her friends and family, photography, and going out on hikes!
Jeetika Sainani- AHSI Online
Jeetika Sainani grew up in Northern Virginia and received her Bachelor's degree in Bioinformatics from Virginia Commonwealth University in December 2019. She is currently a Data Manager for a Clinical Research Organization called Emmes, where she is responsible for overseeing clinical data and maintaining databases for multiple vaccine trials. Her goal is to use data science and analytics to better implement healthcare solutions and improve patient care. She believes that the Master of Science in Applied Health Sciences Informatics program will further enhance her technical skills and lead her to accomplish that goal in the future. During her spare time, Jeetika enjoys working out, watching Hockey and Football games, and spending time with her loved ones.
Gabriel Salvador- AHSI Online
Gabriel Salvador, MD is an academic physician and radiologist from Brazil and currently works at the Brazilian Red Cross- Parana Chapter. Gabriel's main interests include medical education, dissemination of good practices worldwide, and digital inclusion in healthcare in low income regions. Gabriel made several contributions in different medical fields and is currently working in developing AI algorithms to help diagnose diseases in order to improve healthcare in critical regions.
Peter Savio- AHSI Online
Pete has over 11 years' experience as an attending Emergency Department physician. He completed his residency in Emergency Medicine with Drexel University College of Medicine where he previously earned his medical degree. He finished college at Emory University with a BS in biology and a minor in Mathematics. He is married with 2 children and lives near the C and D Canal in Maryland.
Logan Smith – AHSI Online
Logan Smith is a data analyst for the Virginia Department of Health (VDH), and works with hospital and reference laboratories to set up transmission of electronic lab reports (ELR) to Virginia's electronic disease surveillance system. She has been an ASCP certified Medical Technologist since 1989, and has 17 years of laboratory experience, including bacteriology, chemistry, serology and hematology. She earned a Master of Public Health degree in 2015 from Walden University. She completed her capstone project with VDH, which led to her current position and her interest in informatics. Logan has four grown children and five grandchildren. She and her retired husband share their home with two greyhounds, Sadie and Moe.
Shanshan Song– PhD
Shanshan Song received her bachelor level education in chemistry and German from Zhejiang University and National Tsing Hua University, and pursued a career in health informatics during her master's study in chemistry/biostatistics/public health at Boston University and Columbia University. Her long-time goal is making a clear and friendly health information environment a possibility for the public, and harmonizing the medical confidentiality and interoperability for better healthcare service. During her spare time, she actively supports advancement of science, and is enthusiastic about (both natural and artificial) language learning.
Joel Sursas – AHSI Online
Joel Sursas is a Medical Doctor with an interest in Health Informatics. As the Head of Clinical Affairs at Biorithm - a Singaporean start-up, he works with engineers to revolutionize obstetric care through telemonitoring. He oversees Clinical IT requirements as well as plans and governs clinical trials which are underway in Singapore, Australia and the United Kingdom. He also designs and tests the fidelity of the medical mobile/web applications around our device. Finally, he plans the implementation strategy within hospitals' local infrastructure and ensures that the hospitals achieve compliance with international and local regulatory standards which govern data protection and protected health information.
Joan Thul- AHSI Online
Joan Thul has extensive experience since joining the pharmaceutical industry over 15 years ago working with clinical database design, study data analysis and real-world evidence research. Her SAS programming skills have also been applied to real-world data sources such as claims data and electronic health records. She is currently working on CDISC SDTM and ADaM programming for a regulatory submission and has a key role in clinical development and life cycle management programs across multiple therapeutic areas. She is interested in becoming stronger with data analysis in a science-based approach to understanding populations consisting of complex health factors and outcomes, to prevent disease, save and improve lives. She holds a bachelor's degree in Psychology from Rutgers University.
Zack Wang- AHSI 1 Yr Applied On-campus
Zack is excited to join the Applied Health Informatics program. He recently graduated from the University of Chicago with a B.S in Computer Science, and has been tangentially involved with the healthcare sphere for a number of years. Zack's interest was peaked when he worked at the UChicago hospital, and saw how challenging management and daily interactions with EHR were from a first-hand perspective. IHe definitely leaned closer towards the programmatic/data science side of health informatics, especially with his background. Zack is interested in getting more holistic experience and hopes to eventually contribute as a data scientist and developer to lessen the burden EHR poses in healthcare. In his free time he enjoys drawing, playing video/board games, and listening to music.
Ching-Huan Wang- MS 2 Yr Research
Ching-Huan received both a bachelor's degree and master's in pharmacy from Taipei Medical University, Taiwan. He has worked as a drug safety associate at Taiwan's National Adverse Drug Reporting Center, where he developed his research interest in pharmacoepidemiology and pharmacovigilance. Most recently, he has gained research experience at Taipei Medical University, engaging in research projects on drug safety and repurposing issues, electronic health records and their extensive application to clinical decision supporting systems.
Shixiong Wei- 2MS 2 Yr Research
Shixiong Wei is an incoming graduate student in the Health Informatics Research Program. He grew up in Lanzhou, locate in the midwest of China, Gansu province. Shixiong graduated from UC Davis in 2022 and majored in Bioinformatics in college. He has lab experience and is familiar with Python and R language. He wants to learn more informatics skills in the master's program, He believes that informatics can lead the future. Outside of work he works out in the gym, plays basketball, and travels in his free time. In the future, Shixiong wants to attend a Ph.D. program in the Bioinformatics field.
Alisa Zayas- AHSI Online
Alisa Zayas is a Registered Nurse with ER experience mainly in NYC. She decided to pursue a Masters Degree in Applied Health Informatics to expand on her knowledge base and hopefully contribute to the healthcare industry on a more global level.
Shijia Zhang- PhD
Shijia Zhang is currently a Health Science Informatics Ph.D. student at the Department of Biomedical Informatics & Data Science, Johns Hopkins Medicine. He graduated with a B.S in Health Information Management from the University of Pittsburgh and an MS in Biomedical Informatics from Harvard Medical School. He has a strong interest in the application of artificial intelligence to Electronic Health Records(EHR) to develop precision medicine methods and clinical decision support systems. Before joining the Ph.D. program he worked at Mass general Brigham developing case identification algorithms and conducting statistical inferences in clinical research. In his spare time, he likes playing sports and music.
Informatics Faculty
Johns Hopkins University | School of Medicine | my.Johns.Hopkins.edu
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