Jared Sylvester
jsylvest /at/ umd /dot/ edu

Department of Computer Science
University of Maryland
A.V. Williams Building
College Park, MD 20742

@jsylvest

Find me on Mendeley or Academia.edu.
News

May '12:  We have a puppy! Everyone, this is Bonnie, our new three-month-old Westie.

April '12:  Everyone is invited to the first annual UMD AI Day on 9 April. I'll have a poster on display about my current work. When I get a decision back from the editor regarding the journal paper it's based on I'll make a PDF of the poster available here. In the meantime, here's the slide I used for my 90 second spotlight / elevator pitch.

Jan '12:  My Erdős Number is five! (Or no more than five, really. It's tough to tell the exact number since many of my papers, and those of my collaborators, are not in the appropriate databases.) Okay, so plenty of people have Erdős numbers of five. It's not exactly exalted company. (In fact, it's the median.) But it is finite and positive, so I'll take it. Some guy in Ann Arbor once tried to auction off a five. Besides, John Nash and Stephen Hawking have Erdős numbers of four, and Paul Samuelson, Neils Bohr, Francis Crick and Alan Turing have fives, so it's not like I'm in the outer fringes of the Erdősphere.

Nov '11:  I posted my BICA 2011 slides below.

Oct '11: Dissertation proposal: accepted. A.B.D! Now I just have to do a couple of years worth of research and write up several hundred pages about it. No sweat.

Oct '11:  I'm finally adding some content back into the projects section of this site. Not much right now, but I expect next month to flesh it out bit-by-bit.

Aug '11:  I'm setting up some profiles on Mendeley and Academia.edu. Links will be in the header. Does anyone use these services? (For networking, that is. Mendeley is indispensible for organizing research. I literally do not know how I would work without it.)

About Me

I'm a doctoral candidate at the University of Maryland, College Park, in the Department of Computer Science. My main research interest is in biologically-inspired computing, primarily neural networks at this point, though I've done some evolutionary computation in the past as well. More specifically, my dissertation work is on executive function and cognitive control (working memory, decision making, etc.) using neural-inspired systems rather than rule-based ones.

I was graduated in 2006 from the Computer Science and Engineering department at Notre Dame, where I lived in Zahm House.

Outside of academics, I'm interested in economics (especially the George Mason variety) and political philosophy. Film and art — especially sculture, animation and algorithmic art — are also big interests. Recently I've also taken up an interest in baking bread. Our new Westie puppy also takes up a bit of my time.

Resumé / C.V.
Resumé
     (Last updated February, 2012.)
Research

I am currently working with Jim Reggia on exploring neural models of cognitive control. Most cognitive control models are built using symbolic, rule-based paradigms. Such systems are both biologically implausible and often tend towards the homuncular. What neural models do exist are typically very narrowly designed for a particular task and require a great deal of human intervention to tailor them to the objective as well as exhibiting problems scaling to larger problem spaces.

I am exploring creating a more generalizable model of cognitive control using a neural paradigm by creating networks which learn not only memories of environmental stimuli but also the steps necessary for completing the task. The steps are stored in a memory formed by a sequential attractor network I developed so that they can be visited in order. I call my model GALIS, for "Gated Attractor Learning Instruction Sequences."

By generating behavior from the learned contents of a memory rather than the explicit structure of the network itself I believe it will be much easier for the model's behavior to change. Rather than having to rebuild the "hardware" of the network, you can instead load different "software" by training the memory on different patterns. Furthermore, making the model's behavior readily mutable opens the door to it improving its performance as it gains experience. That, in turn, should allow the model to learn the behavior necessary to completing a task on its own.

Basing behavior on memory contents rather than architecture is not unlike the shift from clockwork automata like Vaucanson's "Digesting Duck" to the Jacquard Loom. The latter was an important step in the history of computation because its behavior could be changed simply by swapping in a different set of punchcards — i.e., by changing the contents of its memory. Of course GALIS surpasses the Jacquard loom because the loom was only able to follow instructions, not conduct any computation of its own. GALIS, on the other hand, determines endogenously when and how to modify its working memory, produce outputs, etc.

Prior to GALIS I worked with Jim on two other projects. The first is a computational model of working memory formation. This is being done in conjunction with a wide-ranging study at UMD's Center for Advanced Study of Language into the role of working memory in language tasks. This study of working memory lead into the cognitive control research I am doing now. I have also used machine learning methods to analyze the results of some CASL studies to see if it is possible to determine who will benefit from working memory training based on pre-test results. Please see the 2011 tech report below for more.

The second project, begun in Spring 2007, deals with symmetries in topographic Self-Organizing Maps. By limiting the radius of competition and choosing multiple winners for standard Hebbian learning we can generate cotices with global patterns of symmetric maps. Please see the 2009 Neural Computation paper below for details.

My undergrad research focused on creating and testing a system called EVEN, for "Evolutionary Ensembles." It is a genetic algorithm framework for combining multiple classifiers for machine learning and data mining. It is very flexible, with the ability to combine any type of base classifiers using different fitness metrics. This work was done with Nitesh Chawla, who advised me for my final two years at Notre Dame.

Publications
Journals
Sylvester, J., Reggia, J., Weems, S., and Bunting, M. "Controlling Working Memory with Learned Instructions." Submitted, January 2012.
Sylvester, J., and Reggia, J. "Plasticity-Induced Symmetry Relationships Between Adjacent Self-Organizing Topographic Maps." Neural Computation, vol. 21(12), pp. 3429-3443. 2009. [link, pdf, BibTeX]
Conferences
Sylvester, J., Reggia, J., and Weems, S. "Cognitive Control as a Gated Cortical Net." Proc. of the Int'l Conf. on Biologically Inspired Cognitive Architectures, pp. 371–376. Alexandria, VA, August 2011. [pdf, BibTeX, slides]
Sylvester, J., Reggia, J., Weems, S., and Bunting, M. "A Temporally Asymmetric Hebbian Network for Sequential Working Memory." Proc. of the Int'l Conf. on Cognitive Modeling, pp. 241–246. Philadelphia, PA, August 2010. [pdf, BibTeX]
Reggia, J., Sylvester, J., Weems, S., and Bunting, M. "A Simple Oscillatory Short-term Memory Model." Proc. of the Biologically-Inspired Cognitive Architecture Symposium, AAAI Fall Symposium Series, pp. 103–108. Arlington, VA, 2009. [pdf, BibTeX]
Sylvester, J., Weems, S., Reggia, J., Bunting, M., and Harbison, I. "Modeling Interactions Between Interference and Decay During the Serial Recall of Temporal Sequences." Proc. of the Psychonomic Society Annual Meeting, November 2009. [pdf, BibTeX]
Chawla, N., and Sylvester, J. "Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets." Proc. of Multiple Classifier Systems, pp. 397–406. 2007. [pdf, BibTeX]
Sylvester, J., and Chawla, N. "Evolutionary Ensemble Creation and Thinning." Proc. of IEEE IJCNN/WCCI, pp. 5148–55. 2006. [pdf, BibTeX]
Sylvester, J., and Chawla, N. "Evolutionary Ensembles: Combining Learning Agents using Genetic Algorithms." Proc. of AAAI Workshop on Multi-agent Systems, pp. 46–51. 2005. [pdf, BibTeX]
Reports, etc.
Sylvester, J., Reggia, J., and Weems, S. "Predicting improvement on working memory tasks with machine learning techniques." UMD Center for Adv. Study of Languages. Technical Report. 2011. [pdf]
Sylvester, J. "Maximizing Diffusion on Dynamic Social Networks." 2009. Submitted to satisfy the requirements for my Master's in CS. Originally written as a final project report for BMGT 808L (Complex Systems in Business). [pdf]
Talks
"Attractor Network Models for Cognitive Control." Given for CASL's Lunch Lecture series. College Park, MD. March 13, 2012.
"Modeling Cognitive Control of Working Memory as a Gated Cortical Network." Invited talk at the First Int'l Workshop on Cognitive and Working Memory Training. Introduction by Jim Reggia. Hyattsville, MD. August 23–25, 2011. [pdf] — A chapter based on this material is in preperation.
"Oscillatory Neural Network Models of Sequential Short-Term Memory." Given for CASL's Lunch Lecture series. Introduction by Scott Weems. College Park, MD. June 15, 2010. [pdf]
The Jared Watch

This is a sample of some of the media I've been enjoying when I'm not in the lab.

Extracurricular Projects

Here are some projects I've fooled around with in my spare time. Right now I only have a couple of things web-ready. Hopefully I'll be able to share more soon.

Noise Portrait

"Noise Portrait" is an algorithmic animation I created. It gradually reveals a photo of myself (though of course you can supply any image as source data). The locations of the "brushes" are determined by Perlin Noise, hence the name. The size of the brushes gradually decrease, rendering the underlying photo in more detail. Eventually the brush size increases again, bluring things, before decreasing back to a more detailed mode, then blurring again, etc.

At right is a pre-recorded video showing one run of the animation. (Because of the random nature of Perlin Noise, every run is different.) If you would like to see a live version of the animation, there is an embedded applet on this page that you can test out.

Noise Portrait was created using Processing. You can view the source code here.

I also need to re-render the still frames of this into video using ffmpeg. I've tried my most recent project using that and the difference compared to the older workflow which is responsible for the above is huge.

paletteSOM

This is a Self-Organizing Map used to make an abstract animation based on the colors used in sets of images. This demo clip has been fed four JMW Turner paintings (two nautical sunsets and two of the burning of Parliament). There's a couple others on Vimeo.

I'll post the code and some other samples when I get a moment. Like paletteSOM, I need to re-render this from stills using ffmpeg when I get the chance. Using it has made me realize exactly how embarassingly blurred this render is.

Crossword Generator — Coming soon.

Reading List Formatter — Coming soon.

(This doesn't even rise to the level of a 'project,' as simple as it is. But I struggled for a while to get LaTeX to produce a reading list for my dissertation proposal int he format I wanted, so I finally rolled my own solution. It might be useful to others.)

Asterism

Again, this doesn't rise to 'project' level, just a snippet of LaTeX I put together so that I could use asterisms (⁂) when writing papers. I use them to mark off sections of text which will need further attention when editing.

As I said, this isn't really a project, but I'm putting it up here because hopefully it will lead to me cleaning up and posting more of the macro file I've been piecing together over the last year.

\newcommand{\asterism}{%
  \smash{%
    \begin{minipage}[t]{1.2em}%
      \centering%
      \begin{spacing}{1.0}%
        \raisebox{-.15em}{%
          \setlength{\tabcolsep}{.025em}%
          \renewcommand*{\arraystretch}{0.5}%
          \resizebox{1.05em}{!}{%
            \begin{tabular}{@{}cc@{}}%
              \multicolumn{2}{c}*\\[-0.5em]%
              *&*%
            \end{tabular}%
          }% end resizebox
        }% end raisebox
      \end{spacing}%
    \end{minipage}%
  }% end smash
}

There are other macros floating around out there that will create asterisms, but the ones I tried don't work if you're not using single-spacing, standard leading. This one will — best I can tell — in addition to working with different sized text, etc.

[ Oops. There is one issue with this I just discovered that I haven't ironed out yet. When you put \asterism at the front of a new paragraph LaTeX will begin an unindented line with the asterism, then start another new, indented paragraph for whatever comes after it. To stop this you can insert a non-breaking space ("~\asterism"), which won't take up any additional room but will make everything work correctly. Not ideal, but that's the work-around I'm using until I can dedicate some time to figuring this out. ]

Recommendations

You can access the entire list here, or go to a specific category below.

This isn't a list of my favorite things, but things I think other people should try out. I've tried to a certain extent to keep away from listing obvious things that lots of people already know about or like, since you don't need my suggestion for those. And if I were to just list my favorite things, the page would go on forever. So I've tried to keep the list down to things I've actually recommended to friends in conversation. None-the-less, I have a feeling it's going to grow pretty long.

Links