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440 Huntington Avenue
468 West Village H
Boston, MA 02115


  • BA in Neuroscience, Amherst College
  • MS in Immunology, Harvard Medical School

About me

  • Hometown: Newton, MA
  • Field of Study: Neuroscience, Immunology, Computer Science
  • PhD Advisor: Jan-Willem Van de Meent

What are the specifics of your graduate education (thus far)?

I received my Bachelor’s in Neuroscience from Amherst College. I then worked as a Research Fellow at Harvard University in a protein engineering project. I received my Master’s in Immunology from Harvard Medical School. After that, I worked as a Software Engineer at two genomics analysis companies.

What are your research interests in a bit more detail? Is your current academic/research path what you always had in mind for yourself, or has it evolved somewhat? If so, how/why?

As an undergraduate, I studied Neuroscience at Amherst College. My course of study included broad biology, chemistry, and physics training to understand neurological mechanisms ranging from the scale of single proteins to large anatomical divisions and functional units. During my time there, I also took coursework in Computer Science and Artificial Intelligence. I focused specifically on so-called optogenetic methods; the use of light-sensitive proteins and dyes to extract large-scale realtime activity data from neurons, as well as stimulate neuronal activity in highly targeted regions. This study led me to a position as a research fellow at Harvard University, where I joined work on the development of a fast voltage-sensitive protein capable of resolving individual neuron firings and even sub-threshold changes in neuronal membrane potential. After this role, I pursued a two-year Master’s in Immunology, with coursework again covering biological mechanisms of the immune system from the single-protein level to the whole-organism level. I was particularly interested in the generation of large volumes of unbiased data, especially in DNA and RNA sequencing. I followed this interest to a thesis project analyzing whole-exome DNA sequencing data for patients. This project gave me a tremendous exposure to the methodology for the analysis of high-throughput sequencing, from the chemistry and sensor technology required for raw data generation, to the infrastructure and software for pre-processing, and finally the statistical methods used for context-specific interpretation. The pace of development in the field of genomics continues to be quite rapid, given the somewhat unprecedented challenges in processing high-volumes of sparse, noisy data, while still retaining sufficient information for making meaningful conclusions in a very high dimensionality underlying biological feature space. This project and learning led me to jobs as a Software Engineer in one company performing genomic diagnostic services for patients, and one company developing scalable genomic analysis tools. My time as a Software Engineer only increased my passion for the mathematics and engineering involved in this space, and this helped fuel my decision to pursue my PhD in Computer Science.

My current research interests are in algorithms and machine learning. Specifically, I want to design systems for intelligent summarization and context-sensitive feature extraction. As readers and users of modern data repositories and search engines, we often lack the tools to efficiently distill knowledge, and performing this task manually with high fidelity can sometimes require prohibitive expertise. In order to make more of the world’s information available to more citizens, I feel that this is a fundamental area we must improve upon, and is also an extremely exciting way to increase the pace of discovery and communication.

What’s one problem you’d like to solve with your research/work?

One of my short term research goals is to design a system for intelligent summarization of concepts in a way that is sensitive to the context of a specific user. Intuitively, the system should select the right language, but it should also select the right vocabulary and concepts, and should emphasize the points which are most likely to be salient for that particular user. One of the cruxes of this problem is to choose a suitable space in which to embed articles, concepts, and users.

What aspect of what you do is most interesting/fascinating to you? What aspects of your research (findings, angles, problems you’re solving) might surprise others?

I’m fascinated by two contradictory aspects of human cognition that relate closely to the topic of intelligent summarization and representing human knowledge over time. On the one hand, we have extremely effective heuristics for solving many problems, and we perform quite well at reasoning under uncertainty and extrapolating from few or sparse examples in a way that is robust to noise, distraction, and confusion. This tendency would suggest that we can (sometimes) select just the right few articles to read or just the right experiments to perform to achieve a certain goal or understand a certain phenomenon. On the other hand, human society has performed exhaustive brute-force searches of many problem spaces. This tendency shows that sometimes we lack a “compass” or information gradient to follow, and cannot identify a subset of experiments or questions to ask to solve a particular problem. This latter tendency also probably shows the importance of sharing information more effectively as a society; an easy and relatable example is publishing negative experimental results.

What are your research/career goals, going forward?

In general terms, my main goal is to contribute useful new knowledge through my own research, and to increase the sharing and accessibility of knowledge. I feel this applies both to the content of my research interests (such as in pattern recognition, feature extraction, or automated summarization), as well as to the way I intend to work (keeping all of my research and software open-source, and contributing to projects that follow these principles).

Where did you study for your undergraduate degree? Any reason in particular behind your choice (a program you were excited about, a city you love, a researcher you wanted to work with)?

I grew up in the Boston area, and I have lived here since graduating from college, so I am really excited to continue my studies here!