Knowledge Lab Alumni

Yo Joong "YJ" Choe

PhD Student, Machine Learning, Carnegie Mellon

I am a PhD student studying Machine Learning at Carnegie Mellon. I was born and raised in Seoul, South Korea. I am broadly interested in the theory of machine learning as well as its applications to social models.

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Megan Barnes

Student, University of Chicago

I graduated from the University of Chicago, studying linguistics and computer science.  I am interested in language processing as well as data processing techniques, like machine learning, that help us better understand humans. At Knowledge Lab, I worked on a project to create maps of research activity in key topical areas of interest to research funding agencies. The maps could reveal and help funders analyze a) levels of research activity, b) who is participating, c) and in which topics.  Also, I love music, Twitter, comedy, and the Pacific Northwest. I am currently working for an early stage startup in New York City.

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Cody Braun

, IBM

Cody is a masters student in computer science, a veteran of several tech startups, and a University of North Carolina alumnus. Though he is currently studying high-performance computing, machine learning, and data analysis, his wide range of past experiences include jobs at small-town Southern newspapers, Portuguese olive farms, and Australian construction companies. Among other projects, he is currently working on a browser plugin designed to detect phishing, a bound ePaper book dynamically populated by a web crawler, and a handful of Arduino-based gadgets.

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William Catino

Principal Software Engineer, University of Chicago

William earned a PhD in Electrical and Computer Engineering at the Illinois Institute of Technology in 1997, under the guidance of Joseph L. LoCicero and Henry Stark. His research topic was Constrained Optimization Algorithms for Image Processing, applied to Hologram design. In addition to working at several technology companies, he spent a large portion of his career as an independent consultant.

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Alexander Dunlap

PhD Student, Mathematics, Stanford University

Mathematics PhD student at Stanford. I am interested in machine learning and, more generally, using quantitative ideas to understand complex problems. In my free time I enjoy bicycling and baking.

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Aaron Gerow

Post-doctoral Scholar, Knowledge Lab

I grew up in and around Chicago, went to college in Tacoma at Pacific Lutheran University studying computer science and philosophy, liberal arts style. I received my masters degree from University College Dublin in cognitive science and I began my PhD at Trinity in the Fall of 2010.

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Bowen Lou

PhD Student, University of Pennsylvania, Wharton

I completed my masters study in computer science from University of Chicago and am currently a PhD Student at Wharton. I am a data enthusiast, and passionate about using large amounts of data to solve real world problems. I’m specifically interested in applying or proposing solutions from statistical, natural language processing, and network science in order to understand latent patterns under large-scale texts about individuals and organizations from social media, digital publications and the World Wide Web.

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Grace Lu

Student, Uber

Grace works on self-driving cars at Uber. Grace graduated from the college double majoring in Computer Science and Economics from northeast Ohio. She is interested in big data, data analytics, social media trends, language processing, and using technology to create applications in a variety of different fields. At the Knowledge Lab, she worked on a project to map Wikipedia to understand how past revision history can lead to the creation of new pages. In her free time, she enjoys playing tennis, music, and traveling.

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Prateeti Mohapatra

Research Engineer - Modeling and Simulation, Knowledge Lab

My background is in Statistical Modeling and Analysis, System Dynamics, Speech Processing and Software Engineering. I have also worked in the areas of Parallel Programming, Numerical Methods, Requirements Engineering, Life-cycle Cost Modeling, and Global Software Development at Research Centers (Central Research Laboratory, India, ABB Corporate Research Center, India, and Flash Center for Computational Science, UChicago, USA) and Software Industries (The Mathworks).

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Chris Natoli

PhD Student, Mathematics, CUNY Graduate Center

PhD student in Mathematics at CUNY Graduate Center. I like learning about and using machine learning and probabilistic methods to rigorously study problems in the social sciences. I care a lot about the left, history, and New York City.

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Nate Sauder

Data Scientist, Enlitic

Develop deep learning algorithms for medical image diagnosis

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Feng “Bill” Shi

Post-doctoral Scholar, Knowledge Lab

My background is in applied mathematics, and complex networks in particular. I have been working on various interacting particle systems such as the evolving voter model, percolation of nanocomposites, and virus-antibody interactions.

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Taweewat "Champ" Somboonpanyakul

PhD Student, Physics, MIT

I am a PhD student in Physics at MIT. I am originally from Thailand. I have some experience in Astrophysics, specifically gravitational lensing and exoplanets, but I am also interested in social parts of science, and, in particular, how humans gather new knowledge in science.

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Misha Teplitskiy

Postdoctoral Fellow, Harvard University

I graduated with a Ph.D. in Sociology from the University of Chicago. I was a Graduate Research Fellow at KnowledgeLab. My research focuses on academic publishing, particularly on how scientists evaluate the work of others. How do scientists decide if a finding is worthy of publication, and how valid are these judgments?  To answer these questions I examine the peer review files of academic journals using a variety of machine learning and text processing techniques.

I am also engaged in a variety of collaborative projects. In a KnowledgeLab project with James Evans, we test the robustness of a large sample of claims published in social science journals by testing them on out-of-sample data and “perturbing” the model specifications. In another KnowledgeLab project with Eamon Duede and Grace Lu, we study which scientific findings move from the scientific literature to Wikipedia.

A word of biography: I grew up in Ukraine and moved with my family to Texas, where I attended Rice University to study physics and mathematics. During that time I participated in research on plasma waves at Los Alamos National Laboratory and quantum phase transitions at Rice. Near the end of my undergraduate years I discovered the exciting fields of mathematical and computational social science and began a PhD in sociology at the University of Chicago in 2008.

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Reid Mcllroy-Young

Graduate Student, University of Toronto

I have a Masters in Computational Social Science from the University of Chicago and was a researcher at Knowledge Lab. My current research is on developing new machine learning techniques that are useful in the social sciences. What insites into human nature are presnet in the mass behaviour of people? I have primarily worked with collections of source code and bibliographic sources to examine these, but as my PhD progresses I hope to expand the domain.
 
At Knowledge lab I primarily participated in two projects. First a collection of Jupyter Notebooks to help with James Evans' Content Analysis class, these are detailed examples working through problems relevant to many social scientists, such as entity extraction, auto-encoders or model selection. Secondly, I was the main researcher on quarter million Sloan grant to study how programming languages impact science and thought with James Evans and Gary Lupyan.
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Alexander Belikov

Postdoctoral Scholar, University of Chicago

Alexander Belikov is interested in applications of machine learning and natural language processing to social phenomena and texts. Of particular interest to him are the relation extraction and the convergence of social consensus, which can be studied in conjunction. 
 
Alexander received his B.S. and M.S. from the Moscow Institute of Physics and Technology and his PhD in physics from the University of Chicago.
 
Prior to joining the Knowledge Lab, he held a two-year postdoc at the Institut d'Astrophysique de Paris. He also worked as a quantitative researcher in wholesale risk modeling at JP Morgan Chase and later at the exotic equity derivatives desk at Barclays Capital in New York.
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Jacob G. Foster

Assistant Professor, Sociology, UCLA

I was originally trained as a statistical physicist. Like many physicists, I was drawn to the study of complex systems because it licensed me (after a fashion) to work on all sorts of systems that physicists aren’t “supposed” to—complex networks, evolutionary dynamics, etc. As a graduate student (in physics), I took a spectacular seminar on classical social theory (Marx, Weber, Durkheim, Parsons, Merton, Elias, etc.).

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Lingfei Wu

Postdoctoral Scholar, University of Chicago

My research interest is the sciences of collaboration and innovation. I apply mathematical models and machine learning techniques to analyze collective knowledge production systems, including Web of Science, U.S. patents, Stack Exchange, GitHub. My works were published on journals including Physical Review E, Scientific Reports, PloS ONE, and also generated broad interested among diverse audience in New Scientists and Science Daily. I got my PhD in Communication from the City University of Hong Kong in 2013. Overlapping with the PhD program I spent a year in Baidu as an algorithm engineer (internship). I joined Knowledge Lab in 2016 after working two years in the Center for Behavior, Institutions and the Environment at Arizona State University as a post-doc researcher. I am a core member of Swarma Club, a research network in Beijing with a vision to bridge academic, industry, and government. I love dogs but never got a chance to keep one. I am hosting Lu Gu (meaning yesterday once more in Chinese), a Podcast to encourage guests (mostly junior scientists) to share awkward moments and tough times of life. Find me at lingfeiw AT uchicago.edu if you are not a robot and got real questions to ask.

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Shahab Asoodeh

Postdoctoral Scholar, University of Chicago

I am interested in the applications of discrete (differential) geometry and information theory in machine learning and network science. In particular, my main research at the Knowledge Lab focuses on the following two broad questions:  1) How to quantify geometry of graphs, simplicial complexes, and more generally, hypergraphs and to interpret them in real-world networks? And 2) How to use geometry and information theory to define and quantify fairness and privacy in machine learning and data mining?

I am fortunate to work with James Evans at the Knowledge Lab and Ishanu Chattopadhyay at the Institute of Genomics and System Biology (IGSB).

I received my PhD and MSc both in applied mathematics from Queen’s University, Canada, in 2017 and  2011 and a MSc in Electrical Engineering from ETH Zurich and TU Delft in 2010. 

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Molly Lewis

Special Faculty, Carnegie Mellon University

My research focuses on understanding how linguistic meaning--semantic space--is acquired in cognitive development, changes over historical time, and varies cross-linguistically. I am also interested in issues related to scientific replicability and reproducibility. I received my PhD in Developmental Psychology from Stanford University, where I worked with Michael Frank. Before that, I received a BA in Linguistics from Reed College.

At the Knowledge Lab, my work examined cross-linguistic variability in the alignment of linguistic meaning using large scale corpora. I was co-advised by James Evans at the Knowledge Lab and Gary Lupyan in the Psychology Department at the University of Wisconsin-Madison.

I am currently a faculty member in the Department of Psychology and Social and Decision sciences at Carnegie Mellon University. My personal website:  http://www.andrew.cmu.edu/user/mollylew/

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Nandana Sengupta

Postdoctoral Scholar, University of Chicago

My research revolves around improving the predictive performance of traditional Econometric models using modern Statistics and Machine Learning. I'm very interested in developing these techniques with a special focus on Public Policy applications. I am currently a doctoral candidate of Economics at the Tepper School of Business, Carnegie Mellon University. I also hold a Bachelor's degree in Physics from St. Stephen’s College (New Delhi, India) and a Master's degree in Development Economics from Indira Gandhi Institute of Development Research (Mumbai, India).
 
I've had the opportunity to participate in a number of interdisciplinary research groups including the Machine Learning in Social Sciences group at Carnegie Mellon University and the Computational Social Science Workshop at the Santa Fe Institute. I’m looking forward to continuing this line of work at the Knowledge Lab, where my projects will include developing computational tools to more deeply engage user input as well as developing new techniques to assess and predict the impact of academic research.
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Valentin Danchev

Postdoctoral Scholar, University of Chicago

Valentin Danchev is a computational sociologist. He uses network analysis, computational models, text analysis, and large-scale databases to study how patterns of connectivity in social, spatial, and semantic networks influence differences in outcomes, such as replicable discoveries, innovation, mobility opportunities, and inequality.

At Knowledge Lab, Valentin conducts a large-scale evaluation of the robustness and replicability of tens of thousands of research results published in the biomedical literature and examines what network structures of scientific communities contribute to robust, replicable discoveries. He also examines the interplay of social, biological, and organizational mechanisms inducing robust innovations in oncology research.

Valentin holds a DPhil (PhD) in Development Studies from the University of Oxford, where he was also affiliated with the networks research group at the Mathematical Institute. Prior to that, he received his MA from the University of Essex and his BA from the University of Sofia, both in Sociology.

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Brendan Chambers

Postdoctoral Scholar, University of Chicago

Brendan is a creative data scientist specializing in complex interconnected systems. He performed his PhD research in the MacLean Laboratory for Cortical Circuits and Network Neuroscience, studying emergent activity patterns in the neurons of neocortex. His current work is situated at the intersection between machine learning, communication networks, and the sociology of science.

Brendan has been recognized as an NSF S-STEM Fellow in Computation & Modeling and an NSF IGERT Fellow in the Neural Control of Movement. His work in collaboration with Dr. Jason MacLean was nominated for a Hot Topic Award by the Society for Neuroscience and distinguished as a Top 50 Most-Downloaded Article by PLOS Computational Biology.

Brendan grew up in Iowa and studied computer science at Oberlin College. He is a hobbyist electronic musician and climber. You can find him on Twitter via @societyoftrees.

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Anuraag Girdhar

Graduate Student, University of Chicago

Anuraag is a first-year student in the MA program in Computational
Social Science. While conferring with Professor Mark Granovetter at
Stanford, he spent the past year studying how the structure of social
networks affects opinion polarization. He is more broadly interested
in operationalizing notions of objective truth and theory of mind on
social networks. He is also interested in extending these ideas to
designing social networks that optimize social good.

Anuraag has an A.B. in Mathematics and Economics from Dartmouth
College. Prior to arriving at the University of Chicago, he spent four
years in private industry working as a pharmaceutical statistician at
Gilead Sciences, and as an economic research associate at Bridgewater
Associates.

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Di Tong

Graduate Student, University of Chicago

I’m a student in the Computational Social Science Master’s program with a concentration in sociology. My major research interest lies in stratification and inequality, political sociology as well as computational text analysis. Specifically, I’m interested in studying political discourse, cultural norms, public perceptions and attitudes regarding distributive justice; the causes and consequences of economic disparities and the social conditions that mediate and moderate these processes.

Currently, at the Knowledge lab, I’m applying word embedding techniques on massive-scale job ads data to examine the underlying geometry of skill coordination that shapes social lives centered at the labor division. I’m also working on a project that examines the relationship between perceived inequality and political trust in East Asian societies. My previous work utilizes topic modeling to trace the transforming biopolitics in China from 1956-2003 through the lens of the changing official discourse on the birth planning policy.

Before coming to Chicago, I completed my undergraduate degree at Tsinghua University majoring in English Language and Literature.

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