Knowledge Lab

Chris Arntsen

Whirly Ball Ringer, UChicago

Chris is a member of Knowledge Lab's Whirly Ball Unit.
Chris received his bachelor’s degree in chemistry and mathematics in 2008 from the University of Connecticut. He received his PhD from the University of California, Los Angeles working under Professor Daniel Neuhauser. While at UCLA, Chris studied the electronic properties of organic semiconductors, and helped develop and implement a stochastic approach to the GW approximation. After graduation, Chris taught freshman chemistry at UCLA Extension, and subsequently joined the group of Professor Gregory Voth at the University of Chicago.
Research interests:
My current research interests are within the field of reactive molecular dynamics, where I am studying the behavior of proton dissociation of amino acids in water. Specifically, I am trying to improve the fitting models used to parameterize reactive models.
D. Neuhauser, Y. Gao, C. Arntsen, C. Karshenas, E. Rabani, and R. Baer, “Breaking the theoretical scaling limit for predicting quasiparticle energies: A stochastic GW approach,” Phys. Rev. Lett. 113, 076402 (2014).
J. C. Aguirre, C. Arntsen, S. Hernandez, R. Huber, A. M. Nardes, M. Halim, D. Kilbride, Y. Rubin, S. H. Tolbert, N. Kopidakis, B. J. Schwartz and D. Neuhauser, “Understanding local and macroscopic electron mobilities in the fullerene network of conjugated polymer-based silar cells: time-resolved microwave conductivity and theory,” Adv. Funct. Mater., 24, 784-792 (2014).
R. C. Boutelle, Y. Gao, C. Arntsen and D. Neuhauser, “Nanodoentures and mechanical electrodynamics: 3D relative orientation of plasmonic nanoarches from absorption spectra,” J. Phys. Chem. C, 117, 9381-9385 (2013).
C.Arntsen, R.Reslan, S.Hernandez, Y.Gao, and D.Neuhauser, “Direct delocalization for calculating electron transfer in fullerenes,” Int. J. Quant. Chem., 113, 1885-1889 (2013).
R.Reslan, K.Lopata, C.Arntsen, N.Govind, and D.Neuhauser, “Electron transfer beyond the static picture: a TDDFT/TD-ZINDO study of a pentacene dimer,” J.Chem.Phys., 137, 22A502 (2012).
A. Coomar, C. Arntsen, S. Pistinner, K. Lopata, and D. Neuhauser, “NF: Near-field finite-difference time-dependent method for simulation of electrodynamics on small scales,” J.Chem.Phys., 135, 084121 (2011).
C.Arntsen, K.Lopata, M.R.Wall, L.Bartell, and D.Neuhauser, “Modeling molecular effects on plasmon transport: silver nanoparticles with tartrazine,” J. Chem. Phys., 134, 084101 (2011).
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Nathan Bartley

Graduate Student, University of Chicago

Nathan is Master's student in Computer Science from Los Angeles, California. Having done his B.A. at UChicago in Biology, he is interested in bridging the gap between Computer Science and Biology. This has taken him in many different directions, including (but certainly not limited to): machine learning, natural language processing, big data, and software engineering. While at the Knowledge Lab, he has worked on projects ranging from building multi-target text classifiers and accompanying user interfaces to running an exploratory analysis of public GitHub repositories. In his free time he enjoys team sports, martial arts, and nature excursions. 

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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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Hyunzoo Chai

Sr. Data Scientist, Knowledge Lab

Hyunzoo applies computational analysis to problems in social science. Recent work includes mapping research methods within scientific communities, discovering trends in attention to patient safety in journal articles, building semantic maps of the Occupy Wall Street movement, and uncovering impact factor "goosing" techniques from journal citation patterns. 
Hyunzoo holds a Ph.D. in Computational Mathematics and Applications from the Paris-Sorbonne University, with a thesis on automated semantic annotation. In addition to her academic work, she has several years of industry experience developing natural language processing applications at Systran, Xerox Research, and LG Electronics' mobile phone division. 
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Johan Chu

Assistant Professor of Organizations and Strategy, The University of Chicago Booth School of Business

Johan Chu’s research focuses on understanding large-scale change and stasis. In one stream of research, he explores the factors leading to the durable dominance of companies, products, ideas, and people. This work not only suggests strategies for dominants and would-be dominants, but also sheds light on the causes of inequality and stratification in society. Other streams of research investigate: 1) how dominant actors can change institutions, 2) the changing role of elites in corporate governance and society, and 3) new sources of competitive advantage in the twenty-first century. For his empirical studies, Chu uses very large datasets, social network analysis, simulation, and computational text analysis.
Chu earned a B.S. in physics from the Korea Advanced Institute of Science and Technology (KAIST), a Ph.D. in physics from the California Institute of Technology (Caltech), and a Ph.D. in management & organizations from the University of Michigan’s Ross School of Business.
In between Ph.D.s, Chu spent thirteen years in consulting, start-ups, and executive search. He consulted for clients in the United States, Korea, and China. He founded, grew, and sold an enterprise software platform venture, and was later the CEO for another venture. Chu’s final industry position was at the world's largest private executive search firm, where he was the Asia-Pacific Consumer Practice leader
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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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Eamon Duede

Special Advisor, Knowledge Lab, University of Chicago

Eamon served as the Executive Director of Knowledge Lab from 2013 - 2017. He held a key leadership role in strategic and operational planning and management. He was responsible for coordinating day-to-day research activities, asset allocation and distribution (including a major regranting program), supporting and driving key negotiations, as well as acting as a high-level interface between the Center, its partners, and industry.

Eamon holds a B.A. and M.A. in philosophy and has served as an instructor of various logics, and a lecturer in philosophy (ethics, epistemology, and the history of philosophy) and is currently persuing a Ph.D in the Philosophy and Sociology of Science from the University of Chicago's Committee on the Conceptual and Historical Studies of Science.

His academic interests are focused on the constellation of problems fixed around the concepts ‘perception’, ‘belief’, and ‘knowledge’. Philosophy and Sociology of Science; Bayesian Epistemology; the Science of Data; the Role of Mathematical and Statistical Models in Knowledge Creation, Ratification, Propagation, and Rejection; Statistical Inference (Deduction, Induction, and Abduction).

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James A. Evans

Director, Knowledge Lab; Professor, Sociology, University of Chicago; Senior Fellow, Computation Institute; Faculty Director, Masters Program in Computational Social Sciences

I am Director of Knowledge Lab, Director of the Computational Social Science program, Senior Fellow at the Computation Institute, professor of Sociology and the College, and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. My research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement (and dispute), accumulation of certainty (and doubt), and the texture--novelty, ambiguity, topology--of human understanding. I am especially interested in innovation--how new ideas and technologies emerge--and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery. Much of my work has focused on areas of modern science and technology, but I am also interested in other domains of knowledge--news, law, religion, gossip, hunches and historical modes of thinking and knowing. I support the creation of novel observatories for human understanding and action through crowd sourcing, information extraction from text and images, and the use of distributed sensors (e.g., RFID tags, cell phones). I use machine learning, generative modeling, social and semantic network representations to explore knowledge processes, scale up interpretive and field-methods, and create alternatives to current discovery regimes. My research is funded by the National Science Foundation, the National Institutes of Health, DARPA, Facebook, IBM, Jump! Trading and other sources, and has been published in Science, PNAS, American Journal of Sociology, American Sociological Review, Social Studies of Science, Administrative Science Quarterly, PLoS Computational Biology and other journals. My work has been featured in Nature, the Economist, Atlantic Monthly, Wired, NPR, BBC, El País, CNN and many other outlets.
At Chicago, I sponsor the Computational Social Science workshop (with John Padgett). I teach courses in on augmented intelligence, computational content analysis, the history of modern science, science studies, and Internet and Society. Before Chicago, I received my doctorate in sociology from Stanford University, served as a research associate in the Negotiation, Organizations, and Markets group at Harvard Business School, started a private high school focused on project-based arts education, and completed a B. A. in Anthropology and Economics at Brigham Young University. In the course of these events, I married Jeannie Evans and we now have four (fabulous) children, Noah, Ruth, Anna and Kate.
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Austin Kozlowski

Graduate RA, University of Chicago

I am a doctoral student in the Chicago sociology department. My research focuses on the questions of how belief systems are structured and why certain ideas seem to “go together.” By applying state-of-the-art computational methods, I attempt to shed new light on these age-old questions from the sociology of knowledge and culture.
At Knowledge Lab, I am currently engaged in a project utilizing word embeddings to discover cultural associations and categories in text. This project aims to advance an analytical and relational approach to the study culture, building-up our understanding of how meanings are situated with respect to one another in a cultural system.
Before coming to the University of Chicago, I earned my BA in Sociology at the University of Michigan and worked as a research associate with the Chitwan Valley Family Study at the UM Institute for Social Research. During my time at Michigan, I conducted research on the effects of agricultural technology adoption among subsistence farm households in Nepal.
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Molly Lewis

Postdoctoral Scholar, UChicago

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 examines cross-linguistic variability in the alignment of linguistic meaning using large scale corpora. I am co-advised by James Evans at the Knowledge Lab and Gary Lupyan in the Psychology Department at the University of Wisconsin-Madison.
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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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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 if you are not a robot and got real questions to ask.

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