Metaknowledge Research Network

James A. Evans

Director, Knowledge Lab; Professor, Sociology, University of Chicago; Faculty Director, Masters Program in Computational Social Sciences; External Professor, Santa Fe Institute

James Evans is Director of Knowledge Lab, Professor of Sociology, Faculty Director of the Computational Social Science program, and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. I am also an External Professor at the Santa Fe Institute. His 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. He is 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 his work has focused on areas of modern science and technology, but he is also interested in other domains of knowledge–news, law, religion, gossip, hunches and historical modes of thinking and knowing. He supports 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). He uses 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. His research is funded by the National Science Foundation, the National Institutes of Health, DARPA, Facebook, IBM, the Sloan Foundation, Jump! Trading and other sources, and has been published in Science, PNAS, Nature Human Behavior, Nature Biotech, American Journal of Sociology, American Sociological Review, Social Studies of Science, Administrative Science Quarterly, PLoS Computational Biology and other journals. His work has been featured in Nature, the Economist, Atlantic Monthly, Wired, NPR, BBC, El País, CNN and many other outlets.

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.). I was instantly converted by rich theoretical and empirical resources of the sociological tradition. Markets, manners, state formation, systems of cultural and scientific production — now here were complex systems! Sociology also looked like it would give me the intellectual space to weave together my quantitative training with long-term passions for the history of ideas, cultural and biological evolution, and interpretive modes of scholarship. After a postdoc in the sociology department at the University of Chicago, I joined the sociology department at UCLA as an assistant professor in 2013.

I am interested in the evolution and population dynamics of ideas (broadly construed). How are new ideas born? Why do some spread? What role do ideas play in organizing social structures? And how do social structures affect the genesis, diffusion, and ultimate extinction of ideas? Currently, I work on a particular subset of the world of ideas: the beliefs, practices, and theories that make up scientific knowledge. By fitting rich computational models of discovery and impact to data extracted from articles and patents, I infer the preferences and social processes that give scientific knowledge its particular shape and trajectory. Broadly speaking, I aim to generate new knowledge about the process of knowledge-making; to identify and understand the heuristics, strategies, and institutions that guide scientists as they make sense of the natural world. I hope to thereby shine quantitative light on classic (and not-so-classic) questions in the sociology of science: What makes some scientists unusually successful? How does the tension between tradition and innovation play itself out in the lives of scientists and scientific disciplines? How does science even work at the edge of the knowable? And how are scientists, as particular kinds of knowers, built up from a cognitive substrate shared with every other human on the planet? My approach is informed by a range of traditions, from science studies and contemporary social theory to complex systems and cultural evolution.


Luís Amaral

Professor, Chemical and Biological Engineering Professor, Medicine; HHMI Early Career Scientist, Northwestern University

Professor Amaral, a native of Portugal, conducts and directs research that provides insight into the emergence, evolution, and stability of complex social and biological systems. His research aims to address some of the most pressing challenges facing human societies and the world’s ecosystems, including the mitigation of errors in healthcare settings, the characterization of the conditions fostering innovation and creativity, or the growth limits imposed by sustainability.

Recently, Amaral proposed the development of cartographic methods for the representation of complex biological networks. These methods will enable researchers to accomplish something similar to what travelers now can easily accomplish with, for example, Google Maps, that is, to glean the important information on a given system at the scale of interest to the researcher. These tools hold the promise to enable biomedical researcher to design or re-engineer biological systems for therapeutic purposes.

Professor Amaral has published over a hundred scientific peer-reviewed papers in leading scientific journals. Those papers have been cited in excess of 7 thousand times; ten having accumulated more than 200 citations each. His research has been featured in numerous media sources, both in the US and abroad. Professor Amaral has received a CAREER award from the National Institutes of Health in 2003, was named to the 2006 class of Distinguished Young Scholars in Medical Research by the W. M. Keck Foundation, and has been selected as an Earlier Career Scientist by the Howard Hughes Medical Institute.

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David Blei

Professor, Statistics and Computer Science, Columbia University

I am a professor of Statistics and Computer Science at Columbia University. I am also a member of the Institute for Data Sciences and Engineering. I work in the fields of machine learning and Bayesian statistics.

David’s Ph.D. advisor was Michael Jordan at U.C. Berkeley Computer Science. David was a postdoctoral researcher with John Lafferty at CMU in the Machine Learning department.

His research interests include:

  • Probabilistic graphical models and approximate posterior inference
  • Topic models, information retrieval, and text processing
  • Bayesian nonparametric statistics

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Ian Foster

Director, Computation Institute; Senior Scientist, Mathematics and Computer Science (MCS) at Argonne National Laboratory; Executive Advisory Committee Member and Senior Fellow, Institute for Genomics and Systems Biology (IGSB); Professor, Computer Science, University of Chicago; Professor, Physical Sciences, University of Chicago; Distinguished Fellow, Argonne National Laboratory, University of Chicago

Ian Foster is Director of the Computation Institute, a joint institute of the University of Chicago and Argonne National Laboratory. He is also an Argonne Senior Scientist and Distinguished Fellow and the Arthur Holly Compton Distinguished Service Professor of Computer Science.

Ian received a BSc (Hons I) degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science. His research deals with distributed, parallel, and data-intensive computing technologies, and innovative applications of those technologies to scientific problems in such domains as climate change and biomedicine. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures.

Professor Foster is a fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and the British Computer Society. His awards include the Global Information Infrastructure (GII) Next Generation award, the British Computer Society’s Lovelace Medal, R&D Magazine’s Innovator of the Year, and an honorary doctorate from the University of Canterbury, New Zealand. He was a co-founder of Univa UD, Inc., a company established to deliver grid and cloud computing solutions.

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John P. Ioannidis

C. F. Rehnborg Professor in Disease Prevention, Medicine; Professor, Health Research & Policy; Professor (By courtesy), Statistics, Stanford University

I have worked in the fields of evidence-based medicine, clinical investigation, clinical and molecular epidemiology, clinical research methodology, empirical research methods, statistics, and genomics. I have a strong interest in large-scale evidence (in particular randomized trials and meta-analyses) and in appraisal and control of diverse biases in biomedical research. I am interested in developing and applying new research methods, and in the interdisciplinary enhancement of existing research methods for study design and analysis in biomedicine. Some of my most influential papers in terms of citations are those addressing issues of replication validity of genetic association studies, biases in biomedical research, research synthesis methods, extensions of meta-analysis, genome-wide association studies and agnostic evaluation of associations, and validity of randomized trials and observational research. I have also designed, steered and participated in influential randomized trials (in particular, the major trials that changed decisively the management and outcome of HIV infection, but also trials in nephrology, and in antibiotic use in the community), and large international consortia that have helped transform the efficiency of research in diverse fields of genomic, molecular and clinical epidemiology. I enjoy working with a diverse array of colleagues from very diverse disciplines and to have an opportunity to learn from both senior and junior investigators, and particularly students at all levels.

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David Krakauer

Director, Wisconsin Institute for Discovery; Co-Director, Center for Complexity and Collective Computation; Professor, Genetics, University of Wisconsin, Madison

A graduate of the University of London, where he went on to earn a master’s degree in computer science and mathematics, David Krakauer received his D.Phil. in evolutionary theory from Oxford University in 1995. He remained at Oxford as a postdoctoral research fellow and two years later was named a Wellcome Research Fellow in mathematical biology and lecturer at Pembroke College. In 1999, he accepted an appointment to the Institute for Advanced Study at Princeton University and served as visiting professor of evolution. He moved on to the Santa Fe Institute as a professor three years later and was made faculty chair in 2009. Krakauer has been a visiting fellow at the Genomics Frontiers Institute at the University of Pennsylvania and a Sage Fellow at the Sage Center for the Study of the Mind at the University of California, Santa Barbara.

Krakauer’s research focuses on the evolutionary history of information processing mechanisms in biology and culture. This includes genetic, neural, linguistic and cultural mechanisms. The research spans multiple levels of organization, seeking analogous patterns and principles in genetics, cell biology, microbiology and in organismal behavior and society. At the cellular level, Krakauer has been interested in molecular processes, which rely on volatile, error-prone, asynchronous, mechanisms, which can be used as a basis for decision making and patterning. He also investigates how signaling interactions at higher levels, including microbial and organismal, are used to coordinate complex life cycles and social systems, and under what conditions we observe the emergence of proto-grammars. Much of this work is motivated by the search for ‘noisy-design’ principles in biology and culture emerging through evolutionary dynamics that span hierarchical structures.

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Andrey Rzhetsky

Professor, Department of Medicine; Professor, Department of Human Genetics; Senior Fellow, Computation Institute; Senior Fellow, Institute for Genomics and Systems Biology, University of Chicago

My main interest is in gaining an (asymptotic) understanding how phenotypes, such as human healthy diversity and maladies, are implemented at the level of genes and networks of interacting molecules.

To harvest as much information about known molecular interactions as possible, my group runs a large-scale text-mining effort aiming at analysis of a vast corpus of biomedical publications. Currently we can extract from text automatically about 500 distinct flavors of relations among biomedical entities (such as bind, activate, merystilate, and transport).

To sharpen our text-mining axes, we are actively designing related models and computational applications. Furthermore, in cooperation with our experimentally talented colleagues, we are striving to use text-mined networks to understand, interpret and refine high- or low-throughput experimental data. We are also computationally generating biological hypotheses that our generous collaborators are attempting to test experimentally.

My older passion is in developing and applying computational methods related to phylogenetics and evolutionary biology.

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Michael Alfaro

Associate Professor, Department of Ecology and Evolutionary Biology & Institute for Society and Genetics, UCLA

 ISG Fellow Dr. Michael Alfaro is an Associate Professor in the Ecology and Evolutionary Biology department where he and ISG Associate Director Dr. Jessica Lynch Alfaro run the UCLA Alfaro Lab. The goal of his research is to understand the factors that govern the evolutionary dynamics of organismal diversification. Why are some groups morphologically diverse? Are there general laws or themes that can be used to explain the uneven distribution of diversity in physiological traits across lineages? Does morphological diversity always signal mechanical, functional, or ecological diversity? To address these questions, Alfaro works largely on marine fishes. His research approach is interdisciplinary and quantitative and crosses traditional boundaries among evolutionary morphology, molecular phylogenetics, and theoretical evolution. He identifies and quantifies organismal diversity using morphological and functional morphological techniques; constructs evolutionary trees and tests evolutionary hypotheses using phylogenetic statistical methods; and uses models of trait evolution to explore form-function dynamics.

Carl Bergstrom

Professor, Biology, University of Washington

Carl is a professor in the Department of Biology at the University of Washington and a member of the External Faculty at the Santa Fe Institute.

He received his bachelor’s degree from Harvard University in 1993, where he worked with Naomi Pierce and David Haig. Carl did his Ph.D. work in Biological Sciences at Stanford University with Marc Feldman. processes. After leaving Stanford, he did two years of postdoctoral work at Emory University with Bruce Levin.

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Edward Boyden

Associate Professor, MIT Media Lab and McGovern Institute for Brain Research at MIT, MIT

Professor Boyden leads the MIT Media Lab’s Synthetic Neurobiology research group, which develops tools for mapping, controlling, observing, and building dynamic circuits of the brain, and uses these neurotechnologies to understand how cognition and emotion arise from brain network operation, as well as to enable systematic repair of intractable brain disorders such as epilepsy, Parkinson’s disease, and post-traumatic stress disorder. His research group has invented a suite of “optogenetic” tools that are now in use by thousands of research groups around the world for activating and silencing neurons with light.

Boyden was named to the “Top 35 Innovators Under the Age of 35” by Technology Review in 2006, and to the “Top 20 Brains Under Age 40” by Discover magazine in 2008. He has received the Gabbay Award, National Institutes of Health (NIH) Director’s Pioneer Award and Transformative Research Award, the Society for Neuroscience Research Award for Innovation in Neuroscience, the NSF CAREER Award, the Paul Allen Distinguished Investigator Award, and the New York Stem Cell Robertson Investigator Award. In 2010, his work was recognized as the “Method of the Year” by the journal Nature Methods. Most recently he shared the 2013 Grete Lundbeck European Brain Research Prize for outstanding contributions to European neuroscience–the largest neuroscience prize in the world.

Stephen V. David

Assistant Professor, Oregon Hearing Research Center, Oregon Health and Science University

Neuroscience is a new but rapidly growing field, drawing ideas and methodologies from many other fields, including biology, psychology, physics, mathematics and philosophy. Each neuroscientist brings a unique perspective into their work that reflects this diversity. We are studying how academic mentorship, the hands-on training received at the doctoral and postdoctoral level, influences the work of individuals and permits the synthesis of new experimental approaches. Neurotree ( is a collaborative, open-access website that tracks and visualizes the academic genealogy. After nine years of growth driven by user-generated content, the site has captured information about the mentorship of over 45,000 neuroscientists. It has become a unique tool for a community of primary researchers, students, journal editors, and the press. The database allows us to explore the evolution of new ideas and how mentorship has contributed their development. We are exploring new ways to improve the quality of the existing data and ways to link Neurotree to other datasets, such as publication and grant databases. Inspired by Neurotree’s example, genealogies have been launched for a number of other fields under auspices of the Academic Family Tree, which aims to build a single genealogy across all academic fields.


Jessica Flack

Co-Director, Center for Complexity and Collective Computation, University of Wisconsin-Madison

Jessica’s research focuses on uncertainty reduction, coarse-graining and collective computation in nature and their role in the origins of biological space-time—that is, the evolution and development of hierarchical structure with multiple, functionally significant time and space scales.

Jessica and her colleagues study a wide range of collectives, from group of cells forming neural tissue, to groups of macaques forming animal societies, to groups of online gamers forming virtual societies.

Jessica received her BA with honors from Cornell University in 1996, studying anthropology, evolutionary theory, and biology. She received her PhD from Emory University in 2004, studying animal behavior, cognitive science, and evolutionary theory. For the next eight years she was in residence at the Santa Fe Institute, first as a Postdoctoral Fellow and then as Research Professor, and finally as Professor. She moved to the University of Wisconsin, Madison in 2011. Jessica’s research has empirical and theoretical components and sits at the interface of evolutionary theory, pattern formation, behavior, cognitive science, computer science, information theory, and statistical mechanics. Although most of her work now is of a computational nature, she has spent thousands of hours collecting large behavioral data sets, including highly resolved time-series, from animal societies, and she conducted the first behavioral knockout study on social systems. In that study, she designed an experiment to disable a critical conflict management function—policing—to quantify its role in social system robustness in an animal society.

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Mark Green

Professor Emeritus, Mathematics, UCLA

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César A. Hidalgo

Assistant Professor, Media Arts and Sciences, MIT Media Lab

César A. Hidalgo heads the Macro Connections group at The MIT Media Lab and is also the ABC Career Development Professor of Media Arts and Sciences at MIT. Hidalgo’s work focuses on understanding the evolution of information in natural, social, and economic systems, and on the development of big data visualization engines designed to make available unwieldy volumes of data. Hidalgo’s academic publications have been cited more than 4,500 times and his visualization engines have received more than 5 million visits. He is the author of Why Information Grows (Penguin UK, Basic Books US, Forthcoming June 2, 2015) and the co-author of The Atlas of Economic Complexity (MIT Press).

Konrad Kording

Associate Professor, Physical Medicine and Rehabilitation/Physiology, Northwestern University

The research of the Bayesian Behavior group shows that movement and movement learning can be understood in terms of statistical principles. Our Sensors (Eyes, Ears, Skin etc) are not perfect but are noisy. Moreover, our muscles are noisy and if we try to do the same movement over and over it will be different each time. This means that if we make a movement, say swing a golf club, we will have uncertainty in the potential movement outcomes. Our group studies how people make movement decisions in the presence of such uncertainty.

Our research has four main thrusts:

  • We advance big Data approaches to neuroscience
  • We study experimentally how people move and how their movements are affected by uncertainty.
  • We build computational models using Bayesian statistics to calculate how people could move optimally or learn to move optimally.
  • We build Bayesian Algorithms to solve problems that we find interesting. For example we analyze how neurons are connected in the nervous system.

The main thrust of our current research is to allow for better rehabilitation procedures through an understanding of motor learning.

Our lab is part of Northwestern University, Departments Physiology and PM&R. It is associated with Northwestern Department of applied math. Our laboratory is part of the rehabilitation institute of Chicago.

Hannah Landecker

Associate Professor, Sociology, UCLA

My work takes place at the intersection of the life and social sciences. In general, the social and historical study of biotechnology and life science, from 1900 to now, is my area of specialization. I am currently writing a book called “American Metabolism,” which looks at transformations to the metabolic sciences wrought by the rise of epigenetics, microbiomics, cell signaling and hormone biology. A related project concerns the history of metabolic hormones after 1960 and the rise of the cellular “signal” as a central category of thought and practice in the life sciences.

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Christopher Lee

Professor, Department of Computer Science; Professor, Department of Chemistry & Biochemistry, UCLA

Prof. Lee has been a Faculty member in the Department of Chemistry and Biochemistry since 1998. His training provided an unusual combination of experimental cell biology, biophysics, and algorithm development, which he has applied at UCLA to bioinformatics analysis of genome evolution. He has led efforts to establish a bioinformatics Ph.D. program at UCLA. He has served on the Board of Scientific Counselors, NIH National Center for Biotechnology Information, and serves on the editorial board of Biology Direct . His current research focuses on alternative splicing and its role in genome evolution.

Research Interest: Bioinformatics

  • alternative splicing and genome evolution: genome-wide analysis of the types and functions of alternative splicing, and its apparent role in evolution of mammalian genomes. Alternative splicing appears to have greatly accelerated major evolutionary events such as exon creation, and now is an exciting new area of research in genome evolution
  • protein evolutionary pathways. Using a massive dataset of clinical HIV sequences, we have developed new methods to decode the evolutionary pathways by which HIV evolves drug resistance. We have just shown that our methods can measure the detailed “fitness landscape” describing how HIV proteins can evolve, as a kinetic network showing the actual rate of evolution along every possible evolutionary pathway
  • graph databases for bioinformatics and genomics. We have developed a general framework for working with genomic data as an abstract graph database, for fundamental problems such as multiple genome alignment query and protein interaction network analysis

Hod Lipson

Associate Professor, Department of Mechanical and Aerospace Engineering, Cornell University

Hod Lipson is a professor of engineering at Cornell University in Ithaca, New York, and a co-author of the recent book “Fabricated: The New World of 3D printing”. His work on self-aware and self-replicating robots, food printing, and bio-printing has received widespread media coverage including The New York Times, The Wall Street Journal, Newsweek, Time, CNN, and the National Public Radio. Lipson has co-authored over 200 technical papers and speaks frequently at high-profile venues such as TED and the US National Academies. Hod directs the Creative Machines Lab, which pioneers new ways to make machines that create, and machines that are creative.

Research Interests:

My relatively broad spectrum of research projects focus on what I consider to be two “grand challenges” of engineering: (a) Can we design machines that can design other machines, and (b) Can we make machines that can make other machines. Both of these questions lie at the crux of understanding the engineering process itself, and progress on these fronts can offer huge leverage in our ability to design, make and maintain increasingly complex systems in the future. Biological life has answered these challenges in ways that dwarf the best teams of human engineers; I therefore use primarily biologically-inspired approaches, as they bring new ideas to engineering and new engineering insight into biology. 


Hyot Long

Associate Professor, Japanese Literature, East Asian Languages and Civilizations; Co-Director, Text Lab, University of Chicago

Hoyt comes to Metaknowledge with an interest in how social network analysis, corpus analysis, and other computational methods can facilitate large-scale comparative inquiries into the social dynamics of cultural production. Specifically, he is interested in what these methods can tell us about the diffusion of artistic style and form, the role of formal and informal social ties in shaping such processes, and the emergence of system-level dynamics across linguistic and political boundaries. Along these lines, he Directs a collaborative initiative with Richard Jean So called ‘Text Lab’, which applies these methods to the study of global modernism in the early 20th century.

Hoyt’s research and teaching center on modern Japan, with particular interests in regional literature, publishing history, media and communication, and environmental history. He also has an interest in the application of social-scientific methods to the study of how texts and ideas emerge and circulate within social and material systems.

In a current book project, Hoyt joins this sociological interest with his interest in the history of communication in Japan. Specifically, Hoyt looks at how developments in communications technology at the turn of the last century impacted practices of writing, patterns of social association, and ideas of communication itself. Utilizing a variety of materials (epistolary fiction, letter-writing manuals, correspondence magazines), he uncovers emerging fantasies and beliefs about the meaning of connection in a postal age, particularly as they related to changing notions about handwriting, voice, memory, and brevity.

Gary Lupyan

Assistant Professor, Psychology, University of Wisconsin Madison

Language is one of the defining traits of our species. It, of course, allows for the accumulation and communication of knowledge. But in addition to its uses in communication, the acquisition and use of language appears to augment the human brain in important ways. The aim of my primary line of research is to investigate and delineate these extra-communicative functions of language:

How is our ability to place objects into categories altered by language? Does language literally change what we see? How does naming an object affect visual representations? How does using language change our memories? Do people who speak different languages see and remember things differently? Are there ideas that are unthinkable without language?

In addition, I have investigated the relationship between grammatical structure and social structure (Lupyan & Dale, 2010) and have a continued interest in the ways that the communicative (and cognitive) needs of a population shape the grammatical structure of languages.

I also have a broad interest in the dynamics of neural coding and the way in which perceptual and conceptual representations are dynamically shaped by an individual’s goals, expectations, and task context.

I have employed a wide range of experimental paradigms and tools to address the questions that interest me. These have included behavioral experiments, neural network modeling, large-scale corpus analysis, eye-tracking, neuroimaging (fMRI), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS).

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Gary Lupyan

Assistant Professor, Psychology, University of Wisconsin Madison

Language is one of the defining traits of our species. It, of course, allows for the accumulation and communication of knowledge. But in addition to its uses in communication, the acquisition and use of language appears to augment the human brain in important ways. The aim of my primary line of research is to investigate and delineate these extra-communicative functions of language:

How is our ability to place objects into categories altered by language? Does language literally change what we see? How does naming an object affect visual representations? How does using language change our memories? Do people who speak different languages see and remember things differently? Are there ideas that are unthinkable without language?

In addition, I have investigated the relationship between grammatical structure and social structure (Lupyan & Dale, 2010) and have a continued interest in the ways that the communicative (and cognitive) needs of a population shape the grammatical structure of languages.

I also have a broad interest in the dynamics of neural coding and the way in which perceptual and conceptual representations are dynamically shaped by an individual’s goals, expectations, and task context.

I have employed a wide range of experimental paradigms and tools to address the questions that interest me. These have included behavioral experiments, neural network modeling, large-scale corpus analysis, eye-tracking, neuroimaging (fMRI), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS).

Hoifung Poon

Researcher, Microsoft Research

I am a researcher at Microsoft Research in Redmond, WA. My research interest is in advancing machine learning and natural language processing to automate discovery in genomics and precision medicine. My most recent work focuses on scaling semantic parsing to Pubmed for extracting biological pathways, and on developing probabilistic methods to incorporate pathways with high-throughput genomics data in cancer system biology. I have received Best Paper Awards in NAACL, EMNLP, and UAI.

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Daniel Lord Small

Professor, History, Harvard University

I am a student of early human history; in my teaching and research, I cover a span of time from humanity’s deep history in Africa to Mediterranean Europe in the later middle ages. The overarching intellectual project of my work in recent years has been to identify and develop new frames or narratives for binding human history together into a seamless whole. I work under the assumption that history is not a political science designed to explain the present. It is an anthropological science designed to help us understand humanity. In everything I do, I hope to show how the intellectual projects that drive transnational and global histories work equally well across time, and to offer the deep past as the new intellectual frontier of historical research and historical framing in the twenty-first century.

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Richard Jean So

Assistant Professor, English; Co-Director, Text Lab, University of Chicago

Broadly speaking, Richard’s teaching and research interests center on modern American literature in a transnational context. Within this area, he is interested in theories of cultural transnationalism, the history of media and communications, and the “Pacific” (which includes U.S., Asian American, and East Asian cultures) as a coherent area of study. Richard also does substantial work in the digital humanities. In particular, he is interested in the use of new computational and social scientific methods, such as text mining, to model a form of textual criticism that mediates between distant and close reading approaches.

Rebecca C. Steorts

Assistant Professor of Statistical Science, Duke University

Rebecca C. Steorts is an Assistant Professor of Statistical Science at Duke University with affiliations in the information initiative at Duke (iid) and the Social Science Research Institute (SSRI).  She received her B.S. in Mathematics in 2005 from Davidson College, her MS in Mathematical Sciences in 2007 from Clemson University, and her PhD in 2012 from the Department of Statistics at the University of Florida under the supervision of Malay Ghosh. She is currently a Visiting Assistant Professor in the Statistics Department at Carnegie Mellon University. Rebecca is a recipient of the Graduate Alumni Fellowship Award (2007-2010) from the University of Florida and the U.S. Census Bureau Dissertation Fellowship Award (2010-2011). In 2011, she was awarded the UF Innovation through Institutional Integration Program (I-Cubed) and NSF for development of an introductory Bayesian course for undergraduates. She has also been awarded Finalist for the 2012 Leonard J. Savage Thesis Award in Applied Methodology. She is interested in scalable computational methods for social science applications. Her current works focuses on recovering high dimensional objects from degraded data and determining how to recover the underlying structure. Methods used for this are entity resolution, small area estimation, locality sensitive hashing, and privacy-preserving record linkage as applied to medical studies, fmri studies, human rights violations, and estimation of poverty rates in hard to reach domains. Her research was on record linkage and sparse clustering was recently funded by the John Templeton Foundation, MetaKnowledge Network Grants Awarded, November 2014. Also, her work on privacy and record linkage was just funded by the National Science Foundation. She was recently named to MIT Technology Review’s 35 Innovators Under 35 for 2015 as a humantarian in the field of software. Her work will be profiled in the Septmember/October issue of MIT Technology Review and she will be recognized at a special ceremony along with an invited talk at EmTech in November 2015.



Tim Weininger

Assistant Professor, Department of Computer Science and Engineering, University of Notre Dame

I’m an assistant professor in the College of Engineering at the University of Notre Dame, with appointments in the Interdisciplinary Center for Network Science and Applications (iCeNSA) and the Department of Computer Science and Engineering. I joined the faculty in August 2013, after completing my Ph.D. in Computer Science at the University of Illinois Urbana-Champaign.

I work in network science, with a particular focus on multimodal/heterogeneous information networks. My research lies at the intersection of machine learning and databases and information retrieval.



Jevin D. West

Assistant Professor, Information School, University of Washington

I am an assistant professor in the Information School at the University of Washington. We like maps and information. The research I do aims to map large networks in order to understand the flow of information. I co-founded This research project aims to rank and map science, in hopes of building better tools for navigating the ever expanding scholarly literature.

I have been lucky in my research journal so far. I have had the best graduate and post-doctoral mentors a graduate. I have worked in departments and universities that encourage the type of interdisciplinary work I enjoy doing, and I have pursued questions that keep me thinking inside and outside the lab.

I grew up in the small town of Ammon, Idaho. It doesn’t house any great scientists, but it does offer close proximity to skiing and the outdoors—something I enjoy very much. I attended Utah Sate University, originally to play tennis and to enjoy some of the best snow on earth. After finishing a bachelor’s degree in biology, I took a two year tennis-pro hiatus and traveled. I returned to USU and completed a Masters degree with Keith Mott and David Peak. The research I did with them explored a topic at the cross section of physics, biology and computer science. I looked at how stomatal networks on the surface of a leaf perform a distributed computation. This work hooked my interests to the field of complexity—an area of science that is ambiguous, messy and full of questions that will take generations to sort out.

In the fall of 2004, I visited the University of Washington and met with Carl Bergstrom in the Department of Biology. After a three-day conversation in three hours about the role of information in biology, I knew I had found the right place. During my PhD, I was introduced to citation networks as a model system for studying information flow in social and biological systems. It is also ignited my passion for improving scholarly communication and led to the Eigenfactor Project. The project continues to grow and now includes research in mappinginformation visualizationgender researcheconomics of scholarly publishing, and recommendation For my post-doc, I was fortunate to continue working in the area of networks and mapping while working with Martin Rosvall at Umea University (Sweden).



Lynne G. Zucker

Professor, Department of Sociology; Director, Center for International Science, Technology, and Cultural Policy, UCLA

My training is in organizational sociology, institutional theory, economic sociology, and social psychology. My current major interests are on processes and impact of knowledge transmission from basic science to commercial use, especially impact on economic performance of firms, creation of new organizational populations (some of which become new industries), and on productivity growth. I share with Michael Darby an interest in identifying the major mechanisms of knowledge transfer and the institutional infrastructure that cause metamorphic industry change and rapid economic growth. Within the context of basic scientific breakthroughs that are commercially applicable, we are exploring other measures of success such as IPO returns and examining the impact of other means of knowledge transfer such as joint ventures. We are now studying many of the same processes in nanoscience, a newly emerging basic research area with significant commercial potential. To identify institutional infrastructure effects, we are completing a comparative study of biotech in Japan and the U.S. and embarking on a set of major international analyses of the transmission of scientific breakthroughs to commercial use in nanotechnology.