Luís M. A. Bettencourt is the Pritzker Director of the Mansueto Institute for Urban Innovation and Professor of Ecology and Evolution at the University of Chicago, as well as an External Professor of Complex Systems at the Santa Fe Institute. He was trained as a theoretical physicist and obtained his undergraduate degree from Instituto Superior Técnico (Lisbon, Portugal) in 1992, and his PhD from Imperial College (University of London, UK) in 1996 for research in statistical and high-energy physics models of the early Universe.  He has held postdoctoral positions at the University of Heidelberg (Germany), Los Alamos National Laboratory (Director’s Fellow and Slansky Fellow) and at MIT (Center for Theoretical Physics). He has worked extensively on complex systems theory and on cities and urbanization, in particular. His research emphasizes the creation of new interdisciplinary synthesis to describe cities in quantitative and predictive ways, informed by classical theory from various disciplines and the growing availability of empirical data worldwide. He is the author of over 100 scientific papers and several edited books. His research has been featured in leading media venues, such as the New York Times, Nature, Wired, New Scientist, and the Smithsonian.

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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 understanding. I am especially interested in innovation—how new ideas and practices 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, machine 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 has been supported by the National Science Foundation, the National Institutes of Health, the Air Force office of Science Research, and many philanthropic sources, and has been published in Nature, Science, Proceedings of the National Academy of Science, American Journal of Sociology, American Sociological Review, Social Studies of Science, Research Policy, Critical Theory, Administrative Science Quarterly, and other outlets. My work has been featured in the Economist, Atlantic Monthly, Wired, NPR, BBC, El País, CNN, Le Monde, and many other outlets.

At Chicago, I am Director of Knowledge Lab, which has collaborative, granting and employment opportunities, as well as ongoing seminars. I also founded and now direct on the Computational Social Science program at Chicago, and sponsor an associated Computational Social Science workshop. I teach courses in augmented intelligence, the history of modern science, science studies, computational content analysis, 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 at Brigham Young University.

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Nick Feamster is Neubauer Professor in the Department of Computer Science and the College. He researches computer networking and networked systems, with a particular interest in Internet censorship, privacy, and the Internet of Things. His work on experimental networked systems and security aims to make networks easier to manage, more secure, and more available.

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I lead an interdisciplinary computational and theoretical research group working on materials self-assembly, biomolecular simulation, viral dynamics, and vaccine design. My doctoral training provided me with expertise in molecular simulation, statistical mechanics, and machine learning, in which I developed new nonlinear machine learning approaches to study the conformations and dynamics of proteins, polymers, and confined water. During my post-doctoral fellowship, I acquired knowledge and skills in immunology and viral dynamics, and developed new computational tools for structure-free prediction of antibody binding sites, and the computational design of HIV vaccines using statistical mechanical principles.

Since establishing my independent research program in 2012, I have combined these expertise to establish a dynamic research program in computational materials science and computational virology for which I have attracted over $2.9M in federal research funding, established a strong publication record (60+ papers) in leading journals, and have been recognized with a number of national awards including a 2018 Royal Society of Chemistry Molecular Systems Design and Engineering Emerging Investigator Award, 2017 Dean’s Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award, 2015 ACS Outstanding Junior Faculty Award, 2014 ACS Petroleum Research Fund Doctoral New Investigator Award, 2013 NSF CAREER Award, and I was named the 2013 Institution of Chemical Engineers North America “Young Chemical Engineer of the Year”. I am engaged and active within my professional organization serving on the AIChE Area 1a Programming Committee and as CoMSEF Liaison Director, and in organizing multiple scientific sessions at our national meetings. In addition to independent theoretical work, my research interests lead naturally to close collaboration with experimentalists and clinicians, teaching me the power of mutually reinforcing theoretical and experimental work and the importance of effective communication, planning, budgeting, teamwork and leadership.

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Michael J. Franklin is the inaugural holder of the Liew Family Chair of Computer Science. An authority on databases, data analytics, data management and distributed systems, he also serves as senior advisor to the provost on computation and data science.

Previously, Franklin was the Thomas M. Siebel Professor of Computer Science and chair of the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. There, he co-founded Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), a leading academic big data analytics research center. The AMPLab won a National Science Foundation CISE “Expeditions in Computing” award, which was announced as part of the White House Big Data Research initiative in March 2012, and received support from over 30 industrial sponsors. AMPLab created industry-changing open source Big Data software including Apache Spark and BDAS, the Berkeley Data Analytics Stack. At Berkeley, he also served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments.

An energetic entrepreneur in addition to his academic work, Franklin founded and became chief technology officer of Truviso, a data analytics company acquired by Cisco Systems. He serves on the technical advisory boards of various data-driven technology companies and organizations.

Franklin is a Fellow of the Association for Computing Machinery and a two-time recipient of the ACM SIGMOD (Special Interest Group on Management of Data) “Test of Time” award. His many other honors include the outstanding advisor award from Berkeley’s Computer Science Graduate Student Association. He received the Ph.D. in Computer Science from the University of Wisconsin in 1993, a Master of Software Engineering from the Wang Institute of Graduate Studies in 1986, and the B.S. in Computer and Information Science from the University of Massachusetts in 1983.

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My work is geared towards discovering and describing the principles that are involved in relating linguistic forms to meanings; determining how this mapping is achieved through the interaction of properties of the linguistic system, properties of cognition more generally, and broader features of communicative contexts; and understanding the extent to which structural and typological features of language can be explained in terms of meaning.  Over the past two decades, I have explored these issues primarily through a focused exploration of the language of comparison, amount and degree, though my research has also touched on core issues in the syntax-semantics interface such as ellipsis, anaphora, and quantification.

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Julia Lane is the Executive Director of the Center for Data and Computing, responsible for shaping and executing the strategic vision of CDAC, building new research partnerships and outreach strategies to foster interdisciplinary collaborations, and ensuring that the University continues to broaden applications of data science and computing approaches.

My main research interests are in speech and language processing, as well as related aspects of machine learning.

I am an Associate Professor at TTI-Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus. We are recruiting students to our PhD program and visiting student program, as well as additional faculty, including in speech and language-related areas (more on Speech and Language at TTIC).

I completed my PhD in 2005 at MIT in the Spoken Language Systems group of the Computer Science and Artificial Intelligence Laboratory. In 2005-2007 I was a post-doctoral lecturer in the MIT EECS department. In Feb.-Aug. 2008 I was a Research Assistant Professor at TTI-Chicago.

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Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His latest research is on computational medicine—applying machine learning and other data science tools to produce biomedical insights. In past work he has combined insights from behavioral science with empirical methods—experiments, causal inference tools, and machine learning—to study social problems such as discrimination and poverty. He currently teaches a course on Artificial Intelligence. Outside of research, he co-founded a non-profit to apply behavioral science (ideas42), a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), has worked in government in various roles, and currently serves on the board of the MacArthur Foundation board. He is also a regular contributor to the New York Times.

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Dan Nicolae obtained his Ph.D. in statistics from The University of Chicago and has been a faculty at the same institution since 1999, with appointments in Statistics (since 1999) and Medicine (since 2006). His research focus is on developing statistical and computational methods for understanding the human genetic variation and its influence on the risk for complex traits, with an emphasis on asthma related phenotypes. The current focus in his statistical genetics research is centered on data integration and system-level approaches using large datasets that include clinical and environmental data as well as various genetics/genomics data types: DNA variation, gene expression (RNA-seq), methylation and microbiome.

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Samuel L. Volchenboum, MD, PhD, MS, is an expert in pediatric cancers and blood disorders. He has a special interest in treating children with neuroblastoma, a tumor of the sympathetic nervous system.

In addition to caring for patients, Dr. Volchenboum studies ways to harness computers to enable research and foster innovation using large data sets. He directs the development of the International Neuroblastoma Risk Group Database project, which connects international patient data with external information such as genomic data and tissue availability. The Center he runs provides computational support for the Biological Sciences Division at the University of Chicago, including high-performance computing, applications development, bioinformatics, and access to the clinical research data warehouse.

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Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.  Prof. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group 2007-2011, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Prof. Willett has also held visiting researcher positions at the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare) in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. She is also an instructor for FEMMES (Females Excelling More in Math Engineering and Science; news article here) and a local exhibit leader for Sally Ride Festivals. She was a recipient of the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, the Society of Women Engineers Caterpillar Scholarship, and the Angier B. Duke Memorial Scholarship.

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