Anjali Adukia is an assistant professor at the University of Chicago Harris School of Public Policy and the College. In her work, she is interested in understanding how to reduce inequalities such that children from historically disadvantaged backgrounds have equal opportunities to fully develop their potential.  Her research is focused on understanding factors that motivate and shape behavior, preferences, attitudes, and educational decision-making, with a particular focus on early-life influences.  She examines how the provision of basic needs—such as safety, health, justice, and representation—can increase school participation and improve child outcomes in developing contexts.

Adukia completed her doctoral degree at the Harvard University Graduate School of Education, with an academic focus on the economics of education. Her work has been funded from organizations such as the William T. Grant Foundation, the National Academy of Education, and the Spencer Foundation.  Her dissertation won awards from the Association for Public Policy Analysis and Management (APPAM), Association for Education Finance and Policy (AEFP), and the Comparative and International Education Society (CIES). Adukia received recognition for her teaching from the University of Chicago Feminist Forum.  She completed her masters of education degrees in international education policy and higher education (administration, planning, and social policy) from Harvard University and her bachelor of science degree in molecular and integrative physiology from the University of Illinois at Urbana-Champaign.  She is a faculty research fellow of the National Bureau of Economic Research and a faculty affiliate of the University of Chicago Education Lab and Crime Lab.  She is on the editorial board of Education Finance and Policy.  She was formerly a board member of the Young Nonprofit Professionals Network – San Francisco Bay Area. She continues to work with non-governmental organizations internationally, such as UNICEF and Manav Sadhna in Gujarat, India.

Anna is a Research Data Scientist at the University of Chicago’s Data Science Institute. Her recent work develops methodologies in machine learning for applied problems in medicine, with a particular emphasis on using unsupervised learning of representations to understand image content and structure. She is applying these methods to problems related to understanding and predicting cancer risk, designing personalized cancer screening policies, and designing clinical trials to evaluate AI systems. Previously, Anna served as a postdoctoral fellow at the Data Science Institute, where she was affiliated with the groups of Michael Maire in Computer Science and Olufunmilayo Olopade in the Department of Medicine. She earned her PhD in Physics from the University of Notre Dame, where Kevin Lannon advised her.

Ben Zhao is a Neubauer Professor of Computer Science at University of Chicago. Over the years, he’s followed his own interests in pursuing research problems that he finds intellectually interesting and meaningful. That’s led him to work on a sequence of areas from P2P networks, online social networks, SDR/open spectrum systems, graph mining and modeling, user behavior analysis, to adversarial machine learning. Since 2016, he’s mostly worked on security and privacy problems in machine learning and mobile systems. His meandering interests have led him to publish at a range of top conferences, including Usenix Security/Oakland/CCS, IMC/WWW, CHI/CSCW, and Mobicom/SIGCOMM/NSDI.

Together with Prof. Heather Zheng, he co-directs the SAND Lab (Security, Algorithms, Networking and Data) at University of Chicago. He received his PhD in Computer Science from UC Berkeley in 2004, where he was advised by John Kubiatowicz and Anthony Joseph, and created the Tapestry distributed hash table (dissertation). He received my MS from Berkeley in 2000, and his BS in computer science from Yale in 1997. He is an ACM Distinguished Scientist, a recipient of the NSF CAREER award (2005), MIT Tech Review’s TR-35 Award (Young Innovators Under 35) (2006), IEEE Internet Technical Committee’s Early Career Award (2014), and one of ComputerWorld’s Top 40 Technology Innovators under 40. His papers have somewhere around 28,000 citations and an H-index of 66 (for whatever that’s worth). In some of his “free time,” he writes about research and PhD life on Quora.

Blase Ur is an assistant professor of computer science at the University of Chicago. He founded the UChicago SUPERgroup, an interdisciplinary research collective with dozens of members. Their research spans computer security, privacy, and human-computer interaction (HCI). They are especially interested in using data-driven methods to help users make better security and privacy decisions, as well as to make complex computer systems more usable for non-technical users. Their work has been supported by six NSF grants, as well as grants from Mozilla Research and the Data Transparency Lab.

He has been fortunate to receive three best paper awards (CHI 2017, USENIX Security 2016, and UbiComp 2014), the 2018 SIGCHI Outstanding Dissertation Award, the 2016 John Karat Usable Privacy and Security Student Research Award, an NDSEG fellowship, a Fulbright scholarship, and three honorable mentions for best paper (CHI 2020, CHI 2016, and CHI 2012). Jointly with the other core members of the CMU passwords group, he also received the 2020 Allen Newell Award for Research Excellence and the 2018 IEEE Cybersecurity Award for Practice. He has strong interests in teaching and K-12 outreach, particularly for broadening participation in CS. He earned my AB in computer science from Harvard University and worked for three years at Rutgers University on outreach and diversity programs.

Brian Nord uses artificial intelligence to search for clues on the origins and development of the universe. He actively works on statistical modeling of strong gravitational lenses, the cosmic microwave background, and galaxy clusters. As leader of the Deep Skies Lab, he brings together experts in computer science and technology to study questions of cosmology, including dark energy, dark matter, and the early universe, through large-scale data analysis.

Nord has authored or co-authored nearly 50 papers. He trains scientists in public communication, advocates for science education and funding, and works to develop equitable and just research environments. As co-leader of education and public engagement at the Kavli Institute for Cosmological Physics at UChicago, he organizes Space Explorers, a program to help underrepresented minorities in high school engage in hands-on physics experiences outside the classroom. He is an associate scientist at Fermi National Accelerator Laboratory, where he is a member of the Machine Intelligence Group.

Homepage.

Bryon Aragam is an Assistant Professor and Topel Faculty Scholar in the Booth School of Business at the University of Chicago. He studies high-dimensional statistics, machine learning, and optimization. His research focuses on mathematical aspects of data science and statistical machine learning in nontraditional settings. Some of his recent projects include problems in graphical modeling, nonparametric statistics, personalization, nonconvex optimization, and high-dimensional inference. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Statistical Software.

Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Bryon has also served as a data science consultant for technology and marketing firms, where he has worked on problems in survey design and methodology, ranking, customer retention, and logistics.

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Chenhao Tan is an assistant professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

Website

Dr. Christopher M. Graziul holds a PhD in sociology from the University of Chicago as well as B.S. degrees in physics and applied computational mathematics from Virginia Tech. His work developing and applying novel quantitative methodologies to sensitive data sources spans multiple domains of research, including medical care, education, and vital records. His current research integrates diverse theoretical perspectives and computational techniques to drive the development of theory-based data science in service of public policy, specifically the application of the phenomenological variant of ecological systems theory (PVEST) to understand the psychosocial and developmental mechanisms that link the language of policing to the quality of encounters between law enforcement officers and male minority youth.

Daniel Grzenda is a Staff Data Scientist supporting the partnership with the 11th Hour Project at the University. He engages with social impact organizations across the domains of energy, food and agriculture, human rights, and marine technology, to support applications of data and computer science. Daniel’s work is focused on increasing the data capacity of these organizations, lowering the barriers to mission driven data science, and empowering these organizations through the development of sustainable technical solutions.

David Uminsky joined the University of Chicago in September 2020 as a senior research associate and Executive Director of Data Science. He was previously an associate professor of Mathematics and Executive Director of the Data Institute at University of San Francisco (USF). His research interests are in machine learning, signal processing, pattern formation, and dynamical systems.  David is an associate editor of the Harvard Data Science Review.  He was selected in 2015 by the National Academy of Sciences as a Kavli Frontiers of Science Fellow. He is also the founding Director of the BS in Data Science at USF and served as Director of the MS in Data Science program from 2014-2019. During the summer of 2018, David served as the Director of Research for the Mathematical Science Research Institute Undergrad Program on the topic of Mathematical Data Science.

Before joining USF he was a combined NSF and UC President’s Fellow at UCLA, where he was awarded the Chancellor’s Award for outstanding postdoctoral research. He holds a Ph.D. in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.

Bio: I am a philosopher of science and a computational researcher. My research agenda is broad and advances both our theoretical and empirical understanding of how emerging technologies (particularly AI) affect the way we do science and investigates how we can both study and use computational methods to better understand the joint processes of discovery and justification. I am currently a joint PhD Candidate in the departments of Philosophy and the Committee on the Conceptual and Historical Studies of Science at the University of Chicago. I am a Fellow in the Pritzker School of Molecular Engineering AI-Enabled Molecular Engineering of Materials and Systems for Sustainability program, an Affiliated Researcher at Globus Labs, and a member of the KnowledgeLab.

Talk Title: Digital Doubles of Everything (Even You)

Talk Abstract: Scientists have long made and interrogated doubles of reality. Often developed to describe narrow aspects of otherwise immense systems, traditional models have always been relatively poor substitutes for the bewildering tangle of complexity that pervades the world. However, the rise of AI allows scientists to generate and interrogate increasingly sophisticated digitized captures of entire systems of interest. These “digital doubles” represent everything from system I/O to complete characterizations of internal mechanisms and processes. In this project, we argue that the opportunity with doubles of many such systems is not just expedient exploration or accurate prediction. Instead, it is to combine them, represent interactions between them faithfully, and even enable them to interrogate and explore one another. We probe the possibility of developing digital doubles of dynamic systems of knowledge, values, and commitments, as well as using these doubles to operationalize and automate a logic of anomaly detection and correction, not only in our data but in our theoretical and normative, non-epistemic commitments.

Since joining Fermilab in 2016, my scientific work has focused on the search of new physics in neutrino oscillation experiments.

As a member of the MicroBooNE Collaboration (since 2016), I am mainly interested in searching for sterile neutrinos, as well as in the development of techniques for the reconstruction and analysis of Liquid Argon Time Project Chambers data. I also joined the DUNE Collaboration, where my main interest is CP violation in the neutrino sector.

Previously I worked for about a decade on the CMS experiments, with major contributions to the Higgs boson discovery, SUSY searches and Standard Model measurements.

I served as CMS Track Reconstruction convener in 2013-2014.

Heather Zheng is the Neubauer Professor of Computer Science at University of Chicago. She received my PhD in Electrical and Computer Engineering from University of Maryland, College Park in 1999. Prior to joining University of Chicago in 2017, she spent 6 years in industry labs (Bell-Labs, NJ and Microsoft Research Asia), and 12 years at University of California at Santa Barbara. At UChicago, she co-directs the SAND Lab (Systems, Algorithms, Networking and Data) together with Prof. Ben Y. Zhao.

Homepage.

Dr Jai Yu’s research focuses on understanding the neurophysiological mechanisms that support the formation of knowledge. His lab records and analyzes neural data. Prior to joining the University of Chicago, he worked as a data scientist at a Silicon Valley neuromodulation startup where he used advanced data analytics to guide the development of devices for treating neurological conditions.

Jean-Baptiste Reynier is a recent graduate from the University of Chicago (B.S. in Biology 2018, M.S. in Computer Science 2019). He is currently working as a data science analyst at the Olopade Lab. His work focuses on uncovering the characteristics of the tumor immune micro-environment in breast cancer, using genomic data.

V Hewes is a postdoctoral researcher at Fermi National Accelerator Laboratory working on the DUNE project.

Julia Koschinsky is the Executive Director of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 16 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

Junchen Jiang is an assistant professor of Computer Science at the University of Chicago. He received his PhD degree from Carnegie Mellon University in 2017. His research interests are computer networks and systems and their intersections with machine learning. He is a recipient of Google Faculty Research Award in 2019, best paper award at ACM/IEEE Symposium on Edge Computing, best paper runner-up at ACM CoNEXT 2012, and CMU Computer Science Doctoral Dissertation Award in 2017.

Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the Nimbus project, recognized as the first open source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific datacenters and applications. To facilitate such research for the community at large, Kate leads the Chameleon project, providing a deeply reconfigurable, large-scale, and open experimental platform for Computer Science research. To foster the recognition of contributions to science made by software projects, Kate co-founded and serves as co-Editor-in-Chief of the SoftwareX journal, a new format designed to publish software contributions. Kate is a Scientist at Argonne National Laboratory and a Senior Fellow at the Computation Institute at the University of Chicago.

Kyle Chard is a Research Associate Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

Lorenzo Orecchia is an assistant professor in the Department of Computer Science. His research focuses on applying mathematical techniques from discrete and continuous optimization to design algorithms for computational challenges arising in a variety of applications, including Machine Learning, Numerical Analysis and Combinatorial Optimization.

I earned my PhD from the Committee on Human Development (currently the Department of Comparative Human Development) in the Child and Developmental Psychology Program. Before returning to Chicago, I was the endowed Board of Overseers Professor and Director of the Interdisciplinary Studies of Human Development (ISHD) program and faculty member in the Graduate School of Education at the University of Pennsylvania. At UPenn, I was Director of the University of Pennsylvania’s Center for Health Achievement Neighborhoods Growth and Ethnic Studies (CHANGES) and also guided as its inaugural director, the W. E. B. Du Bois Collective Research Institute. Both appointments continue to frame my developmental scholarship and application of phenomenological variant of ecological systems theory (PVEST). It is a systems theory and, most important, is inclusive of all human experience and provides an identity-focused cultural ecological perspective. I evolved the theory over a decade because non-problematizing perspectives about people of color were generally unavailable.  Accordingly, P-VEST affords an authentic representation of human development processes both for Whites and People of Color as lives evolve in an ethnically, racially and economically diverse world. The conceptual framework addresses resiliency, identity, and competence formation processes for diverse humans—particularly youth—both in the United States and abroad.

Marshini Chetty is an associate professor in the Department of Computer Science at the University of Chicago, where she co-directs the Amyoli Internet Research Lab or AIR lab. She specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from privacy and security to performance, and costs. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from the University of Cape Town, South Africa. In her former lives, Marshini was on the faculty in the Computer Science Department at Princeton University and the College of Information Studies at the University of Maryland, College Park. Her work has won best paper awards at SOUPS, CHI, and CSCW and has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, Facebook, and multiple Google Faculty Research Awards.

Website

Michael Sherman is a Software Engineer at the University of Chicago and Argonne National Laboratory. He works on the ChameleonCloud Testbed, supporting research in edge and cloud computing. Previously he worked on the COSMOS and ORBIT testbeds making repeatable environments available to researchers in real-time and wireless systems. His research interests include software defined networking, edge computing, and reliable systems.

Nico is a Research Data Scientist with the Mansueto Institute. He holds a multi-disciplinary background in data science and urban research with a strong interest in harnessing advances in data and machine learning, which he is now applying to gain deeper insights into the science behind cities. In the past, he worked at Civis Analytics as a Senior Data Scientist where he combined predictive modeling and survey research to provide actionable guidance to organizations in the technology, local government, and political spaces. Before moving to Chicago, he spent six years at the Brookings Institution as an Associate Fellow, publishing numerous reports on global trade and urban development patterns and engineering new data resources to fill gaps in public data.

Nick Feamster is a Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Tech Policy for the Data Science Institute. 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.

Website

Nicole Marwell is Associate Professor in the University of Chicago Crown Family School of Social Work, Policy, and Practice. She is also a faculty affiliate of the Department of Sociology, a faculty fellow at the Center for Spatial Data Science, and a member of the Faculty Advisory Council of the Mansueto Institute for Urban Innovation. Her research examines urban governance, with a focus on the diverse intersections between nonprofit organizations, government bureaucracies, and politics.

Pedro Lopes is an Assistant Professor in Computer Science at the University of Chicago, where he leads the Human Computer Integration lab. Lopes’ research group focuses understanding how to integrate computer interfaces with the human body—creating the interface paradigm that supersedes wearable computing. They created wearable muscle stimulation devices that enable, for example, to: a user to manipulate a tool they never seen before, accelerate our reaction time, read and write information without using a screen, and transform someone’s arm into a plotter so they can solve complex problems with pen and paper. Their work is published at top-tier conferences (ACM CHI, ACM UIST, Cerebral Cortex). Pedro and his students have received one Best Paper award, three Best Talk Awards and two Best Paper nominations. Their work also captured the interest of media, such as MIT Technology Review, NBC, Discovery Channel, NewScientist, Wired and has been shown at Ars Electronica and World Economic Forum. (More: https://lab.plopes.org)

Ravi Madduri is actively involved in developing innovative software and networking technology. For example, as lead architect of the Reliable File Transfer, he designed novel testing and profiling capabilities, ensuring that it met the needs of key communities such as TeraGrid.

He implemented Grid file transfer patterns in the Java CoG Kit and developed a remote application virtualization infrastructure; the Grid-enable extension was incorporated in the Grid Service Authoring Toolkit and is used by NCI Information Systems.

Madduri is applying new technology in diverse science and engineering domains. For example, Ihe is a key contributor to the Cancer Bioinformatics Grid. He played a lead role in the evolution of GridFTP and its adoption by researchers for the Laser Interferometer Gravitational Wave Observatory and the Large Hadron Collider. Moreover, as part of the NEESgrid project, he helped scientific teams incorporate Grid technology into their earthquake engineering research.

Samantha Riesenfeld is an Assistant Professor of Molecular Engineering and of Genetic Medicine, a member of the Committee on Immunology, an Associate Member of the Comprehensive Cancer Center, and co-director of the new Computational and Systems Immunology PhD track in Immunology and Molecular Engineering. She leads an interdisciplinary research program focused on developing and applying genomics-based machine learning approaches to investigate the cellular components, transcriptional circuitry, and dynamics underlying complex biological systems, with a special interest in inflammatory immune responses and solid tumor cancer.

I am an Assistant Professor of Computer Science at the University of Chicago. I received my PhD in Computer Science from Yale University in 2020. My current research explores social dynamics in human-robot interactions, where a robot’s social behaviors lead to positive outcomes for people (e.g., improved team dynamics and performance in a human-robot team, educational learning outcomes for children). During my PhD, I focused on developing robots that improve the performance of human-robot teams by shaping team dynamics to promote inclusion, trust, and cohesion.

Shan Lu’s research focuses on software reliability, particularly detecting, diagnosing, and fixing concurrency bugs and performance bugs in large software systems.

Shan received her Ph.D. at University of Illinois, Urbana-Champaign, in 2008. She was the Clare Boothe Luce Assistant Professor of Computer Sciences at University of Wisconsin, Madison, from 2009 to 2014.

Shan has won Alfred P. Sloan Research Fellow in 2014, Distinguished Alumni Educator Award from Department of Computer Science at University of Illinois in 2013, and NSF Career Award in 2010. Her co-authored papers won ACM-SIGSOFT Distinguished Paper Award at FSE 2014, the Best Paper Award at USENIX FAST in 2013, ACM-SIGPLAN CACM Research Highlight Nomination in 2011, and IEEE Micro Top Picks in 2006. She currently serves as the Information Director of ACM-SIGOPS.

DSI Postdoctoral Scholar 2020-2023

Tarun Mangla joined DSI as a postdoctoral scholar in summer 2020, and was previously a PhD student in the School of Computer Science at the Georgia Institute of Technology, co-advised by Mostafa Ammar and Ellen Zegura. His research interests span video streaming, network measurements, and cellular networks. He completed his bachelors in Computer Science and Engineering from Indian Institute of Technology, Delhi (2014) and MS in Computer Science from Georgia Tech (2018). He is a recipient of the Best Paper Award at IFIP TMA, 2018.

Teodora engages the community of researchers involved in computational image analysis at The University of Chicago across multi-disciplinary areas including: Biology, Physics, Chemistry, Economics, Public Policy, and Cancer Research.

She serves as a catalyst for solving challenging questions in the research teams that she is supporting, such as: predicting oxygen support for COVID-19 patients, detecting prostate cancer, and analysis of messages related to identity in official educational setting. Teodora brings to the field practical expertise in state-of-the-art advanced technology: Supercomputers, Cloud Computing, Machine Learning, Image Analysis Techniques, and Big Data.

Prior to joining RCC, Teodora earned her doctorate degree in Computer Science at Toulouse University in France. She won international challenges (IUS PICMUS 2016) in beamforming for ultrasound medical imaging.

Yuxin Chen is an assistant professor at the Department of Computer Science at the University of Chicago. Previously, he was a postdoctoral scholar in Computing and Mathematical Sciences at Caltech, hosted by Prof. Yisong Yue. He received my Ph.D. degree in Computer Science from ETH Zurich, under the supervision of Prof. Andreas Krause. He is a recipient of the PIMCO Postdoctoral Fellowship in Computing and Mathematical Sciences, a Swiss National Science Foundation Early Postdoc.Mobility fellowship, and a Google European Doctoral Fellowship in Interactive Machine Learning.

His research interest lies broadly in probabilistic reasoning and machine learning. He is currently working on developing interactive machine learning systems that involve active learning, sequential decision making, interpretable models and machine teaching. You can find more information in my Google scholar profile.

Homepage.

Zhuo Zhen is a cloud computing software developer at the University of Chicago and Argonne National Laboratory. She works on the Chameleon Cloud project where she built and operated the national Chameleon testbed which supports research and innovation in cloud computing.

Dr. Allyson Ettinger’s research is focused on language processing in humans and in artificial intelligence systems, motivated by a combination of scientific and engineering goals. For studying humans, her research uses computational methods to model and test hypotheses about mechanisms underlying the brain’s processing of language in real time. In the engineering domain, her research uses insights and methods from cognitive science, linguistics, and neuroscience in order to analyze, evaluate, and improve natural language understanding capacities in artificial intelligence systems. In both of these threads of research, the primary focus is on the processing and representation of linguistic meaning.

Anjali Adukia is an assistant professor at the University of Chicago Harris School of Public Policy and the College. In her work, she is interested in understanding how to reduce inequalities such that children from historically disadvantaged backgrounds have equal opportunities to fully develop their potential.  Her research is focused on understanding factors that motivate and shape behavior, preferences, attitudes, and educational decision-making, with a particular focus on early-life influences.  She examines how the provision of basic needs—such as safety, health, justice, and representation—can increase school participation and improve child outcomes in developing contexts.

Adukia completed her doctoral degree at the Harvard University Graduate School of Education, with an academic focus on the economics of education. Her work has been funded from organizations such as the William T. Grant Foundation, the National Academy of Education, and the Spencer Foundation.  Her dissertation won awards from the Association for Public Policy Analysis and Management (APPAM), Association for Education Finance and Policy (AEFP), and the Comparative and International Education Society (CIES). Adukia received recognition for her teaching from the University of Chicago Feminist Forum.  She completed her masters of education degrees in international education policy and higher education (administration, planning, and social policy) from Harvard University and her bachelor of science degree in molecular and integrative physiology from the University of Illinois at Urbana-Champaign.  She is a faculty research fellow of the National Bureau of Economic Research and a faculty affiliate of the University of Chicago Education Lab and Crime Lab.  She is on the editorial board of Education Finance and Policy.  She was formerly a board member of the Young Nonprofit Professionals Network – San Francisco Bay Area. She continues to work with non-governmental organizations internationally, such as UNICEF and Manav Sadhna in Gujarat, India.

Anna is a Research Data Scientist at the University of Chicago’s Data Science Institute. Her recent work develops methodologies in machine learning for applied problems in medicine, with a particular emphasis on using unsupervised learning of representations to understand image content and structure. She is applying these methods to problems related to understanding and predicting cancer risk, designing personalized cancer screening policies, and designing clinical trials to evaluate AI systems. Previously, Anna served as a postdoctoral fellow at the Data Science Institute, where she was affiliated with the groups of Michael Maire in Computer Science and Olufunmilayo Olopade in the Department of Medicine. She earned her PhD in Physics from the University of Notre Dame, where Kevin Lannon advised her.

Ben Zhao is a Neubauer Professor of Computer Science at University of Chicago. Over the years, he’s followed his own interests in pursuing research problems that he finds intellectually interesting and meaningful. That’s led him to work on a sequence of areas from P2P networks, online social networks, SDR/open spectrum systems, graph mining and modeling, user behavior analysis, to adversarial machine learning. Since 2016, he’s mostly worked on security and privacy problems in machine learning and mobile systems. His meandering interests have led him to publish at a range of top conferences, including Usenix Security/Oakland/CCS, IMC/WWW, CHI/CSCW, and Mobicom/SIGCOMM/NSDI.

Together with Prof. Heather Zheng, he co-directs the SAND Lab (Security, Algorithms, Networking and Data) at University of Chicago. He received his PhD in Computer Science from UC Berkeley in 2004, where he was advised by John Kubiatowicz and Anthony Joseph, and created the Tapestry distributed hash table (dissertation). He received my MS from Berkeley in 2000, and his BS in computer science from Yale in 1997. He is an ACM Distinguished Scientist, a recipient of the NSF CAREER award (2005), MIT Tech Review’s TR-35 Award (Young Innovators Under 35) (2006), IEEE Internet Technical Committee’s Early Career Award (2014), and one of ComputerWorld’s Top 40 Technology Innovators under 40. His papers have somewhere around 28,000 citations and an H-index of 66 (for whatever that’s worth). In some of his “free time,” he writes about research and PhD life on Quora.

Blase Ur is an assistant professor of computer science at the University of Chicago. He founded the UChicago SUPERgroup, an interdisciplinary research collective with dozens of members. Their research spans computer security, privacy, and human-computer interaction (HCI). They are especially interested in using data-driven methods to help users make better security and privacy decisions, as well as to make complex computer systems more usable for non-technical users. Their work has been supported by six NSF grants, as well as grants from Mozilla Research and the Data Transparency Lab.

He has been fortunate to receive three best paper awards (CHI 2017, USENIX Security 2016, and UbiComp 2014), the 2018 SIGCHI Outstanding Dissertation Award, the 2016 John Karat Usable Privacy and Security Student Research Award, an NDSEG fellowship, a Fulbright scholarship, and three honorable mentions for best paper (CHI 2020, CHI 2016, and CHI 2012). Jointly with the other core members of the CMU passwords group, he also received the 2020 Allen Newell Award for Research Excellence and the 2018 IEEE Cybersecurity Award for Practice. He has strong interests in teaching and K-12 outreach, particularly for broadening participation in CS. He earned my AB in computer science from Harvard University and worked for three years at Rutgers University on outreach and diversity programs.

Brian Nord uses artificial intelligence to search for clues on the origins and development of the universe. He actively works on statistical modeling of strong gravitational lenses, the cosmic microwave background, and galaxy clusters. As leader of the Deep Skies Lab, he brings together experts in computer science and technology to study questions of cosmology, including dark energy, dark matter, and the early universe, through large-scale data analysis.

Nord has authored or co-authored nearly 50 papers. He trains scientists in public communication, advocates for science education and funding, and works to develop equitable and just research environments. As co-leader of education and public engagement at the Kavli Institute for Cosmological Physics at UChicago, he organizes Space Explorers, a program to help underrepresented minorities in high school engage in hands-on physics experiences outside the classroom. He is an associate scientist at Fermi National Accelerator Laboratory, where he is a member of the Machine Intelligence Group.

Homepage.

Dacheng Xiu’s research interests include developing statistical methodologies and applying them to financial data, while exploring their economic implications. His earlier research involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.

Xiu’s work has appeared in Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, the Annals of Statistics, and the Journal of Finance. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Journal of Econometrics, the Journal of Business & Economic Statistics, the Journal of Empirical Finance, and Statistica Sinica, and also referees for several journals in the fields of econometrics, statistics, and finance. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, the Fellow of the Journal of Econometrics, the 2018 Swiss Finance Institute Outstanding Paper Award, the 2018 AQR Insight Award, and the Best Conference Paper Prize at the 2017 Annual Meeting of the European Finance Association.

In 2017, Xiu launched a website that provides up-to-date realized volatilities of individual stocks, as well as equity, currency, and commodity futures. These daily volatilities are calculated from the intraday transactions and the methodologies are based on his research of high-frequency data.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he was also a student at the Bendheim Center for Finance. Prior to his graduate studies, he obtained a BS in mathematics from the University of Science and Technology of China.

David Miller’s research focuses on answering open questions about the fundamental structure of matter. By studying the quarks and gluons -—the particles that comprise everyday protons and neutrons —produced in the energetic collisions of protons at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland, Miller conducts measurements using the ATLAS Detector that will seek out the existence of never-before-seen particles, and characterize the particles and forces that we know of with greater precision. Miller’s work into the properties and measurements of the experimental signatures of these quarks and gluons –or jets” –is an integral piece of the puzzle used in the recent discovery of the Higgs bosons, searches for new massive particles that decay into boosted top quarks, as well as the hints that the elusive quark-gluon-plasma may have finally been observed in collisions of lead ions.

Besides studying these phenomena, Miller has worked extensively on the construction and operation of the ATLAS detector, including the calorimeter and tracking systems that allow for these detailed measurements. Upgrades to these systems involving colleagues at Argonne National Laboratory, CERN, and elsewhere present an enormous challenge and a significant amount of research over the next several years. Miller is also working with state-of-the art high-speed electronics for quickly deciphering the data collected by the ATLAS detector.

Miller received his PhD from Stanford University in 2011 and his BA in Physics from the University of Chicago in 2005. He was a McCormick Fellow in the Enrico Fermi Institute from 2011-2013.

Diana Franklin is an Associate Professor in Computer Science. She leads five projects involving computer science education involving students ranging from 3rd grade through university. She is the lead PI for quantum computing education for EPIQC, an NSF expedition in computing. Her research agenda explores ways to create curriculum and computing environments in ways that reach a broad audience. She is a recipient of the NSF CAREER award, NCWIT Faculty Undergraduate Mentoring Award, four teaching awards, three best paper awards (ICER ’17, IPDPS ’14, and Computing Frontiers ’13), and an Honourable Mention from CHI ’18.

Franklin received her Ph.D. from UC Davis in 2002. She was an assistant professor (2002-2007) and associate professor with tenure (2007) in Computer Science at the California Polytechnic State University, San Luis Obispo, during which she held the Forbes Chair. From 2008-2015, she was tenured teaching faculty at UC Santa Barbara. Her research interests include computing education research, architecture involving novel technologies, and ethnic and gender diversity in computing. She is the author of “A Practical Guide to Gender Diversity for CS Faculty,” from Morgan Claypool.

Bio: I am a philosopher of science and a computational researcher. My research agenda is broad and advances both our theoretical and empirical understanding of how emerging technologies (particularly AI) affect the way we do science and investigates how we can both study and use computational methods to better understand the joint processes of discovery and justification. I am currently a joint PhD Candidate in the departments of Philosophy and the Committee on the Conceptual and Historical Studies of Science at the University of Chicago. I am a Fellow in the Pritzker School of Molecular Engineering AI-Enabled Molecular Engineering of Materials and Systems for Sustainability program, an Affiliated Researcher at Globus Labs, and a member of the KnowledgeLab.

Talk Title: Digital Doubles of Everything (Even You)

Talk Abstract: Scientists have long made and interrogated doubles of reality. Often developed to describe narrow aspects of otherwise immense systems, traditional models have always been relatively poor substitutes for the bewildering tangle of complexity that pervades the world. However, the rise of AI allows scientists to generate and interrogate increasingly sophisticated digitized captures of entire systems of interest. These “digital doubles” represent everything from system I/O to complete characterizations of internal mechanisms and processes. In this project, we argue that the opportunity with doubles of many such systems is not just expedient exploration or accurate prediction. Instead, it is to combine them, represent interactions between them faithfully, and even enable them to interrogate and explore one another. We probe the possibility of developing digital doubles of dynamic systems of knowledge, values, and commitments, as well as using these doubles to operationalize and automate a logic of anomaly detection and correction, not only in our data but in our theoretical and normative, non-epistemic commitments.

Hakizumwami Birali Runesha is the Director of Research Computing for the University of Chicago, where he provides leadership and vision for advancing all aspects of research computing strategies at the University. He is responsible for the design, configuration, and administration of centrally managed High-Performance Computing (HPC) systems and related services across the University. In addition, he provides access to advanced technical expertise, user support, advice and training, and access to the University’s HPC facility to the research community.

Runesha is a seasoned professional who brings to the University of Chicago HPC management leadership and more than 17 years of experience in high performance computing and scientific software development. He earned his M.S. and Ph.D. in Civil engineering at Old Dominion University. Prior to joining the University of Chicago, he served as Director of Scientific Computing and Applications at the University of Minnesota Supercomputing Institute (MSI) managing the scientific computing, biological computing, visualization and application development groups. In addition to overseeing strategic planning of HPC resources and leading annual procurement of supercomputing resources at MSI, Runesha created the MSI Application software development group and the MSI Scientific Data Management Laboratory to meet the evolving data management and database development needs of university researchers. Prior to joining the University of Minnesota, he was a research scholar at the Hong Kong University of Science and Technology developing parallel computing algorithms for engineering applications, a research associate for the Multidisciplinary Parallel-Vector Computer Center at Old Dominion University and an Assistant Professor at the University of Kinshasa.

Runesha has developed open source software programs and fast parallel solvers for large-scale finite element applications. He served as principal investigator on a number of research grants and is the author of a number of journal articles, proceedings and conference papers. He has given many invited talks, seminars, courses, and workshops on various HPC topics.

Heather Zheng is the Neubauer Professor of Computer Science at University of Chicago. She received my PhD in Electrical and Computer Engineering from University of Maryland, College Park in 1999. Prior to joining University of Chicago in 2017, she spent 6 years in industry labs (Bell-Labs, NJ and Microsoft Research Asia), and 12 years at University of California at Santa Barbara. At UChicago, she co-directs the SAND Lab (Systems, Algorithms, Networking and Data) together with Prof. Ben Y. Zhao.

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Jean-Baptiste Reynier is a recent graduate from the University of Chicago (B.S. in Biology 2018, M.S. in Computer Science 2019). He is currently working as a data science analyst at the Olopade Lab. His work focuses on uncovering the characteristics of the tumor immune micro-environment in breast cancer, using genomic data.

Julia Koschinsky is the Executive Director of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 16 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the Nimbus project, recognized as the first open source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific datacenters and applications. To facilitate such research for the community at large, Kate leads the Chameleon project, providing a deeply reconfigurable, large-scale, and open experimental platform for Computer Science research. To foster the recognition of contributions to science made by software projects, Kate co-founded and serves as co-Editor-in-Chief of the SoftwareX journal, a new format designed to publish software contributions. Kate is a Scientist at Argonne National Laboratory and a Senior Fellow at the Computation Institute at the University of Chicago.

Kyle Chard is a Research Associate Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

Lorenzo Orecchia is an assistant professor in the Department of Computer Science. His research focuses on applying mathematical techniques from discrete and continuous optimization to design algorithms for computational challenges arising in a variety of applications, including Machine Learning, Numerical Analysis and Combinatorial Optimization.

Marshini Chetty is an associate professor in the Department of Computer Science at the University of Chicago, where she co-directs the Amyoli Internet Research Lab or AIR lab. She specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from privacy and security to performance, and costs. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from the University of Cape Town, South Africa. In her former lives, Marshini was on the faculty in the Computer Science Department at Princeton University and the College of Information Studies at the University of Maryland, College Park. Her work has won best paper awards at SOUPS, CHI, and CSCW and has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, Facebook, and multiple Google Faculty Research Awards.

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Maryellen L. Giger, Ph.D. is the A.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She is also the Vice-Chair of Radiology (Basic Science Research) and the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/ Chair of the Committee on Medical Physics at the University. For over 30 years, she has conducted research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases.

Over her career, she has served on various NIH, DOD, and other funding agencies’ study sections, and is now a member of the NIBIB Advisory Council of NIH. She is a former president of the American Association of Physicists in Medicine and a former president of the SPIE (the International Society of Optics and Photonics) and was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging. She is a member of the National Academy of Engineering (NAE) and was awarded the William D. Coolidge Gold Medal from the American Association of Physicists in Medicine, the highest award given by the AAPM. She is a Fellow of AAPM, AIMBE, SPIE, SBMR, and IEEE, a recipient of the EMBS Academic Career Achievement Award, and is a current Hagler Institute Fellow at Texas A&M University. In 2013, Giger was named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists with the most impact on the field in the last 50 years. In 2018, she received the iBIO iCON Innovator award.

She has more than 200 peer-reviewed publications (over 300 publications), has more than 30 patents and has mentored over 100 graduate students, residents, medical students, and undergraduate students. Her research in computational image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, and response to therapy has yielded various translated components, and she is now using these image-based phenotypes, i.e., these “virtual biopsies” in imaging genomics association studies for discovery.

She is a cofounder, equity holder, and scientific advisor of Quantitative Insights, Inc., which started through the 2009-2010 New Venture Challenge at the University of Chicago. QI produces QuantX, the first FDA-cleared, machine-learning-driven system to aid in cancer diagnosis (CADx). In 2019, QuantX was named one of TIME magazine’s inventions of the year.

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Marynia Kolak, MS, MFA, PhD, is a health geographer using open science tools and an exploratory data analytic approach to investigate issues of equity across space and time. Her research centers on how “place” impacts health outcomes in different ways, for different people, from opioid risk environments to chronic disease clusters. She focuses on quantifying and distilling the structural determinants of health across different environments, tying political ecology models of public health with geocomputational methods and quasi-experimental policy evaluation techniques. She received the 2017 Concordium Innovation Award at AcademyHealth for her open-source visualization of Chicago determinants of health, and “Highest Impact” award in the Prevention Category at the American College of Cardiology 2019 conference for her work in connecting chronic disease rates with social determinants of health. She serves as the Co-I and spatial analytic lead in the ETHIC project investigating the opioid epidemic in Illinois. She is the Assistant Director of Health Informatics and Lecturer in GIScience at the Center for Spatial Data Science, University of Chicago, and serves as a Public Service Intern at the Chicago Department of Public Health. Marynia additionally serves as an Health and Medical Specialty Group (AAG) board member, and chair of the Chicago Public Health GIS Network. She received her Ph.D in Geography at ASU, M.F.A in Writing from Roosevelt University, M.S. in GIS from John Hopkins University, and B.S. in Geology from the University of Illinois at Urbana-Champaign.

Miller Prosser earned a PhD in Northwest Semitic Philology from the University of Chicago in 2010, studying under the guidance of professor Dennis G. Pardee. His PhD Thesis, “Bunušu in Ugaritian Society” addresses the socioeconomic structure of the Late Bronze Age kingdom of Ugarit. Upon completion of his degree, Dr. Prosser began work as a research professional at the Oriental Institute’s Persepolis Fortification Archive Project. He would later take a position as a Research Database Specialist at the OCHRE Data Service of the Oriental Institute, supporting dozens of research projects using the Online Cultural and Historical Research Environment. Over the course of the last decade, Dr. Prosser has presented widely at workshops and conferences and served as a lecturer in the Digital Studies Master’s Degree program at the University of Chicago.

Nick Feamster is a Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Tech Policy for the Data Science Institute. 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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Pedro Lopes is an Assistant Professor in Computer Science at the University of Chicago, where he leads the Human Computer Integration lab. Lopes’ research group focuses understanding how to integrate computer interfaces with the human body—creating the interface paradigm that supersedes wearable computing. They created wearable muscle stimulation devices that enable, for example, to: a user to manipulate a tool they never seen before, accelerate our reaction time, read and write information without using a screen, and transform someone’s arm into a plotter so they can solve complex problems with pen and paper. Their work is published at top-tier conferences (ACM CHI, ACM UIST, Cerebral Cortex). Pedro and his students have received one Best Paper award, three Best Talk Awards and two Best Paper nominations. Their work also captured the interest of media, such as MIT Technology Review, NBC, Discovery Channel, NewScientist, Wired and has been shown at Ars Electronica and World Economic Forum. (More: https://lab.plopes.org)

Qinyun Lin is a postdoctoral fellow at the Center for Spatial Data Science. Her research interests include sensitivity analysis, causal inference, mediation analysis, social network analysis and multi-level models. Her dissertation proposes sensitivity analysis techniques for presence of spillover effects and heterogeneous treatment effects in multi-site randomized control trials. Her dissertation work also looks at unobserved mediator as a post-treatment confounder in causal mediation analysis. Her current research applies a spatial perspective to look at access to medications for opioid use disorder and how it affects opioid-related deaths and HCV infections.

Rajesh Sankaran received his Ph.D. in electrical engineering from Louisiana State University. His research has focused on applications of electrical and computer engineering techniques toward solving problems in related science and engineering fields.

He plays lead technical roles in the Array of Things, WxSeNet, and Smart Windows research initiatives. He is also associated with the EcoSpec projects.

Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago. In his research he builds systems for discovering, preparing, and processing data. The goal of his research is to understand and exploit the value of data. He often uses techniques from data management, statistics, and machine learning. His main effort these days is on building platforms to support markets of data. This is part of a larger research effort on understanding the Economics of Data. He is the faculty co-lead of the DSI’s Data Ecology Research Initiative and he is part of ChiData, the data systems research group at the University of Chicago.

Sandra Schloen is the Manager of the OCHRE (Online Cultural and Historical Research Environment) Data Service of the Oriental Institute at the University of Chicago. She supports all of the research projects using the software. Trained in computer science and in applications of technology to educational contexts, she applies her technical background to the challenges and complexities of representing and managing cultural and historical data in all of its digital forms.

Sanjay Krishnan is an Assistant Professor of Computer Science. His research group studies the theory and practice of building decision systems that are robust to corrupted, missing, or otherwise uncertain data. His research brings together ideas from statistics/machine learning and database systems. His research group is currently studying systems that can analyze large amounts of video, certifiable accuracy guarantees in partially complete databases, and theoretical lower-bounds for lossy compression in relational databases.

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Susan Goldin-Meadow is the Beardsley Ruml Distinguished Service Professor in the Department of Psychology and Committee on Human Development at the University of Chicago. A year spent at the Piagetian Institute in Geneva while an undergraduate at Smith College piqued her interest in the relationship between language and thought, interests she continued to pursue in her doctoral work at the University of Pennsylvania (Ph.D. 1975). At Penn and in collaboration with Lila Gleitman and Heidi Feldman, she began her studies exploring whether children who lack a (usable) model for language can nevertheless create a language with their hands. She has found that deaf children whose profound hearing losses prevent them from learning the speech than surrounds them, and whose hearing parents have not exposed them to sign, invent gesture systems which are structured in language-like ways. This interest in how the manual modality can serve the needs of communication and thinking led to her current work on the gestures that accompany speech in hearing individuals. She has found that gesture can convey substantive information – information that is often not expressed in the speech it accompanies. Gesture can thus reveal secrets of the mind to those who pay attention.

Professor Goldin-Meadow’s research has been funded by the National Science Foundation, the Spencer Foundation, the March of Dimes, the National Institute of Child Health and Human Development, and the National Institute of Neurological and Communicative Disorders and Stroke. She has served as a member of the language review panel for NIH, has been a Member-at-Large to the Section on Linguistics and Language Science in AAAS, and was part of the Committee on Integrating the Science of Early Childhood Development sponsored by the National Research Council and the Institute of Medicine and leading to the book Neurons to Neighborhoods. She is a Fellow of AAAS, APS, and APA (Divisions 3 and 7). In 2001, she was awarded a Guggenheim Fellowship and a James McKeen Cattell Fellowship which led to her two recently published books, Resilience of Language and Hearing Gesture. In addition, she edited Language in Mind: Advances in the Study of Language and Thought in collaboration with Dedre Gentner. She has received the Burlington Northern Faculty Achievement Award for Graduate Teaching and the Llewellyn John and Harriet Manchester Quantrell Award for Excellence in Undergraduate Teaching at the University of Chicago. She is currently the President of the Cognitive Development Society and the editor of the new journal sponsored by the Society for Language Development, Language Learning and Development. Professor Goldin-Meadow also serves as chair of the developmental area program.

Teodora engages the community of researchers involved in computational image analysis at The University of Chicago across multi-disciplinary areas including: Biology, Physics, Chemistry, Economics, Public Policy, and Cancer Research.

She serves as a catalyst for solving challenging questions in the research teams that she is supporting, such as: predicting oxygen support for COVID-19 patients, detecting prostate cancer, and analysis of messages related to identity in official educational setting. Teodora brings to the field practical expertise in state-of-the-art advanced technology: Supercomputers, Cloud Computing, Machine Learning, Image Analysis Techniques, and Big Data.

Prior to joining RCC, Teodora earned her doctorate degree in Computer Science at Toulouse University in France. She won international challenges (IUS PICMUS 2016) in beamforming for ultrasound medical imaging.

Yuxin Chen is an assistant professor at the Department of Computer Science at the University of Chicago. Previously, he was a postdoctoral scholar in Computing and Mathematical Sciences at Caltech, hosted by Prof. Yisong Yue. He received my Ph.D. degree in Computer Science from ETH Zurich, under the supervision of Prof. Andreas Krause. He is a recipient of the PIMCO Postdoctoral Fellowship in Computing and Mathematical Sciences, a Swiss National Science Foundation Early Postdoc.Mobility fellowship, and a Google European Doctoral Fellowship in Interactive Machine Learning.

His research interest lies broadly in probabilistic reasoning and machine learning. He is currently working on developing interactive machine learning systems that involve active learning, sequential decision making, interpretable models and machine teaching. You can find more information in my Google scholar profile.

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Zhuo Zhen is a cloud computing software developer at the University of Chicago and Argonne National Laboratory. She works on the Chameleon Cloud project where she built and operated the national Chameleon testbed which supports research and innovation in cloud computing.