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Data Governance

Data Visualization

Data Security

Implementation and Scalability

Distributed Solutions

Data  Management & Architecture 

Data Platforms

Collection & Curation

Data Performance

Data Evaluation (Metrics; Audits; Evaluation)

Faculty

Nick Feamster is Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Research 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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In my research, I ask what is “the value of data” and explore the potential of data markets to unlock that value. My group collaborates with economists, legal scholars, statisticians, and domain scientists. We build systems to share, discover, prepare, integrate, and process data. I have traditionally worked on distributed query processing systems and continue to do so. I have received a SIGMOD’23 Test-of-time-Award for my PhD work.

Homepage.

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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I am the Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science (CTDS) at the University of Chicago.

I am the Chief of the Section of Biomedical Data Science in the Department of Medicine at the University of Chicago.

I am the Chair of the not-for-profit Open Commons Consortium, which develops and operates clouds to support research in science,
medicine, health care, and the environment. I am also a Partner of Analytic Strategy Partners LLC.

Alex Kale is an Assistant Professor in Computer Science and the Data Science Institute at the University of Chicago. Previously, he earned his PhD at the University of Washington where he worked with Jessica Hullman. Alex leads the Data Cognition Lab, focused on creating data visualization and analysis software that explicitly represents the user’s cognitive processes.

Alex creates and evaluates tools for helping people think with data, specializing in data visualization and reasoning with uncertainty. He publishes in top human-computer interaction and data visualization venues such as ACM CHI and IEEE VIS, where his work has won Best Paper and Honorable Mention Awards. Alex’s work addresses gaps in dominant theories and models of what makes visualization effective for inferences and decision making.

 

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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Tian Li is an Assistant Professor of Computer Science and Data Science starting in July 2024. Her research interests are in distributed optimization, large-scale machine learning, federated learning, and data-intensive systems. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She was a research intern at Google Research in 2022. She received the Best Paper Award at ICLR Workshop on Security and Safety in Machine Learning Systems (2021), was selected as Rising Stars in Machine Learning (2021), and was invited to participate in EECS Rising Stars Workshop (2022).

 

Dr. Ross is an experienced data science executive and academic leader who specializes in leveraging business, engineering, and data to optimize decision-making. His various roles have ranged from architecting and designing production ML/AI systems, to hiring, growing, and leading engineering and data science teams.

Previously, Dr. Ross led the data science and backend engineering efforts at The Meta, an esports training platform used by millions of competitive gamers. Before joining The Meta, Dr. Ross was a Professor of Data Science at the University of San Francisco, where his research focused on how to effectively use data and data science techniques to answer business questions. During this time, he was also the Assistant Director of the University of San Francisco’s Data Institute, where he led and developed academic-industry partnerships to create a world-class masters of data science program. Under his leadership, the Data Institute placed hundreds of students into top data science positions in both the private and public sectors, with a job placement rate of over 90% within 3 months of graduation. As a consultant, he spearheaded data efforts at leading tech companies in the video and online game industry, from early-stage startups to multinational companies.

Dr. Ross received his PhD from UCLA, his Masters from UC Davis, and his Bachelor of Science from UC Berkeley. He has published papers in a variety of journals as well as given talks in both academic and industry settings.

Ce Zhang is an incoming Associate Professor of Computer Science and Data Science for the Summer of 2024.

He is currently an Associate Professor of Computer Science at ETH Zurich. Before joining the department he will spend his time as the CTO of Together, building a decentralized cloud for artificial intelligence. His research looks at the fundamental tension between data, model, computation and infrastructure, with the final goal of democratizing machine learning and artificial intelligence. His current research focuses on building next-​generation machine learning platforms and systems that are data-​centric, human-​centric, and declaratively scalable.

Before joining ETH, Ce finished his PhD at the University of Wisconsin-​​Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and various media outlets such as Atlantic, WIRED, Quanta Magazine, etc. He also currently serves as the co-Editors-in-Chief of DMLR, a new member of the JMLR family focusing on data-centric machine learning research.