Organizing Committee:
Rachel Cummings
Rachel Cummings is an Associate Professor of Industrial Engineering and Operations Research at Columbia University. Her research interests lie primarily in data privacy with connections to algorithm design, economics, optimization, and statistics. A second thread of her work uses her interdisciplinary background to address the technical, legal, and social challenges of bringing differentially private tools to bear in practice and at scale. Cummings has received numerous awards including an NSF CAREER Award, a DARPA YFA, two doctoral dissertation awards, and Best Paper Awards at DISC, CCS, and SaTML. She has previously organized the Workshop on Theory and Practice of Differential Privacy at ICML (2020, 2021), Workshop on Synthetic Data for AI in Finance at ICAIF (2022), Workshop on Synthetic Data Generation: Quality, Privacy, and Bias at ICLR (2021), Workshop on Economic Implications of Data at ICML (2020), Workshop on Privacy-Preserving Artificial Intelligence at AAAI (2020), and the Data Science for Social Good Workshop (2019, 2020).
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Giulia Fanti
Giulia Fanti is an Assistant Professor of Electrical and Computer Engineering at Carnegie Mellon University. Her research interests span the security, privacy, and efficiency of distributed systems, with a focus on synthetic data generation. She has co-organized several conferences and workshops in the past, including as Workshop Chair for NDSS (2018-2019) and a Co-Organizer of the Synthetic Data for AI in Finance Workshop at ICAIF 2022. She has won multiple awards and is a member of NIST’s Information Security and Privacy Advisory Board. She is also a founding co-director of CyLab Africa alongside Assane Gueye.
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Guang Cheng
Guang Cheng is a Professor of Statistics and Data Science and Director of the Trustworthy AI Lab at UCLA. He received his BA in Economics from Tsinghua University in 2002, and PhD in Statistics from University of Wisconsin-Madison in 2006. His research interests include trustworthy AI, synthetic data and statistical machine learning. Cheng is an Institute of Mathematical Statistics Fellow, Simons Fellow in Mathematics, NSF CAREER awardee and was also a member in the Institute for Advanced Study, Princeton. He was a co-organizer of UCLA Synthetic Data Workshop.
Other than government grants, his lab is also sponsored by industry funding from such as Amazon, Meta and Adobe. Please visit his trustworthy AI Lab at http://www.stat.ucla.edu/~guangcheng/
Robert E. Tillman
Robert Tillman is Research Director at Optum Labs, the R&D arm of UnitedHealth Group, where he leads generative AI and foundational disease progression research. Robert’s team is currently focused on data imputation and augmentation challenges with electronic healthcare records, medical claims and signals from wearable medical devices as well as approaches for generating privacy-preserving synthetic healthcare data. A second line of research focuses on applying large language models to healthcare data to develop pretrained models for a variety of disease progression prediction tasks. Previously, Robert was Research Director for Synthetic Data at J.P. Morgan AI Research and Head of Machine Learning at Index Exchange, a supply side ad exchange. He received his Ph.D. from Carnegie Mellon in 2011 where his dissertation research focused on causal inference and probabilistic graphical models.
Vamsi K. Potluru
Vamsi Potluru is a Research Director at JP Morgan AI Research where he leads synthetic data research. Projects include high quality synthetic data for various financial problems such as credit card marketing, anti-money laundering detection and fair lending. Previously, he was a lead researcher at Comcast working on various projects in recommendations, search and speech domains involving sketching and bandit approaches among others. Prior to that he was a postdoctoral research fellow at Rutgers University after having obtained his PhD in matrix factorizations applied to brain imaging data.
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