Sep-23 - I am co-organizing the fith edition of the Privacy Preserving AI workshop at AAAI-24. :sparkles: This year edition will have a particular focus on privacy in generative models. Stay tuned!
- Paper accepted in IEEE PES Innovative Smart Grid Technologies. :sparkles: See publications for details.
Aug-23 - I gave a talk on Privacy and Fairness at the IJCAI-23 workshop on Deep Learning Methods for Social Media.
- New survey on integrating prediction and optimization in end-to-end differentiable systems! :sparkles: See publications for details.
- I am co-organizing the Algorithimc Fairness through the Lens of Time workshop at NeurIPS to spark discussions on how a long-term perspective can help build more trustworthy algorithms in the era of generative models.
- New preprint on the disparate impacts arising in energy optimization. :sparkles: See publications for details.
- :star2: I have moved to the University of Virginia.
Jul-23 - I gave a talk about the integration of Machine Learning and Optimization at the 2023 ACP Summer School. :speech_balloon: Youtube link
Jun-23 - Paper accepted to Electric Power Systems Research! :sparkles: See publications for details.
- I gave a talk about Differential Privacy, Foundation and applications in Energy Systems at the DTU PET Summer School. :speech_balloon: Slides.
- I gave a talk about Machine Learning for Optimization Optimization at the IEEE PES University.
May-23 - Two new exciting preprints on privacy and fairness. :sparkles: See publications for details.
- :tada: Congratulation to Dr. Cuong Tran on a stellar defense! You can check his work here!
- Our NSF-ENG EPCN proposal Physics Informed Real-time Optimal Power Flow has been funded! We’ll integrate machine learning and physics to optimize power flow, integrate systems dynamics, and increase grid reliability. We’ll work on scalable and robust ML for energy solutions. Thank you, NSF!
Apr-23 - :trophy: Notable reviewer award ICLR 2023 (link).
- Four papers accepted to IJCAI 2023! :sparkles: See publications for details.
Mar-23 - Our NSF RI CORE proposal End-to-end Learning of Fair and Explainable Schedules for Court Systems. has been funded! We’ll develop differentiable optimization tools for equitable & explainable scheduling and work on changing the pretrial scheduling process to reduce nonappearance and promote fairness in the American Court system! Thank you, NSF!
- I am co-editing a new book :open_book: on Differential Privacy . It covers topics from foundations, to applications in optimization (including in Census data release, image and video, and medicine), machine learning (including privacy attacks, federated learning, and private algorithms) as well as policy and ethics aspects. Stay tuned!
Feb-23 - I have co-organizing the fourth workshop on Privacy Preserving Artificial Intelligence (PPAI) at AAAI-23.
Jan-23 - Four new exciting preprints on topics including differentiable optimization, data leakage in ML models, differential privacy in language models, and differentially private data disclosure methods. :sparkles: :sparkles: See publications for details.
- Paper accepted to IEEE PES 2023! :sparkles: See publications for details.
- I gave a talk about Differential Privacy and Fairness in Energy Systems at the Grid Science winter school.
- I will be serving as an area chair for FAccT-23 and ECAI-23.
- I will be serving as demo track co-chair for IJCAI-23 and scholarship co-chair for AAMAS-23.
- Paper accepted to AAMAS 2023! :sparkles: See publications for details.