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Research interests
Trustworthy AI
Develop data-driven technology that can be trusted to do the critical work
CPS Security
Protect the critical infrastructure against cyber and physical attacks
Human-centric Technology
Design methods that respect human-centric needs and values
Future Energy Systems
Ensure sustainable and reliable access to power and energy for society
Selected Awards
- 1st Place Winner of the 2021 CityLearn Challenge (2021)
- Siebel Scholar (class of 2018)
- Best paper award, Building and Environment (2018)
- Best paper runner-up award at EAI MobiQuitous (2016)
- Best paper award at UBICOMM (2015)
News
Nov 2022Three papers at AAAI on winning the CityLearn Challenge, approximation/statistical properties of solution functions, and nonstationary risk-sensitive RL
Jun 2022Paper on dynamic regret for online optimization "Dynamic Regret Bounds for Online Nonconvex Optimization" to appear in IEEE Transactions on Control of Network Systems
Mar 2022Paper on general bi-level optimization "Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints"
Mar 2022Thanks The Commonwealth Cyber Initiative (CCI) for supporting our research
Mar 2022Presentation at PMS 406 Autonomy MRE workshop on assured RL for dynamical systems
Feb 2022Paper on learning under specifications "Learning Neural Networks under Input-Output Specifications" (arXiv) to appear in ACC 2022
Jan 2022Paper on adversarial ML "Adversarial Unlearning of Backdoors via Implicit Hypergradient" (arXiv) accepted to ICLR 2022
Dec 2021Paper on safe RL "Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems" (arXiv) to appear in AAAI 2022
Dec 2021Paper on control by proxy "Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach" (arXiv) to appear in IEEE Transactions on Smart Grid
Nov 2021Thanks 4-VA for supporting our research
Nov 2021Two teams that I led (ROLEVT & ZoRL) jointly won the 1st place in the CityLearn Challenge 2021. Congrats to team members: Vanshaj Khattar, Qasim Wani, Zhiyao Chang, and Mingyu Kim
Nov 2021New paper on adversarial ML "Adversarial Unlearning of Backdoors via Implicit Hypergradient" (arXiv)
Nov 2021Talk on assured RL for energy systems at C3.ai Digital Transformation Institute (video)
Oct 2021My group will give an oral presentation on implicit RL in SECC 2021
Oct 2021New paper on implicit RL "Zeroth-Order Implicit Reinforcement Learning for Sequential Decision Making in Distributed Control Systems"
Oct 2021New paper on assured learning of neural networks "Learning Neural Networks under Input-Output Specifications"