Theory, Algorithms and Formal Methods

Faculty working in this area

Faculty Email website
Eric Atkinson Atkinson's Group
Zeyu Ding Ding's Group
William Hallahan Hallahan's Group
Patrick H. Madden Optimality Research Group
Sujoy Sikdar Sikdar's Group

Highlights in this area

Eric Atkinson works on programming languages for uncertainty, including probabilistic programming and nondeterministic belief programming. His research interests include programming languages, program runtimes, program analysis, formal methods and language design for unusual domains. 

Zeyu Ding researches the intersection of data privacy, software security, machine learning and algorithmic fairness. The overarching goal of his work is to protect sensitive personal information from being leaked in unintended ways. His current research focuses on differential privacy and its interactions with software security, formal verification, numerical optimization, statistical inference and machine learning.

William Hallahan  is interested in formal methods, including program analysis, verification and synthesis techniques. He applies theses techniques across a variety of domains, including functional programming languages, networking systems and machine learning models.  

Patrick Madden researches heterogeneous and integrated circuit physical design automation, with an emphasis on placement and routing. Work in this area involves NP-Hard problems that scale with Moore's Law, and computationally efficient heuristics that obtain near-optimal solutions are a primary focus, from both a theoretical and practical perspective.  

Sujoy Sikdar researches the intersection of computer science, artificial intelligence, economics and social science in understanding individual and group preferences, how preferences are aggregated in systems composed of multiple agents, and designing algorithms to make good decisions for groups of heterogeneous agents. Some examples:
  • Designing fair and efficient algorithms for group decision-making problems like fair division and voting.
  • Learning and modeling preferences from data.
  • Understanding human behavior in a variety of social contexts including in social media streams.