Ali Vakilian

Ali Vakilian develops algorithms for large-scale data analysis and machine learning, with expertise in streaming and sublinear algorithms, learning-augmented algorithms, and trustworthy machine learning. His work studies how limited computation, data access, and imperfect predictions can be used to design algorithms with rigorous performance guarantees. He has also developed algorithms for fair clustering and strategic classification, with applications in decision-making settings where individuals may respond strategically to predictive models.

His research includes more than 50 peer-reviewed publications in leading venues in theoretical computer science and machine learning. Vakilian received the NSF CAREER Award in 2026 and an AISTATS Outstanding Student Paper Highlight Award in 2024. He is a faculty member at Rutgers Business School and previously held faculty and research positions at Virginia Tech and the Toyota Technological Institute at Chicago. He received his Ph.D. in computer science from MIT.

AREAS OF EXPERTISE

Algorithms for massive and streaming datasets; the algorithmic foundations of machine learning; algorithmic fairness and bias in automated decision-making, such as hiring, lending, and admissions; how people and firms adapt their behavior to game predictive models; making AI methods efficient enough to run on data too large to store.