Ilias Zadik

Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation.

Education:

• PhD in Operations Research, MIT (2019)
• MASt in Mathematics (Part III), University of Cambridge (2014)
• BA in Mathematics, University of Athens (2013)
• Internship at Microsoft Research New England (2017)

Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds.

Awards:

• MIT Operations Research Best Student Paper Honorable Mention (2017)
• Trinity College Senior Scholarship (2014)
• Onassis Foundation Scholarship (2013-2014)
• SEEMOUS Gold Medal (2011), IMC First Prize (2011)

Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS.

Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.