Farid Alizadeh

Farid Alizadeh is a Professor in the Department of Management Science and Information Systems at Rutgers School of Business, Rutgers University, and is affiliated with the Rutgers Center for Operations Research (RUTCOR). He pioneered semidefinite programming (SDP) in 1990, establishing its foundations and applications to combinatorial optimization.

Education:

• PhD in Computer Science and Engineering, University of Minnesota (1991), Advisor: Ben Rosen
• Postdoctoral Associate, International Computer Science Institute (ICSI), Berkeley (1991-1993), working with Richard Karp

His research spans optimization theory, including SDP, second-order cone programming (SOCP), and their applications in statistical learning (shape-constrained regression, density estimation), combinatorial optimization, and algebraic foundations. Current work focuses on extending simplex-like algorithms to SDP/SOCP for branch-and-bound methods and rule-augmented learning problems where examples incorporate feature-space regions.

Scientific Awards:

• NSF CAREER Award (1995)
• INFORMS Optimization Society Farkas Prize (2014)

Funded by the NSF CAREER award, he mentors graduate students in operations research at Rutgers. His teaching includes Linear Programming, Semidefinite Programming, and Statistical Methods of Business across PhD and Master's programs.

As a core RUTCOR member, he collaborates on interdisciplinary optimization projects spanning theoretical computer science, control theory, and statistical applications, maintaining active research labs in both Piscataway (Livingston Campus) and Newark.