Farzad Yousefian

Assistant Professor

Industrial and Systems Engineering

Phone:848-445-2238
Fax:732-445-5467
Email:farzad.yousefian@rutgers.edu
Office:CoRE Building, Room 218
Office Hours: By appointment
Website: Mathematical Optimization Research Group (MathOptRG)

Farzad Yousefian is an Assistant Professor in the Department of Industrial and Systems Engineering at Rutgers University—New Brunswick. Prior to joining Rutgers, he was an Assistant Professor from 2015 to 2021 and a tenured Associate Professor from 2021 to 2022 at Oklahoma State University (OSU). Prior to that, he was a Postdoctoral Researcher at Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania State University from 2014 to 2015. He received his Ph.D. in Industrial Engineering from the University of Illinois at Urbana-Champaign in 2013. He obtained his B.Sc. and M.Sc. degrees in Industrial Engineering from Sharif University of Technology in 2006 and 2008, respectively. His research interest lies in distributed optimization in multi-agent networks, stochastic and large-scale optimization, nonconvex optimization, hierarchical optimization, variational inequalities, computational game theory, and applications in machine learning and transportation systems. His research has been funded by the National Science Foundation (NSF) Faculty Early Career Development (CAREER) award, the Office of Naval Research (ONR), and the Department of Energy (DOE). He is a recipient (jointly with his co-authors) of the Best Theoretical Paper award at the 2013 Winter Simulation Conference (WSC). His teaching has been recognized through the 2020 OSU College of Engineering, Architecture, and Technology Excellent Teacher Award. He is an active member of Society for Industrial and Applied Mathematics (SIAM), Mathematical Optimization Society (MOS), Institute of Electrical and Electronics Engineers (IEEE), and Institute for Operations Research and the Management Sciences (INFORMS).

Education

  • Ph.D., Industrial Engineering, University of Illinois at Urbana-Champaign
  • M.Sc., Industrial Engineering, Sharif University of Technology
  • B.Sc., Industrial Engineering, Sharif University of Technology

Honors

  • Department of Energy (DOE) grant (Role: PI, Budget: $400K), 2022-2024
  • Office of Naval Research (ONR) grant (Role: PI, Budget: $300K), 2022-2025
  • NSF CAREER Award (Sole PI, Budget: $500K), 2020-2025
  • The 2020 Industrial Engineering & Management Faculty Award, OSU
  • The 2020 College of Engineering, Architecture, and Technology Excellent Teacher Award, OSU
  • Best Theoretical Paper Award, The 2013 Winter Simulation Conference (WSC)

Professional Affiliations

  • Society for Industrial and Applied Mathematics (SIAM)
  • Institute of Electrical and Electronics Engineers (IEEE)
  • Institute for Operations Research and the Management Sciences (INFORMS)
  • Mathematical Optimization Society (MOS)

Research Interests

  • Distributed Optimization in Multi-Agent Networks
  • Stochastic and Large-Scale Optimization
  • Hierarchical and Nonconvex Optimization
  • Variational Inequalities and Computational Game Theory
  • Applications in Transportation Systems and Machine Learning

Selected Publications

[1] Harshal D. Kaushik and Farzad Yousefian, A Method with Convergence Rates for Optimization
Problems with Variational Inequality Constraints, SIAM Journal on Optimization, 31 (2021), pp.
2171–2198. DOI: 10.1137/20M1357378
[2] Afrooz Jalilzadeh, Angelia Nedić, Uday V. Shanbhag, and Farzad Yousefian, A Variable Sample-
Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization,
Mathematics of Operations Research, 47 (2021), pp. 690–719. DOI: 10.1287/moor.2021.1147
[3] Farzad Yousefian, Angelia Nedić, and Uday V. Shanbhag, On Stochastic and Deterministic Quasi-
Newton Methods for Nonstrongly Convex Optimization: Asymptotic Convergence and Rate Analysis,
SIAM Journal on Optimization, 30 (2020), pp. 1144–1172. DOI: 10.1137/17M1152474
[4] Nahidsadat Majlesinasab, Farzad Yousefian, and Arash Pourhabib, Self-tuned Stochastic Mirror
Descent Methods for Smooth and Nonsmooth High-Dimensional Stochastic Optimization, IEEE Transactions
on Automatic Control, 64 (2019), pp. 4377–4384. DOI: 10.1109/TAC.2019.2897889
[5] Farzad Yousefian, Angelia Nedić, and Uday V. Shanbhag, On Stochastic Mirror-Prox Algorithms
for Stochastic Cartesian Variational Inequalities: Randomized Block Coordinate and Optimal Averaging
Schemes, Set-Valued and Variational Analysis, 26 (2018), pp. 789–819. DOI: 10.1007/s11228-018-0472-9
[6] Farzad Yousefian, Angelia Nedić, and Uday V. Shanbhag, On Smoothing, Regularization, and
Averaging in Stochastic Approximation Methods for Stochastic Variational Inequality Problems,
Mathematical Programming, 165 (2017), pp. 391–431. DOI: 10.1007/s10107-017-1175-y
[7] Farzad Yousefian, Angelia Nedić, and Uday V. Shanbhag, Self-Tuned Stochastic Approximation
Schemes for Non-Lipschitzian Stochastic Multi-User Optimization and Nash Games, IEEE Transactions
on Automatic Control, 61 (2016), pp. 1753–1766. DOI: 10.1109/TAC.2015.2478124
[8] Farzad Yousefian, Angelia Nedić, and Uday V. Shanbhag, On Stochastic Gradient and Subgradient
Methods with Adaptive Steplength Sequences, Automatica, 48 (2012), pp. 56–67.
DOI: 10.1016/j.automatica.2011.09.043