Anand D. Sarwate
Associate Professor
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey

Associate Member, Dept. of Statistics
Associate Member, Dept. of Computer Science
Associate Member, WINLAB
Affiliate Member, DIMACS

CoRE Building Rm. 517
Phone: +1-848-445-8516
Twitter: @ergodicwalk
Office Hours (Fall 2020):
Lab Members
My "lab" is a room full of desks. I have thus far resisted giving it a fancy name.
Graduate Students:
Undergraduate Students:
  • Ashley Hart (undergraduate, University of Central Florida), Summer 2021 RiSE REU
  • Harry Fu (undergraduate, University of Michigan), Summer 2021 DIMACS REU
Prospective Students
For prospective graduate students: Rutgers is an exciting place to learn and grow as an engineer. If you are specifically interested in my research, please look at my publications and projects. If you are applying to the department and want to pursue a Ph.D., apply to the Ph.D. program and mention your research interests in your statement. If you have specific questions about my research, feel free to contact me and mention that you have read this particular webpage. Unfortunately, I generally do not have the time to respond to general inquiries from potential students. I will not respond to emails that only contain a CV and a request for admission to my research group.
For undergraduate students: If you're interested in an undergraduate research opportunity, please feel free to email me. Doing an independent research study is a great way to learn about research and expand your horizons beyond the classroom.
Previous Students and Visitors
Graduate Students and Postdocs
  • Konstantinos Nikolakakis (MS/PhD)
    Learning Tree Structured Models from Noisy Data
  • Mohsen Ghassemi
    Nonconvex Matrix and Tensor Recovery with Applications in Machine Learning
  • Hafiz Imtiaz
    Decentralized Differentially Private Algorithms for Matrix and Tensor Factorization
  • Jafar Mohammadi (Postdoc)
  • Liyang Xie (MS, 2015)
    Comparison of two models in differentially private distributed learning
Visiting Graduate Students
Undergraduate Students