Please visit http://prateekjain.org for the latest site and http://prateekjain.org/paper.html for my latest publications.

I am a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research, Bangalore, India. My research interests are in machine learning, large-scale (non-convex optimization), and statistical learning theory. I am also interested in applications of machine learning to privacy, computer vision, text mining and natural language processing. I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.

I am also an adjunct faculty member at IIT Kanpur.


Professional Services

  • Organization:
    • Program Co-Chair, IKDD Conference on Data Sciences (CoDS), 2016.
    • Organizer, Machine Learning Summer School, Microsoft Research, 2015.
    • Organizer, Mysore Park Workshop on Machine Learning, Mysore, India, 2012.
  • Program Committee/Area Chair
    • COLT 2015, 2016
    • NIPS 2012, 2013, 2016


Over the years, I have been very lucky to have worked with some amazing postdocs/interns/research fellows.


  • Purushottam Kar, 2013-2015 (Asst. Prof., IIT Kanpur)
  • Nagarajan Natarajan, 2015- (Postdoc, MSR India)


  • Elena-Madalina Persu, Summer’2015. (Phd Student, MIT)
  • Gautam Kamath, Summer’2015. (Phd Student, MIT)
  • Harikrishna Narasimhan, Summer’2014. (Postdoc, Harvard University)
  • Praneeth Netrapalli, Summer’2012, 2014. (Postdoc, Microsoft Research New England)
  • Srinadh Bhojanapalli, Summer’2013, 2014. (Assistant Professor, TTI Chicago)
  • Pravesh Kothari, Summer’2014. (Phd Student, UT Austin)
  • Purushottam Kar, Summer’2012. (Assistant Professor, IIT Kanpur)
  • Sivakanth Gopi, Summer’2012. (Phd Student, Princeton University)
  • Ankan Saha, Summer’2011. (Software Engineer, LinkedIn)
  • Saurabh Gupta, Summer’2011. (Phd Student, UC Berkeley)

Research Fellows:

  • Kush Bhatia, 2014-2016. (PhD Student, UC Berkeley)
  • Yeshwanth Cherapanamjeri, 2015-. (RF, MSR India)
  • Raajay Viswanathan, 2011-2013. (PhD Student, UWisc Madison)


Resource-efficient ML for Edge and Endpoint IoT Devices

Our objective is to develop a library of efficient machine learning algorithms that can run on severely resource-constrained edge and endpoint IoT devices ranging from the Arduino to the Raspberry Pi. Please see our GitHub page for code release. The Internet of Things (IoT) is poised to revolutionize our world. Billions of microcontrollers and sensors have already been deployed for predictive maintenance, connected cars, precision agriculture, personalized fitness and wearables, smart housing, cities, healthcare, etc. The dominant…

Machine Learning on the Edge

In a few years, the world will be filled with billions of small, connected, intelligent devices. Many of these devices will be embedded in our homes, our cities, our vehicles, and our factories. Some of these devices will be carried in our pockets or worn on our bodies. The proliferation of small computing devices will disrupt every industrial sector and play a key role in the next evolution of personal computing. Most of these devices…

Provable Non-convex Optimization for Machine Learning Problems

Established: April 4, 2014

In this work, we explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc. Talks: Provable Non-convex Optimization for Machine Learning. Summer School on Non-convex Optimization, IIT Bombay, 2015. [part 1] [part 2] Iterative Hard Thresholding for Sparse/Low-rank Linear Regression. INRIA, France, 2015. [pdf version] Iterative Hard Thresholding for Robust Regression.  ITW, 2015. [pdf version] Provable Alternating Minimization methods for…

Virtual Algorithms Center (VIRAL)

Established: December 27, 2013

MSR has a strong group of scientists working on algorithm design, analysis, and experimental evaluation, as well as researchers in related areas (e.g., coding theory), but no formal algorithms group. The Virtual Algorithms Center (VIRAL) brings these individuals together. The goals of the center is to enhance collaboration between algorithms researchers and the rest of MSR, provide internal consulting, and give an external view of the algorithms research at MSR.  







Memory Limited, Streaming PCA
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain, in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States., December 1, 2013, View abstract, View external link








Link description

Panel Q and A


July 27, 2015


Prateek Jain, Chin-Jen Lin, Aditya Gopalan, Suvrit Sra, and Stefanie Jegelka


Microsoft, National Taiwan University, IISc, Max Planck Institute for Intelligent Systems, UC Berkeley



  • Non-convex Optimization for High-Dimensional Statistics
  • Matrix Completion and Low-rank Matrix Recovery
  • Compressive Sensing
  • Learning with Non-decomposable Loss Functions
  • Differential Privacy for Machine Learning
  • Distance Metric Learning