2026 Praneeth Netrapalli: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	41	8900	8649	3788	3638	97	6516

Overview

Praneeth Netrapalli is affiliated with Google in the United States and has contributed extensively to the field of computer science, particularly within artificial intelligence and related subfields. Their research output includes a significant number of publications focusing on various areas within machine learning and optimization techniques.

Their recent papers cover topics such as model-based offline reinforcement learning, simplicity bias in neural networks, nonconvex optimization, domain generalization, and document embeddings. Specifically, notable publications include:

MOReL: Model-Based Offline Reinforcement Learning (2020), published in arXiv (Cornell University)
The Pitfalls of Simplicity Bias in Neural Networks (2020), published in arXiv (Cornell University)
On Nonconvex Optimization for Machine Learning (2021), published in the Journal of the ACM
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition (2020), published in arXiv (Cornell University)
P-SIF: Document Embeddings Using Partition Averaging (2020), published in the Proceedings of the AAAI Conference on Artificial Intelligence

Frequent coauthors who have collaborated with Praneeth Netrapalli include:

Prateek Jain
Anant Raj
Robin Kothari
Suhail Sherif

The primary publication venues for their work are:

arXiv (Cornell University)
Journal of the ACM
Proceedings of the AAAI Conference on Artificial Intelligence
SIAM Journal on Optimization
Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Netrapalli's research spans across several subfields of computer science, including artificial intelligence, management science and operations research, computational mechanics, computer vision and pattern recognition, and statistics and probability.

The main areas of their research work involve:

Advanced Bandit Algorithms Research
Stochastic Gradient Optimization Techniques
Sparse and Compressive Sensing Techniques
Machine Learning and Algorithms
Domain Adaptation and Few-Shot Learning
Adversarial Robustness in Machine Learning
Natural Language Processing Techniques

Best Publications

Low-rank matrix completion using alternating minimization

Prateek Jain;Praneeth Netrapalli;Sujay Sanghavi

1168
Citations
Phase Retrieval Using Alternating Minimization

Praneeth Netrapalli;Prateek Jain;Sujay Sanghavi

645
Citations
How to escape saddle points efficiently

Chi Jin;Rong Ge;Praneeth Netrapalli;Sham M. Kakade

623
Citations
MOReL: Model-Based Offline Reinforcement Learning

Rahul Kidambi;Aravind Rajeswaran;Praneeth Netrapalli;Thorsten Joachims

221
Citations
Learning the graph of epidemic cascades

Praneeth Netrapalli;Sujay Sanghavi

211
Citations
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent

Chi Jin;Praneeth Netrapalli;Michael I. Jordan

207
Citations
Non-convex Robust PCA

Praneeth Netrapalli;Niranjan U N;Sujay Sanghavi;Animashree Anandkumar

190
Citations
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization

Alekh Agarwal;Animashree Anandkumar;Prateek Jain;Praneeth Netrapalli

169
Citations
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Chi Jin;Praneeth Netrapalli;Michael Jordan

150
Citations
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Chi Jin;Praneeth Netrapalli;Rong Ge;Sham M. Kakade

130
Citations
Streaming PCA: Matching matrix bernstein and near-optimal finite sample guarantees for oja's algorithm

Prateek Jain;Chi Jin;Sham M. Kakade;Praneeth Netrapalli

118
Citations
Learning Sparsely Used Overcomplete Dictionaries

Alekh Agarwal;Animashree Anandkumar;Prateek Jain;Praneeth Netrapalli

118
Citations
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification

Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

110
Citations
Efficient Algorithms for Smooth Minimax Optimization

Kiran Koshy Thekumparampil;Prateek Jain;Praneeth Netrapalli;Sewoong Oh

108
Citations
Information-theoretic thresholds for community detection in sparse networks

Jess Banks;Cristopher Moore;Joe Neeman;Praneeth Netrapalli

108
Citations
The Pitfalls of Simplicity Bias in Neural Networks

Harshay Shah;Kaustav Tamuly;Aditi Raghunathan;Prateek Jain

108
Citations
Accelerating Stochastic Gradient Descent for Least Squares Regression

Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

106
Citations
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares

Rong Ge;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

96
Citations
Stochastic Gradient Descent and Its Variants in Machine Learning

Praneeth Netrapalli

96
Citations
Fast Exact Matrix Completion with Finite Samples

Prateek Jain;Praneeth Netrapalli

95
Citations
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization

Rahul Kidambi;Praneeth Netrapalli;Prateek Jain;Sham Kakade

84
Citations
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm

Chi Jin;Praneeth Netrapalli;Rong Ge;Sham M. Kakade

70
Citations
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

Vihari Piratla;Praneeth Netrapalli;Sunita Sarawagi

69
Citations

Frequent Co-Authors

Prateek Jain Google (United States)

Sham M. Kakade Harvard University

Aaron Sidford Stanford University

Sujay Sanghavi The University of Texas at Austin

Rong Ge Duke University

Michael I. Jordan University of California, Berkeley

Anima Anandkumar Nvidia (United Kingdom)

Sewoong Oh University of Washington

Alekh Agarwal Google (United States)

Sunita Sarawagi Indian Institute of Technology Bombay

External Links

Personal website of Praneeth Netrapalli Google Scholar page

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