2026 Karthik Sridharan: 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	38	10200	9913	4288	4112	95	6396

Research.com Recognitions

2018 - Fellow of Alfred P. Sloan Foundation

Overview

Karthik Sridharan is a researcher affiliated with Cornell University in the United States. Their work spans several core areas within computer science and decision sciences, with a focus on advanced algorithms and machine learning methodologies.

The scientist has contributed extensively to topics including:

Advanced Bandit Algorithms Research
Machine Learning and Algorithms
Reinforcement Learning in Robotics
Stochastic Gradient Optimization Techniques
Complexity and Algorithms in Graphs
Sparse and Compressive Sensing Techniques
Adversarial Robustness in Machine Learning

The primary fields of study connected to their publications are:

Computer Science
Decision Sciences

Within these fields, their subfields of study include:

Artificial Intelligence
Management Science and Operations Research
Computational Theory and Mathematics
Mathematical Physics
Computational Mechanics

Frequent publication venues for their work include:

arXiv (Cornell University)
Mathematics of Operations Research
Journal of Pseudo-Differential Operators and Applications

Their recent papers demonstrate a focus on reinforcement learning, stochastic optimization, and online learning, including:

"Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation" (2022, arXiv (Cornell University))
"Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations" (2020, arXiv (Cornell University))
"Small-Loss Bounds for Online Learning with Partial Information" (2022, Mathematics of Operations Research)
"On the Complexity of Adversarial Decision Making" (2022, arXiv (Cornell University))

Karthik Sridharan has collaborated frequently with the following co-authors:

Ayush Sekhari
Dylan J. Foster
Christoph Dann
Yishay Mansour
Mehryar Mohri

The profile of this researcher also includes recognition such as being named a Fellow of the Alfred P. Sloan Foundation in 2018.

Best Publications

Multi-view clustering via canonical correlation analysis

Kamalika Chaudhuri;Sham M. Kakade;Karen Livescu;Karthik Sridharan

951
Citations
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Alexander Rakhlin;Ohad Shamir;Karthik Sridharan

640
Citations
Learnability, Stability and Uniform Convergence

Shai Shalev-Shwartz;Ohad Shamir;Nathan Srebro;Karthik Sridharan

412
Citations
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization

Sham M Kakade;Karthik Sridharan;Ambuj Tewari

354
Citations
Stochastic Convex Optimization.

Shai Shalev-Shwartz;Ohad Shamir;Nathan Srebro;Karthik Sridharan

297
Citations
Smoothness, Low Noise and Fast Rates

Nathan Srebro;Karthik Sridharan;Ambuj Tewari

257
Citations
Online Learning With Predictable Sequences

Alexander Rakhlin;Karthik Sridharan

222
Citations
Better Mini-Batch Algorithms via Accelerated Gradient Methods

Andrew Cotter;Ohad Shamir;Nati Srebro;Karthik Sridharan

216
Citations
Online Optimization : Competing with Dynamic Comparators

Ali Jadbabaie;Alexander Rakhlin;Shahin Shahrampour;Karthik Sridharan

195
Citations
Optimization, Learning, and Games with Predictable Sequences

Sasha Rakhlin;Karthik Sridharan

183
Citations
Fast Rates for Regularized Objectives

Karthik Sridharan;Shai Shalev-shwartz;Nathan Srebro

154
Citations
Online Learning: Random Averages, Combinatorial Parameters, and Learnability

Alexander Rakhlin;Karthik Sridharan;Ambuj Tewari

117
Citations
On the Universality of Online Mirror Descent

Nati Srebro;Karthik Sridharan;Ambuj Tewari

115
Citations
Selective sampling and active learning from single and multiple teachers

Ofer Dekel;Claudio Gentile;Karthik Sridharan

106
Citations
An Information Theoretic Framework for Multi-View Learning

Karthik Sridharan;Sham M Kakade

101
Citations
Optimization, Learning, and Games with Predictable Sequences

Alexander Rakhlin;Karthik Sridharan

96
Citations
Learning in Games: Robustness of Fast Convergence

Dylan J. Foster;Zhiyuan Li;Thodoris Lykouris;Karthik Sridharan

93
Citations
Two-Player Games for Efficient Non-Convex Constrained Optimization

Andrew Cotter;Heinrich Jiang;Karthik Sridharan

83
Citations
Sequential complexities and uniform martingale laws of large numbers

Alexander Rakhlin;Karthik Sridharan;Ambuj Tewari

81
Citations
Relax and Randomize : From Value to Algorithms

Sasha Rakhlin;Ohad Shamir;Karthik Sridharan

68
Citations
Robust Selective Sampling from Single and Multiple Teachers.

Ofer Dekel;Claudio Gentile;Karthik Sridharan

60
Citations
Online Nonparametric Regression

Alexander Rakhlin;Karthik Sridharan

22
Citations
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

Andrew Cotter;Heinrich Jiang;Serena Wang;Taman Narayan

2
Citations

Frequent Co-Authors

Alexander Rakhlin MIT

Ambuj Tewari University of Michigan–Ann Arbor

Ohad Shamir Weizmann Institute of Science

Nathan Srebro Toyota Technological Institute at Chicago

Mehryar Mohri Google (United States)

Shai Shalev-Shwartz Hebrew University of Jerusalem

Satyen Kale Google (United States)

Sham M. Kakade Harvard University

Éva Tardos Cornell University

Yishay Mansour Tel Aviv University

External Links

Personal website of Karthik Sridharan

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring online degrees related to Computer Science can open doors to a range of in-demand careers. Many U.S. universities now offer flexible, affordable options for students seeking a broader technical foundation beyond traditional computer science.

For example, an online degree in mechanical engineering provides problem-solving skills and technical know-how that overlap with computer science fields, especially in areas like robotics and automation.

If your interests lean toward scientific and analytical thinking, pursuing an online bachelor's degree in physics might be the right fit. This path pairs well with computer science in fields like simulation, modeling, and data analysis.

Data science has emerged as a leading field with strong career prospects. Many universities now offer specialized and highly-ranked data science programs online, making it easier to break into areas like machine learning, artificial intelligence, and data analytics.

Finally, for those interested in electronics and digital systems, reviewing electrical engineering online tuition costs can help you understand program affordability and return on investment in another key STEM field with high job demand.