World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
24180
World Ranking
3168
National Ranking
1534

Overview

Elad Hazan is a researcher affiliated with Princeton University in the United States. Their primary fields of study include Computer Science, Engineering, and Decision Sciences, with a significant focus on several subfields such as Artificial Intelligence, Management Science and Operations Research, Control and Systems Engineering, Computational Mechanics, and Computer Networks and Communications.

The main topics that characterize Hazan's body of research comprise Advanced Bandit Algorithms Research, Stochastic Gradient Optimization Techniques, Sparse and Compressive Sensing Techniques, Reinforcement Learning in Robotics, Advanced Control Systems Optimization, Machine Learning and Algorithms, and Respiratory Support and Mechanisms.

Hazan has contributed extensively to the academic literature, with frequent publications appearing predominantly in arXiv (Cornell University), accounting for 48 papers. Other venues include bioRxiv (Cold Spring Harbor Laboratory) and Quantum, with one publication each in these venues.

Among recent papers authored or co-authored by Hazan are:

  • Improper Learning for Non-Stochastic Control, 2020, arXiv (Cornell University)
  • Black-Box Control for Linear Dynamical Systems, 2020, arXiv (Cornell University)
  • Adaptive Regret for Control of Time-Varying Dynamics, 2020, arXiv (Cornell University)
  • Machine Learning for Mechanical Ventilation Control, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • Disentangling Adaptive Gradient Methods from Learning Rates, 2020, arXiv (Cornell University)

Frequent collaborators in Hazan's research projects include the following individuals:

  • Naman Agarwal
  • Udaya Ghai
  • Daniel Suo
  • Paula Gradu
  • Lu Zhou

Hazan's work intersects multiple areas of advanced computational research, focusing on algorithm development and optimization techniques relevant to control systems and machine learning. Projects involving reinforcement learning, sparse sensing methods, and optimization in dynamic and uncertain environments are central themes in their research portfolio.

Best Publications

  • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

    John Duchi;Elad Hazan;Yoram Singer

  • Introduction to Online Convex Optimization

    Elad Hazan

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Amit Agarwal;Satyen Kale

  • The Multiplicative Weights Update Method: A Meta-Algorithm and Applications

    Sanjeev Arora;Elad Hazan;Satyen Kale

  • Competing in the dark: An efficient algorithm for bandit linear optimization

    Jacob D Abernethy;Elad Hazan;Alexander Rakhlin

  • Variance reduction for faster non-convex optimization

    Zeyuan Allen-Zhu;Elad Hazan

  • On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

    Sanjeev Arora;Nadav Cohen;Elad Hazan

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Adam Kalai;Satyen Kale;Amit Agarwal

  • Algorithms for portfolio management based on the Newton method

    Amit Agarwal;Elad Hazan;Satyen Kale;Robert E. Schapire

  • Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization

    Elad Hazan;Satyen Kale

  • Adaptive Online Gradient Descent

    Elad Hazan;Alexander Rakhlin;Peter L. Bartlett

  • On the complexity of approximating k -set packing

    Elad Hazan;Shmuel Safra;Oded Schwartz

  • Finding approximate local minima faster than gradient descent

    Naman Agarwal;Zeyuan Allen-Zhu;Brian Bullins;Elad Hazan

  • Sparse approximate solutions to semidefinite programs

    Elad Hazan

  • Extracting certainty from uncertainty: regret bounded by variation in costs

    Elad Hazan;Satyen Kale

  • Efficient learning algorithms for changing environments

    Elad Hazan;C. Seshadhri

  • Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

    Dan Garber;Elad Hazan

  • Fast algorithms for approximate semidefinite programming using the multiplicative weights update method

    S. Arora;E. Hazan;S. Kale

  • How Hard Is It to Approximate the Best Nash Equilibrium

    Elad Hazan;Robert Krauthgamer

  • Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

    Elad Hazan;Satyen Kale

Frequent Co-Authors

Satyen Kale
Satyen Kale Google (United States)
Sham M. Kakade
Sham M. Kakade Harvard University
Sanjeev Arora
Sanjeev Arora Princeton University
Zeyuan Allen-Zhu
Zeyuan Allen-Zhu Meta Platforms, Inc.
Shai Shalev-Shwartz
Shai Shalev-Shwartz Hebrew University of Jerusalem
Tengyu Ma
Tengyu Ma Stanford University
Shie Mannor
Shie Mannor Technion – Israel Institute of Technology
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University
Nimrod Megiddo
Nimrod Megiddo IBM (United States)

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

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring related fields can expand your career potential after studying Computer Science in the USA. Many students consider pursuing interdisciplinary areas such as engineering or environmental sciences to further diversify their job prospects.

Earning an online computer science degree is an excellent option for those seeking flexibility and accelerated pathways into the tech industry. These programs allow you to study at your own pace and often lead to roles in software development, data analysis, or cybersecurity.

For anyone interested in applying technical skills to sustainability challenges, pursuing environmental engineering degrees online is a smart move. This area merges engineering principles with environmental protection, opening doors to impactful and innovative careers.

Similarly, online mechanical engineering degree programs can prepare you for design, manufacturing, and robotics roles, often overlapping with computer science applications.

If you’re passionate about sustainability, there are also high-paying jobs with environmental science degree backgrounds, especially when paired with computing skills. Adding these fields to your academic journey broadens your career options in both traditional and emerging industries.

Best Scientists Citing Elad Hazan

Trending Scientists