World's Best Scientists 2026 revealed!
Larry Rudolph

Larry Rudolph

D-Index & Metrics

Computer Science

D-Index
52
Citations
12088
World Ranking
5039
National Ranking
2341

Overview

Larry Rudolph is affiliated with MIT in the United States and conducts research primarily in the field of Computer Science. Their work spans several subfields including Artificial Intelligence, Infectious Diseases, Modeling and Simulation, Pollution, and Electrical and Electronic Engineering.

Their research contributions cover a range of topics, notably in SARS-CoV-2 detection and testing, COVID-19 epidemiological studies, energy and environment impacts, reinforcement learning in robotics, adversarial robustness in machine learning, as well as advanced memory and neural computing.

Recent publications by Larry Rudolph include:

  • Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO, 2020, arXiv (Cornell University)
  • ANERGY TO SYNERGY-THE ENERGY FUELING THE RXCOVEA FRAMEWORK, 2020, International Journal for Multiscale Computational Engineering

Frequent co-authors collaborating with Larry Rudolph include:

  • Evelyne Bischof
  • Jantine A.C. Broek
  • Charles R. Cantor
  • Ashley J. Duits
  • Alfredo Ferro

Publications by Larry Rudolph have appeared in venues such as the International Journal for Multiscale Computational Engineering and arXiv (Cornell University).

Best Publications

  • Competitive snoopy caching

    Anna R. Karlin;Mark S. Manasse;Larry Rudolph;Daniel D. Sleator

  • Theory and Practice in Parallel Job Scheduling

    Dror G. Feitelson;Larry Rudolph;Uwe Schwiegelshohn;Kenneth C. Sevcik

  • Dynamic Partitioning of Shared Cache Memory

    G. E. Suh;L. Rudolph;S. Devadas

  • Gang scheduling performance benefits for fine-grain synchronization

    Dror G. Feitelson;Larry Rudolph

  • The Power of Parallel Prefix.

    Clyde P. Kruskal;Larry Rudolph;Marc Snir

  • A new memory monitoring scheme for memory-aware scheduling and partitioning

    G.E. Suh;S. Devadas;L. Rudolph

  • Parallel job scheduling — a status report

    Dror G. Feitelson;Larry Rudolph;Uwe Schwiegelshohn

  • Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors

    Allan Gottlieb;Boris D. Lubachevsky;Larry Rudolph

  • Dynamic decentralized cache schemes for mimd parallel processors

    Larry Rudolph;Zary Segall

  • Towards Convergence in Job Schedulers for Parallel Supercomputers

    Dror G. Feitelson;Larry Rudolph

  • A complexity theory of efficient parallel algorithms

    Clyde P. Kruskal;Larry Rudolph;Marc Snir

  • Parallel Job Scheduling: Issues and Approaches

    Dror G. Feitelson;Larry Rudolph

  • Metrics and Benchmarking for Parallel Job Scheduling

    Dror G. Feitelson;Larry Rudolph

  • A simple load balancing scheme for task allocation in parallel machines

    Larry Rudolph;Miriam Slivkin-Allalouf;Eli Upfal

  • Efficient synchronization of multiprocessors with shared memory

    Clyde P. Kruskal;Larry Rudolph;Marc Snir

  • Distributed hierarchical control for parallel processing

    D.G. Feitelson;L. Rudolph

  • Analytical cache models with applications to cache partitioning

    G. Edward Suh;Srinivas Devadas;Larry Rudolph

  • PIE: A Programming and Instrumentation Environment for Parallel Processing

    Z. Segall;L. Rudolph

  • Application-specific memory management for embedded systems using software-controlled caches

    Derek Chiou;Prabhat Jain;Larry Rudolph;Srinivas Devadas

  • Adaptive reduction parallelization techniques

    Unknown

  • Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

    Logan Engstrom;Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras

  • Implementation Matters in Deep RL: A Case Study on PPO and TRPO

    Logan Engstrom;Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras

Frequent Co-Authors

Dror G. Feitelson
Dror G. Feitelson Hebrew University of Jerusalem
Marc Snir
Marc Snir University of Illinois at Urbana-Champaign
G. Edward Suh
G. Edward Suh Cornell University
Gary L. Miller
Gary L. Miller Carnegie Mellon University
Mark S. Manasse
Mark S. Manasse Microsoft (United States)
Anna R. Karlin
Anna R. Karlin University of Washington
Ravi Kannan
Ravi Kannan Microsoft (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 computer science in the USA can open doors to a variety of related careers and online degree opportunities. Many students find that supplementing their tech knowledge with skills in other fields can enhance their job prospects and versatility.

For instance, those interested in legal and cybersecurity roles may benefit from affordable online criminal justice programs that provide foundational knowledge of law and digital investigations.

Aspiring professionals in finance or business analytics can consider the best online accounting program, giving them an edge in managing accounts or automating financial processes.

Tech-driven roles—such as data analyst or data engineer—often require advanced education. An online master data science degree equips students with specialized skills for today’s data-centric industries.

Lastly, if you have interests at the intersection of technology and infrastructure, an online degree for construction management can help you lead complex building projects using modern digital tools.

These diverse career pathways complement a computer science background and can be pursued conveniently through respected online programs.

Best Scientists Citing Larry Rudolph

Trending Scientists

Recently Published Articles