World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
6724
World Ranking
10176
National Ranking
4284

Overview

Jason Mars is affiliated with the University of Michigan-Ann Arbor in the United States. Their research is primarily situated within the field of Computer Science, with a strong focus on Artificial Intelligence. Other subfields include Information Systems, Computer Networks and Communications, Information Systems and Management, and Computer Vision and Pattern Recognition.

The scientist's work encompasses several major topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Software System Performance and Reliability
  • Cloud Computing and Resource Management
  • Scientific Computing and Data Management
  • Hate Speech and Cyberbullying Detection
  • Software Engineering Research

Jason Mars has published extensively, with frequent contributions to the following venues:

  • arXiv (Cornell University)
  • International Journal of Artificial Intelligence and Robotics Research
  • IEEE Computer Architecture Letters
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Proceedings of the ACM on Programming Languages

Some of their recent papers include:

  • "Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection," 2024, International Journal of Artificial Intelligence and Robotics Research
  • "Towards Personalized Intelligence at Scale," 2022, arXiv (Cornell University)
  • "The Jaseci Programming Paradigm and Runtime Stack: Building Scale-Out Production Applications Easy and Fast," 2023, IEEE Computer Architecture Letters
  • "Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production," 2023, arXiv (Cornell University)
  • "One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI," 2024, arXiv (Cornell University)

Jason Mars frequently collaborates with several researchers, including:

  • Lingjia Tang
  • Yiping Kang
  • Krisztián Flautner
  • Christopher Clarke
  • Ashish Mahendra

Best Publications

  • Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

    Jason Mars;Lingjia Tang;Robert Hundt;Kevin Skadron

  • Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

    Yiping Kang;Johann Hauswald;Cao Gao;Austin Rovinski

  • Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

    Hailong Yang;Alex Breslow;Jason Mars;Lingjia Tang

  • The Architectural Implications of Autonomous Driving: Constraints and Acceleration

    Shih-Chieh Lin;Yunqi Zhang;Chang-Hong Hsu;Matt Skach

  • An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

    Stefan Larson;Anish Mahendran;Joseph J. Peper;Christopher Clarke

  • Sirius: An Open End-to-End Voice and Vision Personal Assistant and Its Implications for Future Warehouse Scale Computers

    Johann Hauswald;Michael A. Laurenzano;Yunqi Zhang;Cheng Li

  • The impact of memory subsystem resource sharing on datacenter applications

    Lingjia Tang;Jason Mars;Neil Vachharajani;Robert Hundt

  • DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers

    Johann Hauswald;Yiping Kang;Michael A. Laurenzano;Quan Chen

  • Whare-map: heterogeneity in "homogeneous" warehouse-scale computers

    Jason Mars;Lingjia Tang

  • SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers

    Yunqi Zhang;Michael A. Laurenzano;Jason Mars;Lingjia Tang

  • Gist: efficient data encoding for deep neural network training

    Animesh Jain;Amar Phanishayee;Jason Mars;Lingjia Tang

  • GrandSLAm: Guaranteeing SLAs for Jobs in Microservices Execution Frameworks

    Ram Srivatsa Kannan;Lavanya Subramanian;Ashwin Raju;Jeongseob Ahn

  • Prophet: Precise QoS Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers

    Quan Chen;Hailong Yang;Minyi Guo;Ram Srivatsa Kannan

  • Contention aware execution: online contention detection and response

    Jason Mars;Neil Vachharajani;Robert Hundt;Mary Lou Soffa

  • Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting

    Chang-Hong Hsu;Yunqi Zhang;Michael A. Laurenzano;David Meisner

  • Baymax: QoS Awareness and Increased Utilization for Non-Preemptive Accelerators in Warehouse Scale Computers

    Quan Chen;Hailong Yang;Jason Mars;Lingjia Tang

  • Heterogeneity in “Homogeneous” Warehouse-Scale Computers: A Performance Opportunity

    J. Mars;Lingjia Tang;R. Hundt

  • Octopus-Man: QoS-driven task management for heterogeneous multicores in warehouse-scale computers

    Vinicius Petrucci;Michael A. Laurenzano;John Doherty;Yunqi Zhang

  • Compiling for niceness: mitigating contention for QoS in warehouse scale computers

    Lingjia Tang;Jason Mars;Mary Lou Soffa

  • Evaluating Indirect Branch Handling Mechanisms in Software Dynamic Translation Systems

    Jason D. Hiser;Daniel Williams;Wei Hu;Jack W. Davidson

  • Directly characterizing cross core interference through contention synthesis

    Jason Mars;Lingjia Tang;Mary Lou Soffa

Frequent Co-Authors

Lingjia Tang
Lingjia Tang University of Michigan–Ann Arbor
Mary Lou Soffa
Mary Lou Soffa University of Virginia
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Scott Mahlke
Scott Mahlke University of Michigan–Ann Arbor
Dean M. Tullsen
Dean M. Tullsen University of California, San Diego
Jack W. Davidson
Jack W. Davidson University of Virginia
Thomas F. Wenisch
Thomas F. Wenisch University of Michigan–Ann Arbor
Kevin Skadron
Kevin Skadron University of Virginia
Danny H. K. Tsang
Danny H. K. Tsang Hong Kong University of Science and Technology

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