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D-Index & Metrics

Computer Science

D-Index
36
Citations
6412
World Ranking
11145
National Ranking
4622

Overview

Lingjia Tang is affiliated with the University of Michigan-Ann Arbor in the United States. Their research contributions lie primarily within the field of computer science, with a significant focus on subfields such as artificial intelligence, information systems, computer networks and communications, information systems and management, and computer vision and pattern recognition.

Their scholarly output includes a range of topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Cloud Computing and Resource Management
  • Software System Performance and Reliability
  • Machine Learning and Data Classification
  • Scientific Computing and Data Management
  • Context-Aware Activity Recognition Systems

Lingjia Tang has contributed to various research venues, frequently publishing in:

  • arXiv (Cornell University)
  • IEEE Computer Architecture Letters
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Proceedings of the ACM on Programming Languages

Recent papers authored or co-authored by Lingjia Tang include:

  • "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)
  • "A Benchmarking Framework for Interactive 3D Applications in the Cloud," 2020, arXiv (Cornell University)

Frequent collaborators of Lingjia Tang include:

  • Jason Mars
  • 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

  • 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

  • Directly characterizing cross core interference through contention synthesis

    Jason Mars;Lingjia Tang;Mary Lou Soffa

  • Treadmill: attributing the source of tail latency through precise load testing and statistical inference

    Yunqi Zhang;David Meisner;Jason Mars;Lingjia Tang

  • Proceedings of the 2017 International Symposium on Code Generation and Optimization

    Vijay Janapa Reddi;Aaron Smith;Lingjia Tang

Frequent Co-Authors

Jason Mars
Jason Mars 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
Thomas F. Wenisch
Thomas F. Wenisch University of Michigan–Ann Arbor
Kevin Skadron
Kevin Skadron University of Virginia
Martin Schulz
Martin Schulz Technical University of Munich
Danny H. K. Tsang
Danny H. K. Tsang Hong Kong University of Science and Technology

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