Baolin Li

Baolin Li

Ph.D. candidate

Northeastern University

Biography

I am a Ph.D. candidate in Computer Engineering at Northeastern University, working under the guidance of Professor Devesh Tiwari. My research focuses on optimizing High Performance Computing (HPC) and Cloud Computing systems for Machine Learning (ML) applications. Throughout my doctoral studies, I have taken the lead on several research projects tackling various challenges in terms of cost-effectiveness, resource sharing, and sustainability of such HPC/cloud systems for machine learning. I am dedicated to addressing real-world challenges faced by modern computing systems and developing solutions that are both efficient and sustainable.

For more information and publications, please refer to my full resumé .

Interests
  • HPC
  • Cloud Computing
  • Systems for Machine Learning
Education

  • Ph.D. in Computer Engineering, 2024

    Northeastern University

  • M.S. in Electrical and Computer Engineering, 2017

    The University of Texas at Austin

  • B.Eng. (honours) in Electrical and Electronic Engineering, 2015

    The University of Manchester

Experience

 
 
 
 
 
Research Assistant
Northeastern University
Jul 2019 – Present Boston, MA
Currently active in research of efficient ML system, Multi-Instance GPU, and sustainable HPC for AI.
 
 
 
 
 
Machine Learning Intern, Research
Netflix
Jun 2023 – Sep 2023 Los Gatos, CA
Scalable machine learning platform for foundation model training
 
 
 
 
 
Research Intern
Bosch Research
Jun 2022 – Sep 2022 Sunnyvale, CA
Cloud optimization for ML inference applications
 
 
 
 
 
System Engineer
Silicon Labs
Jun 2017 – Jun 2019 Austin, TX
Testing software development for for ultra-low power IoT microcontroller.

Publications

Baolin Li, Yankai Jiang, Vijay Gadepally, Devesh Tiwari (2024). Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference. Under Review.

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Baolin Li, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari (2023). Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service. In SC ‘23.

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Baolin Li, Rohan Basu Roy, Daniel Wang, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari (2023). Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems. In SC ‘23.

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Dan Zhao, Siddharth Samsi, Joseph McDonald, Baolin Li, David Bestor, Michael Jones, Devesh Tiwari, Vijay Gadepally (2023). Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale. In SoCC ‘23.

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Baolin Li, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari (2023). Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources. In HPDC ‘23.

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Baolin Li, Tirthak Patel, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari (2022). MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant GPU Clusters. In SoCC ‘22.

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Baolin Li, Rohin Arora, Siddharth Samsi, Tirthak Patel, Rohan Basu Roy, Vijay Gadepally, Devesh Tiwari (2022). AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications. In HPCA ‘22.

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Joseph McDonald, Baolin Li, Nathan Frey, Devesh Tiwari, Vijay Gadepally, Siddharth Samsi (2022). Great Power, Great Respobsibility: Recommendations for Reducing Energy for Training Language Models. In NAACL ‘22 Findings.

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Adnan Maruf, Sashri Brahmakshatriya, Baolin Li, Devesh Tiwari, Gang Quan, Janki Bhimani (2022). Do Temperature and Humidity Exposures Hurt or Benefit Your SSDs?. In DATE ‘22 (Best Paper Finalist).

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Baolin Li, Rohan Basu Roy, Tirthak Patel, Vijay Gadepally, Karen Gettings, Devesh Tiwari (2021). RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances. In SC ‘21.

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Baolin Li, Vijay Gadepally, Siddharth Samsi, Mark Veillette, Devesh Tiwari (2021). Serving Machine Learning Inference Using Heterogeneous Hardware. In HPEC ‘21 (Outstanding Student Paper Award).

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Tirthak Patel, Abhay Potharaju, Baolin Li, Rohan Basu Roy, Devesh Tiwari (2020). Experimental Evaluation of NISQ Quantum Computers: Error Measurement, Characterization, and Implications. In SC ‘20 (Best Paper and Best Student Paper Finalist).

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Tirthak Patel, Baolin Li, Rohan Basu Roy, Devesh Tiwari (2020). UREQA: Leveraging Operation-Aware Error Rates for Effective Quantum Circuit Mapping on NISQ-Era Quantum Computers. In ATC ‘20.

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