Curriculum vitae

Download CV (PDF)

David Jin
jindavid.me · GitHub

51 Vassar St, Building 45, Room 421G, Cambridge, MA 02139

Education

Massachusetts Institute of Technology

Cambridge, MA · May 2027 (expected)

M.S./Ph.D. in Computational Science and Engineering · GPA: 5.0/5.0
Advisors: Sungho Shin and Yoon Kim
Amazon AI Fellow, 2025–2027

California Institute of Technology

Pasadena, CA · June 2022

B.S. in Information and Data Science · GPA: 4.2/4.3

Grinnell College

Grinnell, IA · May 2021

B.A. in Physics · GPA: 4.0/4.0
Barry Goldwater Scholar, 2021–2022

Research interests

GPU computing, numerical optimization, and efficient machine learning.

Professional experience

Jump Trading

Quantitative Researcher Intern · New York, NY · May–August 2026

Designed and implemented GPU-accelerated algorithms for large-scale portfolio optimization.

Argonne National Laboratory

Research Intern · Lemont, IL · June–September 2025

Developed a distributed optimization framework across multiple supercomputers with heterogeneous architectures, including Aurora and Frontier.

Symbotic

Machine Learning Intern · Wilmington, MA · June–August 2024

Built a training pipeline for pose and shape estimation for large-scale multi-robot systems (more than 1,000 robots) in automated warehouses.

Open-source software

BlockDSS.jl

Creator, contributor, and maintainer · 2024–present

• Developed GPU-accelerated direct solvers for structured linear systems, including block-tridiagonal systems.
• Benchmarked against MA57, CHOLMOD, and cuDSS, demonstrating 20–50× speedups over CPU methods on large-scale problems.
• Integrated with Julia’s scientific computing and optimization ecosystem, including MadNLP.

OpenMatch

Contributor · 2021–2022

• Contributed implementations of neural information retrieval models and support for building customized retrieval systems.
• Reproduced results on widely used information retrieval benchmarks.

Publications

Manuscripts

1. Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling

   Oliver Sieberling, Bharat Runwal, David Jin, Ryan Chin, Rameswar Panda, Yoon Kim

   Preprint

   Paper

   BibTeX

   @misc{sieberling2026triadic,
     title = {Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling},
     author = {Sieberling, Oliver and Runwal, Bharat and Jin, David and Chin, Ryan and Panda, Rameswar and Kim, Yoon},
     year = {2026},
     eprint = {2609.36529},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     url = {https://arxiv.org/abs/2609.36529},
   }
   

1. AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

   B. Islam Bouli*, David Jin*

   Submitted

   BibTeX

   @unpublished{bouli_aaac,
     title = {{AAAC}: Activation-Aware Adaptive Codebooks for 4-bit {LLM} Weight Quantization},
     author = {Islam Bouli, B. and Jin, David},
     note = {Submitted},
   }
   

Conference and journal publications

1. BiLoCo: Binary Low-Rank Corrections for Post-Training Quantization

   David Jin*, B. Islam Bouli*, T. Goel, G. Han, Yoon Kim

   NeurIPS 2026

   Paper

   BibTeX

   @inproceedings{jin_biloco,
     title = {{BiLoCo}: Binary Low-Rank Corrections for Post-Training Quantization},
     author = {Jin, David and Islam Bouli, B. and Goel, T. and Han, G. and Kim, Yoon},
     year = {2026},
     booktitle = {NeurIPS 2026},
     eprint = {2610.32243},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     url = {https://arxiv.org/abs/2610.32243},
   }
   

2. GPU-Accelerated Solution of Block-Tridiagonal Systems for Large-Scale Optimization

   David Jin, Sungho Shin

   IFAC World Congress 2026

   Code

   BibTeX

   @inproceedings{jin2025gpu,
     title = {{GPU}-Accelerated Solution of Block-Tridiagonal Systems for Large-Scale Optimization},
     author = {Jin, David and Shin, Sungho},
     year = {2025},
     booktitle = {IFAC World Congress 2026},
   }
   

3. GMKF: Generalized Moment Kalman Filter for Polynomial Systems with Arbitrary Noise

   S. Teng, Harry Zhang*, David Jin*, A. Jasour, M. Ghafarri, Luca Carlone

   NeurIPS 2025

   BibTeX

   @inproceedings{teng2025gmkf,
     title = {{GMKF}: Generalized Moment Kalman Filter for Polynomial Systems with Arbitrary Noise},
     author = {Teng, S. and Zhang, Harry and Jin, David and Jasour, A. and Ghafarri, M. and Carlone, Luca},
     year = {2025},
     booktitle = {NeurIPS 2025},
   }
   

4. CRISP: Object Pose and Shape Estimation with Test-Time Adaptation

   J. Shi, R. Talak, Harry Zhang, David Jin, Luca Carlone

   CVPR 2025

   BibTeX

   @inproceedings{shi2025crisp,
     title = {{CRISP}: Object Pose and Shape Estimation with Test-Time Adaptation},
     author = {Shi, J. and Talak, R. and Zhang, Harry and Jin, David and Carlone, Luca},
     year = {2025},
     booktitle = {CVPR 2025},
   }
   

5. Physics-Informed Neural Operator for Learning Partial Differential Equations

   Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, Anima Anandkumar

   ACM/JMS Journal of Data Science, 1(3), 1–27 · 2024

   Paper

   BibTeX

   @article{li2024pino,
     title = {Physics-Informed Neural Operator for Learning Partial Differential Equations},
     author = {Li, Zongyi and Zheng, Hongkai and Kovachki, Nikola and Jin, David and Chen, Haoxuan and Liu, Burigede and Azizzadenesheli, Kamyar and Anandkumar, Anima},
     year = {2024},
     journal = {ACM/JMS Journal of Data Science},
     volume = {1},
     number = {3},
     pages = {1--27},
     url = {https://arxiv.org/abs/2111.03794},
   }
   

6. Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval

   S. Yu, C. Fan, C. Xiong, David Jin, Z. Liu, Z. Liu

   COLING 2024

   BibTeX

   @inproceedings{yu2024fusion,
     title = {{Fusion-in-T5}: Unifying Document Ranking Signals for Improved Information Retrieval},
     author = {Yu, S. and Fan, C. and Xiong, C. and Jin, David and Liu, Z. and Liu, Z.},
     year = {2024},
     booktitle = {COLING 2024},
   }
   

7. Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds

   David Jin, Sushrut Karmalkar, Harry Zhang, Luca Carlone

   ICRA 2024

   Paper

   BibTeX

   @inproceedings{jin2024multimodel3dregistrationfinding,
     title = {Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds},
     author = {Jin, David and Karmalkar, Sushrut and Zhang, Harry and Carlone, Luca},
     year = {2024},
     booktitle = {ICRA 2024},
     eprint = {2402.10865},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     url = {https://arxiv.org/abs/2402.10865},
   }
   

Talks

• GPU-Accelerated Solution of Block-Tridiagonal Systems for Large-Scale Optimization. IFAC World Congress, August 2026, Busan, South Korea.
• GPU-Accelerated Solution of Block-Tridiagonal Systems for Large-Scale Optimization. SIAM Parallel Processing, March 2026, Berlin, Germany.
• GPU-Accelerated Solution of Block-Tridiagonal Systems for Large-Scale Optimization. AIChE Annual Meeting, November 2025, Boston, USA. Plenary talk.
• Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds. ICRA, May 2024, Yokohama, Japan.
• Emulating Ray Tracing Using Machine Learning. Joint Mathematics Meeting, January 2021, online (Washington, DC).
• Laminar Chaos in an Electronic Mackey-Glass Variable Time Delay System. APS March Meeting, March 2020, Denver, CO. Canceled.
• The Effects of Gaps on the Correlation Dimension of Nonlinear Systems. Midstates Consortium for Math and Science, November 2018, Washington University in St. Louis, St. Louis, MO.

Teaching

Massachusetts Institute of Technology

Teaching Assistant

• Numerical Computation for Mechanical Engineers · Fall 2022

California Institute of Technology

Teaching Assistant

• Statistical Inference · Spring 2022
• Machine Learning and Data Mining · Winter 2022
• Foundations of Machine Learning and Statistical Inference · Winter 2022
• Introduction to Probabilistic Models · Fall 2021

Grinnell College

Teaching Assistant

• General Physics I · Spring 2020
• Modern Physics · Fall 2019
• Classical Mechanics · Spring 2019
• General Physics I · Fall 2018