I’m a first year Ph.D. student in the MURGe-Lab at the University of North Carolina at Chapel Hill, advised by Prof. Mohit Bansal. Prior to joining UNC, I received my master’s degrees in Computer Science and Financial Engineering from Columbia University, where I was a member of the DVMM Lab advised by Prof. Shih-Fu Chang and a member of the ROAM Lab advised by Prof. Matei Ciocarlie and Prof. Shuran Song. I also work closely with Prof. Krzysztof Choromanski.

My recent research focuses on generative models, multimodal learning, and LLMs. I’m also broadly interested in theory-grounded algorithms for efficient machine learning.

You can find my CV here.

Feel free to email/wechat me if you would like to chat about any research ideas! :)

Publications

2024

• (Preprint 2024) CTRL-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model🔥

  Han Lin*, Jaemin Cho*, Abhay Zala, Mohit Bansal
  [Paper][Project Page]

• (Preprint 2024) EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents

  Abhay Zala*, Jaemin Cho*, Han Lin, Jaehong Yoon, Mohit Bansal
  [Paper][Project Page]

2023

• (Preprint 2023) DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning

  Abhay Zala, Han Lin, Jaemin Cho, Mohit Bansal
  [Paper][Project Page]

• (Preprint 2023) VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning

  Han Lin, Abhay Zala, Jaemin Cho, Mohit Bansal
  [Paper][Project Page]

• (ICML 2023) Efficient Graph Field Integrators Meet Point Clouds

  Krzysztof Choromanski*, Arijit Sehanobish*, Han Lin*, Yunfan Zhao*, Eli Berger, Alvin Pan, Tetiana Parshakova, Tianyi Zhang, David Watkins, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Avinava Dubey, Deepali Jain, Tamas Sarlos, Snigdha Chaturvedi, Adrian Weller
  [Paper]

• (CVPR 2023) Supervised Masked Knowledge Distillation for Few-shot Transformers

  Han Lin*, Guangxing Han*, Jiawei Ma, Shiyuan Huang, Xudong Lin, Shih-Fu Chang
  [Paper][Code][Slides]

• (ICRA 2023) Active Tactile Exploration for 3D Object Recognition

  Jingxi Xu*, Han Lin*, Shuran Song, Matei Ciocarlie
  [Paper][Project Page][Video]

2022

• (ICML 2022) From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers

  Krzysztof Choromanski*, Han Lin*, Haoxian Chen*, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten
  [Paper][Code][Poster]

• (ICLR 2022) Hybrid Random Features

  Krzysztof Choromanski*, Han Lin*, Haoxian Chen*, Yuanzhe Ma*, Arijit Sehanobish*, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
  [Paper][Code][Video][Slides]

2021

• (Preprint 2021) Graph Kernel Attention Transformers

  Krzysztof Choromanski*, Han Lin*, Haoxian Chen*, Jack Parker-Holder
  [Paper][Code]

2020

• (NeurIPS 2020) Demystifying Orthogonal Monte Carlo and Beyond

  Han Lin*, Haoxian Chen*, Tianyi Zhang, Clement Laroche, Krzysztof Choromanski
  [Paper][Code][Video]

* Equal contribution.

Teaching Assistants

• COMS 4231 Analysis of Algorithms, Fall 2022, Columbia University
• COMS 4732 Computer Vision 2: Learning, Spring 2022, Columbia University
• COMS 4721 Machine Learning for Data Science, Spring 2022, Columbia University
• QMSS 5073 Machine Learning for Social Science, Fall 2021, Columbia University
• IEOR 4007 Optimization Models & Methods for FE, Fall 2019, Columbia University
• IEOR 4418 Transportation Analytics & Logistics, Spring 2019, Columbia University

Academic Services

• Conference Reviewer: ICLR 2024, ICML 2022/2023/2024; NeurIPS 2022/2023
• Conference Volunteer: RSS 2022