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RaptorMai/README.md

Hi there 👋

My name is Zheda(Marco) Mai, and I am a third-year Ph.D. student from the Department of Computer Science and Engineering at the Ohio State University, advised by Professor Wei-Lun (Harry) Chao. My research interests lie in Efficient Foundation Model Adaptation (CVPR23, NeurIPS23, NeurIPS24), Multimodal LLM (NeurIPS24), Continual Learning (AAAI-21, CVPR21-W, Neurocomputing, 1st place of CVPR20-Competition, AIJ), Learning with Imperfect Data (NeurIPS23-W).

+ I am actively looking for a research internship! 
+ If you are aware of any opportunities or have any recommendations,
+ I would greatly appreciate your insights and referrals. Please feel free to reach out!

I obtained my MASc. from the University of Toronto advised by Prof. Scott Sanner. I mostly worked on Continual Learning and Recommender Systems during my master collaborating with LG AI Research.

Prior to that, I completed my BASc. in Engineering Science at the University of Toronto, where I was fortunate to work with Dr. Erkang Zhu.

You can find more information about me at my personal page: https://zheda-mai.github.io/.

You can contact me at [email protected] or by LinkedIn.

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  1. online-continual-learning online-continual-learning Public

    A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neuroc…

    Python 382 57

  2. CVPR20_CLVision_challenge CVPR20_CLVision_challenge Public

    1'st Place approach for CVPR 2020 Continual Learning Challenge

    Python 46 4

  3. Deep-AutoEncoder-Recommendation Deep-AutoEncoder-Recommendation Public

    Keras implementation of AutoRec and DeepRecommender from Nvidia.

    Jupyter Notebook 61 21

  4. CompBench CompBench Public

    CompBench evaluates the comparative reasoning of multimodal large language models (MLLMs) with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence,…

    Jupyter Notebook 31 1

  5. OSU-MLB/PETL_Vision OSU-MLB/PETL_Vision Public

    Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Transfer Learning (PETL) in Visual Recognition

    Jupyter Notebook 25