Jordy Homing Lam

About

I am a postdoctoral fellow at the University of Southern California, supported by the Croucher Foundation of Hong Kong . I work at the Center for New Technologies in Drug Discovery and Development and the Center for Advanced Research Computing . I am advised by Prof. Aiichiro Nakano and Prof. Vsevolod Katritch. I have extensive experience in Chemical Physics, Numerical Algorithms and Parallel Computing.

"https://estija.github.io/" ============================================= REF here s my current fpcous I work on the foundations of machine learning, and my interests mostly lie in the intersection of machine learning, theoretical computer science and statistics. The goal of my research is to study and discover the underlying principles which govern learning, and to leverage this understanding to build practical machine learning systems which are more efficient, fair and robust. A large part of my work aims to inspect questions which arise from modern applications and challenges of machine learning. If you're interested in learning more, here are some questions I've recently been working on: Maksim is a bioinformatics software engineer at the Irwin Lab. Maksim attained his Bachelor's of Chemistry at the University of California, Berkeley in 2020. At Berkeley, Maksim did undergraduate research in the advanced applications of nuclear magnetic resonance. After Berkeley, Maksim started working as a research chemist at a large corporation. Maksim always had an interest in computer science and that interest drew him to join the Irwin Lab, which merges both his interests in chemistry and software. What is the role of computational and statistical constraints in learning and optimization? Are there inherent trade-offs between the amount of memory required for learning or optimization, and the amount of data or computation required? How do we solve learning tasks with limited data, such as by designing optimal data augmentation and regularization techniques? What are appropriate notions of fairness in various domains, and how do we train models which respect these notions? Similarly, how do we ensure trained models are robust, such as when evaluated on data distributions which differ from the original training distribution? How can we understand deep neural networks and foundation models in the context of some of the above considerations? My research is supported by an NSF CAREER award (2023), and Amazon Research Awards (2022 and 2024). This support is very gratefully acknowledged. Here is my CV

News

Publications

Most recent publications on Google Scholar. (The outbound hyperlinks for the papers/videos may take a little longer to redirect!)
^‡ indicates equal contribution.

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Vitæ

Full Resume (likely not the most updated!) in PDF.

• University of Southern California (USC) 2024 - present

  Croucher Foundation Postdoctoral Fellow (Supervisor: Prof. Vsevolod Katritch)
  Department of Quantitative and Computational Biology
• University of Southern California (USC) 2019 - 2024

  Ph.D. in Bioinformatics (Supervisor: Prof. Aiichiro Nakano and Prof. Vsevolod Katritch)
  Department of Quantitative and Computational Biology
• King Abdullah University of Science and Technology (KAUST) 2018

  Research Associate (Supervisor: Prof. Xin Gao)
  Department of Computer, Electrical and Mathematical Sciences and Engineering
• Hong Kong University of Science and Technology (HKUST) 2017

  M.Phil., Chemistry (Supervisor: Prof. Xuhui Huang)
  Department of Chemistry
• Institut Català d'Investigació Química 2016

  Summer Intern (Supervisor: Prof. Feliu Maseras)
  Department of Chemistry
• The Chinese University of Hong Kong (CUHK) 2015

  B.Sc. (Hons)
  Department of Chemistry

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jhmlam.github.io by Jordy Homing Lam is licensed under CC BY-NC-SA 4.0