wang.hanchen at gene.com | hanchenw at cs.stanford.edu |
|
My first-author papers (Nature, NeurIPS etc.) are highlighted by 1000+ tweets, 50+ press including: A new series in Nature on "AI for Science", DeepMind, The Economist, Harvard Medical School, Chemical & Engineering News, Tech Crunch, Yahoo! News, a few Wikipedia pages, Tech Xplore, University of Cambridge, healthcare-in-europe. Besides AI/ML, I'm trained and have published in Physics (my foundation and childhood's favorite, High School Olympiad), Biology (wild learner, also High School Olympiad), Chemistry, Materials / Electronics, Medical Science, and Clinical Trials :) |
PostDoc, in Therapeutics & AI for Science, Aug 2023-26/7Along my three to four-year PostDoc (with full fellowship), I target challenges in Therapeutic Science, emphasizing Genomics. I'm developing frontier AI tools (e.g., FM, GenAI, MLSys) for fundamental cell biology and medicine. I'm keen to advance novel therapeutic strategies, via new observations (e.g., imaging), modalities (e.g., organoid), and paradigms (e.g., lab in a loop). Concurrently, I'm devising data-driven methods to benefit a wide spectrum of sciences, aiming for substantial scientific, industrial and social impacts. Trained and published in multiple domains, my path naturally aligns with AI for Science :) |
|
Hanchen Wang, Jure Leskovec#, Aviv Regev# in submission |
PhD, in Machine Learning, 2019-22During my three-year Ph.D (with full fellowship), I first shifted to AI, published my 1st paper in AI till the end of my 2nd year. I have been working on data including votes from polls and surveys, point clouds, CT/CXR scans, histological and pathological images, molecular and relational graphs, as well as single-cell transcriptomics. I develop methods that can learn structures from these data with weak supervisions. Aside from this, I spent some time on quantum computation also did four interns in tech and biotech industries (Google -> Amazon -> BioMap -> Iambic). Back in 2018, I co-founded a startup (as the CTO) with Stanford and Berkeley alumni, aimed at transforming electronic health records via language models, with partnerships among 37 hospitals. Though things didn't work out then, my commitment to harnessing AI's power in Healthcare remains steadfast. |
|
Hanchen Wang, University of Cambridge, Doctoral Thesis |
| - upcoming, also on large language models |
| - upcoming, on large language models |
|
- upcoming, on robotics-driven lab |
|
Hanchen Wang et al., Chao Tang#, John Marioni# in submission |
|
Lijun Chen et al., Tian Xia# Phase IIa Trial in revision, Cell |
|
Hanchen Wang, James Zou in revision, Nature Machine Intelligence |
|
Tsinghua University Press 2023, Table of Contents also in press, Springer Nature |
|
Hanchen Wang* et al., Connor Coley, Yoshua Bengio, Marinka Zitnik# [Team] > Nature 2023, accepted in principle before revisions, ONLY survey during my entire PhD :) |
|
Jiefeng Gan*, Hanchen Wang*, Hui Yu*, et al., Tian Xia# iScience 2023 |
|
Hanchen Wang*, Jean Kaddour*, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu NeurIPS 2023, Datasets and Benchmarks Track |
|
Shengchao Liu et al., in revision, TMLR |
|
Dingmin Wang et al., ECAI 2023 |
|
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang ICLR 2022 |
|
Hanchen Wang*, M. N.* (in contribution order) ICASSP 2022, Invited by Editors, with Travel Award |
|
Hanchen Wang, Yunlong Jiao, Jordan Massiah contributed talk at Amazon Machine Learning Conference 2021 |
|
Xiang Bai (Prof.)* Hanchen Wang*, Liya Ma*, Yongchao Xu*, Jiefeng Gan*, ..., Carola Schönlieb#, Tian Xia# Nature Machine Intelligence 2021 |
|
Weiyang Liu*, Zhen Liu*, Hanchen Wang*, Liam Paul, Bernhard Schölkopf, Adrian Weller NeurIPS 2021, Spotlight |
|
Yun-Hao Cao, et al., Pattern Recognition 2021 |
|
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner ICCV 2021 |
|
Hanchen Wang, Nina Grgić-Hlača, Preethi Lahoti, Krishna. P. Gummadi, Adrian. V. Weller axXiv, presented at NeurIPS HCML 2019, with Travel Award |
Undergraduate, in Physics, 2014-18In addition to studying Theoretical Physics, I researched next-gen electronic devices such as transistors and solar cells, a field at the nexus of Solid-State Physics, Material Sciences and Electrical Engineering. I primarily worked in Xinran Wang's and Ali Javey's groups. I also accumulated experience in the finance sector, initially focusing on bonds then transitioning to roles in quantitative trading of equity and crypto. These diverse experiences ultimately cemented my commitment in advancing Science and Engineering with the aim of improving human life. Finance is boring, technology is fun and the future :) |
|
Nature Nanotechnology 2019 |
|
Advanced Energy Materials 2019 |
|
ACS Energy Letters 2018 |
|
Zhihao Yu*, Hanchen Wang* et al., Xinran Wang# IEDM 2017, Oral, NJU's 1st IEDM. It is where Intel, NVIDIA, TSMC, AMD etc sharing their secret sauce :) |
|
Journal of Physical Chemistry B 2017 |
|
Scientific Reports 2017 |
|
Weisheng Li*, Jian Zhou*, Hanchen Wang* et al., Xinran Wang# Invited Review, Acta Physica Sinica 2017 |