Hanchen Wang    |  Publication  |  Misc  |  Bio  | wang.hanchen at gene.com  |  hanchenw at stanford.edu

My first-author papers (Nature, NeurIPS etc.) are reported by 1000+ tweets, 50+ press including: A special collection of Nature, 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 Sciences, and Clinical Trials :)

Postdoc, Aug 2023-26 Building on omics and AI for better therapeutics and sciences!

Biomni: A General Biomedical AI Agent Kexin Huang*#, Serena Zhang*, Hanchen Wang*, Yuanhao Qu*, Yingzhou Lu*, et al., Aviv Regev, Jure Leskovec# release soon!

SpatialAgent: An Autonomous AI Agent for Spatial Biology Hanchen Wang*#, Yichun He*, Paula P. Coelho*, Matt Bucci*, et al., Jure Leskovec, Aviv Regev# release soon!

PerTurboAgent: A Self-Planning agent for boosting sequential perturb-seq experiment Minsheng Hao*, Yongju Lee*, Hanchen Wang, Gabriella Scalia, Aviv Regev# release soon!

3D Interaction Geometric Pre-training for Molecular Relational Learning Namkyeong Lee, et al., Chanyoung Park# in review

Fine-tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design Chenyu Wang*, Masatoshi Uehara*, et al., Tommi Jaakkola#, Sergey Levine#, Hanchen Wang#, Aviv Regev# ICLR 2025

Learning Multi-cellular Representations of scRNA Data Enables Characterization of Patient-level Disease States Tianyu Liu*, Edward De Brouwer*, et al., Aviv Regev#, Graham Heimberg# RECOMB 2025, Oral. long version in review

Considerations for Building and Using Integrated Single-Cell Atlases Karin Hrovatin*, Lisa Sikkema*, et al., Fabian Theis#, Malte Luecken# Nature Methods 2024, part of Human Cell Atlas collection

Metric Mirages in Cell Embeddings Hanchen Wang, Jure Leskovec#, Aviv Regev# Nature Biotechnology (accepted in principle), <1 month from submission to revision

PhD, in Machine Learning, Oct 2019-22 Thesis: Learning from Structured Data with Weak Supervision During my 3-year PhD (supported by two fellowships), I'm all in AI and published my 1st ML paper till the end of 2nd year. I develop methods (many are pre-training) that can learn structures from data with weak supervisions. I've been working on polls, point clouds, CT/CXR scans, histological and pathological images, molecular and relational graphs, and scRNA-seq. I also explored quantum computing, and interned in tech then biotech (Google -> Amazon -> BioMap -> Iambic). Back in 2019, I co-founded a startup (as the CTO) with friends from Stanford and Cal, building language models on EHRs, partnering with 37 hospitals. Though short-lived, it deepened my resolve to advance sciences, therapeutics and healthcare with AI.

Last batch of works (~3 pieces) are near completion. Stay tuned!!!

Unsupervised Discovery of Steerable Factors in Graphs Shengchao Liu et al., Jian Tang TMLR 2024

Scientific Discovery in the Age of AI Hanchen Wang et al., Connor Coley, Yoshua Bengio, Marinka Zitnik# Nature 2023, accept without revisions

Evaluating Self-supervised Learning for Molecular Graph Embeddings Hanchen Wang*, Jean Kaddour*, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu NeurIPS 2023, Datasets and Benchmarks Track

Focalizing Regions of Relevance Facilitates Biomarker Prediction on Histopathological Images Jiefeng Gan*, Hanchen Wang*, Hui Yu* et al., Tian Xia# iScience 2023

Augmenting Message Passing by Retrieving Similar Graphs Dingmin Wang et al., Qi Liu ECAI 2023

Machine Learning Systems: Design and Implementation Tsinghua University 2023, Table of Contents English version is in press, Springer Nature

Matching Point Sets with Quantum Circuits Learning Hanchen Wang*, M. N.* (in contribution order) ICASSP 2022, invited by editors, with travel award

Pre-training Molecular Graph Representation with 3D Geometry Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang ICLR 2022

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in AI Xiang Bai (Prof.)* Hanchen Wang*, Liya Ma*, Yongchao Xu*, Jiefeng Gan* et al., Carola Schönlieb#, Tian Xia# Nature Machine Intelligence 2021

Unsupervised Point Cloud Pre-training via Occlusion Completion Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner ICCV 2021

Iterative Teaching by Label Synthesis Weiyang Liu*, Zhen Liu*, Hanchen Wang*, Liam Paul, Bernhard Schölkopf, Adrian Weller NeurIPS 2021, Spotlight

Graph Denoising with Edge Editing Hanchen Wang, Yunlong Jiao, Jordan Massiah contributed talk at Amazon Machine Learning Conference 2021

Neural Random Subspace Yun-Hao Cao et al., Pattern Recognition 2021

An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision Hanchen Wang, Nina Grgić-Hlača, Preethi Lahoti, Krishna. P. Gummadi, Adrian. V. Weller axXiv 1910.10255, presented at NeurIPS HCML 2019, with travel award

Undergraduate, in Physics, Sep 2014-18 In addition to studying Physics, I researched next-gen electronic devices such as transistors and solar cells, an intersection 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 :)

Current-controlled Propagation of Spin Waves in Antiparallel, Coupled Domains Nature Nanotechnology 2019

Dopant‐Free Partial Rear Contacts Enabling 23% Silicon Solar Cells Advanced Energy Materials 2019

Stable dopant-free asymmetric heterocontact silicon solar cells with efficiencies above 20% ACS Energy Letters 2018

Negative Capacitance 2D MoS2 Transistors with Sub-60mV/dec Subthreshold Swing over 6 Orders, 250 μA/μm Current Density, and Nearly-Hysteresis-Free Zhihao Yu (Ph.D)*, Hanchen Wang (B.S)* et al., Xinran Wang# IEDM 2017, Oral, NJU's 1st IEDM. It is where Intel, NVIDIA, TSMC, AMD etc sharing their secret sauce on chip design :)

Molecular Mechanism of Self-Assembly of Aromatic Oligoamides into Interlocked Double-Helix Foldamers Journal of Physical Chemistry B 2017

Microchannel contacting of crystalline silicon solar cells Scientific Reports 2017

Logical integration device for two-dimensional semiconductor transition metal sulfide Weisheng Li*, Jian Zhou*, Hanchen Wang* et al., Xinran Wang# Invited Review, Acta Physica Sinica 2017