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

Only co-lead projects are listed here. They are covered by Amazon Web Services, Anthropic, Cambridge Engineering School, Google DeepMind, Harvard Medical School, healthcare-in-europe, Nature, Stanford Engineering School, The Economist, etc.

Postdoc, Aug 2023-26 Building Agents! Also do modeling & analysis for multi-omics, spatial transcriptomics, live-cell imaging, and perturbation assays, with a translational focus on autoimmune, neurological, and cancer therapeutics.

Spatiotemporal Profiling Reveals the Role of Inflammatory Niche in Driving Prostate Cancer Abbas Nazir*, Hanchen Wang*, Ziyu Lu*, et al., Levi Garraway#, Aviv Regev# release soon!

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing Shuvom Sadhuka, Drew Prinster, Clara Wong-Fannjiang, Gabriella Scalia, Aviv Regev, Hanchen Wang# in review

Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles Kejun Ying*, Alexander Tyshkovskiy*, Alibek Moldakozhayev*, Hanchen Wang*, et al., Tony Wyss-Coray, Vadim Gladyshev# in review

Biomni: A General-Purpose Biomedical AI Agent Kexin Huang*#, Serena Zhang*, Hanchen Wang*, Yuanhao Qu*, Yingzhou Lu*, et al., Le Cong, Aviv Regev, Jure Leskovec# in review

AREIAL: Towards AI Research Assistant for Expert-Involved Learning Tianyu Liu*, Simeng Han*, Hanchen Wang*#, et al., James Zou, Hongyu Zhao# in review

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

Limitations of Cell Embedding Metrics Assessed Using Drifting Islands Hanchen Wang, Jure Leskovec#, Aviv Regev# Nature Biotechnology 2025

PhD, in Machine Learning, Oct 2019-22 Thesis: Learning from Structured Data with Weak Supervision During my 3-year PhD (supported by 2 fellowships), I shifted to AI and didn't publish ANY paper till the end of the 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. 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 it is short-lived, it deepened my commitment to advance sciences, therapeutics and healthcare with AI.

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

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

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 worked in Xinran Wang's and Ali Javey's groups. I also had some fun in finance, starting with bonds, then moving to equity and crypto trading. Yet these experiences clarified my priorities towards making real-world impacts, like using AI to push Science forward and improve human life qualities.

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 insights on chip design :)

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