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

My co-lead projects are covered by: Anthropic, DeepMind, Genentech, Amazon Web Services, Nature, The Economist, Stanford Engineering School, Harvard Medical School, Cambridge Engineering School, healthcare-in-europe, and more.

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.

something on llm post-training :) Hanchen Wang et al., release soon!

GDT: A Longitudinal Clinical LLM as Digital Twins of Cancer Patients Nikita Makarov et al., Michael P Menden# release soon!

CellMAPP: A Multiplexed Organelle Imaging Platform for Perturbation Profiling Paula Coelho, Carolina Leote, et al., John Marioni, Aviv Regev# release soon!

Cocoa: Equipping Biomedical Agents with Computer Use Chang Ma, Linh Trinh, et al., Aviv Regev, Hanchen Wang# release soon!

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing Shuvom Sadhuka, Drew Prinster, Clara Wong-Fannjiang, Gabriella Scalia, Bonnie Berger, Aviv Regev, Hanchen Wang# release soon!

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!

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

AutoScreen: An Agentic System for Target Discovery in Functional Genomics Yuanhao Qu, Jingwen Hui, et al., Russ Altman, Jure Leskovec, Aviv Regev, Mengdi Wang, Le Cong# release soon!

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

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

PerTurboAgent: A Self-Planning agent for boosting sequential perturb-seq experiment Minsheng Hao*, Yongju Lee*, Hanchen Wang, Gabriella Scalia, Aviv Regev# MLCB 2025

3D Interaction Geometric Pre-training for Molecular Relational Learning Namkyeong Lee, et al., Chanyoung Park# NeurIPS 2025, Spotlight

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; also in Cell Systems 2025 (in press)

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

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

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 the startup is short-lived, it deepened my ego to advance sciences, therapeutics and healthcare with AI.

a few more co-author works in revision / in drafting

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

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

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 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 sharpened my ego to make real-world impacts, like using AI to push science forward and improve lives.

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

a few more co-author papers on Electronics, Solid-State Physics and Computation Chemistry