Liu Lab — AI, Digital Health & Genomics

AI for Digital Health and Genomics

Our lab works at the intersection of artificial intelligence, digital health, and genomics. We develop interpretable AI models that connect biosensor (e.g., wearables) signals with molecular variation to map disease trajectories and mechanisms.

Research Areas

We focus on precision neurology: building interpretable AI that links real-world physiology and behavior to molecular mechanisms to understand the brain. Our work combines digital and wearable time-series modeling, statistical genetics, and causal inference across three pillars:

Digital Health

Wearables → Disease

From continuous biosensor data (activity, sleep, HRV, gait) to robust digital phenotypes that capture symptoms, progression, and treatment response in real-world settings.

Integrating Genomics

Digital Phenotypes → Genes

Map digital traits to genetic architecture using GWAS/eQTL/regulatory annotation to identify pathways, risk mechanisms, and biologically grounded biomarkers.

Translation

Therapeutics & Trials

Turn signals into mechanisms and targets; design digital biomarkers and endpoints for neurology trials, enabling earlier detection, stratification, and precision intervention.

Team

Jason J. Liu, PhD

Assistant Professor
Dept. AI & Human Health
Dept. Psychiatry

We’re recruiting

Postdocs and students excited about AI, wearables, and genomics. See Open Positions.

Selected Publications

Liu J*, Borsari B*, Li Y, Liu S, Gao Y, Xin X, Lou S, Jensen M, Garrido-Martín D, Verplaetse T, Ash G, Zhang J, Girgenti M, Roberts W, Gerstein M.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell. 2025;188(2):515-529.e15. DOI: 10.1016/j.cell.2024.11.012. PMID: 39706190.

Emani P*, Liu J*, Clarke D*, Jensen M*, Warrell J*, Gupta C*, Meng R*, Lee C*, Xu S*, Dursun C*, Lou S*, Chen Y, Chu Z, ... Gerstein M, Geschwind D.

Single-cell genomics and regulatory networks for 388 human brains. Science. 2024;384:eadi5199. PMID: 38781369. PMCID: PMC11365579. DOI: 10.1126/science.adi5199.

Rozowsky J*, Gao J*, Borsari B*, Yang Y*, Galeev T*, Gürsoy G*, Epstein C*, Xiong K*, Xu J*, Li T*, Liu J*, Yu K, ... Gerstein M, Gingeras T.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell. 2023;186:1493-1511.e40. PMID: 37001506. PMCID: PMC10074325. DOI: 10.1016/j.cell.2023.02.018.

Liu J*, Spakowicz DJ*, Ash GI, Hoyd R, Ahluwalia R, Zhang A, Lou S, Lee D, Zhang J, Presley C, Greene A, Stults-Kolehmainen M, Nally LM, Baker JS, Fucito LM, Weinzimer SA, Papachristos AV, Gerstein M.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions. PLOS Comput Biol. 2021;17:e1009303. PMID: 34424894. PMCID: PMC8412351. DOI: 10.1371/journal.pcbi.1009303.

Open Positions

Postdoctoral Researchers — AI, Digital Health & Genomics

Join us to build interpretable AI that links wearable-derived digital phenotypes with genomics and clinical data for precision neurology.

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Research opportunities in the lab include:

Building novel AI/ML models for multi-modal digital health time-series and genomic data
Developing digital biomarkers from real-world digital health (e.g., wearable) data
Integrating genomics/genetics with digital health to identify therapeutic targets
Translating computational findings into mechanistic & clinical insights

Qualifications:

PhD in computational biology, computer science, statistics, math, neuroscience, or related field
Strong background in AI/ML, coding, data science, and statistics
Enthusiasm for interdisciplinary research and collaborative team science

What we offer:

A highly collaborative environment at the intersection of AI and medicine
Access to large-scale datasets and Mount Sinai’s advanced computing resources
Mentorship tailored for academic and industry career paths

Interested candidates should send a CV and a brief statement of research interests.

Apply via Email General Questions

Graduate Students (PhD or MD-PhD)

PhD students can join the lab through Mount Sinai’s GSBS PhD or MD-PhD Program

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PhD trainees interested in the Liu Lab must apply centrally to the Icahn School of Medicine at Mount Sinai Graduate School of Biomedical Sciences (GSBS) PhD Program (or MD-PhD Program).

How to join the lab as a PhD student

Apply to the Mount Sinai GSBS PhD Program (Biomedical Sciences).
Indicate interests in AI and Emerging Technologies.
Once admitted, request a rotation in the Liu Lab.
After rotations, select the Liu Lab for dissertation research.

GSBS PhD Admissions Rotation Inquiry

Master’s Students / Research Assistant

Project-based roles in biosensor analytics, AI model development, and genomics integration.

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We welcome master’s students as well as RAs on a project-basis. These roles provide exposure to lab methods and may lead to extended appointments.

How to apply

Send a CV and brief statement of interests
Mention the pillar you’re most excited about and why! (Wearables → Disease; Digital Phenotypes → Genes; Translation/Trials)

Email Application

Research Interns and Undergraduates

Case-by-case opportunities for motivated students wanting to engage in biomedical research

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We occasionally host motivated research interns or external undergraduates who are interested in contributing to digital health and genomics research.

Send interest