Data, Machines, and AI (DMAI) Reading Group

Data, Machines, and AI (DMAI) is a reading group where we discuss advanced AI topics and most recent research in machine learning and data science, all in the context of biomedical data.


Reading group meetings are held every two weeks. We have a speaker for every meeting. The speaker selects a paper and sends it to the group coordinator one week before the presentation. The coordinator shares the information with the group. Everyone is expected to read the paper carefully before the group meeting.

Each meeting lasts for 1 hour and consists of three parts:

  • Paper presentation (20 minutes): The speaker presents the paper, including background, motivation, key challenges, methods and modeling assumptions, datasets, and experiments. The focus is on models, algorithms, and learning methods.
  • Insights (5-10 minutes): The speaker shares their insights with the group. This includes insights related to the merits of the research, possible future directions, how can this research can be used in our ongoing projects, any drawbacks and ideas on how to fix them, any related-papers (other approaches to study the same problem), etc.
  • Discussion (25-30 minutes): Q&A and broader debate, brainstorming what questions in biomedical data these methods can help us answer, availability of data and source code implementation, any other topic attendees want to talk about.

These are interactive events; questions and comments are welcome throughout the meeting!


Every meeting has a dedicated speaker whose responsibility is to select a paper and present it. We use a rotation system for speakers. Members of the reading group can expect to speak 1-3 times a year.

Paper selection

Generally, papers presented at the reading group are published in top-tier venues (conferences and journals) in the last two years:

  • Interdisciplinary journals:
    • Nature, Science, and Cell family, NEJM, PNAS, Lancet journals, JAMA journals
  • Machine learning and data science:
    • Bioinformatics conferences: ISMB, RECOMB, PSB
    • Journals: TPAMI, JMLR, TKDD, TKDE, Bioinformatics, JAMIA, PLoS Computational Biology, BMC journals, ACS family

Please note this is only a sampling of relevant venues. We are generally interested in exciting, intriguing, and thought-provoking papers, no matter their publication venue, including preprints (e.g., arXiv, bioRxiv, and medRxiv).


Every other Thursday at 4:30-5:30pm.


Virtual meetings on Zoom. We will resume in-person meetings as soon as we return to campus.


If you have any questions, email the reading group coordinator (Xiang Zhang, <>). Suggestions are very much welcome!

Latest News

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details.

Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

Mar 2022:   Multimodal Learning on Graphs

New preprint! We introduce REMAP, a multimodal AI approach for disease relation extraction and classification. Project website.

Feb 2022:   Explainable Graph AI on the Capitol Hill

Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!

Feb 2022:   Graph Neural Networks for Time Series

Hot off the press at ICLR 2022. Check out Raindrop, our graph neural network with unique predictive capability to learn from irregular time series. Project website.

Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

Jan 2022:   Inferring Gene Multifunctionality

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

Jan 2022:   Probing GNN Explainers Accepted to AISTATS

Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

Jan 2022:   Therapeutics Data Commons User Meetup

We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

Jan 2022:   Workshop on Graph Learning Benchmarks

Dec 2021:   NASA: Precision Space Health System

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth independence. Delighted to be working with NASA and can share our recommendations!

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics