Data, Machines, and AI (DMAI) Reading Group

Data, Machines, and AI (DMAI) is a reading group to discuss AI theory and methods, with a strong bent towards understanding what AI methods are most suitable for problems in biology and medicine, and how to advance state-of-the-art AI algorithms.

Schedule

Reading group meets 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 before the group meeting.

Each meeting lasts for 1 hour and has three parts:

  • Paper presentation (30 minutes): The speaker presents the paper, including background, motivation, key challenges, methods and modeling assumptions, datasets, and experiments. The focus is on algorithms and carefully considering what makes them suitable for biomedical applications and why.
  • 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 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 (20-25 minutes): Q&A and broader debate, brainstorming what biological or medical questions these methods can help us answer, dataset availability, source code implementation, any other topic attendees want to discuss.

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

Speakers

Every meeting has a dedicated speaker responsible for selecting a paper and presenting it. We use a rotation system for speakers. Members of the reading group can expect to speak 1-3 times a year.

Paper selection

Papers presented at the reading group are published at various venues over the last two years. Following is a sampling of relevant journals and conferences.

  • Journals:
    • Nature, Science, and Cell family, NEJM, PNAS, Lancet journals, JAMA journals
  • Machine learning and data science:
    • Conferences: ICML, NeurIPS, ICLR, ACL, EMNLP, KDD, ICDM, AAAI, IJCAI, UAI, FAT*, AISTATS, WSDM, SIGIR
    • Bioinformatics conferences: ISMB, RECOMB, PSB
    • Journals: TPAMI, JMLR, TKDD, TKDE, Bioinformatics, JAMIA, PLoS Computational Biology, BMC journals, ACS family

We are generally interested in exciting, intriguing, and thought-provoking papers, no matter their publication venue, including preprints (e.g., arXiv, bioRxiv, and medRxiv).

Time

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

Location

Hybrid meetings on Zoom and in-person in Countway Bldg.

Coordinator

Reach out to the reading group coordinator (Zhenglun Kong) with questions and suggestions.

Latest News

Oct 2025:   A Scientist's Guide to AI Agents in Nature

A piece on AI agents in Nature highlights ongoing projects in our group, including methods for evaluating scientific hypotheses, challenges in benchmarking AI agents, and the open ToolUniverse ecosystem.

Sep 2025:   ToolUniverse: AI Agents for Science and Medicine

New paper: ToolUniverse introduces an open ecosystem for building AI scientists with 600+ scientific and biomedical tools. Build your AI co-scientists at https://aiscientist.tools.

Sep 2025:   Democratizing "AI Scientists" with ToolUniverse

Our new initiative: Use Tool Universe to build an AI scientist for yourself from any language or reasoning model, whether open or closed. https://aiscientist.tools

Sep 2025:   InfEHR in Nature Communications

Collaboration with Ben and Girish on clinical phenotype resolution through deep geometric learning on electronic health records published in Nature Communications.

Sep 2025:   PDGrapher in Nature Biomedical Engineering

New paper in Nature Biomedical Engineering introducing PDGrapher, a model for phenotype-based target discovery. [Harvard Medicine News]

Sep 2025:   AI and Net Medicine: Path to Precision Medicine

Aug 2025:   CUREBench - Reasoning for Therapeutics

Update from CUREBench: 650+ entrants, 100+ teams and 500+ submissions. Thank you to the CUREBench community. Working on AI for drug discovery and reasoning in medicine? New teams welcome. Tasks, rules, and leaderboard: https://curebench.ai.

Aug 2025:   Drug Discovery Workshop at NeurIPS 2025

Excited to organize a NeurIPS workshop on Virtual Cells and Digital Instruments. Submit your papers.

Aug 2025:   AI for Science Workshop at NeurIPS

Excited to organize a NeurIPS workshop on AI for Science. This is our 6th workshop in the AI for Science series. Submit your papers.

Jul 2025:   Launching CUREBench

Launched CUREBench, the first competition in AI reasoning for therapeutics. Colocated with NeurIPS 2025. Start at https://curebench.ai.

Jul 2025:   Launching TxAgent Evaluation Portal

Launched TxAgent evaluation portal, our global evaluation of AI for drug decision-making and therapeutic reasoning. Participate in TxAgent evaluations! [TxAgent project]

Jul 2025:   SPATIA Model of Spatial Cell Phenotypes

Jul 2025:   AI-Enabled Drug Discovery Reaches Clinical Milestone

Jun 2025:   Knowledge Tracing for Biomedical AI Education

New preprint on biologically inspired architecture for knowledge tracing. The study on the use of generative AI in education with prospective evaluation of knowledge tracing in the classroom.

Jun 2025:   Few shot learning for rare disease diagnosis

Jun 2025:   One Patient, Many Contexts: Scaling Medical AI

Jun 2025:   ToolUniverse - 211+ Tools for "AI Scientist" Agents

ToolUniverse now offers access to over 211 cutting-edge biological and medical tools, all integrated with Model Context Protocol (MCP). Any “AI Scientist” agent can tap into these tools for biomedical research. [Tutorial] [ToolUniverse] [TxAgent]

May 2025:   What Perturbation Can Reverse Disease Effects?

In press at Nature Biomedical Engineering: PDGrapher AI predicts chemicals to reverse disease phenotypic effects — with applications to drug target identification.

May 2025:   Decision Transformers for Cell Reprogramming

New preprint: Decision transformers for generating reach-avoid policies in sequential decision making — with applications from robotics to cell reprogramming.

May 2025:   COMPASS: Immunotherapy Outcome Prediction

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics