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 groundbreaking problems in biology and medicine, and how to advance the state-of-the-art.

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

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

Coordinator

Reach out to the reading group coordinator (Huan He, <huan_he@hms.harvard.edu>) with questions, comments, and suggestions.

Latest News

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Jul 2022:   New Frontiers in Graph Learning at NeurIPS

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

Jul 2022:   AI4Science at NeurIPS

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Jul 2022:   Graph AI for Precision Medicine at ISMB

Jul 2022:   Welcoming Fellows and Summer Students

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Jun 2022:   Broadly Generalizable Pre-Training Approach

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Jun 2022:   Welcoming New Postdocs

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

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.

May 2022:   Building KGs to Support Precision Medicine

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. http://www.ai4science.net/icml22

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.

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