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.

Schedule

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!

Speakers

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:
    • 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

Please note this is only a selection of relevant venues. We are generally interested in exciting and thought-provoking papers, including preprints (e.g., arXiv, bioRxiv, and medRxiv).

Time

Every other Thursday at 4:30-5:30pm. The first meeting is on Thursday, September 24, 2020.

Location

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

Coordinator

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

Latest News

Jan 2021:   Workshop on AI in Health at the Web Conference

We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.

Jan 2021:   Tutorial on ML for Drug Development

We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

Dec 2020:   Two New Papers Published

We are excited to see published our paper on DeepPurpose, a deep learning library for drug-target interaction prediction, and a paper on skipGNN, a graph neural network for predicting molecular interactions.

Dec 2020:   Bayer Early Excellence in Science Award

Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!

Nov 2020:   Therapeutics Data Commons (TDC)

We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.

Nov 2020:   National Symposium on the Future of Drugs

On behalf of the NSF, we are organizing the National Symposium on Drug Repurposing for Future Pandemics. We have a stellar lineup of invited speakers! Register at www.drugsymposium.org.

Oct 2020:   MARS: Novel Cell Types in Single-cell Datasets

Hot off the press! MARS, an approach for discovering novel cell types across heterogeneous single-cell experiments is just published in Nature Methods.

Sep 2020:   Four Papers Accepted at NeurIPS

Thrilled that our lab has 4 papers accepted at NeurIPS 2020! Congratulations to fantastic students and collaborators, Michelle, Xiang, Kexin, Sam, and Emily.

Sep 2020:   MITxHarvard Women in AI Interview

The MITxHarvard Women in AI initiative talked with Marinka about AI, machine learning, and the role of new technologies in biomedical research.

Aug 2020:   Trustworthy AI for Healthcare

We are excited to be co-organizing a workshop at AAAI 2021 on Trustworthy AI for Healthcare! We have a stellar lineup of speakers. Details to follow soon!

Aug 2020:   Network Drugs for COVID-19

What are network drugs? Drugs for COVID-19 predicted by network medicine, our graph neural networks (GNNs), and our rank aggregation algorithms, followed by experimental confirmations. The full paper is finally out!

Jul 2020:   Podcast on ML for Drug Development

Tune in to the podcast with Marinka about machine learning to drug development. The discussion focuses on open research questions in the field, including how to limit the search space of high-throughput screens, design drugs entirely from scratch, and identify likely side-effects of combining drugs in novel ways.

Jul 2020:   Postdoctoral Research Fellowship

We have a new opening for a postdoctoral research fellow in novel machine learning methods to combat COVID-19! Submit your application by September 1, 2020.

Jul 2020:   DeepPurpose Library

DeepPurpose is a deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening.

Jun 2020:   Subgraph Neural Networks

Subgraph neural networks learn powerful subgraph representations that create fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.

Jun 2020:   Defense Against Adversarial Attacks

GNNGuard can defend graph neural networks against a variety of training-time attacks. Remarkably, GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

Jun 2020:   Graph Meta Learning via Subgraphs

G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.

May 2020:   The Open Graph Benchmark

A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

May 2020:   Special Issue on AI for COVID-19

Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.

May 2020:   Multiscale Interactome

A new preprint on discovering disease treatment mechanisms through the multiscale interactome.

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