Mentoring & Advising


Open positions
<a href=Marinka Zitnik" />

Marinka Zitnik

Assistant Professor

<a href=Michelle M. Li" />

Michelle M. Li

PhD Student

<a href=Yasha Ektefaie" />

Yasha Ektefaie

PhD Student

<a href=Xiang Zhang" />

Xiang Zhang

Postdoctoral Fellow

<a href=Chirag Agarwal" />

Chirag Agarwal

Postdoctoral Fellow

<a href=Josh Pan" />

Josh Pan

Postdoctoral Researcher
Broad Institute of MIT & Harvard

<a href=Yucong Lin" />

Yucong Lin

Research Fellow

<a href=Marissa Sumathipala" />

Marissa Sumathipala

Graduate Researcher

<a href=Haoxin Li" />

Haoxin Li

Graduate Researcher

<a href=Kexin Huang" />

Kexin Huang

Graduate Researcher

<a href=Ayush Noori" />

Ayush Noori

Undergraduate Researcher

<a href=Jingyi Liu" />

Jingyi Liu

Masters Student

<a href=Mert Erden" />

Mert Erden

Visiting from Tufts CS

<a href=Owen Queen" />
<a href=Payal Chandak" />

Payal Chandak

Visiting from Columbia CS

<a href=Lydia Fozo" />

Alumni:

  • Yujie Shao (Masters Student, 2020)
  • Stone Chen (Masters Student, 2020)
  • Kathleen Sucipto (Masters Student, 2020)
  • Min Jean Cho (Masters Student, 2020)

Latest News

Jul 2021:   Best Paper Award at ICML Interpretable ML for Healthcare

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at ICML 2021 Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.

Jul 2021:   Five presentations at ICML 2021

Jun 2021:   Theory and Evaluation for Explanations

We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.

Jun 2021:   Deep Contextual Learners for Protein Networks

New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.

May 2021:   New Paper Accepted at UAI

Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.

Apr 2021:   Hot Off the Press: COVID-19 Repurposing in PNAS

Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Mar 2021:   Fair and Stable Graph Representation Learning

We are thrilled to share the latest preprint on fair and stable graph representation learning.

Feb 2021:   New Preprint on Therapeutics Data Commons

Jan 2021:   An Algorithmic Approach to Patient Safety

The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.

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

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.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics