Postdoctoral Research Fellow Position

Overview

Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.

The selected candidate will be expected to lead research in novel machine learning methods for knowledge graphs and graph representation learning. In addition, the candidate will also devise novel explainable algorithms and use them for applications in biomedical discovery, drug discovery and development, and therapeutics.

Qualifications

We seek highly-motivated applicants with background in one or more of the following areas: machine learning, explainable AI/ML, computational healthcare, and network science. Successful applicants will be strong technically as well as have an inclination towards real-world problems.

We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in machine learning, artificial intelligence, and data mining (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI), and/or top-tier interdisciplinary journals (e.g., Nature family of journals, PNAS).

Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and experience with machine learning and/or its applications to biology and medicine are required.

Application process

The position is available immediately and can be renewed annually. Interested applicants should submit the following documents via email to Prof. Zitnik and use the subject line “Postdoctoral Fellowship Application”:

  • Curriculum Vitae (please include links to your academic webpage and any software you developed)
  • Two representative publications (preprints are acceptable)
  • Statement of Research (2 pages) describing prior research experience and future research plans
  • Three letters of recommendation (will be solicited after the initial review)

We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible.

Advisor

Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik is a computer scientist studying machine learning, focusing on challenges brought forward by data in science, medicine, and health. Before Harvard, she was a postdoctoral fellow in Computer Science at Stanford and also a member of the Chan Zuckerberg Biohub.

Dr. Zitnik has published extensively in top ML venues (e.g., NeurIPS, ICLR, ICML) and leading interdisciplinary journals (e.g., Nature Methods, Nature Communications, PNAS). She has organized numerous workshops and tutorials in the nexus of AI, deep learning, drug discovery, and medical AI at leading conferences (NeurIPS, ICLR, ICML, ISMB, AAAI, WWW), where she is also in the organizing committees. She also organized the National Symposium on drugs for future pandemics on behalf of the NSF.

Dr. Zitnik’s algorithms have had a tangible impact, which has garnered the interests of government, academic, and industry researchers and has put new tools in the hands of practitioners. Her methods are used by major institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, Massachusetts General Hospital, and the pharmaceutical industry.

Her research won Bayer Early Excellence in Science Award and numerous best paper and research awards from the International Society for Computational Biology. She was named a Rising Star in Electrical Engineering and Computer Science (EECS) by MIT and also a Next Generation in Biomedicine by Broad Institute of MIT and Harvard, being the only young scientist who received such recognition in both EECS and Biomedicine.


Harvard is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.

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