Postdoctoral Positions in the Lab of Marinka Zitnik
Areas of Focus
- Machine learning to combat COVID-19
- Representation learning and embeddings for biomedical networks and knowledge graphs
The selected candidate will be expected to lead research in novel machine learning methods to combat COVID-19. More specifically, this fellowship will focus on leveraging recent advances in explainable and interpretable AI/ML to help with the diagnosis and treatment of COVID-19. For instance, the candidate will be developing explainable methods which not only facilitate early detection of COVID-19 as well as its spread across various communities, but also provide interpretable insights into these aspects. In addition, the candidate will also devise novel explainable algorithms that can detect and filter out misinformation about COVID-19.
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, FAT*, AIES), 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 its applications to healthcare are required.
The position is available immediately and can be renewed annually. Interested applicants should apply via https://forms.gle/kDKoGPJHJe2sjBWYA and submit the following documents:
- 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 and preferably by September 1st, 2020. We will continue accepting applications after this deadline if the position is not filled.
Marinka Zitnik (https://zitniklab.hms.harvard.edu) is an Assistant Professor at Harvard with appointments in the Department of Biomedical Informatics, Blavatnik Institute, Broad Institute of MIT and Harvard, and Harvard Data Science Initiative. Dr. Zitnik is a computer scientist studying applied machine learning with a focus on challenges brought forward by data in science, medicine, and health. She has published extensively on the topics of representation learning, knowledge graphs, network science, and graph ML in top-tier AI venues (NeurIPS, JMLR, IEEE TPAMI, KDD, ICLR), top-tier bioinformatics venues (Bioinformatics, ISMB, RECOMB), and journals (Nature Methods, Nature Communications, PNAS). Some of her methods are used by major institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, and Massachusetts General Hospital. Her work received several best paper, poster, and research awards from the International Society for Computational Biology. She has recently been named a Rising Star in EECS by MIT and also a Next Generation in Biomedicine by the Broad Institute, being the only young scientist who received such recognition in both EECS and Biomedicine.
Hima Lakkaraju (https://himalakkaraju.github.io) is an Assistant Professor at Harvard University with appointments in the Business School and the Department of Computer Science. Her research focuses on enabling machine learning models to complement human decision making in high-stakes settings such as law, healthcare, and public policy. At the core of her research lie rigorous computational techniques spanning ML and data mining. She has published extensively on the topics of fair and interpretable ML in various top-tier ML and AI conferences including NeurIPS, AISTATS, KDD, AAAI, and AIES. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review, and was featured as one of the innovators to watch by Vanity Fair. She has received several prestigious awards including the best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. Her research has also been covered by popular media outlets including the New York Times, MIT Tech Review, Harvard Business Review, TIME, Forbes, Business Insider, and Bloomberg.
The Department of Biomedical Informatics (DBMI) at Harvard Medical School and the Zitnik Lab are looking for a Postdoctoral Fellow for research in novel biomedical machine learning and data science methods. Biomedical data involve rich interactions that span from the molecular level to the level of connections between diseases in a patient and to the societal level encompassing all human interactions. These interactions at different levels give rise to a bewildering degree of complexity, which is only likely to be fully understood through data-driven and computationally enabled study. We are investigating machine learning for biomedical sciences, focusing on new methods for learning and reasoning over rich interaction data and on translation of these methods into solutions for biomedical problems. This scientific approach not only opens up new avenues for understanding nature, analyzing health, and developing medicines to help people but can impact on the way predictive modeling is performed today at the fundamental level.
Among others, possible research projects include:
- Representation learning for biomedicine in an effort to set sights on new frontiers in genomics, drug discovery, and precision health beyond classic applications of neural networks on image and sequence data.
- Network embedding methods in an effort to bridge the divide between basic science and patient data.
- Fusion of diverse data into knowledge graphs in an effort to combine biomedical data in their broadest sense to reduce redundancy and uncertainty and make them amenable to analyses.
- Next-generation algorithms for networks, focusing on large networks of interactions between biomedical entities and their applications to network biology and medicine.
- Contextually adaptive AI in an effort to advance algorithms to train more with less data and reason about never-before-seen phenomena as algorithms encounter new patients, diseases, or cell types.
We seek a highly-motivated candidate with strong research skills and background in machine learning and/or applications in bioinformatics and computational medicine. Candidates must have a Ph.D. or equivalent degree in a field relevant to biomedical data science, such as computer science, statistics, engineering, biomedical informatics or computational biology. Strong programming skills and experience with large-scale data analysis and deep learning frameworks are a plus.
The position is available immediately and can be renewed annually.
How to apply
Submit your application with a letter indicating your research interests and experience, a CV, names and email addresses of 2-3 references, and copies of two representative publications via email to firstname.lastname@example.org. We encourage applicants also to include links to any software they have developed.
Harvard Medical School is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.