The Clarity- and Virtue-guided Algorithms (CAVA) Lab at Boston Children's Hospital / Harvard Medical School is seeking a post-doctoral research fellow to advance the interpretability and fairness of machine learning (ML) models deployed in critical healthcare settings.  The fellow will join a multi-disciplinary team of computer scientists, informaticists, clinicians, engineers and bioethicists to develop and assess clinical prediction algorithms and advance our understanding of the behavior of machine learning models deployed in health settings. The fellow will help us think critically about how machine learning methods affect clinical practice and outcomes; in particular, 1) the conditions under which ML provides or fails to provide insight into disease pathologies, and 2) the conditions under which ML exacerbates or mitigates treatment and outcome disparities between patient subgroups. 

Prediction models are an increasingly important technology in the digital health landscape, and can produce large-scale changes in  health care via their interactions with patients, clinicians, and hospital operations. This postdoctoral fellowship provides an opportunity to study these issues more deeply in order to improve our ability to diagnose and intervene in a more trustworthy and equitable way. 

The fellowship includes an academic appointment at Harvard Medical School, as well as a hospital appointment at Boston Children’s Hospital. This position provides an excellent opportunity for the Research Fellow to work within a multidisciplinary research team to explore advanced areas in health information technology. CHIP is home to 20 faculty working at the forefront of research areas extending beyond clinical prediction algorithms to domains like clinical NLP, digital epidemiology, clinical genomics, and app ecosystems for health records. CHIP and the CAVA Lab value diversity and believe that it is essential to our research goals. We therefore strongly encourage candidates from underrepresented groups to apply.  

Admissions

The position is available immediately and is renewable annually.

Qualifications
  • PhD degree in computer science, information science, biomedical informatics, data mining, engineering, applied mathematics, or a closely related field.
  • A track record of high-quality research that demonstrates the ability to independently identify important research topics and carry out experiments. 
  • Candidates with strong experience in machine learning, preferably both in the assessment of ML algorithms in data science applications and in the development of novel methods. 
  • Experience and familiarity with the machine learning literature on interpretability and fairness. 
  • Experience working with large, heterogeneous data collections, especially electronic health records, multi-omics data, or other health data.
  • Programming experience in Python, R, and/or C++.
  • Experience with collaborative software development (revision control, continuous integration, etc) strongly preferred 
  • Experience with open science practices (preprints, reproducible workflows, etc) strongly preferred
  • Strong written and oral communication skills.
  • Ability to work both independently and as a team player.
How to apply

Interested candidates should email a CV, three letters of reference, and a sample publication to Dr. William La Cava, PI Clarity and Virtue-guided Algorithms Lab: william.lacava@childrens.harvard.edu.

Publications

Hopper RK, Abman SH, Elia EG, Avitabile CM, Yung D, Mullen MP, Austin ED, Bates A, Handler SS, Feinstein JA, Ivy DD, Kinsella JP, Mandl KD, Raj JU, Sleeper LA, . Pulmonary Hypertension in Children with Down Syndrome: Results from the Pediatric Pulmonary Hypertension Network Registry. The Journal of pediatrics 2022.

Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T, . SurvMaximin: Robust federated approach to transporting survival risk prediction models. Journal of biomedical informatics 2022.

Torous J, Stern AD, Bourgeois FT. Regulatory considerations to keep pace with innovation in digital health products. NPJ digital medicine 2022.

Levy S, Wisk LE, Minegishi M, Ertman B, Lunstead J, Brogna M, Weitzman ER. Association of Screening and Brief Intervention With Substance Use in Massachusetts Middle and High Schools. JAMA network open 2022.

Nielsen M, Presti M, Sztupinszki Z, Jensen AWP, Draghi A, Chamberlain CA, Schina A, Yde CW, Wojcik J, Szallasi Z, Crowther MD, Svane IM, Donia M. Co-existing alterations of MHC class I antigen presentation and IFNγ signaling mediate acquired resistance of melanoma to post-PD-1 immunotherapy. Cancer immunology research 2022.