Gabriel Loewinger

Machine Learning Research Scientist

Machine Learning Core, National Institute of Mental Health, NIH

Biography

I am a research scientist at the NIMH Machine Learning Core. I develop statistical and machine learning methods with a focus on functional data analysis, causal inference, and longitudinal data analysis. I completed my PhD in the Department of Biostatistics at Harvard where I was an NIH Graduate Fellowship recipient (NRSA: F31). I was advised by Dr. Giovanni Parmigiani and collaborated closely with Dr. Rahul Mazumder at MIT. My graduate methods research focused on transfer learning and mixed integer optimization. My subject area interests include neuroscience, psychiatry and chemical dependence. Please feel free to reach out to me at gloewinger@gmail.com. This site represents my opinions, not my employer’s.

Download my CV.

Interests

Biostatistics
Machine Learning
Optimization
Neuroscience

Education

PhD in Biostatistics, 2022

Harvard University
AM in Biostatistics, 2019

Harvard University
BA in Neuroscience, 2012

Pitzer College

About Me

I grew up in Washington, DC and studied neuroscience at Pitzer College (of the Claremont Colleges). After graduating, I was fortunate to spend two years conducting research abroad on Watson (Peru, Brazil, Thailand and Vietnam) and research Fulbright (Nepal) fellowships. I also spent a couple years as an NIH postbac fellow in the laboratory of Dr. David Lovinger.

My graduate research in biostatistics focused on developing machine learning algorithms that borrow information across different datasets to improve model generalizability. In addition to my advisor, Dr. Giovanni Parmigiani, I collaborated with Dr. Rahul Mazumder at MIT and Dr. Rajarshi Mukherjee at Harvard.

At the NIMH I develop machine learning and statistical methods. I also actively engage in collaborative applied work with clinicians, neuroscientists, and mental health researchers.

In my free time, I train Brazilian Jiu Jitsu. I also like chess, Vipassana meditation and studying languages.

Awards

Teaching Award, Department of Biostatistics, Harvard University. 2021-2022
Harvard Medical School Computational Data Neuroscience Symposium Best Abstract Award. Oct 2020
NIH National Research Service Award Pre-Doctoral Fellowship (F31), National Institute on Drug Abuse. Aug 2020
Rose Fellowship, Harvard School of Public Health. Nov 2019
NIH Technical Intramural Research Training Award. Feb 2015
Fulbright Research Fellowship. May 2013
Watson Fellowship. May 2012
Amgen Scholarship. Mar 2011
Claremont Colleges Summer Neuroscience Research Fellowship. Mar 2011

Software Development

fastFGEE

CRAN Package

Apr 2026 – Present

I developed the “fastFGEE” R package that is freely available on CRAN. The package implements one-step functional generalized estimating equations for function-on-scalar, scalar-on-function and function-on-function regression for large longitudinal functional datasets. Click on “CRAN Package” above for a link to download.

fastFMM

CRAN Package

Nov 2023 – Present

I codeveloped the “fastFMM” R package that is freely available on CRAN. The package implements an approach to functional generalized linear mixed models. Click on “CRAN Package” above for a link to download.

sMTL

CRAN Package

Feb 2023 – Present

I developed and maintain the “sMTL” R package that is freely available on CRAN. The package implements numerous sparse Multi-Task Learning methods. The scalable algorithms implemented in the package are based on block coordinate descent and combinatorial local search. They are coded in Julia for computational efficiency and are called from an easy-to-use R wrapper. Click on “CRAN Package” above for a link to download. Named as one of R Views “Top 40” new CRAN packages of February 2023!

studyStrap

CRAN Package

Feb 2020 – Present

I developed and maintain the “studyStrap” R package that is freely available on CRAN. The package implements numerous methods for training prediction algorithms with multiple training datasets. Click on “CRAN Package” above for a link to download. ~ 11,000 downloads. Named as one of R Views “Top 40” new CRAN packages of February 2020!

Publications

Gabriel Loewinger, Alex Levis, Erjia Cui, Francisco Pereira (2026). Fast Penalized Generalized Estimating Equations for Large Longitudinal Functional Datasets. Biometrics (To Appear), https://doi.org/10.48550/arXiv.2506.20437.

Gabriel Loewinger*, Alex Levis*, Francisco Pereira (2026). Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes. Journal of the American Statistical Association (To Appear), https://doi.org/10.48550/arXiv.2405.18597.

Kayhan Behdin*, Gabriel Loewinger*, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder (2025). Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives. Journal of the Royal Statistical Society B, https://doi.org/10.1093/jrsssb/qkaf076.

Al W Xin, Erjia Cui, Francisco Pereira, Gabriel Loewinger (2025). Capturing instantaneous neural signal-behavior relationships with concurrent functional mixed models. eLife, https://elifesciences.org/reviewed-preprints/109428.

Gabriel Loewinger, Mats Julius Stensrud, Sandeep M Nayak, David B Yaden, Alex W Levis (2025). Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond. medRxiv, doi.org/10.64898/2025.12.05.25341713.

Alex Levis*, Gabriel Loewinger*, Francisco Pereira (2024). Causal inference in the closed-loop: marginal structural models for sequential excursion effects. NeurIPS 2024, https://doi.org/10.52202/079017-3464.

Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder, Giovanni Parmigiani (2024). Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation. Statistics in Medicine, (9), 43, pp. 1774–1789.

Gabriel Loewinger, Erjia Cui, David Lovinger, Francisco Pereira (2023). A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments. eLife, https://elifesciences.org/reviewed-preprints/95802.

Gabriel Loewinger, Prasad Patil, Kenneth T. Kishida, Giovanni Parmigiani (2022). Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing. The Annals of Applied Statistics, (16), 4, pp. 2145–2165, https://doi.org/10.1214/21-AOAS1574.

Brian Rush, Olivia Marcus, Sara Garcia, Anja Loizaga-Velder, Gabriel Loewinger, Ariane Spitalier, Fernando Mendive (2021). Protocol for Outcome Evaluation of Ayahuasca-Assisted Addiction Treatment: The Case of Takiwasi Center. Frontiers in Pharmacology, (12), pp. 1203, https://doi.org/10.3389/fphar.2021.659644.

Inmaculada Marquez, Gabriel Loewinger, Juan Pedro Vargas, Juan Carlos Lopez, Estrella Diaz, Guillem Esber (2021). Surprise-Induced Enhancements in the Associability of Pavlovian Cues Facilitate Learning across Behavior Systems. Behavioral Neuroscience, https://doi.org/10.1101/2021.06.14.448382.

Shana Augustin, Gabriel Loewinger, Timothy O'Neal, Alexxai Kravitz, David Lovinger (2020). Dopamine D2 Receptor Signaling on iMSNs is Required for Initiation and Vigor of Learned Actions. Neuropsychopharmacology, (45), 12, pp. 2087–2097, https://doi.org/10.1038/s41386-020-00799-1.

Kari Johnson, Lucas Voyvodic, Gabriel Loewinger, Yolanda Mateo, David Lovinger (2020). Operant Self-Stimulation of Thalamic Terminals in the Dorsomedial Striatum is Constrained by Metabotropic Glutamate Receptor 2. Neuropsychopharmacology, (45), 9, pp. 1454–1462, https://doi.org/10.1038/s41386-020-0626-y.

Gabriel Loewinger, Bishnu Sharma, Deepak Kumar Karki, Prasana Khatiwoda, Sher Kainee, Krishna Poudel (2016). Low Knowledge and Perceived Hepatitis C Risk Despite High Risk Behaviour among Injection Drug Users in Kathmandu, Nepal. International Journal of Drug Policy, (33), pp. 75-82, https://doi.org/10.1016/j.drugpo.2016.05.005.

Kate Wassum, Sean Ostlund, Gabriel Loewinger, Nigel Maidment (2013). Phasic Mesolimbic Dopamine Release Tracks Reward Seeking During Expression of Pavlovian-to-Instrumental Transfer. Biological Psychiatry, (73), 8, pp. 747–755, https://doi.org/10.1016/j.biopsych.2012.12.005.

Gabriel Loewinger, Erik Oleson, Joseph Cheer (2013). Using Dopamine Research to Generate Rational Cannabinoid Drug Policy. Drug Testing and Analysis, (5), 1, pp. 22-26, https://doi.org/10.1002/dta.1410.

Gabriel Loewinger, Michael Beckert, Hugo Tejeda, Joseph Cheer (2012). Methamphetamine-Induced Dopamine Terminal Deficits in the Nucleus Accumbens are Exacerbated by Reward-Associated Cues and Attenuated by CB1 Receptor Antagonism. Neuropharmacology, (62), 7, pp. 2192-2201, https://doi.org/10.1016/j.neuropharm.2012.01.013.