Scholar
Gabriel Loewinger
Google Scholar ID: ZGBjoDsAAAAJ
Machine Learning Research Scientist at National Institute of Mental Health
Citations & Impact
Citations
216
H-index
7
i10-index
7
Publications
20
Co-authors
0
Publications
Resume
Academic Achievements
- Teaching Award, Department of Biostatistics, Harvard University (2021–2022).
- Best Abstract Award, Harvard Medical School Computational Data Neuroscience Symposium (Oct 2020).
- NIH NRSA Predoctoral Fellowship (F31) from NIDA (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 and Claremont Colleges Summer Neuroscience Research Fellowship (Mar 2011).
- Developed and maintain the 'sMTL' R package on CRAN (since Feb 2023) for sparse Multi-Task Learning.
- Co-developed the 'fastFMM' R package on CRAN (since Nov 2023) for functional generalized linear mixed models.
Background
- Currently a Machine Learning Research Scientist at the National Institute of Mental Health (NIMH/NIH), developing statistical and machine learning methods.
- Research interests include biostatistics, machine learning, optimization, neuroscience, and chemical dependence.
- PhD research focused on transfer learning methodologies, particularly domain generalization and multi-source domain adaptation with multiple training datasets.
- At NIH, works on functional data analysis and causal inference methods.
- Actively collaborates with clinicians, neuroscientists, and mental health researchers on statistical projects.