Randy Ellis, PhD

Randy Ellis, PhD

Postdoctoral Fellow at Harvard Medical School

Investigating neurodegenerative disease using human biobank data, including blood biomarkers, proteomics, genomics, electronic health records, and methods from machine learning and statistical modeling in the Department of Biomedical Informatics.

Background

Currently

I am currently a Postdoctoral Fellow in Chirag Patel's lab in the Department of Biomedical Informatics at Harvard Medical School working on neurodegenerative disease using human biobank data including blood biomarkers (e.g., pTau-217), amyloid-PET and other types of brain imaging, cerebrospinal fluid, genomics, proteomics, electronic health records, and methods from machine learning and statistical modeling.

Previous Experience

Tufts University (2023): Postdoctoral Fellow in Michael Levin's lab working on single-cell genomics and calcium imaging analysis.

Icahn School of Medicine at Mount Sinai (2022): Earned PhD in Yasmin Hurd's lab, supported by F31 (NIDA) and T32 (NIGMS) National Research Service Awards. Worked at the interface of genomics, machine learning, and behavioral neuroscience to understand the molecular underpinnings of opioid use disorder. I also wrote a snapshot on opioid abuse neurobiology, studied the effects of prenatal cannabis exposure on motivation and the epigenetics of the nucleus accumbens, and published essays on the intersection of metascience and neuroscience and common pitfalls of clinical machine learning.

From 2017-2018, I was a rotation student in Avi Ma’ayan’s lab and developed machine learning algorithms to predict diagnosis of substance use disorders using electronic medical records.

National Institute on Drug Abuse (2015-2017): Postbac in Michael Michaelides' lab on an Intramural Research Training Award. Worked on projects including the decoding of natural scenes from calcium imaging data curated by the Allen Institute, DREADD actuator metabolism, the effects of zinc on on cocaine-seeking, the effects of obesity on GPCR signaling in the striatum, and a textbook chapter on neuroimaging similarities between substance use and overeating disorders.

Florida Atlantic University (2014): B.S. in Neuroscience & Behavior with Minor in Psychology. Pursued research in coordination dynamics in J. A. Scott Kelso's lab and neural circuits underlying cognitive behaviors in Robert Vertes' lab.

Featured Research

Using random feature baselines as performance benchmarks for -omic machine learning

In high-dimensional data settings, we propose randomly chosen feature sets of varying sizes as simple and effective benchmarks for evaluating the performance of data-driven machine learning models.
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Machine learning-based identification of translational targets of opioid use disorder

Identified Shisa7 as a novel biomarker for opioid use disorder via a machine learning approach with human brain RNA-seq and validated it in a translational rodent model.
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Publications

  1. Isaac, S.K., Ellis, R.J., Gusev, A., Murthy, V.L., Udler, M.S. and Patel, C.J.
    medRxiv, 2025
  2. Ellis, R.J., Ferland, J.M.N., Rahman, T., Landry, J.L., Callens, J.E., Pandey, G., Lam, T., Kanyo, J., Nairn, A.C., Dracheva, S. and Hurd, Y.L.
    Biological Psychiatry, 2024
  3. Ellis, R. J., Airaud, A., Patel, C. J.
    Machine Learning For Health (ML4H), 2024
  4. Luo, R., Zeraatkar, D., Glymour, M., Ellis, R. J., Estiri, H., Patel, C. J.
    BMC Medicine, 2024
  5. Ferland, J. M. N., Ellis, R. J., Betts, G., Silveira, M. M., de Firmino, J. B., Winstanley, C. A., & Hurd, Y. L.
    JAMA Psychiatry, 2023
  6. Ellis, R. J.
    eNeuro, 2022
  7. Ellis, R. J., Sander, R. M., Limon, A.
    Intelligence-Based Medicine, 2022
  8. Ferland, J.-M. N., Ellis, R. J., Rompala, G., Landry, J. A., Callens, J. E., Ly, A., Frier, M. D., Uzamere, T. O., Hurd, Y. L.
    Molecular Psychiatry, 2022
  9. Ellis, R. J.*, Bara, A.*, Vargas, C. A.*, Frick, A. L., Loh, E., Landry, J., Uzamere, T. O., Callens, J. E., Martin, Q., Rajarajan, P., Brennand, K., Ramakrishnan, A., Shen, L., Szutorisz, H. & Hurd, Y. L.
    Biological Psychiatry, 2021
  10. Ellis, R. J., Rahman, T., Sherman, J. & Hurd, Y. L.
    Cell, 2021
  11. Suprun, M., Ellis, R. J., Sampson, H. A. & Suárez-Fariñas, M.
    Bioinformatics, 2021
  12. Gomez, J. L., Bonaventura, J., Keighron, J., Wright, K. M., Marable, D. L., Rodriguez, L. A., Lam, S., Carlton, M. L., Ellis, R. J., Jordan, C. J., Bi, G., Solis, O., Pignatelli, M., Bannon, M. J., Xi, Z.-X., Tanda, G. & Michaelides, M.
    Translational Psychiatry, 2021
  13. Egervari, G., Akpoyibo, D., Rahman, T., Fullard, J. F., Callens, J. E., Landry, J. A., Ly, A., Zhou, X., Warren, N., Hauberg, M. E., Hoffman, G., Ellis, R., Ferland, J.-M. N., Miller, M. L., Keller, E., Zhang, B., Roussos, P. & Hurd, Y. L.
    Nature Communications, 2020
  14. Ellis, R. J., Wang, Z., Genes, N. & Ma'ayan, A.
    BioData Mining, 2019
  15. Michaelides, M., Miller, M. L., Egervari, G., Primeaux, S. D., Gomez, J. L., Ellis, R. J., Landry, J. A., Szutorisz, H., Hoffman, A. F., Lupica, C. R., Loos, R. J. F., Thanos, P. K., Bray, G. A., Neumaier, J. F., Zachariou, V., Wang, G.-J., Volkow, N. D. & Hurd, Y. L.
    Molecular Psychiatry, 2018
  16. Ellis, R. J. & Michaelides, M.
    bioRxiv, 2018
  17. Ellis, R. J., Michaelides, M. & Wang, G.-J.
    Processed Food Addiction: Foundations, Assessment, and Recovery (CRC Press), 2017
  18. Gomez, J. L., Bonaventura, J., Lesniak, W., Mathews, W. B., Sysa-Shah, P., Rodriguez, L. A., Ellis, R. J., Richie, C. T., Harvey, B. K., Dannals, R. F., Pomper, M. G., Bonci, A. & Michaelides, M.
    Science, 2017

Consulting Services

Machine Learning for Biology
Alzheimer's Biomarker Development
Biobank-scale Analysis
Bioinformatics Pipeline Development
Statistical Modeling & Analysis
Data Visualization & Reporting

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