Descriptive CV Concise CV

Michelle Lynn Gill, PhD

Applied Research Manager, NVIDIA Virtual Cell Team

Overview

I am an Applied Research Manager on NVIDIA’s Virtual Cell team. Our team uses data, deep learning, and HPC to develop models that simulate cellular states. My focus is on the benchmarks and development of supporting tools that help evaluate and improve these models.

Previously I was a Senior Machine Learning Engineer and Data Scientist at BenevolentAI and a member of Arthur Palmer’s research group where I developed and applied nuclear magnetic resonance (NMR) spin relaxation experiments to understand how enzyme dynamics are critical to biological function.

Education

2003-2006 Ph.D., Molecular Biophysics & Biochemistry
Yale University, New Haven, CT

  • Thesis: Development of 205Tl NMR methods for the direct study of monovalent metal ions and ligands in nucleic acids

  • Advisors: J. Patrick Loria and Scott Strobel

2001-2003 M.Phil., Molecular Biophysics & Biochemistry
Yale University, New Haven, CT

1997-2001 B.S., Biochemistry
University of Kansas, Lawrence, KS

  • Highest Distinction and Honors (Summa Cum Laude)

Experience

2022-Present Applied Research Manager, NVIDIA, Virtual Cell Team

  • Lead benchmarking efforts for virtual cell models, including internal and external collaborations

  • Developed benchmarking framework and tooling for model assessment and reproducibility

  • Collaboration with Chan Zuckerberg Initiative on cz-benchmarks for evaluating virtual cell models

2019-2023 Scientific Lead, BioNeMo, NVIDIA

  • R&D Manager and Scientific Lead for Clara Discovery, NVIDIA’s platform for accelerating drug discovery

  • Developed BioNeMo for pre-training and fine tuning of large language models for cheminformatics and proteomics

2019-2023 Senior AI and Deep Learning Scientist, NVIDIA

  • Focused on proteomics including deep learning models to predict peptide spectral matches with >95% F1

  • Led team using GCNNs to predict molecular properties

2018-2019 Senior Data Scientist and Machine Learning Engineer, BenevolentAI

  • Matrix factorization and GCNNs for drug mechanism importance in knowledge graphs

  • 3D CNNs for ligand pose and affinity prediction

2017-2018 Senior Deep Learning Consultant, NVIDIA

  • Assisted clients in pharmaceutical and materials science in utilizing deep learning

2014-2016 Scientist, National Cancer Institute, NIH

  • Developed parallelized, compressed sensing methods for NMR data reconstruction

2008-2014 Postdoctoral Research Fellow, Columbia University, Department of Biochemistry and Molecular Biophysics

  • Demonstrated conformational selection in DNA methyltransferase AlkB

  • Developed multiple quantum NMR spin relaxation experiments

  • Advisor: Professor Arthur G. Palmer, III

Publications

  1. S. Dicks, L. Heumos, S. Jimenez, P. Angerer, I. Gold, I. Virshup, F. Fischer, L. May, C. J. Nolet, M. Gill, M. Boerries, and F. Theis "Accelerating single-cell analysis with GPU-enabled RAPIDS-singlecell" In preparation (2025).
  2. E. Sevgen, J. Moller, A. Lange, J. Parker, S. Quigley, J. Mayer, P. Srivastava, S. Gayatri, D. Hosfield, M. Korshunova, M. Livne, M. Gill, R. Ranganathan, A.B. Costa, and A.L. Ferguson "ProT-VAE: Protein transformer variational autoencoder for functional protein design" Proceedings of the National Academy of Sciences (2025).
  3. P. St. John, D. Lin, P. Binder, M. Greaves, V. Shah, J. St. John, A. Lange, P. Hsu, R. Illango, A. Ramanathan, A. Anandkumar, D.H. Brookes, A. Busia, A. Mahajan, S. Malina, N. Prasad, S. Sinai, L. Edwards, T. Gaudelet, C. Regep, M. Steinegger, B. Rost, A. Brace, K. Hippe, L. Naef, K. Kamata, G. Armstrong, K. Boyd, Z. Cao, H.Y. Chou, S. Chu, A. dos Santos Costa, S. Darabi, E. Dawson, K. Didi, C. Fu, M. Geiger, M. Gill, D.J. Hsu, G. Kaushik, M. Korshunova, S. Kothen-Hill, Y. Lee, M. Liu, M. Livne, Z. McClure, J. Mitchell, A. Moradzadeh, O. Mosafi, Y. Nashed, S. Paliwal, Y. Peng, S. Rabhi, F. Ramezanghorbani, D. Reidenbach, C. Ricketts, B.C. Roland, K. Shah, T. Shimko, H. Sirelkhatim, S. Srinivasan, A.C. Stern, D. Toczydlowska, S.P. Veccham, N.A.E. Venanzi, A. Vorontsov, J. Wilber, I. Wilkinson, W.J. Wong, E. Xue, C. Ye, X. Yu, Y. Zhang, G. Zhou, B. Zandstein, A. Chacon, P. Sohani, M. Stadler, C. Hundt, F. Zhu, C. Dallago, B. Trentini, E. Kucukbenli, T. Rvachov, E. Calleja, J. Israeli, H. Clifford, R. Haukioja, N. Haemel, K. Tretina, N. Tadimeti, and A.B. Costa "BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery" arXiv (2024).
  4. M.T. Strauss, I. Bludau, W.F. Zeng, E. Voytik, C. Ammar, J. Schessner, R. Illango, M.L. Gill, F. Meier, S. Willems, and M. Mann "AlphaPept, a modern and open framework for MS-based proteomics" Nature Communications (2024).
  5. M.L. Gill "The rise of the machines in chemistry" Magnetic Resonance in Chemistry (2022).
  6. D. Reidenbach, M. Livne, R.K. Illango, M.L. Gill, and J.I. Israeli "Improving small molecule generation using mutual information machine" arXiv (2022).
  7. M.L. Gill, A. Hsu, and A.G. Palmer "Detection of chemical exchange in methyl groups of macromolecules" Journal of Biomolecular NMR (2019).
  8. M. Tong, J. Pelton, M.L. Gill, W. Zhang, F. Picart, and M. Seeliger "Survey of solution dynamics in Src kinase reveals cross talk between the ligand binding and regulatory sites" Nature Communications (2017).
  9. M.L. Gill, R.A. Byrd, and A.G. Palmer "Dynamics of GCN4 facilitate DNA interaction: a model-free analysis of an intrinsically disordered region" Physical Chemistry and Chemical Physics (2016).
  10. S. Sun, M.L. Gill, Y. Li, M. Huang, and R.A. Byrd "Efficient and generalized processing of multidimensional NUS NMR data: the NESTA algorithm and comparison of regularization terms" Journal of Biomolecular NMR (2015).
  11. B. Ergel, M.L. Gill, L. Brown, B. Yu, A.G. Palmer, and J.F. Hunt "Protein dynamics control the progression and efficiency of the catalytic reaction cycle of AlkB" Journal of Biological Chemistry (2014).
  12. M.L. Gill, and R.A. Byrd "Dynamic activation of apoptosis: conformational ensembles of cIAP1 are linked to a spring-loaded mechanism" Nature Structural Molecular Biology (2014).
  13. M.L. Gill, and A.G. Palmer "Local isotropic diffusion approximation for coupled internal and overall molecular motions in NMR spin relaxation" Journal of Physical Chemistry B (2014).
  14. M.L. Gill, and A.G. Palmer "Multiplet-filtered and gradient-selected zero-quantum TROSY experiments for 13C1H3 methyl groups in proteins" Journal of Biomolecular NMR (2011).
  15. J.D. Ramsey, M.L. Gill, T.J. Kamerzell, E.S. Price, S.B. Joshi, S.M. Bishop, C.N. Oliver, and C.R. Middaugh "Using empirical phase diagrams to understand the role of intramolecular dynamics in immunoglobulin G stability" Journal of Pharmaceutical Sciences (2009).
  16. M.L. Gill, S.A. Strobel, and J.P. Loria "Crystallization and characterization of the thallium form of the Oxytricha nova G-quadruplex" Nucleic Acids Research (2006).
  17. H. Beach, R. Cole, M.L. Gill, and J.P. Loria "Conservation of µs-ms enzyme motions in the apo- and substrate-mimicked state" Journal of the American Chemical Society (2005).
  18. M.L. Gill, S.A. Strobel, and J.P. Loria "205Tl NMR methods for the study of monovalent metal binding sites in nucleic acids" Journal of the American Chemical Society (2005).
  19. P.L. Adams, M.R. Stahley, M.L. Gill, A.B. Kosek, J. Wang, and S.A. Strobel "Crystal structure of a group I intron splicing intermediate" RNA (2004).
  20. C.M. Wiethoff, M.L. Gill, G.S. Koe, J.G. Koe, and C.R. Middaugh "A fluorescence study of the structure and accessibility of plasmid DNA condensed with cationic gene delivery vehicles" Journal of Pharmaceutical Sciences (2003).
  21. C.M. Wiethoff, M.L. Gill, G.S. Koe, J.G. Koe, and C.R. Middaugh "The structural organization of cationic lipid-DNA complexes" Journal of Biological Chemistry (2002).
  22. S. Silchenko, M.L. Sippel, O. Kuchment, D.R. Benson, A.G. Mauk, A. Altuve, and M. Rivera "Hemin is kinetically trapped in cytochrome b5 from rat outer mitochondrial membrane" Biochemical and Biophysical Research Communications (2000).

Patents

2025 Automated FEP Path Generation and Optimization by Molecule Foundation Models and Generative AI

  • Peng, Y., Kucukbenli, E., Zhou, G., Gill, M.L., Livne, M., Korshunova, M., Rvachov, T., Israeli, Y.
  • US Patent Application No. 19/208,119; Filing Date: 2025/05/14

2024 Extraction of Informative Embeddings from Encoder-Decoder Models

  • Livne, M., Gill, M.L.
  • US Patent Application No. 18/957,301; Filing Date: 2024/11/22

2024 Contrastive Framework for Unified Generative and Discriminative Representation Learning

  • Livne, M., Gill, M.L.
  • US Patent Application No. 18/957,294; Filing Date: 2024/11/22

2024 Efficient Data Loading for Deep Learning Workloads

  • Darabi, S., Korshunova, M., Grewal, J., Gill, M.L., Morkisz, P.
  • US Patent Application No. 18/885,428; Filing Date: 2024/09/13

2024 Guardrails for Molecular Generation

  • Korshunova, M., Peng, Y., Zhou, G., Rvachov, T., Gill, M.L., Kucukbenli, E., Israeli, Y.
  • US Patent Application No. 18/807,805; Filing Date: 2024/08/16

2024 Guardrails for Conditional Molecular Generation

  • Korshunova, M., Peng, Y., Zhou, G., Rvachov, T., Gill, M.L., Kucukbenli, E., Israeli, Y.
  • US Patent Application No. 18/807,808; Filing Date: 2024/08/16

2024 Training-Time Guardrails for Molecular Generation

  • Korshunova, M., Peng, Y., Zhou, G., Rvachov, T., Gill, M.L., Kucukbenli, E., Israeli, Y.
  • US Patent Application No. 18/807,811; Filing Date: 2024/08/16

2024 Guardrails for Instruction-Tuned Molecular Generation

  • Korshunova, M., Peng, Y., Zhou, G., Rvachov, T., Gill, M.L., Kucukbenli, E., Israeli, Y.
  • US Patent Application No. 18/807,814; Filing Date: 2024/08/16

2024 End to End Deep Learning Workflow for In Silico Molecule Design

  • Stern, A., Gill, M.L., Stepniewska-Dziubinska, M., Grzegorzek, T., Nowaczynski, A., Toczydlowska, D., Israeli, Y., Ribalta Lorenzo, P.
  • US Patent Application No. 18/412,168; Filing Date: 2024/01/12

2023 Small Molecule Generation Using Machine Learning Models

  • Livne, M., Reidenbach, D., Gill, M.L., Ilango, R., Israeli, Y.
  • US Patent Application No. 18/450,745; Filing Date: 2023/08/16

Presentations

2024 Scientific Discovery: From the Lab Bench to the GPU, Andy Byrd Retirement Symposium

  • Invited Keynote, April 19, 2024, Institute for Bioscience & Biotechnology Research, University of Maryland, NIST, Bethesda, MD
  • Slides · Program

2023 Scientific Discovery: From the Lab Bench to the GPU, PyData NYC

2023 NVIDIA BioNeMo: A framework and service for development and use of generative AI in drug discovery, 6th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Conference

  • Invited Keynote, September 5, 2023, Cambridge, UK
  • Slides · Program

2021 Exploring Molecular Space and Accelerating Drug Discovery on the GPU with Clara Discovery, Gates Foundation Grand Challenges: Applications of Artificial Intelligence in Machine Learning

  • Invited Talk, November 10, 2021, Virtual

2021 Accelerating Drug Discovery with Clara Discovery and MegaMolBART, 4th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry

  • Invited Talk, September 27, 2021, Virtual
  • Slides

2020 Real Time, GPU-Accelerated Analysis and Visualization in the Life Sciences, Ken Kennedy Institute Data Science Conference

  • Michelle Gill and Avantika Lal
  • Invited Keynote, October 26-27, 2020, Virtual
  • Slides · Abstract · Program

2019 Artificial intelligence driven drug discovery, NYC R Conference

  • Invited Presentation, May 10, 2019, New York, NY
  • Slides

2019 Panel: Careers in data science, Tri-Institutional Career Symposium

  • Panel, April 9, 2019, Memorial Sloan Kettering Cancer Center, The Rockefeller University and Weill Cornell Medicine, New York, NY
  • Program

2019 Machine learning for target identification and lead optimization in drug discovery, New York Area Group for Informatics and Modeling

  • Alix Lacoste and Michelle Gill
  • Invited Presentation, February 26, 2019, New York, NY
  • Abstract

2018 Accelerating the journey from data to medicine, NeurIPS

  • Amir Saffari, Dan Neil, Alix Lacoste, and Michelle Gill
  • Expo Talk, 2018, Montreal, Canada
  • Abstract

2018 Artificial intelligence as a catalyst for scientific discovery, JupyterCon

2018 From structural biology to AI: a holistic approach to studying molecular machines, Brookhaven National Laboratory

  • Invited Presentation, 2018, Upton, NY
  • Slides

2017 Efficient image search and identification: the making of Wine-O.AI, SciPy Conference

2006 Development of 205Tl NMR methods for the direct study of monovalent metal ions and ligands in nucleic acids, Ph.D. Thesis Defense, Yale University

Awards

2009-2012 NIH Postdoctoral Research Fellowship (F32 GM089047)

2007 Global BCG Strategy Olympics, Winning Team

2002-2006 NSF Graduate Research Fellowship

2000-2001 Barry M. Goldwater Scholar

2001 Outstanding Undergraduate Honors Research Thesis

1997-2001 Kansas Board of Regents Full Tuition Merit Scholarship

Service

2022 Judge, Preliminary and Final Rounds, NYC STEM Fair, New York, NY

  • Evaluated submissions in the biochemistry track

2018 Program Chair, PyData NYC, New York, NY

  • Responsible for conference content, proposal review, speaker selection, and scheduling

2018 Machine Learning Symposium Co-Chair, SciPy Conference, Austin, TX

  • Co-chair of machine learning / deep learning symposium

2018 Proposal Reviewer, JupyterCon

2018-2019 Reviewer, Journal of Open Source Software (JOSS)