Ph.D., Molecular Biophysics & Biochemistry, 2003–2006
M.Phil., Molecular Biophysics & Biochemistry, 2001–2003
Yale University, New Haven, CT
Thesis: Development of ²⁰⁵Tl NMR methods for the direct study of monovalent metal ions and ligands in nucleic acids THESIS DEFENSE
Advisors: J. Patrick Loria and Scott Strobel

B.S., Biochemistry, 1997-2001
Highest Distinction and Honors (Summa Cum Laude)
University of Kansas, Lawrence, KS

Applied Research Manager, 2022-Present
Scientific Lead, BioNeMo, 2019-2023
Senior AI and Deep Learning Scientist, Cheminformatics and Proteomics, 2019-2023
Senior Deep Learning Consultant, 2017-2018
NVIDIA
On the Virtual Cell team, I lead benchmarking efforts, including both internal and external collaborations to develop a benchmarking framework and tooling that support model assessment and reproducibility across datasets and tasks. This scope includes a collaboration with the Chan Zuckerberg Initiative (CZI) to develop cz-benchmarks for evaluating virtual cell models. NVIDIA's contributions include the first perturb seq benchmark task and improvements to usability of the framework during the model development process. This was announced as part of a large scale collaboration between NVIDIA and CZI on 10/28/2025 to coincide with NVIDIA's GTC 2025 conference.

Previously, I was the R&D Manager and Scientific Lead for Clara Discovery, NVIDIA's platform for accelerating the drug discovery process through deep learning, molecular dynamics, and HPC. The team developed BioNeMo, which enabled pre-training and fine tuning of large language models for cheminformatics and proteomics tasks. My responsibilities included applied research, product cycle planning, and external collaborations with researchers in pharma, biotech, and academia.

Senior Machine Learning Engineer, 2019
Senior Data Scientist, 2018-2019
BenevolentAI
Utilized scientific and machine learning methods to improve outcomes for target identification and chemical validation. Focused on matrix factorization and graph convolutional neural networks (GCNNs) to determine the importance of drug mechanisms in knowledge graphs, and 3D CNNs with cheminformatics methods to predict ligand pose and affinity within a target.

Senior Data Scientist, 2016-2017
Metis
Co-instructed 12-week data science bootcamps, and developed a 12-week machine learning course for F100 company.

Scientist, 2014-2016
National Cancer Institute, National Institutes of Health
Developed parallelized, compressed sensing methods for reconstruction of non-uniformly sampled NMR data.

Postdoctoral Research Fellow, 2008-2014
Columbia University, Department of Biochemistry and Molecular Biophysics
Part of a collaboration that demonstrated conformational selection is critical in the highly concerted mechanism of the DNA methyltransferase, AlkB. Developed multiple quantum NMR spin relaxation experiments for quantifying the slow timescale (microsecond – millisecond) motions of methyl sidechains.
Advisor: Professor Arthur G. Palmer, III

Consultant, 2006–2007
The Boston Consulting Group
Worked with clients in the finance and pharmaceutical sectors to streamline organizational structure and identify novel investment opportunities. I was part of the case team that won the 2007 Global BCG Strategy Olympics for our work with a pharmaceutical client.

Dicks, S., Heumos, L., Jimenez, S., Angerer, P., Gold, I., Virshup, I., Fischer, F., May, L., Nolet, C. J., Gill, M., Boerries, M., Theis, F.
Accelerating Single-Cell Analysis with GPU-Enabled Rapids-singlecell, In preparation

Sevgen, E., Moller, J., Lange, A., Parker, J., Quigley, S., Mayer, J., Srivastava, P., Gayatri, S., Hosfield, D., Korshunova, M., Livne, M., Gill, M., Ranganathan, R., Costa, A.B., Ferguson, A.L. BIORXIV DOI PDF
ProT-VAE: Protein transformer variational autoencoder for functional protein design
Proceedings of the National Academy of Sciences (PNAS), Special Feature on Machine Learning in Chemistry, 2025, 122, 41

Strauss, M.T., Bludau, I., Zeng, W.F., Voytik, E., Ammar, C., Schessner, J., Illango, R., Gill, M.L., Meier, F., Willems, S., Mann, M. BIORXIV DOI PDF
AlphaPept, a modern and open framework for MS-based proteomics
Nature Communications, 2024, 15

Reidenbach, D., Livne, M., Illango, R.K., Gill, M.L., Israeli, J.I. DOI PDF
Improving small molecule generation using mutual information machine
arXiv, 2022
ICLR Machine Learning for Drug Discovery (MLDD) Workshop, 2023 ABSTRACT POSTER

Gill, M.L. DOI
The rise of the machines in chemistry, Invited Prospectus
Magnetic Resonance in Chemistry, 2022, 60, 1044-1051

Gill, M.L., Hsu, A., Palmer, A.G. CHEMRXIV DOI PDF
Detection of chemical exchange in methyl groups of macromolecules
Journal of Biomolecular NMR, 2019, 73, 443-450

57^th Experimental Nuclear Magnetic Resonance Conference, 2016, Pittsburgh, PA POSTER

Tong, M., Pelton, J., Gill, M.L., Zhang, W., Picart, F., Seeliger, M. DOI PDF
Survey of solution dynamics in Src kinase reveals cross talk between the ligand binding and regulatory sites
Nature Communications, 2017, 8, 2160

Gill, M.L., Byrd, R.A., Palmer, A.G. DOI PDF
Dynamics of GCN4 facilitate DNA interaction: a model-free analysis of an intrinsically disordered region
Physical Chemistry and Chemical Physics, 2016, 18, 5839–5849

International Conference of Magnetic Resonance in Biological Sciences, 2014, Dallas, TX POSTER
57^th Experimental Nuclear Magnetic Resonance Conference, 2016, Pittsburgh, PA POSTER

Gill, M.L., Sun, S., Li, Y., Byrd, R.A.
NESTA-NMR: efficient and quantitative processing of multidimensional NUS NMR data

57th Experimental Nuclear Magnetic Resonance Conference, 2016, Pittsburgh, PA POSTER

*Sun, S., *Gill, M.L., Li, Y., Huang, M., Byrd, R.A. DOI PDF
Efficient and generalized processing of multidimensional NUS NMR Data: the NESTA algorithm and comparison of regularization terms
Journal of Biomolecular NMR, 2015, 62, 105–117
* Authors contributed equally

56^th Experimental Nuclear Magnetic Resonance Conference, 2015, Monterey, CA POSTER

Gill, M.L., Byrd, R.A. DOI PDF
Dynamic activation of apoptosis: conformational ensembles of cIAP1 are linked to a spring-loaded mechanism
Nature Structural Molecular Biology, 2014, 21, 1022–1023

Gill, M.L., Palmer, A.G. DOI PDF
Local isotropic diffusion approximation for coupled internal and overall molecular motions in NMR spin relaxation
Journal of Physical Chemistry, Series B, 2014, 118, 11120–11128

Ergel, B., Gill, M.L., Brown, L., Yu, B., Palmer, A.G., Hunt, J.F. DOI PDF
Protein dynamics control the progression and efficiency of the catalytic reaction cycle of AlkB
Journal of Biological Chemistry, 2014, 289, 29584–29601
International Conference of Magnetic Resonance in Biological Sciences, 2012, Lyon, France POSTER

Gill, M.L. and Palmer, A.G. DOI PDF
Multiplet-filtered and gradient-selected zero-quantum TROSY experiments for ¹³C₁H₃ methyl groups in proteins
Journal of Biomolecular NMR, 2011, 51, 245–251
52nd Experimental Nuclear Magnetic Resonance Conference, 2011, Monterey, CA POSTER

Ramsey, J.D., Gill, M.L., Kamerzell, T.J., Price, E.S., Joshi, S.B., Bishop, S.M., Oliver, C.N., Middaugh, C.R. DOI PDF
Using empirical phase diagrams to understand the role of intramolecular dynamics in immunoglobulin G stability
Journal of Pharmaceutical Sciences, 2009, 98, 2432–2447

Gill, M.L., Strobel, S.A., and Loria, J.P. DOI PDF
Crystallization and characterization of the thallium form of the Oxytricha nova G-quadruplex
Nucleic Acids Research, 2006, 34, 4506–4514

Gill, M.L., Strobel, S.A., and Loria, J.P. DOI PDF
²⁰⁵Tl NMR methods for the study of monovalent metal binding sites in nucleic acids
Journal of the American Chemical Society, 2005, 127, 16723–16732

Beach, H., Cole, R., Gill, M.L., and Loria, J.P. DOI PDF
Conservation of µs-ms enzyme motions in the apo- and substrate-mimicked state
Journal of the American Chemical Society, 2005, 127, 9167–9176

Adams, P.L., Stahley, M.R., Gill, M.L., Kosek, A.B., Wang, J., and Strobel, S.A. DOI PDF
Crystal structure of a group I intron splicing intermediate
RNA, 2004, 12, 1867–1887

Wiethoff, C.M., Gill, M.L., Koe, G.S., Koe, J.G., and Middaugh, C.R. DOI PDF
A fluorescence study of the structure and accessibility of plasmid DNA condensed with cationic gene delivery vehicles
Journal of Pharmaceutical Sciences, 2003, 92, 1272–1285

Wiethoff, C.M., Gill, M.L., Koe, G.S., Koe, J.G., and Middaugh, C.R. DOI PDF
The structural organization of cationic lipid-DNA complexes
Journal of Biological Chemistry, 2002, 277, 44980–44987

Silchenko, S., *Sippel, M.L., Kuchment, O., Benson, D.R., Mauk, A.G., Altuve, A., and Rivera, M. DOI PDF
Hemin is kinetically trapped in cytochrome b₅ from rat outer mitochondrial membrane
Biochemical and Biophysical Research Communications, 2000, 273, 467–472
* M.L. Gill is formerly M.L. Sippel

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

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

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

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

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

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

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

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

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

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

Scientific Discovery: From the Lab Bench to the GPU SLIDES PROGRAM
Andy Byrd Retirement Symposium, Invited Keynote, April 19, 2024, Institute for Bioscience & Biotechnology Research, University of Maryland, NIST, Bethesda, MD

Scientific Discovery: From the Lab Bench to the GPU SLIDES ABSTRACT VIDEO
PyData NYC, Invited Keynote, November 2, 2023, New York, NY

NVIDIA BioNeMo: A framework and service for development and use of generative AI in drug discovery SLIDES PROGRAM
6th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Conference, Invited Keynote, September 5, 2023, Cambridge, UK

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

Accelerating Drug Discovery with Clara Discovery and MegaMolBART SLIDES
4th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry, Invited Talk, September 27, 2021, Virtual

Real Time, GPU-Accelerated Analysis and Visualization in the Life Sciences PROGRAM ABSTRACT SLIDES
Michelle Gill and Avantika Lal
Ken Kennedy Institute Data Science Conference, Invited Keynote, October 26-27, 2020, Virtual

Artificial intelligence driven drug discovery SLIDES
NYC R Conference, Invited Presentation, May 10, 2019, New York, NY

Panel: Careers in data science PROGRAM
Tri-Institutional Career Symposium, April 9, 2019, New York, NY
Memorial Sloan Kettering Cancer Center, The Rockefeller University and Weill Cornell Medicine

Machine learning for target identification and lead optimization in drug discovery ABSTRACT
Alix Lacoste and Michelle Gill
New York Area Group for Informatics and Modeling, Invited Presentation, February 26, 2019, New York, NY

Accelerating the journey from data to medicine ABSTRACT
Amir Saffari, Dan Neil, Alix Lacoste, and Michelle Gill
NeurIPS, Expo Talk, 2018, Montreal, Canada

Artificial intelligence as a catalyst for scientific discovery ABSTRACT SLIDES VIDEO
JupyterCon, Invited Keynote, 2018, New York, NY

From structural biology to AI: a holistic approach to studying molecular machines SLIDES
Brookhaven National Laboratory, Invited Presentation, 2018, Upton, NY

Efficient image search and identification: the making of Wine-O.AI SLIDES VIDEO CODE
SciPy Conference, Selected Presentation, 2017, Austin, TX

Service

Judge, Preliminary and Final Rounds
NYC STEM Fair, Feb 18 - March 27, 2022

Evaluated preliminary and final round submissions in the biochemistry track.

Program Chair
PyData NYC, 2018

Responsible for conference content including: recruitment of proposal reviewers, solicitation and aggregation of proposal feedback, selection of presentations, notification of selected speakers, and scheduling of talks.

Machine Learning Symposium Co-Chair
SciPy Conference, 2018

Co-chair of machine learning / deep learning symposium at SciPy 2018. Responsible for recruiting reviewers, reviewing proposals, selecting talks, and running symposium.

Proposal Reviewer
JupyterCon, 2018
Responsible for reviewing and scoring proposal submissions.

Reviewer
Journal of Open Source Software (JOSS), 2018–2019
Review submissions to JOSS within my expertise area as needed.

Awards

NIH Postdoctoral Research Fellowship (F32 GM089047), 2009–2012
Global BCG Strategy Olympics, Winning Team, 2007
NSF Graduate Research Fellowship, 2002–2006
Barry M. Goldwater Scholar, 2000–2001
Outstanding Undergraduate Honors Research Thesis, 2001
Kansas Board of Regents Full Tuition Merit Scholarship, 1997–2001

NIH Postdoctoral Research Fellowship (F32 GM089047), 2009–2012
NSF Graduate Research Fellowship, 2002–2006
Barry M. Goldwater Scholar, 2000–2001
Kansas Board of Regents Full Tuition Merit Scholarship, 1997–2001

Press

Deep Learning at NVIDIA (with Michelle Gill) AUDIO
DataFramed, DataCamp, Podcast Interview, 2018
Interview about my deep learning work at NVIDIA

How to Keep Your Job Regardless of AI ARTICLE
International Business Times, 2017
Profile of my transition from biophysicist to data scientist