About

I am a Postdoctoral Fellow in the lab of Prof Rhiju Das at Stanford University. In my research, I am integrating high-throughput experimentation with machine learning to investigate and design molecular interactions mediated by RNA. Prior to my postdoc, I completed my PhD with Prof Charlotte Deane at the University of Oxford, where I developed machine learning models to predict and optimize sequence- and structure-based properties of antibodies. The ultimate aim of my research is to design better therapeutics at reduced timescales and costs.



Education & Experience

Stanford University logo

2024-present

Postdoctoral Scholar

Stanford School of Medicine Dean's Fellow (2025)

Stanford University

High-throughput experiments and machine learning for RNA design


University of Oxford logo

2020-2024

DPhil in Statistics

University of Oxford

Machine learning for antibody design


Harvard Medical School logo

2023

Visiting PhD Student

Harvard Medical School

Machine learning for antibody design


University of Cambridge and LMB logo

2019-2020

MPhil in Molecular Biology

MRC Laboratory of Molecular Biology, University of Cambridge, UK

Integrative computational analysis of GPCR-RAMP interactions


University of Oxford logo

2015-2019

MBiochem in Biochemistry

University of Oxford, UK

Awarded first class degree

Research & Publications

Primary research interests:

  • Structure-based molecular design
  • Inter-molecular interactions
  • Machine learning
Antibody sequence to bits to structure

Current and past research projects:



  1. Høie, M.H.*, Hummer, A.M.*, Olsen, T.H, Aguilar-Sanjuan, B., Nielsen, M. and Deane, C.M. AntiFold: Improved structure-based antibody design using inverse folding. Bioinformatics Advances (2025, In Press).

  2. Condado-Morales, I., Dingfelder, F., Waibel, I., Turnbull, O.M., Patel, B., Cao, Z., Bjelke, J.R., Grell, S.N., Bennet, A., Hummer, A.M., Raybould, M.I.J., Deane, C.M., Egebjerg, T., Lorenzen, N. and Arosio, P. A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs. mAbs 16(1):2403156 (2024).

  3. Gordon, G. L., Greenshields-Watson, A., Agarwal, P., Wong, A., Boyles, F., Hummer, A.M., Lujan Hernandez, A.G. and Deane, C.M. PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature. Nucleic Acids Research 53(D1):D535–D542 (2024).

  4. Glaser, P., Paul, S., Hummer, A.M., Deane, C.M., Marks, D.S. and Amin, A.N. Kernel-Based Evaluation of Conditional Biological Sequence Models. International Conference on Machine Learning (2024).

  5. Chinery, L.*, Hummer, A.M.*, Mehta, B.B.*, Akbar, R., Rawat, P., Slabodkin, A., Le Quy, K., Lund-Johansen, F., Greiff, V., Jeliazkov, J.R. and Deane, C.M. Baselining the Buzz. Trastuzumab-HER2 Affinity, and Beyond. bioRxiv (2024).

  6. Hummer, A.M. and Deane, C.M. Designing stable humanized antibodies. Nature Biomedical Engineering News & Views 8:3-4 (2024).

  7. Hummer, A.M.†, Schneider, C., Chinery, L. and Deane, C.M.† Investigating the volume and diversity of data needed for generalizable antibody-antigen ∆∆G prediction. bioRxiv (2023).

  8. Hummer, A.M.*, Abanades B.* and Deane, C.M. Advances in computational structure-based antibody design. Current Opinions in Structural Biology 74:102379 (2022).

  9. Marks, C., Hummer, A.M., Chin, M. and Deane, C.M. Humanization of antibodies using a machine learning approach on large-scale repertoire data. Bioinformatics 37(22):4041–4047 (2021).

  10. Bolla, J.R., Corey, R.A., Sahin, C., ..., Hummer, A.M., ..., Stansfeld, P.J., Robinson, C.V., Landreh, M. A Mass-Spectrometry-Based Approach to Distinguish Annular and Specific Lipid Binding to Membrane Proteins. Angewandte Chemie 59(9):3523-3528 (2020).

  11. Li, D., Li. N., Zhang, Y.F., ..., Hummer, A.M., ..., Ho, M. Persistent Polyfunctional Chimeric Antigen Receptor T Cells That Target Glypican 3 Eliminate Orthotopic Hepatocellular Carcinomas in Mice. Gastroenterology 158(8):2250-2265 (2020).

  12. * Equal contribution; † Co-corresponding author


Biotech Entrepreneurship

Nucleate logo

Nucleate Oxford, Co-Managing Director
(2022-2024)

  • Co-founded and co-led the Oxford chapter of Nucleate, an international student-run organization that facilitates the formation of pioneering life sciences companies
  • Grew Oxford leadership team from 2 to >20 in 1 year
  • Key focus on supporting students/postdocs to spin out their research into companies – Nucleate Activator
  • Additional focuses included planning events to bolster the Oxford biotech entrepreneurship community, as well as research to better understand the ecosystem


OUBT logo

Oxford University Biotech Society, President (2021-2022)

  • Led committee of 9 students in organising events promoting diversity and entrepreneurship in biotech
  • Organised events including "Careers in Biotech", "Women in Biotech" and "Diversity in Biotech" with speakers from various backgrounds
  • Ran 8-week biotech ideation programme (Catalyse Oxford) with team formation, mentorship and pitching events - led to the formation of 3+ startups between the 2021 and 2022 cohorts
  • Raised >£8,000 in sponsorship as well as 3-month lab space prize