Peter Eckmann

Peter Eckmann

Fourth-year Undergraduate
UC San Diego
B.S. Computer Science

Welcome, I’m a fourth-year undergraduate student at University of California San Diego pursuing a B.S. in Computer Science. I am a member of Dr. Rose Yu’s Spatiotemporal Machine Learning Lab. My primary research interests are machine learning and its applications to small molecule drug discovery. My work has involved deep generative modeling, molecular dynamics, and few-shot learning.

I am also involved with the FAIR Data Informatics (FDI) Lab at UC San Diego following a high school internship, where I use machine learning tools to quantify research quality and reproducibility.

Education

  • Undergraduate, University of California San Diego (Sept. 2021 - June 2025)
    • B.S. Computer Science, 3.952 GPA
  • High school student, University City High School, San Diego (Sept. 2017 - June 2021)
    • 4.86 GPA

Honors and Awards

  • Finalist, Outstanding Undergraduate Researcher Award, North American colleges and universities, Computing Research Association (Dec. 2023)
  • NSF Research Experiences for Undergraduates Award (June - Sept. 2023)
  • Regents Scholarship, UC San Diego (2021 - 2025)
  • Salutatorian, University City High School (2021)
  • Finalist, National Merit Scholarship Corporation (2021)
  • Broadcom MASTERS Top National 300, Science Fair project (2017)

Conference Publications

  • Eckmann P, Wu D, Heinzelmann G, Gilson MK, Yu R (2024). MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning. AIDrugX and Machine Learning in Structural Biology workshops at the Conference on Neural Information Processing Systems (NeurIPS) (2024, December 15). [PDF]
  • Eckmann P, Sun K, Zhao B, Feng M, Gilson MK, Yu R (2022). LIMO: Latent Inceptionism for Targeted Molecule Generation. Proceedings of the 39th International Conference on Machine Learning (ICML), in Proceedings of Machine Learning Research 162:5777-5792 (2022, June 17-23). [PDF] [Code] [Website]

Journal Publications

  • Eckmann P, Anderson J, Yu R, Gilson MK (2024). Ligand-Based Compound Activity Prediction via Few-Shot Learning. Journal of Chemical Information and Modeling, 64 (14), 5492-5499. [PDF] [Code]
  • Ayoubi R, Ryan J, Biddle MS, Alshafie W, Fotouhi M, Bolivar SG, Moleon VR, Eckmann P, Worrall D, McDowell I, Southern K, Reintsch W, Durcan TM, Brown C, Bandrowski A, Virk H, Edwards AM, McPherson P, Laflamme C (2023). Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications. eLife, 12:RP91645. [PDF]
  • Eckmann P, Bandrowski A (2023). PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing. PLoS ONE 18(3): e0281659. [PDF] [Code]
  • Bandrowski A, Pairish M, Eckmann P, Grethe J, Martone ME (2023). The Antibody Registry: ten years of registering antibodies. Nucleic Acids Research, 51(D1), D358-D367. [PDF]
  • Schulz R, Barnett A, Bernard R, Brown NJ, Byrne JA, Eckmann P, Gazda MA, Kilicoglu H, Prager EM, Salholz-Hillel M, Ter Riet G, Vines T, Vorland CJ, Zhuang H, Bandrowski A, Weissgerber TL (2022). Is the future of peer review automated? BMC Research Notes, 15(1), 1-5. [PDF]
  • Menke J, Eckmann P, Ozyurt IB, Roelandse M, Anderson N, Grethe J, Bandrowski A (2022). Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality. Journal of Medical Internet Research, 24(6), e37324. [PDF]
  • Weissgerber T, Riedel N, Kilicoglu H, Labbé C, Eckmann P, Ter Riet G, Byrne J, Cabanac G, Capes-Davis A, Favier B, Saladi S, Grabitz P, Bannach-Brown A, Schulz R, McCann S, Bernard R, Bandrowski A (2021). Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? Nature Medicine 27:6-7, 2021. [PDF]

Preprints

  • Thumuluri V, Eckmann P, Gilson MK, Yu R (2024). Technical report: Improving the properties of molecules generated by LIMO. arXiv:2407.14968 [cs.LG]. [PDF]
  • Eckmann P, Wu D, Heinzelmann G, Gilson MK, Yu R (2024). MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling. arXiv:2402.10387 [q-bio.BM]. [PDF]

Invited Talks

  • Learning on Graphs and Geometry (LoGG) Reading Group, Valence Labs (Mar. 2024). MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling. [Link]

Patents

  • Yu R, Eckmann P, Sun K, Zhao B, Feng M, Gilson MK (2023). Computational architecture to generate representations of molecules having targeted properties. United States patent pending US20240005179A1. June 16, 2023. [Link]

Conference Abstracts

  • Eckmann P, Bandrowski A. PreprintMatch: a new tool to match manuscripts across multiple similarity metrics. International Neuroinformatics Coordinating Facility Assembly, held virtually (2021, April 19-29) [Poster presentation]. [Link]
  • Eckmann P, Riedel N, Kilicoglu H, Labbé C, Ter Riet G, Byrne J, Cabanac G, Capes-Davis A, Favier B, Saladi S, Grabitz P, Bannach-Brown A, Schulz R, McCann S, Bernard R, Weissgerber T, Bandrowski A. Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? Annual Meeting, Association for Interdisciplinary Meta-Research and Open Science (AIMOS), held virtually (2020, December 3-4) [Poster presentation]. [Link]
  • Eckmann P, Riedel N, Kilicoglu H, Labbé C, Ter Riet G, Byrne J, Cabanac G, Capes-Davis A, Favier B, Saladi S, Grabitz P, Bannach-Brown A, Schulz R, McCann S, Bernard R, Weissgerber T, Bandrowski A. Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? Research Reproducibility 2020, held virtually (2020, December 2-3) [Oral presentation]. [Link]

Review Activity

Reviewer, International Conference on Learning Representations 2025