Dr Gabriel Reder
Gabe Reder is dedicated to harnessing informatics, robotic automation, and artificial intelligence towards transforming biological laboratory research.
With experience spanning metabolomics, systems biology, laboratory robotics, data science, and computational reasoning, he is especially interested in the interface between symbolic knowledge representation, generative AI, and physical automation for self-driving biological experimentation and discovery.
In the past, Gabe has worked on computational mass spectrometry metabolomics, developing machine learning and AI techniques for automating complex sample analysis. He has also worked on physical mass spectrometry automation, interfacing AI agents with ion mobility instrumentation for a robotic metabolomics laboratory. He currently works on AI-symbolic systems for designing experiments, generating hypothesis, interpreting results, and linking to scientific knowledge.
Gabe holds a PhD in Bioengineering from Stanford University where he researched computational methods for identifying gut microbiome metabolites from mass spectrometry data. He additionally holds a MS in Applied Mathematics from Columbia University and an AB in Computer Science from Princeton University.
Select publications
- I. A. Tiukova et al., “Genesis: Towards the Automation of Systems Biology Research,” Sep. 04, 2024, arXiv: arXiv:2408.10689. doi: 10.48550/arXiv.2408.10689.
- W. Lei, C. Fuster-Barceló, G. Reder, A. Muñoz-Barrutia, and W. Ouyang, “BioImage.IO Chatbot: a community-driven AI assistant for integrative computational bioimaging,” Nat Methods, vol. 21, no. 8, pp. 1368–1370, Aug. 2024, doi: 10.1038/s41592-024-02370-y.
- A. H. Gower et al., “The Use of AI-Robotic Systems for Scientific Discovery,” Jun. 25, 2024, arXiv: arXiv:2406.17835. doi: 10.48550/arXiv.2406.17835.
- G. K. Reder et al., “AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting,” J. Am. Soc. Mass Spectrom., vol. 35, no. 3, pp. 542–550, Mar. 2024, doi: 10.1021/jasms.3c00396.
- D. Brunnsåker et al., “High-throughput metabolomics for the design and validation of a diauxic shift model,” npj Syst Biol Appl, vol. 9, no. 1, Art. no. 1, Apr. 2023, doi: 10.1038/s41540-023-00274-9.
- G. K. Reder et al., “Genesis-DB: a database for autonomous laboratory systems,” Bioinformatics Advances, vol. 3, no. 1, p. vbad102, Jan. 2023, doi: 10.1093/bioadv/vbad102.
- G. K. Reder et al., “Supervised topic modeling for predicting molecular substructure from mass spectrometry,” F1000Res, vol. 10, p. 403, May 2021, doi: 10.12688/f1000research.52549.1.