Instructors

As a postdoctoral fellow at the Institute for Protein Innovation, he delved into the realm of de novo protein design using Rosetta-based protein design tools like SEWING and Rosetta Layer design. He then joined AI Proteins as one of the founding members and has focused on miniprotein de novo design, incorporating sophisticated deep learning methods such as TR-Rosetta/AF2 hallucination, RFDiffusion and ProteinMPNN into his work.

Helen is a graduate student in the labs of David Baker and Neil King in the Institute for Protein Design (IPD) at the University of Washington. She is broadly interested in using deep-learning to design de novo self-assembling protein-based nanomaterials for applications such as electron microscopy, vaccine design, and cellular delivery of biomolecules. Helen's background before UW is in gene and cell therapies, but since joining the IPD has been working on designing with and developing RoseTTAFold Diffusion.

Sergey is a John Harvard Distinguished Science Fellow at Harvard University. His lab is interested in developing a unified statistical model of protein evolution to better understand phylogenetics, protein folding, origins of life, and to mine metagenomic “dark matter” sequences to discover new protein families, functions, and protein-protein interactions. He co-developed ColabFold for using AlphaFold2 on the cloud, as well as ColabDesign and AF2Rank. 

Felipe is a passionate doctoral researcher and entrepreneur with a focus on shaping a sustainable future. With a background in Bioinformatics, Biophysics, and AI, he is working on the intersection of Geometric Deep Learning, Bayesian Statistics, Machine Learning Directed Evolution, and Large Language Models. Currently pursuing his PhD at Leipzig University, his research involves the use of AlphaFold2, ProteinMPNN, ESM2, Autonomous Agents, and RFDiffusion to advance the field of protein design.

Susana, a 4th year PhD student at the Baker lab, focuses on designing protein binders to bioactive peptides. With a background in biochemistry, biophysics, and protein design, she is powering artificial proteins to generate diagnostic tools to crucial peptide hormones associated with diabetes and Alzheimer's. Simultaneously, she employs deep learning tools to devise antagonists against snake venom toxins, aiming to yield affordable and broadly effective therapeutics for neglected tropical diseases.

Michael is an NSF GRFP Fellow and PhD student in Chemical & Biomolecular Engineering at Johns Hopkins University, in the Gray Lab group. Michael is conducting research at the interface of antibody engineering and deep learning, and engaged in developing computational models to design and enhance therapeutic antibody candidates. His ongoing research projects encompass the development of a diffusion-based generative model to facilitate the design of antigen-specific therapeutic antibodies.