Speaker Series Schedule
Every other Tuesday 4-5pm EST unless otherwise noted
2024 Schedule:
February- March 2024
February 6th — Nabil Ibtehaz, PhD student (Purdue)
Domain-PFP allows protein function prediction using function-aware domain embedding representations
February 20th — Tianhao Yu, PhD student (UIUC)
Enzyme function prediction using contrastive learning
March 5th — GoCurator group (Fudan University)
CAFA5 Protein Function Prediction via Kaggle
*** note special time for talk at 7PM, EST
March 19th — Alexander Kroll, PhD student (Heinrich Heine University)
A general model to predict small molecule substrates of enzymes based on machine and deep learning
2023 Archived Schedule:
January - March 2023
Jan 17th — Emily Makowski, PhD— University of Michigan
Multi-objective engineering of therapeutic antibodies
Jan 31st — Gina El Nesr — PhD Student, Stanford University
Singular value decomposition of protein sequences as a method to visualize sequence and residue space
Feb 14th — Joe Watson, PhD & David Juergens — Institute for Protein Design, University of Washington
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Feb 28th— Samantha Petti, PhD — Postdoctoral Fellow, Harvard
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
March 14th— Zhuoran Qiao, PhD — Lead ML Scientist, Entos Inc.
Dynamic-backbone protein-ligand complex structure prediction with multiscale generative diffusion models
March 28th— Simon Dürr — PhD Candidate , EPFL
Deploying protein machine learning models on the web
April - June 2023
April 11— Pascal Notin — PhD Candidate , University of Oxford
Hybrid protein language models for fitness prediction
April 25— Jacob Rapp — PhD Candidate , UW-Madison
A Self-Driving Laboratory System for Protein Engineering
May 9— Simon Kozlov, Stephen Rettie — U of Washington and Harvard
Cyclic peptide structure prediction and design using AlphaFold
May 23— Hannah Wayment-Steele, PhD — Brandeis University
Predicting (and discovering) proteins with multiple conformational states
June 6— David Ding, PhD — Harvard and UC Berkeley
Site-wise mutation effects enable combinatorial protein variant design
June 20 — Sam Gelman — PhD candidate, UW-Madison
Open-source use of METL models for proteins
September - November 2023
September 19 — Kevin Yang, PhD — Senior Researcher, Microsoft
Protein generation with evolutionary diffusion: sequence is all you need
September 26 — Nathan Frey, PhD — ML Scientist, Prescient Design
Protein Discovery with Discrete Walk-Jump Sampling
October 3 — Kotaro Tsuboyama, PhD — Institute of Industrial Science (IIS), UTokyo
Mega-scale experimental analysis of protein folding stability in biology and design
*** note special time for talk at 7PM, EST
October 10 — Karolis Martinkus, PhD — Machine Learning Scientist, Prescient Design, Genentech, Roche
AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies
October 17 — Stephanie A. Wankowicz, PhD — Scientist, UCSF
Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
October 31 — Craig J. Markin, PhD — Research Scientist, Stanford
Decoupling of catalysis and transition state analog binding from mutations throughout a phosphatase revealed by high-throughput enzymology
November 14th — Samuel Stanton, PhD (Prescient Design) + Nate Gruver (PhD student, NYU)
Protein Design with Guided Discrete Diffusion
November 28th — Zaixiang Zheng, PhD — ByteDance Research
Structure-informed Language Models Are Protein Designers
2022 Archived Schedule:
February - March 2022
Feb 1st — Eli Weinstein — PhD Candidate, Harvard Biophysics
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models
Feb 15th — Bruce Wittman — PhD Candidate, Caltech Bioengineering
Machine Learning-Assisted Protein Engineering with ftMLDE and evSeq
+ introduction by Prof. Frances Arnold
March 1st — Chloe Hsu — PhD Student, UC Berkeley
Learning Protein Fitness Models from Evolutionary and Experimental Data
March 15th — Erika Alden DeBenedictis — Postdoc, University of Washington
Systematic molecular evolution using PRANCE
March 22nd — Moderated discussion panel with all speakers
April - May 2022
April 5th — Doug Tischer — Postdoc, University of Washington
David Juergens — PhD Candidate, University of Washington
Scaffolding protein functional sites using deep learning
April 19th — Wengong Jin — Postdoc, Broad Institute
Iterative Refinement Graph Neural Network for Antibody Docking and Design
May 3rd — Noelia Ferruz — Postdoc, University of Bayreuth
A Deep Unsupervised Language Model for Protein Design
May 17th — Moderated discussion panel with all speakers
June - July 2022
June 7th — Brian Hie — Stanford Science Fellow, Stanford & Visiting Researcher, Meta AI
Efficient evolution of human antibodies from general protein language models and sequence information alone
June 21st — Danny Diaz — PhD Candidate, UT Austin
Engineering Proteins with 3D Convolutional Neural Networks
July 5th — Gabriel Foley — Postdoctoral Fellow, University of Queensland
Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)
July 19th — Moderated discussion panel with all speakers
Aug - Sep 2022
Aug 2nd — Namrata Anand — PhD Graduate, Stanford
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
Sep 6th — Brian Trippe, Columbia University & University of Washington+ Jason Yim, MIT
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Sep 20th — Justas Dauparas, University of Washington
Robust deep learning based protein sequence design using ProteinMPNN
Sep 27th — Moderated discussion panel with all speakers
Oct - Nov 2022
Oct 4th — Jonathan Greenhalgh, PhD— Data Scientist, A-Alpha Bio
Machine-learning guided engineering of fatty acyl-ACP reductases
Oct 18th — Clara Wong-Fannjiang — PhD Candidate, UC Berkeley
Conformal prediction for the design problem
Nov 1st — Daniel Berenberg — PhD Candidate, New York University
Multi-segment preserving sampling for deep manifold sampler
Presentation Recording
Nov 15th — Moderated discussion panel with all speakers