Speaker Series Schedule
Every other Tuesday 4-5pm EST unless otherwise noted
2025 Schedule:
January-February 2025:
January 7th — Seyone Chithrananda (UC Berkeley)
Mapping the combinatorial coding between olfactory receptors and perception with deep learning
January 21st — Elana Simon (Stanford University)
InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders
February 4th — Aya Abdelsalam, PhD (Guide Labs) & Nathan Frey, PhD (Prescient Design)
CONCEPT BOTTLENECK LANGUAGE MODELS FOR PROTEIN DESIGN
February 18th — Bowen Jing, PhD student (MIT) & Hannes Stark, PhD student (MIT)
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
March-April 2025:
March 4th — Karsten Kreis, PhD (NVIDIA Research)
Proteina: Scaling Flow-based Protein Structure Generative Models
March 18th — Alan Amin, PhD (Courant Institute, NYU)
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
April 1st — Anthony Marchand, PhD (Novate Technologies)
Targeting protein–ligand neosurfaces with a generalizable deep learning tool
April 15th — Garyk Brixi, PhD student (Stanford)
Genome modeling and design across all domains of life with Evo 2
April 22nd — Zhuoqi Zheng, PhD student (Shanghai Jiao Tong University)
MotifBench: A standardized protein design benchmark for motif-scaffolding problems
April 30th — Gina El Nesr, PhD student (Stanford) and Hannah K. Wayment-Steele, PhD (UW-Madison)
Learning millisecond protein dynamics from what is missing in NMR spectra
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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
April 2nd— Zhangzhi Peng, PhD student (Duke)
PTM-Mamba: A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks
April 16th — Eric Nguyen, PhD student and Brian Hie, PhD (Stanford)
Sequence modeling and design from molecular to genome scale with Evo
May 7th— Jeff Ruffolo, PhD and Stephen Nayfach, PhD
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
May 28th — Zaixiang Zheng, PhD (ByteDance Research)
Diffusion Language Models Are Versatile Protein Learners
June 18th— Kieran Didi, MSc, Simon Mathis, PhD student and Francisco Vargas, PhD student (Cambridge)
July 2nd— Yeqing Lin, PhD student and Minji Lee, PhD student (Columbia)
July 9th — Neil Thomas + David Belanger
July 16th — Francesca-Zhoufan Li, PhD student (CalTech)
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
(No recording publicly available)
July 23rd — Roshan Rao, PhD (EvolutionaryScale)
ESM3: Simulating 500 million years of evolution with a language model
July 30th — Jason Yang, PhD student (CalTech)
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
August 6th— Alex Tong, PhD (Mila) and Guillaume Huguet, PhD (Université de Montréal)
Sequence-Augmented SE (3)-Flow Matching For Conditional Protein Backbone Generation
September 3rd — Kaiyi Jiang, PhD Candidate (MIT)
Rapid protein evolution by few-shot learning with a protein language model
September 17th — Jeff Ruffolo, PhD (Profluent Bio)
Adapting protein language models for structure-conditioned design
October 1st — Amy Lu, PhD student (UC Berkeley)
Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure
October 15th — Kapil Devkota, PhD (Duke)
Template-based protein editing using Raygun
October 29th — Andre Cornman, PhD (Tatta Bio)
The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling
November 12th — Arda Goreci (Ligo Biosciences)
Lessons from implementing AlphaFold3 in the wild
November 19th — Will Hua (McGill University)
AI-Assisted De Novo Enzyme Design
November 26th — Jin Su (Westlake University)
ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning
December 12th — Jacob Gershon, Sidney Lisanza & Sam Tipps (University of Washington)
Multistate and functional protein design using RoseTTAFold sequence space diffusion
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Jan 17th — Emily Makowski, PhD— University of Michigan
Multi-objective engineering of therapeutic antibodiesJan 31st — Gina El Nesr — PhD Student, Stanford University
Singular value decomposition of protein sequences as a method to visualize sequence and residue spaceFeb 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 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 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
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***talks and speakers grouped according to themes, to later serve on a panel
**please note: Panel Discussion were not usually recorded and uploaded to our channel unless otherwise stated
Feb 1st — Eli Weinstein — PhD Candidate, Harvard Biophysics
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence ModelsFeb 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 DataMarch 15th — Erika Alden DeBenedictis — Postdoc, University of Washington
Systematic molecular evolution using PRANCE
March 22nd — Moderated discussion panel with all speakersApril 5th — Doug Tischer — Postdoc, University of Washington
David Juergens — PhD Candidate, University of Washington
Scaffolding protein functional sites using deep learningApril 19th — Wengong Jin — Postdoc, Broad Institute
Iterative Refinement Graph Neural Network for Antibody Docking and DesignMay 3rd — Noelia Ferruz — Postdoc, University of Bayreuth
A Deep Unsupervised Language Model for Protein DesignMay 17th — Moderated discussion panel with all speakers
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 aloneJune 21st — Danny Diaz — PhD Candidate, UT Austin
Engineering Proteins with 3D Convolutional Neural NetworksJuly 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 2nd — Namrata Anand — PhD Graduate, Stanford
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic ModelsSep 6th — Brian Trippe, Columbia University & University of Washington+ Jason Yim, MIT
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemSep 20th — Justas Dauparas, University of Washington
Robust deep learning based protein sequence design using ProteinMPNNSep 27th — Moderated discussion panel with all speakers
Oct 4th — Jonathan Greenhalgh, PhD— Data Scientist, A-Alpha Bio
Machine-learning guided engineering of fatty acyl-ACP reductasesOct 18th — Clara Wong-Fannjiang — PhD Candidate, UC Berkeley
Conformal prediction for the design problemNov 1st — Daniel Berenberg — PhD Candidate, New York University
Multi-segment preserving sampling for deep manifold sampler
Nov 15th — Moderated discussion panel with all speakers