22 citations · 75 across the 13 of their papers we have counts for
15 papers
On the Selectivity of Generative Models in Structure-Based Drug Design
Ella Miray Rajaonson, Jungyoon Lee, William Chau +5
Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that cont…
DIA: Denoising Intermediate Advantage for Diffusion Policy Optimization
Arjun Sohal, Yuchi Zhao, Miroslav Bogdanovic +1
Diffusion-based robot policies have become widely used in robotic manipulation, where they are typically trained with behavior cloning. However, policies trained purely from demons…
Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal +5
Action chunking has become a standard design in modern robot policies, from diffusion/flow policies to vision-language-action models, where the policy predicts a sequence of action…
Discrete Feynman-Kac Correctors
Mohsin Hasan, Viktor Ohanesian, Artem Gazizov +5
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences. Sample generation via gradual denoising…
Informing Acquisition Functions via Foundation Models for Molecular Discovery
Qi Chen, Fabio Ramos, Alán Aspuru-Guzik +1
Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidat…
RoboCulture: A Robotics Platform for Automated Biological Experimentation
Kevin Angers, Kourosh Darvish, Naruki Yoshikawa +6
Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems.…
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian +6
While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner…
Bayesian Optimization for General Reaction Conditions
Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser +6
General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and hi…
AnyPlace: Learning Generalized Object Placement for Robot Manipulation
Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo +5
Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. To address this, we propose AnyPlace, a two-stag…
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
Austin Cheng, Cher Tian Ser, Marta Skreta +8
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it i…
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
Yuanqi Du, Michael Plainer, Rob Brekelmans +5
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large sp…
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang, Marta Skreta, Cher-Tian Ser +11
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutiona…
RePLan: Robotic Replanning with Perception and Language Models
Marta Skreta, Zihan Zhou, Jia Lin Yuan +3
Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have al…
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta +3
Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior d…
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization
Kourosh Darvish, Marta Skreta, Yuchi Zhao +9
Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure fa…