Doors open, attendees can put up posters, and workshop organizers help set up room.

Opening remarks from the workshop organizers.

Invited talk from Jeff Schneider.

Invited talk from Ava Soleimany.

Coffee break.

(Update) Poster Setup/Discussion and Extended Coffee Break

Spotlight talks from a subset of accepted papers.

Lunch break.

Invited talk from Ruth Misener.

Title: Autonomous research machines: Self-optimizing new chemistry

Abstract: Our research seeks to boost R&D efficiency in the chemicals industry. As an example, consider "micro reactor flow systems", which are transforming chemical manufacturing by enabling flexible prototyping. Because these high-throughput microfluidic devices can control reaction conditions online, they are ideal for quantitatively characterizing diverse chemical synthesis techniques along new reaction pathways. The challenge is: How do we automate the design of experiments to "self-optimise" new chemistry?

Together with the BASF Data Science for Materials & Chemistry teams, we’re interested to solve Bayesian optimization challenges which may simultaneously exhibit: multiple objectives, mixed-feature spaces, asynchronous decisions, large batch sizes, input constraints, multi-fidelity observations, hierarchical choices, and costs associated with switching between experimental points. We review the machine learning contributions that we’ve found useful towards achieving these goals and discuss our own methodological and software contributions.

This work is a collaboration between Imperial (Jose Pablo Folch, Alexander Thebelt, Shiqiang Zhang, Jan Kronqvist, Calvin Tsay, Ruth Misener) and BASF (Robert Lee, Behrang Shafei, Nathan Sudermann-Merx, David Walz).

Coffee Break

Invited talk from Peter Frazier.

Title: Bayesian Optimization in Biochemistry

Abstract: Excitement is growing in the chemical sciences about Bayesian optimization and other methods for AI-based adaptive experimentation for accelerating the design of drugs, materials, and chemical formulations. Query-efficient methods like BayesOpt seem well-suited to these problems because measuring chemical and biological properties is often time-consuming and/or expensive, because they can incorporate scientific expertise into Bayesian prior distributions, and because the automation they provide allows getting the full benefits of robotics and high-throughput experimentation.

At the same time, barriers remain: designing a drug, material or consumer product is a multi-stage process involving many goals and evaluation methods, not a single optimization problem; poorly performing recommended molecules can destroy trust; training data often lacks negative examples; humans are hard to beat; experimentalists can have unrealistic expectations; and disciplinary differences in approach, expertise, and language can hinder collaboration.

The talk will focus on the speaker's experience from 3 biochemical design problems in which experimentalists performed wet lab experiments based on algorithmic recommendations from a BayesOpt or other AI algorithm. Drawing on these experiences, we suggest approaches for overcoming these challenges, including grey-box BayesOpt methods, methods for BayesOpt with preference learning, and human-focused approaches supporting effective collaboration with chemists.

Invited talk from Christopher Langmead.

Title: Active Learning for the Design of Therapeutic Proteins

Abstract: I will discuss ongoing work at Amgen to use Active Learning to train predictive models relevant to the design of therapeutic proteins.

Invited talk from Caroline Uhler.

Title: Optimal Design of Interventions for Causal Discovery in Genomics

Best student paper award announcement.

Closing remarks and poster session kickoff.

In-person poster session.