Ji Won Park

Ji Won Park

High-dimensional inference and sampling.

Talks, tutorials, and panels

Multivariate tails for active molecular design

April 15, 2025

Invited talk, Molecule Maker Lab Institute Symposium 2025, Urbana, IL

LLMs are optimized for average-case behavior, whereas drug design requires us to consider rare, extreme combinations of molecular properties. I present two recent projects: a novel multi-objective acquisition function for Bayesian optimization and multi-target conformal calibration. Both projects use nonparametric vine copulas to model flexible tail dependence, which gives us the structure we need to explore where it matters most. [Event Page] [Slides]

Winning in the age of AI

October 16, 2024

Invited panel, 2024 AI Summit, South San Francisco, CA

A panel organized by Genentech CMG (Commercial, Medical, and Government Affairs) moderated by Amit Akhelikar (COO & Managing Partner, Lynx Analytics) on what it takes to gain competitive advantage as a pharma in the age of AI.

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

March 01, 2024

Invited talk, 2024 SIAM Conference on Uncertainty Quantification, Trieste, Italy

Many scientific and industrial applications require the joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-efficient framework for identifying Pareto-optimal solutions. At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives. In this paper, we show a natural connection between non-dominated solutions and the extreme quantile of the joint cumulative distribution function (CDF). Motivated by this link, we propose the Pareto-compliant CDF indicator and the associated acquisition function, BOtied. BOtied inherits desirable invariance properties of the CDF, and an efficient implementation with copulas allows it to scale to many objectives. Our experiments on a variety of synthetic and real-world problems demonstrate that BOtied outperforms state-of-the-art MOBO acquisition functions while being computationally efficient for many objectives. [Event Page] [Slides]

Plenary panel

January 20, 2024

Invited panel, 2024 APS CUWiP, Stanford, CA

Invited plenary panelist at the American Physical Society’s 2024 Conference for Undergraduate Women in Physics (CUWiP) at Stanford University. The goal of CUWiP is to encourage undergraduate women and underrepresented minorities to continue in physics. [Event Page]

A Tale of Many Tails: Multi-Objective Bayesian Optimization for Molecular Design

January 06, 2024

Invited talk, KASBP-SF Symposium 2024, South San Francisco, CA

Active design of therapeutic molecules requires the joint optimization of multiple, potentially competing properties. Multi-objective Bayesian optimization (MOBO) offers a sample-efficient framework for identifying Pareto-optimal drug candidates. MOBO proceeds in cycles, a single iteration of which involves (1) sampling molecules from a combinatorially vast design space, (2) inferring multiple properties of interest, and (3) selecting the most promising subset for wet-lab evaluation. In this talk, I highlight the importance of modeling the tails – extreme, low-probability events –- in biological applications and propose algorithms designed to accommodate complex tail behavior in each of these steps. Together, the algorithms enable modeling flexibility beyond that afforded by the common log-concave (e.g., Gaussian) assumption. [Event Page]