Trung Dang

Hi! I am currently a fourth-year Ph.D. student at The University of Texas at Austin, advised by Shuchi Chawla. Before joining UT Austin, I completed my BS at Purdue University, working with Alex Psomas and Paul Valiant.

I am interested in designing algorithms under data uncertainty that still achieves provable guarantees against the best one can do given all the information. Some variants of this theme include algorithms under noisy data, algorithms with few samples, algorithms against adversarial data corruption, and algorithms with prediction.

I am also particularly interested in the application of algorithms in economics applications (such as mechanism design and pricing algorithms), and generally interested in approximation algorithms, online algorithms, machine learning theory, and theoretical computer science at large.

My full name in Vietnamese is Đặng Đoàn Đức Trung.

news

Apr 03, 2026	I will join Google Market Algorithms Team as a Student Researcher for Summer 2026, hosted by Mingfei Zhao and Gagan Aggarwal!
Dec 18, 2025	Our manuscript of Reward Selection with Noisy Observations was accepted to ALT’26!
Sep 15, 2025	Our manuscripts of Multi-Unit Combinatorial Prophet Inequalities and Commitment Gap via Correlation Gap were accepted to WINE’25!
Apr 22, 2025	Our manuscript of Robust Max Selection was accepted to ISIT’25!
Oct 04, 2024	Our manuscript of OCRS for Subadditive Revenue Maximization was accepted to SODA’25!

selected publications

Manuscript

Single-Sample Prophet Inequalities: A Combinatorial to Single-Item Reduction

Shuchi Chawla and Trung Dang

2026

Abs arXiv

We reduced single-sample combinatorial prophet inequality to single-sample single-item prophet inequality using support prices and free-disposal algorithms. This approach leads to a modular analysis of the problem, and leading to better competitive ratio than the state of the art.
SODA

A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization

Shuchi Chawla, Dimitris Christou, Trung Dang, Zhiyi Huang, Gregory Kehne, and Rojin Rezvan

In Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms, 2025

Abs arXiv

We derive a novel multi-dimensional online contention resolution scheme with the objective of maximizing revenue. In particular, we obtain polylog upper and lower bounds to the ex-ante buy-many objective, achieved via an sequential dynamic item pricing mechanism.
WINE

Commitment Gap via Correlation Gap

Shuchi Chawla, Dimitris Christou, and Trung Dang

In International Conference on Web and Internet Economics, 2025

Abs arXiv

We study the general model of Costly Information Combinatorial Selection (CICS). In this problem, a decision maker needs to select a feasible subset of stochastic variables, and can only learn information about their values through a series of costly steps, modeled by a Markov decision process. The algorithmic objective is to maximize the total value of the selection *minus* the cost of information acquisition. In this work, we obtain improved bounds on the commitment gap of CICS through a reduction to a simpler problem of Bayesian Combinatorial Selection where information is free. By establishing a close relationship between these problems, we are able to relate the commitment gap of CICS to ex ante free-order prophet inequalities. As a consequence, we obtain improved approximation results for CICS, including the well-studied variant of Pandora’s Box with Optional Inspection under matroid feasibility constraints.