2 citations · 2 across the 11 of their papers we have counts for
13 papers
Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation
Dae Woong Ham, Dae Woong, Ham +3
Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgme…
Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost
Mojtaba Abdolmaleki, Stefanus Jasin, Boyu Wang
Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost. A natural deployment policy…
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
Fenghua Yang, Preet Baxi, Yi Zhang +4
Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, opti…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
Preet Baxi, Jiannan Xu, Jane Yi Jiang +1
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems. We…
A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
Dae Woong Ham, Stefanus Jasin, Xuejun Zhao
Sequential hypothesis tests are widely adopted as a principled way to perform multiple tests on data that arrives over time. In particular, researchers frequently utilize group seq…
Sequential Sampling for Binary Classification: Two LLMs are (Almost) All You Need
Guokai Li, Alys Liang, Mo Liu +4
We study how to orchestrate multiple large language models (LLMs) for reliable binary classification when models differ in accuracy, monetary cost, and response time. A decision-ma…