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…
Multi-LLM Query Optimization
Arlen Dean, Zijin Zhang, Stefanus Jasin +1
Deploying multiple large language models (LLMs) in parallel to classify an unknown ground-truth label is a common practice, yet the problem of optimally allocating queries across h…
Minimizing Type 2 Errors in an Experiment-Rich Regime via Optimal Resource Allocation
Fenghua Yang, Dae Woong Ham, Stefanus Jasin
Randomized experiments (often known as "A/B tests") are widely used to evaluate product and service innovations. We study how to allocate limited experimentation resources across M…
Decentralized Multi-product Pricing: Diagonal Dominance, Nash Equilibrium, and Price of Anarchy
Boxiao Chen, Jiashuo Jiang, Stefanus Jasin
Decentralized decision making in multi--product firms can lead to efficiency losses when autonomous decision makers fail to internalize cross--product demand interactions. This pap…
From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems
Guokai Li, Pin Gao, Stefanus Jasin +1
Assortment optimization seeks to select a subset of substitutable products, subject to constraints, to maximize expected revenue. The problem is NP-hard due to its combinatorial an…
Phase Transitions in Learning and Earning under Price Protection Guarantee
Qing Feng, Ruihao Zhu, Stefanus Jasin
Motivated by the prevalence of ``price protection guarantee", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called p…
Adaptive Risk Mitigation in Demand Learning
Parshan Pakiman, Boxiao Chen, Selvaprabu Nadarajah +1
We study dynamic pricing of a product with an unknown demand distribution over a finite horizon. Departing from the standard no-regret learning environment in which prices can be a…
Algorithmic Transparency with Strategic Users
Qiaochu Wang, Yan Huang, Stefanus Jasin +1
Should firms that apply machine learning algorithms in their decision-making make their algorithms transparent to the users they affect? Despite growing calls for algorithmic trans…