Scalable onion routing with torsk. - Jon McLachlan; Andrew Tran; Nicholas Hopper; Yongdae Kim (CCS, 2009)

Hashing it out in public: common failure modes of DHT-based anonymity schemes. - Andrew Tran; Nicholas Hopper; Yongdae Kim (WPES, 2009)

Word sense distribution in a web corpus. - Ping Chen 001; David Brown; Andrew Tran; Noble Ozoka; Rafael Ortiz (IEEE ICCI, 2010)

TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain. - Andrew Tran; Chris Bowes; David Brown; Ping Chen 001; Max Choly; Wei Ding 003 (SemEval@ACL, 2010)

MoBI-Box: A next generation Mobile Brain-Body Imaging Platform. - Ying Choon Wu; Jeffery Wang; Andrew Tran; Alex Schperberg; John Caldwell; Tzyy-Ping Jung; Po-Chih Kuo (CogSIMA, 2016)

Visual Analytic Process to Familiarize the Average Person with Ways to Apply Machine Learning. - Andrew Tran; Yamini Dasu; Anna Baynes (Visualization and Data Analysis, 2019)

Extend: A Framework for Increasing Energy Access by Interconnecting Solar Home Systems. - Santiago Correa; Noman Bashir; Andrew Tran; David E. Irwin 001; Jay Taneja (COMPASS, 2020)

arcos and arcospy: R and Python packages for accessing the DEA ARCOS database from 2006 - 2014. - Steven Rich; Andrew Tran; Aaron Williams 003; Jason Holt; Jeffery Sauer; Taylor Oshan (J. Open Source Softw., 2020)

Generating Diverse Code Explanations using the GPT-3 Large Language Model. - Stephen MacNeil; Andrew Tran; Dan Mogil; Seth Bernstein; Erin Ross; Ziheng Huang (ICER (2), 2022)

GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning. - Xiangru Tang; Andrew Tran; Jeffrey Tan; Mark Gerstein (ClinicalNLP@ACL, 2023)

Generating Multiple Choice Questions for Computing Courses Using Large Language Models. - Andrew Tran; Kenneth Angelikas; Egi Rama; Chiku Okechukwu; David H. Smith IV; Stephen MacNeil (FIE, 2023)

Using Large Language Models to Automatically Identify Programming Concepts in Code Snippets. - Andrew Tran; Linxuan Li; Egi Rama; Kenneth Angelikas; Stephen MacNeil (ICER (2), 2023)

Comparing Code Explanations Created by Students and Large Language Models. - Juho Leinonen 001; Paul Denny 001; Stephen MacNeil; Sami Sarsa; Seth Bernstein; Joanne Kim; Andrew Tran; Arto Hellas (ITiCSE (1), 2023)

The Implications of Large Language Models for CS Teachers and Students. - Stephen MacNeil; Joanne Kim; Juho Leinonen 001; Paul Denny 001; Seth Bernstein; Brett A. Becker; Michel Wermelinger; Arto Hellas; Andrew Tran; Sami Sarsa; James Prather; Viraj Kumar (SIGCSE (2), 2023)

Automatically Generating CS Learning Materials with Large Language Models. - Stephen MacNeil; Andrew Tran; Juho Leinonen 001; Paul Denny 001; Joanne Kim; Arto Hellas; Seth Bernstein; Sami Sarsa (SIGCSE (2), 2023)

Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book. - Stephen MacNeil; Andrew Tran; Arto Hellas; Joanne Kim; Sami Sarsa; Paul Denny 001; Seth Bernstein; Juho Leinonen 001 (SIGCSE (1), 2023)

Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances. - Stephen MacNeil; Andrew Tran; Joanne Kim; Ziheng Huang; Seth Bernstein; Dan Mogil (CoRR, 2023)

Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models. - Stephen MacNeil; Paul Denny 001; Andrew Tran; Juho Leinonen 001; Seth Bernstein; Arto Hellas; Sami Sarsa; Joanne Kim (ACE, 2024)

Moderating Technostress for Enhanced Student Learning Experiences: A Stress-is-Enhancing Mindset Approach. - Andrew Tran; Zixiu Guo; Carmen Leong; Giuseppe Daniele Ibello (ICIS, 2024)

MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations. - Xiangru Tang; Andrew Tran; Jeffrey Tan; Mark B. Gerstein (Bioinform., 2024)

Hacking Student Leadership: Peer Mentorship and Leadership Skill Development Among Hackathon Organizers. - Kush Patel; Andrew Tran; Christine Kapp; Daniel Bicalho; Yatri Patel; Chiku Okechukwu; Egi Rama; Stephen MacNeil (SIGCSE (2), 2025)