CV

PhD Candidate in Computer Science
University of Massachusetts Amherst
hjeong[at]umass.edu · Website · LinkedIn

Download Concise · Full academic CV

Research Interests

I study security, privacy, and behavioral properties of AI agent systems, where LLMs interact with tools, memory, and external environments over multiple steps. In these settings, agent behavior—including actions, tool use, and execution traces—reveals properties that cannot be captured by analyzing model outputs alone.

My work focuses on identifying weaknesses, strengths, and emergent behaviors unique to agentic AI, such as network-level information leakage, behavioral drift under persuasion, and persistent bias patterns across LLM families.

Education

Ph.D. in Computer Science (2023 – Present)
University of Massachusetts Amherst
Advisors: Amir Houmansadr, Eugene Bagdasarian

M.S. in Computer Science (2021 – 2023)
SungKyunKwan University (SKKU), South Korea
Advisor: Tai-Myoung Chung · GPA: 4.5/4.5

B.S. in Computer Science (2015 – 2020)
Stony Brook University (SBU), New York
Security & Privacy Specialization · Dean’s List (5x)

Publications & Presentations

Peer-Reviewed

  • H. Jeong, M. Teymoorianfard, A. Kumar, A. Houmansadr, E. Bagdasarian. Network-Level Prompt and Trait Leakage in Local Research Agents. USENIX Security 2026. [Paper] [Code] [Dataset]
  • H. Jeong, S. Ma, A. Houmansadr. Bias Similarity Measurement: A Black-Box Audit of Fairness Across LLMs. ICLR 2026. [Paper] [Code]
  • H. Jeong, H. Son, S. Lee, J. Hyun, T.-M. Chung. FedCC: Robust Federated Learning Against Model Poisoning Attacks. SecureComm 2025. [Paper] [Code] [Slides]
  • H. Jeong, T.-M. Chung. Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare. FDSE 2022. [Paper]
  • J.H. Yoo, H. Jeong, J. Lee, T.-M. Chung. Open Problems in Medical Federated Learning. IJWIS 2022. [Paper]
  • J.H. Yoo, H. Jeong (co-first), J. Lee, T.-M. Chung. Federated Learning: Issues in Medical Application. FDSE 2021. [Paper]
  • H. Jeong, J. An, J. Jeong. Are You a Good Client? Client Classification in Federated Learning. ICTC 2020. [Paper] [Code]

Preprints / Under Review

  • H. Jeong, A. Houmansadr, S. Zilberstein, E. Bagdasarian. Persuasion Propagation in LLM Agents Preprint [Paper] [Code]

  • H. Jeong, S. Ma, A. Houmansadr. SoK: Challenges and Opportunities in Federated Unlearning. Preprint, under review (IEEE Big Data 2025). [Paper] [Slides]

Patent

  • T.-M. Chung, J.H. Yoo, H. Jeong, H.J. Jeon. Data Processing Method for Depressive Disorder Using AI Based on Multi-indicator. Patent No. 1024322750000.

Research Experience

Research Assistant, University of Massachusetts Amherst (2023 – Present)

  • Conduct research on security, privacy, and behavioral risks in LLM-based AI agents.
  • Led work on network-level prompt and trait leakage in web/research agents, demonstrating inference attacks from encrypted metadata (USENIX Security).
  • Investigated persuasion propagation in agentic LLMs, analyzing how task-irrelevant beliefs affect downstream behavior in web navigation and coding tasks.
  • Studied bias similarity across LLM families, comparing open- and closed-source models using behavioral and representation-level metrics (ICLR).
  • Initiated a survey project on federated unlearning, identifying evaluation gaps and limitations in existing approaches.
  • Designed large-scale experimental pipelines and released open-source code and datasets.

Research Assistant, SungKyunKwan University (SKKU), South Korea (2021 – 2023)

  • Studied defenses against backdoor and poisoning attacks in federated learning.
  • Conducted privacy-preserving federated learning research in medical settings; co-authored peer-reviewed publications.

Undergraduate Research Assistant, Stony Brook University (SBU) (2019)

  • Built and validated a GPS spoofing detection pipeline using sensor fusion and camera-based signals.

Selected Projects

  • Exploring Model Inversion on Unlearned Samples (2024) — Reconstructed unlearned samples by contrasting representations between original and unlearned models.
  • Federated Unlearning as Backdoor Mitigation (2023) — Evaluated unlearning defenses against backdoor attacks in FL. [Code]
  • Malicious Client Detection in Federated Learning (2022) — Proposed client classification using model weight heatmaps to detect backdoors/data poisoning. [Code]
  • Covert C\&C and Data Exfiltration (2020) — Developed Python client/server for covert command-and-control and encrypted data exfiltration to AWS. [Code]
  • Distributed Typosquatting Detector (2019) — Built distributed app to detect typosquatting domains via headless Chrome scanning and automated reporting. [Code]

Teaching Experience

  • Teaching Assistant, CS 690: Trustworthy & Responsible AI, UMass Amherst (Fall 2025) — Organized and graded group assignments; led paper discussions; mentored teams on programming assignments and a security-focused final project.
  • Teaching Assistant, CS 360: Introduction to Computer & Network Security, UMass Amherst (Spring 2025) — Assisted with lectures; designed and graded weekly assignments (SHA-256, web security, AI security); advised semester projects with research-style final reports.
  • Tutor, KT Corp. Aivle School, South Korea (Feb–May 2022) — Tutored in AI model interpretation and CS fundamentals; supported projects in ML/DL, NLP, and Django-based web apps.
  • Teaching Assistant, Global Capstone Design Course, SKKU (Spring 2022) — Guided teams through ideation → prototyping → evaluation; projects applied AI techniques to deployable products.
  • Teaching Assistant, Web Design and Programming, SBU (Spring 2018) — Taught web design wireframing and documentation; graded assignments; led recitation sections.

Service & Affiliations

  • Ph.D. Mentor, UMass Amherst (Summer 2025) — Mentored undergraduates in an 11-week project on AI web agent security; guided research design and poster preparation. [Poster]
  • URV Mentor, UMass Amherst (2023–2024) — Supervised undergraduates in semester-long research projects; supported planning, experiments, and poster presentations.
  • Reviewer, IEEE Transactions on Information Forensics & Security (TIFS) (2024–)
  • Member, UMass Amherst AI Security (AISEC) Lab (2025–)
  • Member, The Secure, Private Internet (SPIN) Lab (2023–)

Honors & Awards

  • Dean’s List, Stony Brook University (5 semesters)
  • Graduate Research Assistantship, UMass Amherst (2023–Present)

Technical Skills

Languages: Python, Java, C, LaTeX, JavaScript, PHP, SQL, R
Frameworks/Tools: PyTorch, TensorFlow, Django, Git, Docker
Areas: Security & Privacy, Federated Learning, LLMs, Unlearning, Deep Learning

Last updated: February 2026