CV

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

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Research Interests

I study security and privacy in AI systems, including large language models (LLMs) and autonomous AI agents.
My current work focuses on vulnerabilities in agent pipelines, missing security properties, and risks of persuasion or persona manipulation. I have also conducted research on fairness and bias similarity in LLMs and on federated learning with emphasis on robustness, privacy, and unlearning. More broadly, I am interested in trustworthy and responsible AI and in developing privacy-preserving methods for collaborative and agent-based learning.

Education

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

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, 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, M. Teymoorianfard, A. Kumar, A. Houmansadr, E. Bagdasaryan. Network-Level Prompt and Trait Leakage in Local Research Agents. arXiv:2508.20282, under review (USENIX 2026). [Paper] [Code] [Dataset]
  • H. Jeong, S. Ma, A. Houmansadr. Bias Similarity Measurement: A Black-Box Audit of Fairness Across 30 LLMs. arXiv:2410.12010, under review (ICLR 2026). [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)

  • Designed attacks inferring prompts/persona traits from browsing traces; released datasets & tools.
  • Built cross-family bias comparison pipelines across 30+ LLMs; led first-author manuscripts.
  • Initiated and led a systematization-of-knowledge (SoK) project on federated unlearning.

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

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

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

  • Built and validated a GPS spoofing detection pipeline using a sensor and a camera.

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: September 2025