Huzaifa Arif

About Me

I am a fifth-year Ph.D. candidate and researcher with multiple first-author publications in trustworthy AI. My work focuses on exposing and mitigating privacy and safety vulnerabilities in Large Language Models, demonstrated through research at IBM and LLNL. I am seeking a full-time Research Scientist position starting December 2025 where I can apply my expertise in LLM alignment and privacy to build verifiably safe AI systems.

Quick Links

• 📄 CV
• 🎓 Google Scholar
• 💻 GitHub

Professional Experience

IBM T.J Watson Research Center - Yorktown Heights, NY

AI Research Extern - Trustworthy AI | Jun 2025–Aug 2025
Mentors: Pin-Yu Chen, Ching-Yun Ko, Keerthiram Murugesan, Payel Das

Lawrence Livermore National Laboratory - Livermore, CA

Data Science Intern | May 2024–Aug 2024
Mentors: Bhavya Kailkhura, James Diffenderfer

IBM T.J Watson Research Center - Yorktown Heights, NY

AI Research Extern - Trustworthy AI | Jun 2023–Aug 2023
Mentors: Pin-Yu Chen, Keerthiram Murugesan, Payel Das

IBM T.J Watson Research Center - Yorktown Heights, NY

AI Research Extern - Trustworthy AI | Jun 2022–Aug 2022
Mentor: Pin-Yu Chen

Education

Rensselaer Polytechnic Institute - Troy, NY

Ph.D. in Electrical and Computer Systems Engineering | Expected Dec 2025

Lahore University of Management Sciences - Lahore, Pakistan

B.S. in Electrical Engineering

Ongoing work

Project 1: Parameter-Efficient LLM Safety Alignment: I am developing a lightweight, prefix-based method to steer LLMs towards safe behavior without needing to retrain the entire model. By combining Supervised Fine-Tuning (SFT) with Direct Preference Optimization (DPO), this work efficiently instills safety, mitigates toxicity, and can be adapted to prevent demographic bias and PII leakage. (Under Review).

Project 2: Exposing Association Leakage in LLMs: We consider the privacy vulnerability, “association leakage,” where LLMs can be prompted to reveal sensitive linked information (e.g., a name and its corresponding private data). This work introduces a new post-hoc attack that amplifies this leakage by manipulating attention heads at inference time, defining a new threat model for LLM safety audits. (Under Review).

Publications

1. Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming

Conference: IEEE Conference on Secure and Trustworthy Machine Learning, February 2023
Authors: Huzaifa Arif, Alex Gittens, Pin-Yu Chen
🎥 Talk | 💻 Code | 📄 Paper

2. PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks

Conference: IEEE Conference on Secure and Trustworthy Machine Learning, April 2025
Authors: Huzaifa Arif, Keerthiram Murugesan, Payel Das, Alex Gittens, Pin-Yu Chen
📄 Paper

3. Group Fair Federated Learning via Stochastic Kernel Regularization

Journal: Transactions on Machine Learning Research, April 2025
Authors: Huzaifa Arif, Pin-Yu Chen, Keerthiram Murugesan, Alex Gittens
📄 Paper

4. Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models

Conference (Workshop): AAAI Workshop on AI to Accelerate Science and Engineering
Authors: Huzaifa Arif, Pin-Yu Chen, Alex Gittens, James Diffenderfer, Bhavya Kailkhura
📄 Paper

Patents

• Differentially Private Federated Learning using Model Reprogramming

  Inventors: Huzaifa Arif, Pin-Yu Chen, Bo Wu, Zhengfang Chen, Chuang Gan
  Patent No. US20240256894A1

• Method for Quantifying Private Leakage in Pretrained Neural Networks

  Inventors: Huzaifa Arif, Keerthiram Murugesan, Payel Das, Pin-Yu Chen

• Patching LLM: Steering Large Language Models with Soft Prompting

  Inventors: Huzaifa Arif, Keerthiram Murugesan, Payel Das, Irene-Ko, Pin-Yu Chen

Book Chapters

• “Utility-privacy Tradeoff in Federated Learning” Book chapter in Federated Learning for Medical Imaging, Elsevier, 2025
  Huzaifa Arif, Alex Gittens, and Pin-Yu Chen
  Link

Preprints

DP-Compressed VFL is secure for Model Inversion Attacks

Paper | Code

Additional Research Work

• Our adaptation of Hypergradient Descent Project
• PhD Qualifier Report Paper

Professional Service

Reviewer Experience

• ICLR 2025
• International Conference on Acoustics, Speech, and Signal Processing
  • ICASSP 2025
  • ICASSP 2023
• Artificial Intelligence and Statistics (AISTATS) 2023
• IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023)

Recent News & Achievements

• Sept 2025: Awarded Founders Award of Excellenece (Award to 1% RPI Graduate Students every year for excellence in academics and overall leadership)
• Apr 2025: 2nd Place runners-up at 3MT for my thesis Listen to my Pitch
• Apr 2025: Gave a talk at RPI School of Mathematical Sciences Algorithmic Designs to Investigate Trustworthiness in AI models
• Apr 2025: Successfully defended my PhD Candidacy Exam
• Dec 2024: Awarded the Belsky Award for Computational Science and Engineering 2025 (Top 6 Graduate Students for Excellence in Research) Award Learn more
• Dec 2024: Composite gradient-based bias mitigation in Federated Learning accepted to TMLR 2025 (Journal)
• Dec 2024: LLNL internship work (WAPPO) accepted at AAAI 2025
• Dec 2024: Data Leakage in Residual Networks accepted at SATML 2025 (29% Acceptance Rate)
• Aug 2024: Patent “Reprogrammable FL” publicly available (US20240256894A1)
• Apr 2023: Repgrogrammable FL accepted at SATML (26% Acceptance Rate)
• Apr 2022: Passed PhD Qualification Exam (Rank: Top-5)
• Apr 2022: Completed work with Dr. Stacy Patterson Preprint: DP-Compressed VFL is secure for Model Inversion Attacks
• Jan 2021: Joined RPI as a PhD student

Contact Information

📧 Email: arifh@rpi.edu | huzaifaarif20@gmail.com
📞 Phone: (518) 961-8482

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