Arman Riasi

Ph.D. Candidate at Virginia Tech · Visiting Scholar at Purdue University

I am a Ph.D. candidate in Computer Science at Virginia Tech and a visiting scholar at Purdue University. My research focuses on trustworthy AI, particularly verifiable computation (e.g., zero-knowledge proofs), privacy-preserving AI/ML, and federated learning.

Arman Riasi

Education

M.S. in Computer Science, Virginia Tech 2025
B.S. in Computer Engineering, Isfahan University of Technology 2022

Experience

Visiting Scholar, TCLS Lab, Purdue University · West Lafayette, Indiana Aug 2026–Present

Host: Prof. Zahra Ghodsi

Graduate Research Assistant, VT-ASAP Lab, Virginia Tech · Blacksburg, Virginia Aug 2022–Present
AI Agents and Data Support Intern, Virginia Tech · Blacksburg, Virginia Jul 2026–Aug 2026

Publications

2025

Trustworthy Federated Learning with Local Differential Privacy

Rouzbeh Behnia, Jeremiah Birrell, Arman Riasi, Reza Ebrahimi, Kaushik Dutta, and Thang Hoang

Workshop on Information Technologies and Systems (WITS)

Best Paper Award

Paper DOI

Zero-Knowledge AI Inference with High Precision

Arman Riasi, Haodi Wang, Rouzbeh Behnia, Viet Vo, and Thang Hoang

ACM Conference on Computer and Communications Security (ACM CCS)

Acceptance rate: 13.9%

Paper Code Slides DOI

2024

Privacy-Preserving Verifiable Neural Network Inference Service

Arman Riasi, Jorge Guajardo, and Thang Hoang

Annual Computer Security Applications Conference (ACSAC)

Acceptance rate: 19.7%

Paper Code Slides DOI

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning

Rouzbeh Behnia, Arman Riasi, Mohammadreza Ebrahimi, Sherman S. M. Chow, Balaji Padmanabhan, and Thang Hoang

Annual Computer Security Applications Conference (ACSAC)

Acceptance rate: 19.7%

Paper Code DOI

Breaking Privacy in Model-Heterogeneous Federated Learning

Atharva Haldankar, Arman Riasi, Hoang-Dung Nguyen, Tran Phuong, and Thang Hoang

International Symposium on Research in Attacks, Intrusions and Defenses (RAID)

Acceptance rate: 25%

Paper Code DOI

Patents

Method and System for Next-Location Prediction Using Federated Learning with Heterogeneous Mobility Data

Arman Riasi, Rouzbeh Behnia, Thang Hoang, and Kaushik Dutta

U.S. Provisional Patent Application No. 63/969,801

Filed January 28, 2026 · Inventor

Awards

Prabuddha De Best Paper Award, WITS 2025 (best paper among 156 papers) Dec 2025
CCI SWVA Cyber Innovation Scholarship, two-time recipient Mar 2025, Mar 2026
ACSAC Student Conferenceship Grant, NSF-funded Oct 2024
BitShares Graduate Student Fellowship Jun 2023

Professional Service

Artifact Evaluation Program Committee

  • IEEE S&P (2027)
  • PETS (2027)
  • NDSS (2026–2027)
  • ACM CCS (2025–2026)
  • USENIX Security (2026)

Poster & Demo Program Committee

  • ACM CCS (2026)

Journal Reviewer

  • IEEE TIFS (2026)
  • IEEE TNSE (2026)
  • IEEE TDSC (2024–2026)
  • IEEE TCC (2023–2025)

Conference Reviewer

  • IEEE S&P (2026–2027)
  • PETS (2023–2027)
  • ACM CCS (2026)
  • ACISP (2026)
  • ACM ASIACCS (2025)
  • ACSAC (2023–2025)

Teaching

Virginia Tech

CS 2114: Software Design & Data Structures · 314 students Spring 2026
CS 5704: Software Engineering · 195 students Fall 2022, 2025
CS 2505: Introduction to Computer Organization I · 412 students Spring 2025
CS 4824/ECE 4424: Machine Learning · 65 students Fall 2024
CS 2506: Introduction to Computer Organization II · 69 students Summer II 2024
CS 5594: Blockchain Technologies · 53 students Spring 2024
CS 4604: Introduction to Database Management Systems · 139 students Spring 2023