홍상현 오레곤 주립 대학교

Oregon State University
Computer Science Dept.
Cybersecurity | AI
Contact Information

Office: Room 4103, Kelley Engineering Center (KEC)
2500 NW Monroe Ave
Corvallis, OR 97331 USA
Office Hours: Tu/Th: 2 - 3 pm

          

Press
06.2023

OSU AI News

04.2022

TechXplore
Techradar.pro

06.2021

TechTalks

05.2021

Dev Podcast
MIT Tech Review

02.2021

USENIX Enigma 2021
(Ted Talk for Security)

Teaching
Spring 24 CS370: Intro to Sec.
Fall 23 CS499/579: TML
Spring 23 CS370: Intro to Sec.
CS499/579: TML
Winter 23 CS344: OS I
Spring 22 CS344: OS I
Winter 21 CS499/599: MLSec.
Students [Full list]

Fiona Victoria (PhD, AI)
Tahmid Prato (PhD, CS)
Jose Escamilla (PhD, CS
  co-advise w. Huazheng Wang)
Gabriel Ritter (PhD, CS
  co-advise w. Rakesh Bobba)
Anirudh Kanneganti (MS, CS)
Zach Coalson (BS, CS)
Evan Mrazik (BS, CS)
Leo Marchyok (BS, CS)
Colin Pannikkat (BS, CS)
Brielle Choquer (BS, CS)
Nyx (CS)

Alumni

'23: Hoang Le (MS, CS)
'22: Peter M-Stevens (BS, CS)
'22: Ryan Little (BS, CS)
  Now a PhD student at UMD

I am an Assistant Professor of Computer Science at Oregon State University.
I work at the intersection of computer security, privacy and machine learning.

Research Interests

I am an AI hacker working on building trustworthy and socially-responsible AI-enabled systems so that humans use those systems to improve our lives and society in the future. Thus far, I’ve been interested in characterizing the security/privacy and dependability issues of AI-enabled systems from a holistic view (i.e., systems security perspective). I received the Google Faculty Research Award 2023 and the Samsung Global Research (GRO) Award 2022. I am selected as a DARPA Riser (2022) and was invited as a speaker at USENIX Enigma (2021).

Please drop me an email with your CV if you're interested in working with me.

Bio

I earned my Ph.D. from the University of Maryland, College Park, under the supervision of Prof. Tudor Dumitras in 2021. I received my bachelor's degree from Seoul National University in 2015. I was fortunate to spend a winter at Google Brain in 2021 (working with Dr. Nicholas Carlini and Dr. Alexey Kurakin) and to spend 6-months at Frame.io in 2017 (working with Dr. Abhinav Srivastava).

News

Sep. 21, 2023
My student (Zachery)'s first paper will be in NeurIPS 2023. Congratulations!
Jun. 15, 2023
Our team's story of advancing AI systems is on OSU AI Newsletter
Apr. 24, 2023
One paper is accepted at ICML 2023
Apr. 19, 2023
Received Google Faculty Research Award 2023. Thanks Google Research!
Mar. 4, 2023
One paper is accepted at ICLR Workshop on
  Pitfalls of Limited Data and Computation for Trustworthy ML 2023
Jan. 31, 2023
Received NSF SFS Award (co-PI). Thanks NSF!
Jan. 13, 2023
One paper is accepted at ACM CHI 2023
Nov. 15, 2022
One paper is accepted at IEEE SaTML 2023
Oct. 25, 2022
Received Samsung 2022 GRO Award. Thanks Samsung!
Sep. 14, 2022
One paper is accepted at NeurIPs 2022 [Oral]
Jul. 16, 2022
One paper is accepted at IEEE VIS 2022
Jun. 13, 2022
One paper is accepted at ICML Workshop on Continuous-Time Methods for ML 2022
May. 10, 2022
Selected as a DARPA Riser 2022.

Selected Publications [Full list]

Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
Sicheng Zhu, Bang An, Furong Huang, and Sanghyun Hong
International Conference on Machine Learning (ICML). 2023.
PDF | Code | Talk & Poster (on ICML'23 Website)

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong,
Nicholas Carlini (*authors ordered reverse-alphabetically)
The ACM Conference on Computer and Communications Security (CCS), 2022.
PDF | Code | Media

Data Poisoning Won't Save You From Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramer
International Conference on Learning Representations (ICLR) 2022.
PDF | Code | Poster

Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, and Tudor Dumitraș
Advances in Neural Information Processing Systems (NeurIPS) 2021.
PDF | Code | Poster

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
*Sanghyun Hong, *Yigitcan Kaya, Ionuţ-Vlad Modoranu, and Tudor Dumitraș(* equal contribution)
International Conference on Learning Representations (ICLR) 2021. [Spotlight]
PDF | Code | Spotlight Presentation

How to 0wn NAS in Your Spare Time
Sanghyun Hong, Michael Davinroy, Yigitcan Kaya, Dana Dachman-Soled, and Tudor Dumitraș
International Conference on Learning Representations (ICLR) 2020.
PDF | Code | Poster

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks
Under Hardware Fault Attacks
Sanghyun Hong, Pietro Frigo, Yigitcan Kaya, Cristiano Giuffrida, and Tudor Dumitraș
Proceedings of The 28th USENIX Security Symposium (USENIX Security) 2019.
PDF | Presentation

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitraș
International Conference on Machine Learning (ICML) 2019.
PDF | Code