Sanghyun Hong 홍상현 메릴랜드 대학교


       
Ph.D Candidate in Computer Science
at University of Maryland, College Park, advised by Prof. Tudor Dumitraș.

** I am on the job market this year (2020-21)! **

Research Interests
My research objective is to solve the security and privacy problems of machine learning (ML) systems. For this, I am particularly interested in characterizing the vulnerable interactions between ML algorithms and their surrounding environments such as hardware/systems where ML algorithms are deployed [1, 2, 3] or datasets that we use to train them [4]. This effort often leads to contributions to ML privacy [5] and security services for cloud infrastructures [6, 7].

Short Bio
I received my B.S. in Electrical Engineering and Computer Science (EECS) from Seoul National University in 2015. During my undergraduate years, I carried out projects with LG Electronics Inc. (LGE) as a lead researcher (as part of my mandatory military service in South Korea). Also, I founded Openwise Inc. (2013-15), a start-up company where I worked as a chief technology officer, and supervised research projects carried out with Samsung Advanced Institute of Technology (SAIT).


Contacts
Email: shhong@cs.umd.edu
Office: [Maryland Cybersecurity Center (MC2)]
5112 Brendan Iribe Center for Computer Science and Engineering, College Park, MD, USA

News
(New) Nov. 2020: My talk proposal has been accepted to the USENIX Enigma 2021. (Super-excited!)
Oct. 2020: I started my internship at Google Brain under the supervision of Dr. Nicholas Carlini.
May. 2020: I did my thesis proposal. Now I became a Ph.D. candidate :)
Mar. 2020: I was awarded the Ann G. Wylie Dissertation Fellowship.
Jan. 2020: I was selected as a 2020-21 Future Faculty Fellow by the Clark School of Engineering.

Selected Publications
— Practical Hardware Attacks on Deep Learning (Project website)
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, 2020 (ICLR).
[ Paper | Talk ]

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.
USENIX Security Symposium, 2019 (USENIX).
[ Paper | Talk ]

— Practical Data Poisoning Attacks and Defenses
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping.

Sanghyun Hong, Varun Chandrasekaran, Yigitcan Kaya, Tudor Dumitraș, and Nicolas Papernot.
arXiv Pre-print 2020.
[ Paper ]

— Characterizing Internal Behaviors of Deep Neural Networks
On the Effectiveness of Regularization Against Membership Inference Attacks.

Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitraș.
arXiv Pre-print 2020.
[ Paper ]

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking.

Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitraș.
International Conference on Machine Learning, 2019 (ICML).
[ Paper | Website ]