Stealthy Poisoning Attack on Certified Robustness
- Akshay Mehra
- Bhavya Kailkhura
- et al.
- 2020
- NeurIPS 2020
Pin-Yu Chen
Title
Principal Research Scientist; Chief Scientist, RPI-IBM AI Research Collaboration
Bio
Pin-Yu Chen is a Principal Research Staff Member of the Trusted AI Group, IBM Thomas J. Watson Research Center. He is also the Chief Scientist of RPI-IBM AI Research Collaboration program and a PI of MIT-IBM Watson AI Lab projects.
His recent research focuses on adversarial machine learning of neural networks for robustness and safety, and more broadly, making machine learning trustworthy.
♦ Pin-Yu's research highlights can be found from HERE
♦ Pin-Yu is open to collaboration with highly motivated researchers!
Pin-Yu received his Ph.D. degree in electrical engineering and computer science (EECS) and M.A. degree in Statistics from the University of Michigan Ann Arbor in 2016, under the supervision of Prof. Alfred Hero.
♦ Please refer to his personal webpage for more details - www.pinyuchen.com
Publications
Reprogramming Language Models for Molecular Representation Learning
- Ria Vinod
- Pin-Yu Chen
- et al.
- 2020
- NeurIPS 2020
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications
- Sijia Liu
- Pin-Yu Chen
- et al.
- 2020
- IEEE SPM
Fast learning of graph neural networks with guaranteed generalizability: One-hidden-layer case
- Shuai Zhang
- Meng Wang
- et al.
- 2020
- ICML 2020
Proper network interpretability helps adversarial robustness in classification
- Akhilan Boopathy
- Sijia Liu
- et al.
- 2020
- ICML 2020
Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources
- Yun Yun Tsai
- Pin-Yu Chen
- et al.
- 2020
- ICML 2020
Do You Sign Your Model?
- Omid Aramoon
- Pin-Yu Chen
- et al.
- 2020
- ICML 2020
Towards verifying robustness of neural networks against a family of semantic perturbations
- Jeet Mohapatra
- Tsui Wei Weng
- et al.
- 2020
- CVPR 2020
Ai explainability 360: An extensible toolkit for understanding data and machine learning models
- Vijay Arya
- Rachel Bellamy
- et al.
- 2020
- JMLR
Towards an efficient and general framework of robust training for graph neural networks
- Kaidi Xu
- Sijia Liu
- et al.
- 2020
- ICASSP 2020
Patents
- CN
- ZL201980030031.2
- US
- 12061991
- US
- 12020480
- JP
- 7507851
- CN
- ZL202010827714.5
- GB
- 2603391
- CN
- ZL 202010055649.9
- US
- 11880765
- US
- 11853713
- US
- 11853702
Blog posts
Serving customized AI models at scale with LoRA
ResearchKim MartineauWhat’s an LLM context window and why is it getting larger?
NewsKim MartineauWhat is red teaming for generative AI?
ExplainerKim MartineauAccelerating molecular optimization with AI
Deep DivePayel Das, Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, and Pin-Yu Chen11 minute read- See more blog posts
Top collaborators
PD
Payel Das
Principal Research Scientist and Manager, IBM Research AI
KV
Kush Varshney
IBM Fellow
MP
Mark Purcell
Research Manager - Security Research, AI Security & PETs
AR
Ambrish Rawat
Senior Research Scientist, AI Security and Privacy, Master Inventor