- Book: 'Adversarial Learning and Secure AI', Cambridge University Press, 2023
- Multiple research projects funded by NSF, DARPA, Facebook, Cisco, etc.
Research Experience
- Adversarial AI – recent papers
- NSF CPS: Clustering, Auto-Encoding, and Generative Modelling of 3D Object Representations for Manufacturing
- NSF CNS: Collaborative Research: Rethinking Multi-User VR - Jointly Optimized Representation, Caching and Transport
- Cisco Systems Ltd Gift: Adversarial Learning Towards a Comprehensive Framework to Secure Deep Learning
- NSF CNS: Principled Methodologies and Systems Support for Automated Cost-Effective Service Blending in the Emerging Public Cloud
- Facebook Research Award: Virtual Reality Support at the Edge Cloud
- NSF CCF: Cross-layer Design for Cost-Effective HPC in the Cloud
- Navy ROTC Projects on Cyber Security: Adversarial AI/ML
- Cisco Systems Ltd Gift: Online Active Learning for Classification and Zero-Day Exploit Discovery in Large-Scale Datasets
- AFOSR DDDAS: Adversarial Learning & Active Learning
- NSF CSR: Using Burstable Instances For Cost-Effective Tenant Orchestration in the Public Cloud
- DARPA XD3: Democratizing DDoS Defense using Secure Indirection Networks
- NSF NeTS: Competition, Neutrality and Service Quality in Cellular Wireless Access
- Cisco Systems Gift: Low-latency detection of networks with fast-flux both in IP address proxies and in domain names
- NSF SaTC TWC: Towards Securing Coupled Financial and Power Systems in the Next Generation Smart Grid
- Cisco System Ltd URP: Unsupervised flow-level clustering in network routers for anomaly detection
- NSF NeTS: Inter-provider dynamics in neutral and non-neutral networks
- NSF EAGER: GENI Experiments to Explore Adoption of New Security Services
- NSF NeTS: Supporting unstructured peer-to-peer social networking
- NSF NetSE: Unsupervised flow-based clustering
- Cisco Ltd URP: Per-flow state management in Internet routers: mass purging and heavy-hitter detection
- NSF Cyber Trust and Cyber Infrastructure: Router Models and Downscaling Tools for Scalable Security Experiments
- DHS/NSF Cyber Trust: Testing and benchmarking methodologies for future network security mechanism
Education
- Ph.D. (1992 – Networking and Performance Evaluation), EECS, University of California at Berkeley, advisor: Jean Walrand
- M.S. (1990 – Machine Learning and Stochastic Optimization), EECS, U.C. Berkeley, advisor: Eugene Wong
- B.A.Sc. (1988) in Electrical Engineering, University of Waterloo, Canada
Background
Research interests include networking and performance evaluation, machine learning, and stochastic optimization. He is a professor in the CSE and EE Depts at The Pennsylvania State University.