I am a computer science and electrical engineering researcher. I have a joint professor appointment in both the computer science department and the electrical engineering department at the University of Connecticut. My research focus is on adversarial machine learning and the security of machine learning systems. You can find my resume at this link. I currently have four major papers in adversarial machine learning (with one accepted at ICCV 2021). These works include:
- “On the Robustness of Vision Transformers to Adversarial Examples (ICCV 2021): Accepted Paper Link
- “Besting the Black-Box: Barrier Zones for Adversarial Example Defense” (IEEE Access 2021): Accepted Paper Link
- “Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples” (Entropy 2021): Accepted Paper Link
- “Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks” (IEEE Access 2021): Accepted Paper Link
I also have a number of other papers in hardware security, IoT security and underwater localization. A list of my related publications and citations can be found here: https://scholar.google.com/citations?user=axn5PtMAAAAJ. Related code I have publicly released can be found here: https://github.com/MetaMain.
Current Research:
I work with a variety of academic institutions and industry collaborators, in both pure machine learning and applications of machine learning, My current projects include:
- Spiking Neural Networks (SNNs) security and robustness to adversarial machine learning, in collaboration with North Carolina State University: E-print.
- Multi-Task Learning (MTL) robustness and security, in collaboration with the University of Massachusetts Amherst: E-print.
- Communication system design via machine learning, in collaboration with the Electrical Engineering Department at the University of Connecticut.
- Machine learning for processing ecological video data, in collaboration with the Ecology and Evolutionary Biology Department at the University of Connecticut.
- Game theoretic analyses of adversarial machine learning (my research group): E-print.
- Voting system security with respect to adversarial machine learning (my research group).
My Background:
I am a West coaster, born in Richland, WA (U.S. citizen, native English speaker), that happens to reside on the East coast. I grew up in Connecticut and I received my bachelors in electrical engineering in 2013, my masters in electrical engineering in 2016 and my masters in computer science in 2017, all from the University of Connecticut. I completed my PhD in computer science (with a focus on adversarial machine learning) in 2021. I am currently serving as an Assistant Professor in Residence (APIR) in the Computer Science and Engineering (CSE) department at the University of Connecticut and also as an Assistant Research Professor (ARP) in the Electrical and Computer Engineering (ECE) department.
Research is a highly collaborative and rewarding endeavor. Therefore, I am always looking for exciting new projects and collaborations. If you are interested feel free to contact me: kaleel[dot]mahmood[at]uconn[dot]edu