Ellick Chan, Ph.D.
Senior Associate, Statistical & Data Sciences Department at Exponent
Dr. Chan is a computer scientist and a data scientist who specializes in the use of deep learning and machine learning techniques to solve difficult problems across areas ranging from computer vision to computer security. He has applied his expertise to research new methodologies in detection to include anomaly and threat detection in Ground Penetrating Radar (GPR) data. Dr. Chan continually evaluates the latest techniques in data analytics and teaches a class on deep learning at Northwestern University.
Prior to joining Exponent, Dr. Chan performed post-doctoral research at Stanford on methodologies to prevent de-anonymization of medical record data. For his Ph.D. work, Dr. Chan developed software and algorithms to address many facets of computer security and privacy. His work included development of computer forensic and recovery tools for analyzing live memory dumps of devices operating on critical infrastructures such as power grid monitoring equipment. He also identified security vulnerabilities in embedded microprocessor architectures and operating systems running on them.
Dr. Chan has also worked in the software industry on developing malware analysis tools for Windows, Linux-based mobile operating systems; and ARM microprocessor simulation. He has experience with low-level analysis of ARM and X86 machine code, operating system internals, and analysis of software packages.
Dr. Chan is a computer scientist and data scientist who specializes in the use of deep learning and machine learning to solve difficult problems in computer vision and computer security. He has applied his expertise to research new methodologies in detection. This includes anomaly and threat detection such as detecting buried explosives (IEDs) using Ground Penetrating Radar (GPR) by using state-of-the-art anomaly detection algorithms. Dr. Chan continually evaluates the latest techniques and teaches a class on deep learning at Northwestern University.