Ellick Chan
Ellick Chan, Ph.D.
Senior Associate
Statistical & Data Sciences
  • Menlo Park

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.

CREDENTIALS & PROFESSIONAL HONORS

  • Postdoctoral Scholar, Stanford University, 2014
  • Ph.D., Computer Science, University of Illinois, Urbana-Champaign, 2011
  • M.B.A., Business Administration, University of Illinois, Urbana-Champaign, 2011
  • M.S., Computer Science, University of Illinois, Urbana-Champaign, 2004
  • B.S., Computer Engineering, University of Illinois, Urbana-Champaign, 2004, highest honors
  • Siebel Scholarship, 2004

Publications

Chan E, Lam P, Mitchell J. Understanding the challenges with medical data segmentation for privacy. USENIX Workshop on Health Information Technologies, August 2013.

Cui W, Peinado M, Xu Z, Chan E. Tracking rootkit footprints with a practical memory analysis system. Proceedings, 21st USENIX Security Symposium, USENIX, August 2012.

Chan E, Venkataraman S, Tkach N, Larson K, Gutierrez A, Campbell R. Characterizing data structures for volatile forensics. IEEE Systematic Approaches to Digital Forensic Engineering (SADFE), 2011. Best student paper award; best presentation award.

Chan E, Venkataraman S, Chaugule A, Campbell R. Forenscope: A framework for live forensics. Annual Computer Security Applications Conference (ACSAC), 2010.

Chan E, Chaugule A, Larson K, Campbell R. Performing live forensics on insider attacks. Proceedings, CAE Workshop on Insider Threat, 2010.

Farivar R, Verma A, Chan E, Campbell R. MITHRA: Multiple data Independent Tasks on a heterogeneous resource architecture. IEEE Cluster, 2009.

David F, Chan E, Carlyle J, Campbell R. CuriOS: Improving reliability through operating system structure. USENIX Symposium on Operating Systems Design and Implementation, December, 2008.

Chan E, Carlyle J, David F, Farivar R, Campbell R. BootJacker: Compromising computers using forced restarts. ACM Conference on Computer and Communications Security, October, 2008.

David F, Chan E, Carlyle J, Campbell R. Cloaker: Hardware supported rootkit concealment. IEEE Symposium on Security and Privacy, May, 2008.

Invited Talks and Presentations

Chan E. Deep learning approaches to digging data out of digitized paper documents. INFORMS Annual Meeting. November, 2015.

Chan E. Open panel discussion. Inside Data Science. November, 2015.

Chan E. Analytical approaches to detection buried objects in cluttered environments. INFORMS Conference on Business Analytics and Operations Research. April, 2015.

Chan E. Anomaly detection algorithms for detecting insider threat. Insider Threat Summit. March, 2015.

Chan E. Understanding the challenges with medical data segmentation for privacy. USENIX Workshop on Health Information Technologies, August 12, 2013.

Chan E. Characterizing data structures for volatile forensics. IEEE Systematic Approaches to Digital Forensic Engineering (SADFE), May 26, 2011. Best student paper award; best presentation award.

Chan E. Forenscope: A framework for live forensics. Annual Computer Security Applications Conference (ACSAC), December 10, 2010.

Chan E. Performing live forensics on insider attacks. Proceedings, CAE Workshop on Insider Threat, November 2010.

Chan E. BootJacker: Compromising computers using forced restarts. ACM Conference on Computer and Communications Security, October 30, 2008.

Prior Experience

Research Intern, Microsoft Research, Summer 2010

Software Engineering Intern, Australian Semiconductor Technology Company, Summer 2008

Senior Software Engineer, Motorola, 2005–2007

Academic Appointments

Postdoctoral Scholar, Stanford University, August 2014

Research Assistant, University of Illinois, 2009–2011

Adjunct Lecturer, Northwestern University, Introduction to Deep Learning, April 2016

Professional Affiliations

Association for Computing Machinery—ACM

Institute of Electrical and Electronics Engineers—IEEE

INFORMS Analytics

Project Experience

Privacy

  • Analysis of medical record de-anonymization techniques 
  • Analysis of redaction techniques for medical records 

Security

  • Rootkit and malware analysis 
  • Crash dump analysis 
  • Analysis of computer architectures and software systems for vulnerabilities 

Forensics 

  • Forensic analysis of memory dumps 
  • Development of tools for mapping out program data structures in-memory 

Computer Architecture/Embedded Systems

  • Embedded CPU simulation 
  • Analysis of embedded architecture vulnerabilities 
  • Analysis of power usage on mobile devices 
  • Compilers and software tool chain for mobile devices 

Knowledge

News & Events

CREDENTIALS & PROFESSIONAL HONORS

  • Postdoctoral Scholar, Stanford University, 2014
  • Ph.D., Computer Science, University of Illinois, Urbana-Champaign, 2011
  • M.B.A., Business Administration, University of Illinois, Urbana-Champaign, 2011
  • M.S., Computer Science, University of Illinois, Urbana-Champaign, 2004
  • B.S., Computer Engineering, University of Illinois, Urbana-Champaign, 2004, highest honors
  • Siebel Scholarship, 2004