Faculty Advisors

Dr. Chen is an Associate Professor of Computer Science at Cal Poly Pomona. Dr. Chen has expertise in wireless network security, data privacy, incentive mechanisms and health informatics. She has published over 40 referred papers in journals and conferences, including top-tier conferences such as ACM MobiCom, IEEE INFOCOM, and many IEEE or ACM Transactions. She has received funding and gifts from NSF, Amazon Inc., Oklahoma Center for the Advancement of Science and Technology, and some smaller organizations. She is a co-PI of the current NSF SFS program at Cal Poly Pomona. Dr. Chen has served as a technical program committee member for several well-recognized international conferences, including IEEE INFOCOM. She reviews research papers for a good number of journals, across the domains of computer security, network communications, health informatics, and machine learning.

Dr. Husain is an Associate Professor of Computer Science at CPP. He directs NSF Award 1504526 (2015-2020), “SFS (Scholarship Track): An Interdisciplinary, Learning-by-Doing Approach to Identify and Prepare Cyber Warriors”. Co-PI Husain is also the founding program chair of SFSCon, a national cybersecurity training workshop for the CyberCorps SFS students across the country offered in collaboration with National Security Agency (NSA), NSF Cybersecurity Center of Excellence (Trusted CI), and NIWC-Pacific. Dr. Husain established the PolySec cybersecurity lab, a center for cybersecurity education, research and outreach. His current research focuses on psychology of security, embedded cloud computing security and Android security. He has mentored many undergraduate and graduate students in research projects on coercion-resistant passwords, cloud storage forensics, psychology of phishing attacks, etc.

Dr. Ji is an Assistant Professor of Computer Science at Cal Poly Pomona. His research interests include big data analysis, high-performance computing, and large-scale linear algebra, with a focus on developing efficient computational algorithms for data intelligence applications. Dr. Ji is the PI of an NSF Award 1828644 (2018-2021) “MRI: Acquisition of a Mass Storage System for Data Intelligence Research”. He has mentored/supervised more than 25 undergraduate students in data intelligence research. He has also served as a faculty mentor in the XSEDE EMPOWER (Expert Mentoring Producing Opportunities for Work, Education, and Research) program, the Cal Poly Pomona’s SURE (Search Results Web results Summer Undergraduate Research Experience) program and ECRA (Early Career Research Apprenticeship) program.

Dr. Monemi is a Professor of Electrical & Computer Engineering and founder of Smart Grid Laboratory at CPP.  Dr. Monemi’s research area covers: Smart Grid Technologies, Renewable Energy, Model Integrated Computing, Simulation and Diagnostics of Power Grid, and Fault Management Analysis in Power Systems.  He has received several research grants to support his research work from Department of Energy (DOE), National Science Foundation (NSF), NASA, Microsoft, ETAP, SKM and JWEMC. The DOE project, using RTDS and building a prototype model of a real-world like Smart Grid, has a total of about 100 undergraduate students involved in the last 5 years at Cal Poly Pomona.

Dr. Amamra is an Assistant Professor of Computer Science at Cal Poly Pomona. Dr. Amamra’s research expertise is in using machine learning in cybersecurity with focuse on network security, smartphone security, anomaly detection and gaming cybersecurity. Dr. Amamra has published over 20 referred papers, technical reports, and book chapters. His research has been supported by NSF REU Site in Big Data Security and Privacy at CPP under direction of Dr. Chen. In addition, Dr. Amamra is a director of Cyberstar program. This program focuses on gaming cybersecurity curriculum; it is collaboration between NSF, NYU and SANS institution. Dr. Amamra is also a co-PI of the NSF SFS program in Cal Poly Pomona. In the last three years, Dr. Amamra advised over 10 undergraduate and graduate students’ research in Cal Poly Pomona. 

Dr. Korah is an Assistant Professor in Computer Science at CPP. His research interests include cybersecurity, computational frameworks for data science with applications to computational health policy, large scale information processing and retrieval, dynamic social network analysis, and computational social systems. His contributions to teaching and outreach includes development of a summer research internship program at the University of Texas at El Paso, aimed at creating pathways for minority students to careers in homeland security. His work has been funded by multiple agencies within the Department of Defense (DoD) and the Department of Homeland Security (DHS).

Dr. Eger will join Computer Science Department as an Assistant Professor in January 2021. Dr. Eger's research involves communication over a variety of channels in games. In his previous work, he has investigated how information exogenous to a game, most notably timing, can be correlated to in-game events. In one project, he used Machine Learning to predict a player's play style from that player's actions in the game, as well as their deliberation time. As a graduate student at NC State University, Dr. Eger has directly supervised two NSF REU students. Dr. Eger is also currently supervising three graduate students in the Master's program at the University of Costa Rica as a Visiting Professor.

Dr. Marin is an Assistant Professor in the Computer Science Department at CPP. His currently multidisciplinary research explores proactive cyber-threat intelligence, where artificial intelligence, data mining, machine learning, and social network analysis techniques are applied into the cybersecurity domain to predict future cyber-threats against organizations. Among all his scientific publications in the area, Dr. Marin authored two of the first books ever written about malicious hacking on the darkweb - "Darkweb Cyber Threat Intelligence Mining" and "Exploring Malicious Hacker "Communities", demonstrating how technical and social data collected from the underground hacker communities can be leveraged by learning models to predict future cyber-attacks