Faculty
Mahdi Ebrahimi, Ph.D.
Assistant Professor, College of Engineering and Computer Science, CSUN
COMP 644: Big Data
COMP 698DS: Graduate Project
Dr. Mahdi Ebrahimi is an Assistant Professor of Computer Science at CSUN. He holds a Ph.D. in Data Management from Wayne State University and an M.S. from Amirkabir University of Technology. His research focuses on big data management in cloud computing, with expertise in developing metaheuristic optimization algorithms for cloud data placement and workflow scheduling.
Dr. Ebrahimi has published extensively in peer-reviewed journals and conferences, including the IEEE Conference on Big Data and Big Data Congress. He is a member of IEEE and is particularly interested in large-scale scientific workflows within cloud-based big data management.
Vanessa Klotzman, M.S.
Adjunct Lecturer, College of Engineering and Computer Science, CSUN
COMP 641: Fundamentals of Data Science
Vanessa Klotzman is a Ph.D. Candidate in Software Engineering at UC Irvine. She holds a B.S. in Applied Mathematics (2017) and an M.S. in Software Engineering (2019) from CSUN. Her research focuses on recommender systems, natural language processing, disease modeling, software development optimization, and health care analytics.
Since 2022, Vanessa has been a Lecturer at CSUN, teaching data mining and software engineering. She is also a Research Intern at CHOC Children’s, where she collaborates on deep learning and AI applications in healthcare. Her Ph.D. research, in partnership with CHOC, examines telehealth patient populations, neurological conditions, and NLP applications. She has also contributed to studies on Acute Kidney Injury and COVID-19 in pediatric patients.
Alex Modarresi, Ph.D.
Assistant Professor, College of Engineering and Computer Science, CSUN
COMP 642: Advanced Databases and Data Visualization
COMP 640: Database System Design
Dr. Alex Modarresi holds a Ph.D. in Computer Science from the University of Kansas and an M.S. from Ryerson University. His research focuses on network resilience, software-defined networking, and the Internet of Things (IoT), with a recent emphasis on smart agriculture systems. He has extensive experience in network protocol simulation and system dynamics modeling for agricultural applications. He also holds multiple certifications from Cisco Systems, further strengthening his expertise in computer networking.
Abhishek Verma, Ph.D.
Assistant Professor, College of Engineering and Computer Science, CSUN
COMP 542: Machine Learning
COMP 643: Deep Learning
Dr. Abhishek Verma is an Assistant Professor of Computer Science at CSUN and the Director of the Data Science and AI Research Lab. He is the lead creator and academic lead of the self-support M.S. in Data Science program offered through Tseng College. A researcher and instructor since 2006, he has authored over 55 publications on machine learning, deep learning, AI, and big data analytics.
Dr. Verma serves on the editorial boards of the International Journal of Artificial Intelligence and Machine Learning and the International Journal of Data Science. He is also a program committee member for the IEEE International Conference on Tools with Artificial Intelligence. Additionally, he is a faculty member of CSUN’s Autonomy Research Center for STEAHM (ARCS), a NASA-sponsored Center of Excellence.
Taehyung (George) Wang, Ph.D.
Professor, College of Engineering and Computer Science, CSUN
COMP 541: Data Mining
COMP 698DS: Graduate Project
Dr. Wang earned his Ph.D. in Computer Science from UC Irvine, an M.S. from Western Illinois University, and a B.S. from Seoul National University. His research focuses on video and image object detection, natural language processing, and agent-based applications. His recent work includes deep learning for prosthetic limb control, password security, and traffic sign recognition. He has published in IEEE conferences and secured multiple NSF grants supporting data science research and career development.
At CSUN, Dr. Wang teaches data mining and graduate research, guiding students in applying AI and machine learning to real-world challenges.
