Rate My Professor Rushit Dave

Dr. Rushit Dave is an Associate Professor and Program Director for Computer Science in the Department of Computer Information Science at Minnesota State University, Mankato. He earned his Doctor of Philosophy in Computer Science from North Carolina A&T State University in Greensboro, North Carolina, his Master of Science in Computer Science from New York Institute of Technology in New York, and his Bachelor of Technology in Computer Engineering from Dharmasinh Desai University in India. Prior to his current position, Dr. Dave served as an Assistant Professor in the Computer Science Department at the University of Wisconsin-Eau Claire from 2020 to 2022. As an educator, he teaches a range of courses including analysis of algorithms, programming languages, data structures, database systems, machine learning, introduction to data mining, software engineering, mobile application development, and operating systems. His commitment to teaching is evident in his involvement in faculty development workshops, such as those on prompt engineering for effective communication with AI.

Dr. Dave's research interests focus on machine learning and deep learning applications across various domains, including cybersecurity, biometrics authentication schemes, IoT security in wireless networks, machine learning applications in computing education, object detection using machine learning, human activity recognition using machine learning, Canvas System 2.0, touch and click stream analysis, data mining, deep learning, and reinforcement learning. He founded and directs the Artificial Intelligence, Machine Learning & Security Research (AIMS) Group at Minnesota State University, Mankato, which collaborates with North Carolina A&T State University, Northern Kentucky University, and University of Wisconsin-Eau Claire to advance research in these areas. Dr. Dave has published multiple journal and conference papers in reputed platforms and serves on the editorial and reviewer boards of numerous computer science and engineering journals and conferences. His mentorship has guided numerous graduate students in their theses, covering topics such as deepfake detection using machine learning, mouse dynamics for user authentication, sentiment analysis in healthcare, automation readiness in course scheduling, and music genre recognition. Through these efforts, Dr. Dave contributes significantly to the academic field by bridging theoretical advancements with practical applications in computer science.