Dr. Ram Prakash Rustagi

Dr. Ram Prakash Rustagi

Professor · Non-Tenure Track

Department of Computer Science and Electrical Engineering

College of Engineering and Information Technology

He/Him/His/Himself

About

Dr Ram P Rustagi is currently working as Professor of Practice, CSEE Dept, University of Maryland Baltimore County, Maryland, USA, and honed up his academic skills with Ph.D from IIT Delhi, India and M. Tech from IISc Bangalore, India.

At UMBC, Dr Rustagi teaches both undergraduate and graduate students in the area
of Computer Network, Security and Databases. He is also working as Principal Investigator on a Research Grant project funded by United States India Science and Technology Endowment Fund, and aims to develop "Hyper Local Air Quality - AI Enabled Context Aware Content System". Prior to joining UMBC, he was instrumental in setting Cyber Range which provides a sandbox environment where students can conduct real time experiments and carry out hands on exercises related to Computer Network, Security, Operating Systems as well as in other areas of Computer Science. Prior to UMBC, he was Department Chair of AI/ML Dept at KS Institute of Technology, Bangalore, India. He also advised a technology startup in the area of Air Pollution, where he mentored new technology development using Machine Learning techniques. His previous engagements cover senior positions in engineering in various startup technology companies in USA/India. The professional spectrum of 35+ years consists of Academic Institutes, and Technology start-ups as well as large companies.

Research interests

Network Security
Experiential Learning approach to teaching

Teaching interests

Computer Networks and Security
Databases and ​Concurrency Control
Big Data Analytics

Education

  • Ph D, Computer Science & EngineeringIndian Institute of Technology, Delhi (1998)
    Studies in Concurrency Control for Centralized, Distributed and Nested Transaction Systems
  • MS, Information ScienceIndian Institute of Science, Bangalore (1981)
    A Comparative Study of Sklansky's Piecewise Linear Classifiers with Clustering Algorithm ISODATA