Prof. ABHISHEK RAGHUVANSHI is Associate Professor & Head of Departments- Computer Science & Engineering, Information Technology, Artificial Intelligence and Data Science. He is having teaching experience of 17 years, research experience of 12 years and administrative experience of 6 years. He is also working as Chief Coordinator of Network Cell, Remote Centre coordinator IIT Bombay. He is also AAKASH Project Coordinator. AAKASH Tablet project is funded by the MHRD. He is about to submit his PhD thesis on IoT Security. He has completed his AMIETE (BTech) in Computer science & Engineering and Master of Technology (M.Tech) in Computer Science. He has also completed B.Sc. in Computer Science & MCA (Master of Computer Applications). He has also qualified GATE (CSE) several times and NET (Computer Science) for Assistant Professorship. He has published 78 research papers including publications in SCI indexed journal from renowned publisher’s like- Inderscience, Wiley, Hindawi, Elsevier, IEEE. He has also published 2 Indian patents. He has also worked as Principal Investigator in TEQIP 3 funded research project on “Towards Mitigation of Vulnerabilities and Risk in Cloud and IOT Based Smart Cities” He has also conducted 11 ISTE workshops in association with IIT Bombay. 15 MTech students have completed their project under his guidance.
Why to Choose Computer Science & Engineering:
Because computing skills are in incredibly high demand…..
Computer Science will build your fundamentals of how to code.It will teach you what to code for Industry.
Mahakal Institute of Technology established the Department of Computer Science and Engineering (CSE), in to lead computing education, research, and innovation. The academic programs and research by faculty and students advance computing for the public good and broadening societal impact.
Specific courses provide fundamental knowledge and skills in core aspects of computing like programming, data structure, operating system, algorithms, computer networks, databases. Students also gain proficiency in interdisciplinary subfields of computing like machine learning, data mining, and data science.