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Mr. Devasane Mallesham
Asst. Professor
Qualification
M.Tech
Professional Exp.
5+ Years
Registration Number
ACET05137
About :
Mr. Devasane Mallesham is a highly qualified academician with over 15 years of extensive experience in teaching, research, and technical training in the field of Computer Science and Engineering. He completed his M.Tech from the prestigious Indian Institute of Technology Bombay (IIT Bombay) in 2009 and has since worked as a consultant, educator, and researcher, contributing significantly to competitive exam training and higher education.
He has taught a wide range of subjects including C Programming, Data Structures, Algorithms, Compiler Design, Theory of Computation, Machine Learning, Artificial Intelligence, and Database Management Systems. His teaching methodology emphasizes conceptual clarity, real-time examples, and problem-solving, making him a popular instructor in GATE CS and AI/ML domains.
Beyond teaching, Mr. Mallesham is actively engaged in research focusing on Quantum Computing, Artificial Intelligence–driven data compatibility, and Machine Learning applications.
Educational Details
- M.Tech in Computer Science & Engineering – IIT Bombay (2009)
- B.Tech in Computer Science & Engineering – Kakatiya University (2007)
Subjects Taught:
- C Programming
- Discrete Mathematics
- Compiler Design
- Theory of Computation
- Python
Core Research Domains:
- Artificial Intelligence (AI)
- Machine Learning & Deep Learning
- Quantum Computing
- Data Mining & Predictive Modelling
- Natural Language Processing (NLP)
- Big Data & Distributed Systems
Research Focus:
- Developing hybrid Quantum–AI frameworks for enhancing data compatibility across heterogeneous systems.
- Building predictive models for financial risk analysis, including credit default prediction using machine learning.
- Designing AI-driven solutions for automated data transformation, schema mapping, and semantic alignment.
- Investigating quantum-optimized algorithms such as QAOA, Grover Search, and quantum kernels for accelerating data integration.
- Applying ML and NLP techniques for tasks like toxic comment classification, pattern recognition, and large-scale text analytics.
- Exploring scalable AI systems capable of handling high-volume, high-velocity data with improved accuracy and computational efficiency.