About
Mr. Rajan Mani is a dedicated academician with more than 15+ years of experience in teaching, academic administration, and 3+ years in research. He has been actively involved in delivering core computer science subjects, guiding student projects, and contributing to institutional development. His areas of interest include Machine Learning, Federated Learning, and Edge Computing, along with practical exposure to modern web technologies and software development.
Professional Experience
Assistant Professor
Galgotias University • Jan 2026 – Present
Currently engaged in teaching and academic responsibilities, contributing to student learning, curriculum delivery, and institutional academic activities.
Assistant Professor & Registrar
LDC Institute of Technical Studies • Total 7+ Years
Served initially as Assistant Professor and later handled additional responsibilities as Registrar. Involved in teaching core computer science subjects, managing academic records, coordinating examinations, and supporting administrative and institutional operations.
Assistant Professor
Shambhunath Institute of Engineering & Technology • 2+ Years
Delivered lectures, mentored students, and participated in academic activities including project guidance and evaluation.
Assistant Professor
LDC Institute of Technical Studies • 3+ Years (Initial Phase)
Started academic career by teaching fundamental computer science subjects and guiding students in practical and theoretical learning.
Education
Ph.D. (Pursuing)
Computer Science & Engineering • National Institute of Technology Meghalaya
Research Area: Federated Learning & Edge Computing
M.Tech
Computer Science & Engineering
Specialization: Machine Learning
B.Tech
Information Technology
Graduated with Honors
Research Areas
Research interests are centered around Artificial Intelligence and Machine Learning, with a focus on Federated Learning and Edge Computing. The work primarily explores building privacy-preserving, communication-efficient, and scalable learning models for distributed environments, addressing real-world challenges such as data security, limited bandwidth, and decentralized data processing.
Artificial Intelligence & Machine Learning
Designing and implementing machine learning models for classification, prediction, and data-driven decision-making, with emphasis on real-world applicability and performance optimization.
Federated Learning
Developing decentralized learning frameworks that enable collaborative model training without sharing raw data, focusing on privacy preservation, secure aggregation, and efficient communication.
Edge Computing
Exploring edge-based model deployment and optimization techniques to reduce latency and improve system efficiency in resource-constrained and distributed environments.
Technical Expertise
Artificial Intelligence & Machine Learning
Machine Learning, Deep Learning, Model Training, Classification & Prediction Models, Data Preprocessing, Feature Engineering, Model Evaluation.
Federated Learning & Distributed Systems
Decentralized Model Training, Secure Aggregation, Communication-Efficient Learning, Privacy-Preserving Techniques, Distributed AI Systems.
Edge Computing
Edge-based Model Deployment, Low-latency Processing, Resource Optimization, Distributed Data Handling, Real-time AI Applications.
Full Stack Development
HTML, CSS, JavaScript, Node.js, React, MongoDB, REST APIs, Responsive Web Design, Application Development.
Programming Languages
Python, C, C++, SQL with strong understanding of data structures, algorithms, and problem-solving techniques.
Tools & Frameworks
TensorFlow, Keras, Scikit-learn, Git, Database Management Systems, Development & Research Tools.
Let’s Connect
Available for academic collaboration, research discussions, and professional opportunities.
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