Mr. Aryan Kundal

Assistant Professor

Aryan Kundal is an emerging researcher and educator in the field of Computer Science and Engineering, with strong expertise in Machine Learning, Deep Learning, Time-Series Forecasting, and Energy-Aware Intelligent Systems. He is currently serving as an Assistant Professor at Dayananda Sagar University, Bengaluru, where he is actively involved in teaching, research, and mentoring students across undergraduate and postgraduate programs.

He holds an M.Tech. in Data Science and Engineering from Dr B R Ambedkar National Institute of Technology Jalandhar (NIT Jalandhar), where he pursued advanced coursework in Data Engineering and Visualization, Advanced Machine Learning, Statistics, Deep Learning, and Big Data Analytics. Prior to this, he earned his B.Tech. in Computer Science and Engineering from Shri Mata Vaishno Devi University (SMVDU), Katra, Jammu & Kashmir, with coursework spanning Computer Architecture, Data Structures and Algorithms, Networking, and Database Management Systems.

Mr. Kundal brings practical industry and research experience alongside his academic credentials. He worked as an IT System Engineer at RVS Informatics IT Private Limited, Jammu & Kashmir, where he provided expert technical support to UK-based clients, resolving 90% of initial queries and ensuring smooth operations using tools such as Salesforce, Office 365, SharePoint, N-central, and CRM platforms. Earlier, he completed a Work-Based Learning Internship at the National Institute of Electronics & Information Technology (NIELIT), Jammu & Kashmir, where he configured and deployed EIGRP on IPv6 for seamless inter-departmental connectivity and implemented IPv6 Access Control Lists (ACLs) to strengthen network security across a university campus environment.

His research contributions include work on hybrid foundation-model architectures for interpretable multi-horizon energy consumption forecasting. His paper, "Foundation-Enhanced Hybrid Framework for Interpretable Multi-Horizon Energy Consumption Forecasting" (co-authored with Deepak Kumar Gupta, NIT Jalandhar), proposes FEHF — a novel architecture that integrates frozen Chronos encoder representations with STL (Seasonal-Trend decomposition using LOESS) and an energy-aware attention mechanism. The manuscript is accepted in Fourth International Conference on Secure Cyber Computing and Communications .

Mr. Kundal is deeply interested in interdisciplinary and collaborative research at the intersection of Artificial Intelligence, Foundation Models, Explainable AI, Time-Series Analysis, Networking, and Data Engineering. He regularly engages with the latest developments in large language models, energy systems intelligence, and reproducible machine learning research. He is keen on contributing to both the academic community and applied industry through principled, honest, and high-impact research.

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Phone: +91-9682606410

LinkedIn: https://www.linkedin.com/in/aryan-kundal