Speaker: Mahima Arrawatia

Title: Millimeter wave Transceiver Design

Abstract: The talk will focus on the design challenges of millimeter transceiver IC's. The talk will provide details of the IC's designed by our group for 5G and 6G applications.

Biography: Dr. Mahima Arrawatia is faulty in the Department of Electronics and Electrical Engineering at IIT Guwahati. She joined the institute in July 2017. She completed her Masters and Ph.D from IIT Bombay. She is having two granted Indian patents on RF energy harvesting. She has authored/co-authored more than 20 papers in international journals and conferences. She is the recipient Early Research Grant from SERB, India for working on RF power amplifier for 5G applications. She has also received a grant for designing low-power IoT transmitter/ receiver from the Department of Science and Technology, India. In addition, she is also a part of the project funded by MeiTy to develop an mm-wave transceiver for 5G applications and 6G THz Test Bed funded by DoT. Her research interests are RFIC design, RF circuit design and antenna design.

Speaker: Dr. Lilapati Waikhom

Tittle: Introduction to Graph Neural Networks (GNNs)

Abstract: This presentation provides a comprehensive introduction to Graph Neural Networks (GNNs), combining foundational concepts from graph theory and neural networks. It explores how GNNs leverage both the structure of graph data and the learning capabilities of neural networks to solve tasks like node classification, link prediction, and community detection. Core mechanisms such as message passing and neighborhood aggregation are explained. The presentation also highlights challenges like positional unawareness in GNNs and introduces Position Observant GNNs (PO-GNNs) as a solution. Applications across domains such as drug discovery, social network analysis, and smart cities illustrate the practical impact of GNNs.

Biography: Dr. Lilapati Waikhom is an Assistant Professor in the Department of Computer Science and Engineering, NERIST, with a specialization in Graph Neural Networks (GNNs). She earned her Ph.D. from the National Institute of Technology (NIT) Silchar in July 2024. Her doctoral thesis, titled "Design and Development of Deep Learning-based Models for Inherent Tasks in Graph Structures", was supervised by Dr. Ripon Patgiri. She has a keen interest in cutting-edge research in GNNs, especially in their applications to bioinformatics and adversarial robustness.