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Innovative ML model -switching approach for real-time traffic monitoring on smartphones

Four CSE second-year students of IIITH’s demonstrated a dynamic ML model switching approach on smartphones for real-time traffic monitoring. students have come up with a dynamic machine learning (ML) model-switching technique on smartphones, enabling real-time traffic monitoring that adapts to changing conditions depending on the traffic flow. The team comprising undergraduate second-year CSE students – Kriti Gupta, Ananya Halgatti, Priyanshi Gupta, and Larissa Lavanya – under PhD student Akhila Matathammal’s mentorship and guidance of Prof. Vaidyanathan, who is part of software Architecture 4 Sustainability group at Software Engineering Research Centre, worked on a dynamic model switching approach titled EdgeML Balancer, for object detection on edge devices such as smartphones.

IIITH’s SCLL plays pivotal role in TSDSI’s White Paper on Smart City Solutions

The rapid urbanization of modern cities has driven the evolution of Smart City initiatives, emphasizing sustainability, citizen-centric services, and enhanced quality of life. As cities worldwide strive to optimize infrastructure and resource management, intelligent enabling technologies continue to play a critical role in this transformation. TSDSI, India’s Telecom SDO and a Type 1 partner of the global oneM2M Partnership Project, conducted the oneM2M Stakeholders Day on 12 February 2025 at the Research & Innovation Park, IIT Delhi. The white paper on Innovations For Sustainable Urban Living: Insights into Smart City Solutions was officially launched during the event, marking a significant milestone in contributions to Smart Cities.

IIITH researchers work on unlearning AI biases

As part of the Techforward Research Seminar series, Prof. Ponnurangam Kumaraguru briefly touches upon the pitfalls of LLMs and the ways in which they can be made to unlearn or forget content. In today’s world we rely on technology to accelerate response times to our tasks or queries, to gather accurate information and assist us efficiently. Let’s take the example of 3 everyday technological tools that almost everyone lives with – Google Translate, ChatGPT and WhatsApp. Now, let’s look at some of their imperfections. For instance, a test – that anyone can conduct – across these 3 tools reveals the gender biases that are present. In Google Translate, the prompt for “My friend is a doctor” will translate it to “Mera friend ek doctor hai” while “My friend is a nurse” translates it to “Meri friend ek doctor hai”.

Distinguished Lecture by Prof. Elisa Bertino (4 March 2025)

Speaker: Elisa Bertino, Samuel Conte Distinguished Professor of Computer Science at Purdue University.

Title: Applying Machine Learning to to securing cellular networks

Date: 4 March 2025

Summary of the talk: Cellular network security is more critical than ever, given the increased complexity of these networks and the numbers of applications that depend on them, including telehealth, remote education, ubiquitous robotics and autonomous vehicles, smart cities, and Industry 4.0. In this talk, I will first present a comprehensive threat analysis in the context of 5G cellular networks to give a concrete example of the magnitude of the problem of cellular network security. Then, I will present two specific applications of ML techniques for the security of cellular networks. The first application focuses on the use of natural language processing techniques to the problem of detecting inconsistencies in the “natural language specifications” of cellular network protocols. The second application addresses the design of an anomaly detection system able to detect the presence of malicious base stations and determine the type of attack. Then I’ll conclude with a discussion on research directions.

About Elisa Bertino: Elisa Bertino is a Samuel Conte Distinguished Professor of Computer Science at Purdue University. She serves as Director of the Purdue Cyberspace Security Lab (Cyber2Slab). Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. Her recent research focuses on security and privacy of cellular networks and IoT systems, and on edge analytics for cybersecurity.  Elisa Bertino is a Fellow member of IEEE, ACM, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for “For outstanding contributions to database systems and database security and advanced data management systems”, the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”, the 2019-2020 ACM Athena Lecturer Award, and the 2021 IEEE 2021 Innovation in Societal Infrastructure Award. She is currently serving as ACM Vice-president.