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.

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.

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”.