IIIT Hyderabad’s TechForward Research Seminar Series, an academia-industry confluence around emerging technologies is currently in its 12th edition, having successfully completed a year of deep insights, directional talks, and industry/business outlooks from highly accomplished thought leaders, on one theme every month. The talks were converted into features that are compiled into a monthly dispatch as a ready reckoner for technology directions.The research seminar series covered themes across various domains such as computer vision, artificial information, robotics, quantum computing, AI on the Edge, healthcare, tech in financial services, software architecture, LLMs, sustainable mobility and agentic AI. Over the past year, IIITH collaborated with leading companies such as Qualcomm, ISB, Google, GoldmanSachs, Bosch, Llyods Technology Centre, Meta, Accenture Evernorth Health Services, ZF India and ServiceNow.

In a significant step toward accelerating urban innovation, Smart City Research Centre at IIITH and KIET signed a MoU on 17 June 2025 at the KIET Campus to support the Kakinada Smart City Mission. The collaboration aims to bring cutting-edge research, real-time urban solutions, and emerging technologies to the forefront by establishing a Smart City Living Lab at the KIET campus and extending the same to Kakinada Smart City. The initiative, launched in the presence of the Kakinada District Collector Shri Sagili Shan Mohan and other dignitaries, marks a new chapter in bridging academia, government, and technology for smarter, more sustainable cities. The lab will serve as a collaborative ecosystem to support the research, development, and deployment of smart city solutions, enabling students, researchers, and city stakeholders to tackle practical urban challenges.

At the recently concluded TechForward series seminar on AI in Vehicles, Prof. C V Jawahar described the mobility-related research efforts being undertaken at IIITH. Here’s a summarized version. At IIITH, we have been aiming to solve problems on Indian roads and driving conditions using data-driven technologies. With the help of techniques from machine learning, computer vision, computer graphics, computational sensing, and allied areas, we have been creating solutions and transferring these solutions across diverse practical conditions on roads. One of the research studies that was undertaken includes early anticipation of driver actions before the onset of a manoeuvre, such as a turn, a lane change, a sudden halt and so on. When you know the driver in front of you is going to take a right turn without indicating with a signal, you anticipate and get ready for it perhaps by slowing down or changing lanes.