In a first-of-its-kind initiative in India, IIIT-H convened the Learning & Longevity Symposium (LLS) placing learning—not medicine, fitness, or finance—at the centre of the longevity conversation. Hosted by IIIT-H’s Third Age Learning (3AL) Research and Design Group, the symposium brought together leading voices from cognitive neuroscience, molecular biology, geriatric medicine, AI, game design, eldercare technology, and learning sciences. With an aim to explore a bold, unifying hypothesis — learning is the most powerful intervention for healthy aging. The symposium unfolded across three thematic tracks: The Biology of Longevity, Third Age Learning, and The Longevity Ecosystem. The format encouraged unlikely but necessary intersections — neuroscientists engaging with game designers, dementia specialists in dialogue with edtech entrepreneurs, and cognitive scientists exchanging ideas with architects of the emerging silver economy.

The selective national research fellowship is backing the development of a low-cost smart mattress by Prof Aftab Hussain that can detect falls, track sleep, and improve elderly care – offering a privacy-first alternative to cameras and wearables. The sunset years come with their own set of challenges. Ageing is one of the key risk factors for falls. According to the WHO, older people have the highest risk of death or serious injury arising from a fall and the risk only increases with age. In fact monitoring the elderly during sleep is just as vital as keeping an eye on them while moving. It is the reason why elderly homes, hospitals and now even families employ nursing staff or attendants to monitor the well-being of elderly patients through the night. At IIIT-H, Prof. Aftab Hussain, Centre for VLSI and Embedded System Technologies, is particularly concerned about falls that go unnoticed. According to him, “In many cases, help arrives too late – not because care is unavailable, but because no one knows an incident has occurred.”

From flood relief to farming and the frontlines, Prof. Spandan Roy is rethinking how machines learn to act in the real world. “Even if you don’t know the system… can you still control it?” It’s not the kind of question that usually opens a talk on robotics. But for Prof. Spandan Roy, it defines everything and sets the context for his work at the Robotics Research Center at IIITH. In theory, engineering is neat. Systems obey equations; forces can be calculated; outcomes predicted. In practice, especially in the world of flying machines, things are far less tidy. A drone in flight isn’t just governed by clean physical laws; it’s constantly negotiating wind, drag, shifting payloads, and environmental disturbances that are difficult, if not impossible, to model precisely. For someone without a traditional mechanical background, as Prof. Roy admits of himself, this gap becomes even more pronounced.