We are pleased to announce the following keynote speakers for PerCom 2026:
Nicholas D. Lane
University of Cambridge, Flower Labs
Federated AGI: A Generational Opportunity for Pervasive Computing
ABSTRACT
Current scaling laws indicate that future advances in AI will hinge on access to massive amounts of compute and data. How will we obtain the computing power and data resources required to sustain continued AI progress? I believe all roads lead to federated learning, and approaches of this kind. And, in turn, this change represents the biggest opportunity for pervasive computing in a generation: with the right research priorities and execution, pervasive systems can provide the critical infrastructure for tomorrow’s frontier AI, supplying both compute and data; serving as the missing piece for the AI miracle to continue.
The AI world may not realize it yet, but it is ready for this radical change. Today, frontier models are trained on essentially the same web-scraped data and rely on data centers that can only scale through unsustainable capital investment. Instead, pervasive systems of sensors, personal devices, scientific instruments, space satellites, vehicles, and embedded computers offer both a limitless stream of new data and a modular, scalable network of compute. Once resources of this scale and quality are unlocked, today’s AI infrastructure paradigm will not compete. Soon, decentralized and federated techniques built on pervasive systems will be how the strongest LLMs are trained; and in time, how aspirational capabilities like AGI will finally be achieved.
In this talk, I will describe why the future of AI will be federated, and describe early solutions from Flower Labs and the Cambridge ML Systems Lab (CaMLSys), which address the technical challenges of this shift and place pervasive computing and communication at the core of how tomorrow’s AI is built.
BIOGRAPHY
Nic Lane (http://niclane.org) is a full Professor in the department of Computer Science and Technology at the University of Cambridge and holds a Royal Academy of Engineering Chair in Decentralized AI. He is also a Fellow of St. John’s College. At Cambridge, Nic leads the Cambridge Machine Learning Systems lab (CaMLSys; https://mlsys.cst.cam.ac.uk/). The mission of CaMLSys is to invent the next-generation of breakthrough ML-centric systems. Alongside his academic roles, Nic is the co-founder and Chief Scientific Officer of Flower Labs (https://flower.ai), a venture-backed AI company (YC W23) behind the Flower open-source federated learning framework. Flower Labs seeks to enable an AI future that is collaborative, open and decentralized. Nic has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering research, performed during his PhD thesis, that devised machine learning algorithms used today on devices like smartphones. Nic was the 2020 ACM SIGMOBILE Rockstar award winner for his contributions to “the understanding of how resource-constrained mobile devices can robustly understand, reason and react to complex user behaviors and environments through new paradigms in learning algorithms and system design.” In 2011, Nic received his Ph.D. from Dartmouth College. He also holds an M.Eng from Cornell University and a BSc(Hons) from the Uni. of Waikato.
Silvia Santini
Università della Svizzera italiana, Mobile and Wearable Computing Group
Beyond Accuracy: The Good, the Bad, and the Unknown in Sensor‑Based Human Behavior Modeling
ABSTRACT
Over twenty years, ubiquitous computing research has transformed everyday environments into rich sources of behavioral insight. Today, data‑driven models built on mobile, wearable, and physiological sensors can infer activities, routines, affect, and even identity – often beyond what system designers intended. This keynote revisits the field’s evolution through the lens of “the good, the bad, and the unknown”: the good –powerful models enabling health, education, and well‑being applications; the bad – fragility, bias, and opaque inference pipelines; and the unknown – emergent, unintended capabilities in AI‑driven sensing systems. I will outline open research directions for a future where pervasive systems remain trustworthy, meaningful, and aligned with human values.
BIOGRAPHY
Silvia Santini is an Associate Professor at the Faculty of Informatics at the Università della Svizzera italiana (USI), where she leads the Mobile and Wearable Computing Group. Before joining USI in 2016, she held faculty positions at TU Dresden (Associate Professor in Embedded Systems) and TU Darmstadt (Assistant Professor for Wireless Sensor Networks), and was previously a postdoctoral researcher at ETH Zurich and a visiting scholar at Stanford University. She received a PhD in computer science from ETH Zurich in 2009, and a Master’s degree in electrical engineering from the Sapienza Università di Roma in 2004. Silvia is an active member of the scientific community: she has served on the technical program committees of several international conferences, including UbiComp, PerCom, MobiSys, SenSys, e-Energy and INFOCOM, and as a reviewer for numerous venues. She was a founding Editor (2016–2019) and later Editor-in-Chief (2020–2023) of the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (PACM IMWUT). She is also a member of the Swiss Federal Energy Research Commission. Her current research spans wearable sensing and affective computing, sleep and well‑being modeling, human activity recognition, and interpretable models for human‑centered artificial intelligence.