IEEE Global Communications Conference
7–11 December 2021 // Madrid, Spain // Hybrid: In-Person and Virtual Conference
Connecting Cultures around the Globe


Mirco Musolesi

Mirco Musolesi is Full Professor of Computer Science at the Department of Computer Science at University College London and a Turing Fellow at the Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence. He is also Full Professor of Computer Science at the University of Bologna. Previously, he held research and teaching positions at Dartmouth, Cambridge, St Andrews, and Birmingham. His research interests lie at the interface of machine learning, artificial intelligence, computational modelling of user/human behaviour & social systems, and ubiquitous computing. More information about his research profile can be found at:

Sensing and Modelling Human Behaviour for Social Good

Today's mobile phones are far from mere communication devices they were just fifteen years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. Information about users’ behaviour can also be gathered by means of wearables and IoT devices as well as by sensors embedded in the fabric of our cities. Inference is not only limited to physical context and activities, but in the recent years mobile phones have been increasingly used to infer users' emotional states. The applications of these techniques are several, from positive behavioural intervention to more natural and effective human-mobile device interaction.
In this talk, I will discuss the work of my lab in the area of mobile sensing for modelling and predicting human behaviour for social good. I will also discuss our research directions in the broader area of modelling human behaviour and social systems, outlining the open challenges and opportunities.

Vijay Janapa Reddi

Vijay Janapa Reddi is an Associate Professor at Harvard University, VP and a founding member of MLCommons (, a nonprofit organization aiming to accelerate machine learning (ML) innovation for everyone. His research sits at the intersection of machine learning, computer architecture and runtime software. He specializes in building computing systems for tiny IoT devices, as well as mobile and edge computing. Dr. Janapa-Reddi is a recipient of multiple honors and awards, including the National Academy of Engineering (NAE) Gilbreth Lecturer Honor (2016), IEEE TCCA Young Computer Architect Award (2016), Intel Early Career Award (2013), Google Faculty Research Awards (2012, 2013, 2015, 2017, 2020), and more. He has been inducted into the MICRO and HPCA Hall of Fame (in 2018 and 2019, respectively). He is passionate about widening access to applied machine learning for STEM, Diversity, and using AI for social good. He designed the Tiny Machine Learning (TinyML) series on edX, a massive open online course (MOOC) that sits at the intersection of embedded systems and ML that thousands of global learners can access and audit free of cost. Dr. Janapa-Reddi received a Ph.D. in computer science from Harvard University, an M.S. from the University of Colorado at Boulder and a B.S from Santa Clara University

TinyML: The Future of Machine Learning is Tiny and Bright

Tiny machine learning (TinyML) is a fast-growing field at the intersection of ML algorithms and low-cost embedded systems. TinyML enables on-device analysis of sensor data (vision, audio, IMU, etc.) at ultra-low-power consumption (<1mW). Processing data close to the sensor allows for an expansive new variety of always-on ML use-cases that preserve bandwidth, latency, and energy while improving responsiveness and maintaining privacy. This talk introduces the vision behind TinyML and showcases some of the novel humanitarian applications that TinyML is enabling in the field, from wildlife conservation to supporting public health initiatives. Yet, there are still numerous challenges to address. Tight memory and storage constraints, hardware/software heterogeneity, and a lack of relevant large-scale datasets pose a substantial barrier to developing TinyML applications. To this end, the talk also touches upon some future research opportunities for unlocking the full potential of TinyML for social good.