Picture of Fahim Kawsar

Fahim Kawsar

Cambridge, UK
Research Lead

Education

 

 

Biography

Fahim  leads the Internet of Things research at Bell Labs and holds a Design United Professorship at TU Delft. His current research explores novel algorithms and system design techniques to build transformative multi-sensory systems for disruptive mobile, wearable and IoT services. He borrows tenets from Social Psychology, learns from Behavioural Economics and applies Computer Science methods to drive his research. He is a frequent keynote, panel and tutorial speaker, hold 15+ patents, organised and chaired numerous conferences, (co-)authored 100+ publications and has had projects commissioned. He is a former Microsoft Research Fellow and has worked before at Nokia Research, and Lancaster University. His work and publications can be viewed at http://www.fahim-kawsar.net.

 

 

 

Selected Articles and Publications

An early characterisation of wearing variability on motion signals for wearables

Unsupervised Domain Adaptation for Design of Robust Sensory Systems

WellComP 2019: Second international workshop on computing for well-being

Degradable inference for energy autonomous vision applications

The city as a personal assistant

Scaling Crowdsourcing with Mobile Workforce : A Case Study with Belgian Postal Service

A vision for adaptive and generalizable audio-sensing systems

Automatic Smile and Frown Recognition with Kinetic Earables

On Tracking the Physicality of Wi-Fi: A Subspace Approach

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

On Indoor Human Sensing using Commodity Radar

Monitoring Daily Activities of Multiple Sclerosis Patients with Connected Health Devices

On Robustness of Cloud Speech APIs: An Early Characterization

Mindful Interruptions: A Lightweight System for Managing Interruptibility on Wearables

Poster: On-Wearable AI to Model Human Interruptibility

Earables for Personal- scale Behaviour Analytics

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices

Squeezing Deep Learning into Mobile and Embedded Devices

Can Mobile Workforce Revolutionize Country-Scale Crowdsourcing?

A Closer Look at Quality-Aware Runtime Assessment of Sensing Models in Multi-Device Environments

An Early Characterisation of Wearing Variability onMotion Signals for Wearables

On Hyper-local Conversational Agents in Urban Settings

Contact

Blog posts
Workshop @ MobiCom 2015
Introducing the IoT Team