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Since January 2015, I have been a Principal Scientist at Bell Labs where I drive the mobile sensing agenda of the broader Internet of Things research activity directed by Fahim Kawsar. Before joining Bell Labs, I spent four years at Microsoft Research based in Beijing. There I was a Lead Researcher within the Mobile and Sensing Systems group (MASS) led by Feng Zhao. In March 2011, I received a Ph.D. from Dartmouth College under the supervision of Andrew T. Campbell and Tanzeem Choudhury.

My dissertation pioneered community-guided techniques for learning models of human behavior that enable mobile systems to better cope with diverse user populations encountered in the real-world. More broadly, my research interests revolving around the systems and modelling challenges that arise when computers collect and reason about people-centric sensor data. At heart, I am an experimentalist who likes to build prototype mobile sensing systems based on well-founded computational models.

Please visit my publications page or google scholar profile to learn more about my work.

I can be reached at: niclane at acm dot org

Jan '16 Two papers provisionally accepted to IPSN '16. The first details unobtrusive monitoring of internal body states (such as, eating and drinking) using a wearable that exploits PHY-level radio transmissions; a first-of-its kind device developed with collaborators at MSU. The second presents DeepX -- a software-based accelerator for deep learning models that enables wearable-class hardware to cope with the deepest forms of this important direction in machine learning.
Oct '15 Initial results of our measurement study examining the system resource overhead of deep learning inference phases on wearables, phones and embedded devices will appear at the IoT-App workshop at SenSys.
Sept '15 DeepEar wins best paper at UbiComp '15! Congrats to all my co-authors.
Aug '15 I am joining the editorial board of ACM SIGMOBILE Mobile Computing and Communications Review, “GetMobile Magazine”; there I will be working with Robin Kravets on a column covering the latest and greatest in mobile computing research.
DeepEar, and our broader work into deep learning for wearable and mobile platforms, is featured in the New Scientist -- 'Eavesdropping app will turn your smartphone into a virtual PA'.
Jul '15 Three papers accepted at UbiComp '15 -- DeepEar, Compressive CrowdSensing and Prime! Very excited about the new ground we are breaking in deep learning for wearables/mobiles, low burden crowdsensing and the bootstrapping collaborative apps. Congrats to my co-authors at Microsoft Research, University of Cambridge, UC Davis, Tsinghua University, UC Santa Barbara, and University of Bologna.