B.S. Rutgers School of Engineering, 1999B.A. Rutgers College, 1999M.S. University of California, Berkeley, 2003Ph.D. University of California, 2006
"My research lab is developing new techniques for sensing and control of computer systems in the physical world."
Kamin Whitehouse, Commonwealth Associate Professor
Research interests include:
Internet of Things, Autonomy and Controls/Control Systems, Smart Buildings/Cities, Computer Networks
Kamin Whitehouse's research lab develops new technologies at the frontier of Cyber-Physical Systems (CPS), including RF sensing, safety-critical wireless communication, wearable sensors, occupancy sensing, smart buildings, and coordinated control of distributed systems and autonomous drones. His team develops techniques at the intersection of signal processing, control theory, and machine learning. The technologies created by these projects have been downloaded 50,000+ times, have been used by over half a dozen companies to create new products, and are currently running in millions of embedded devices around the world. He has patents granted and pending in a range of CPS techniques. Prof. Whitehouse is serving as Director of the Link Lab, whose mission is to enhance excellence in CPS at the University of Virginia. He is a past TPC chair for ACM BuildSys, ACM SenSys, ACM/IEEE IPSN, and EWSN and serves as associate editor of ACM Transactions on Sensor Networks (TOSN) and The P of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). He earned a B.A. in Philosophy and a B.S. in Electrical Engineering and Cognitive Science from Rutgers University. He earned M.S. and Ph.D. degrees in Computer Science from UC Berkeley.
BuildSys’16 Audience Choice AwardNov 2016
BuildSys’16 Best Paper Runner Up Nov 2016
BuildSys’16 Best Demo Award Nov 2016
BuildSys’16 Best Presentation Award Nov 2016
MobiSys’16 Best Paper Award Jun 2016
BuildSys’15 Best Paper Runner Up Nov 2015
BuildSys’15 Best Paper Runner Up Nov 2015
BuildSys’14 Best Talk Award Nov 2014
Commonwealth Endowed Chair Aug 2013
IPSN’13 Best Paper Runner Up Apr 2013
NSF CAREER Award Jan 2008
UVA Excellence in Diversity fellowship award Sep 2006
Siebel fellowship award Sep 2005
GOF two-year fellowship award Sep 2003
NSF three-year fellowship (monetary award not accepted) Sep 2000
NDSEG three-year fellowship award Sep 2000
Internet of Things
Autonomy and Controls/Control Systems
High-dimensional Time Series Clustering via Cross-Predictability. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS'17). Apr 20-22, 2017. Fort Lauderdale, Florida, USA. Dezhi Hong, Quanquan Gu and Kamin Whitehouse.
Matrix Factorisation for Scalable Energy Breakdown. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17). San Francisco, California, USA. February 4-9, 2017. Nipun Batra, Hongning Wang, Amarjeet Singh, Kamin Whitehouse.
How does eco-coaching help to save energy? assessing a recommendation system for energy-efficient thermostat scheduling. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2016. Rayoung Yang, Devika Pisharoty, Soodeh Montazeri, Kamin Whitehouse, Mark W. Newman.
Gemello: Creating a Detailed Energy Breakdown from Just the Monthly Electricity Bill. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. Nipun Batra, Amarjeet Singh, Kamin Whitehouse.
Reactive Control of Autonomous Drones. The 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), 2016. Endri Bregu, Nicola Casamassima, Daniel Cantoni, Luca Mottola, Kamin Whitehouse.
Clustering-based Active Learning on Sensor Type Classification in Buildings. The 24th ACM International Conference on Information and Knowledge Management (CIKM'15). October 19-23, 2015. Melbourne, Australia. Dezhi Hong, Hongning Wang, Kamin Whitehouse.
Object Hallmarks: Identifying Object Users using Wearable Wrist Sensors. The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'15). September 7-11, 2015. Osaka, Japan. Juhi Ranjan, Kamin Whitehouse.
How Hot is Piping Hot? Lower Energy Consumption with Smarter Hot Water Delivery. The 14th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'15). April 13-17, 2015, Seattle, WA. Yong Sun, Anindya Prodhan, Erin Griffiths, and Kamin Whitehouse.
Team-level Programming of Drone Sensor Networks. In the Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems (SenSys 2014). November 3-5, 2014. Memphis, USA. Luca Mottola, Mattia Moretta, Kamin Whitehouse, and Carlo Ghezzi.
An RF Doormat for Tracking People’s Room Locations. To Appear in The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). September 8-12, 2013. Zurich, Switzerland. Juhi Ranjan and Kamin Whitehouse.
FixtureFinder: Discovering the Existence of Electrical and Water Fixtures. To Appear in The 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'13). April 8-11, 2013, Philadelphia, PA. Vijay Srinivasan, Jack Stankovic, Kamin Whitehouse.
Doorjamb: Unobtrusive Room-level Tracking of People in Homes using Doorway Sensors. The 10th ACM Conference on Embedded Networked Sensing Systems (SenSys'12). November 7-9, 2012, Toronto, Canada. Timothy Hnat, Erin Griffiths, Raymond Dawson, and Kamin Whitehouse
Being SMART About Failures: Assessing Repairs in Smart Homes. The 14th ACM International Conference on Ubiquitous Computing (UbiComp'12). September 5-8, 2012, Pittsburgh, PA. Krasimira Kapitanova, Enamul Hoque, John A. Stankovic, Sang H. Son, and Kamin Whitehouse.
Smart Blueprints: Automatically Generated Maps of Homes and the Devices within Them. To appear in The 10th Conference on Pervasive Computing (Pervasive'12). June 18-22, 2012, Newcastle, UK. (ppt) Jiakang Lu and Kamin Whitehouse.
SunCast: Fine-grained Prediction of Natural Sunlight Levels for Improved Daylight Harvesting. The 11th ACM Conference on Information Processing in Sensor Networks (IPSN'12). April 17-19, 2012, Beijing, China. Jiakang Lu and Kamin Whitehouse.
The Hitchhiker's Guide to Successful Residential Sensing Deployments. The 9th ACM Conference on Embedded Networked Sensing Systems (SenSys). November 2-4, 2011, Seattle, WA. Timothy Hnat, Vijay Srinivasan, Jiakang Lu, Tamim Sookoor, Raymond Dawson, John Stankovic, Kamin Whitehouse.
The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes. The 8th ACM Conference on Embedded Networked Sensing Systems (SenSys). November 3-5, 2010, Zurich, Switzerland. Jiakang Lu, Tamim Sookoor, Vijay Srinivasan, Gao Ge, Brian Holben, John Stankovic, Eric Field, Kamin Whitehouse.
Automatic and Robust Breadcrumb System Deployment for Indoor Firefighter Applications. The 8th Annual International Conference on Mobile Systems, Applications and Services (MobiSys). June 15-18, 2010, San Francisco, CA, USA. Hengchang Liu, Jingyuan Li, Zhiheng Xie, Shan Lin, Kamin Whitehouse, John A. Stankovic, David Siu.
Run Time Assurance of Application-level Requirements in Wireless Sensor Networks. The 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN '10). Stockholm, Sweden, April 12-16, 2010. Yafeng Wu, Krasimira Kapitanova, Jingyuan Li, John A. Stankovic, Sang H. Son, and Kamin Whitehouse.
Buildings account for 40% of the total US energy budget, the largest energy consuming sector in the country, and many government and public organizations agree that a national grand challenge is to achieve a 70% reduction in building energy by 2030. However, energy retrofits are extremely costly. We are developing computational alternatives to conventional retrofits that use a combination of embedded sensing, intelligence, and control to save energy at 10x to 100x lower cost than conventional approaches. A cornerstone of this work is new technology to identify and track individual people, recognize common activity patterns, and detect object usage. These sensing technologies allow buildings to provide heating and cooling, lighting, and water heating services that respond to the dynamics of occupant presence, activities, and goals. Our current data predicts that, if they were deployed in every home, these techniques would reduce total US energy consumption by almost 3%, more than the energy used by the entire commercial airline industry.
Many forecast that the Internet of Things will grow to over 1 trillion objects in the next two decades. However, people cannot practically coordinate the 1000’s of connected objects they will encounter every day to help with their daily tasks. Networks of coordinating devices are too complex to operate with conventional human controls such as dials, switches and knobs. We are creating new tools and techniques to dynamically compose objects in a goal-driven fashion, adapting in real-time to changes in either network resources or high-level objectives. We have demonstrated these techniques in both static networks and networks that include aerial drones. In addition to goal-oriented tasking, we have shown that these tools enable system-level analysis, run-time visibility and debugging, and testing and verification, even in the face of severe limitations on energy, memory, and bandwidth.