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Fan Ye Phone : +1 631 632 8393
Office : Light Engineering 217 |
I'm a Professor in the Electrical and Computer Engineering Department at Stony Brook University, also Director of Communications and Devices Devision at New York State Center of Excellence for Wireless and Information Technology (CEWIT). I am affiliated with the Computer Science Department, and Smart Energy Techologies (SET Cluster). Before joining Stony Brook I worked at IBM T.J. Watson Research Center as a Research Staff Member since 2004.
My research interests are in mobile and embedded sensing systems, AI/ML algorithms and infrastucture for "computational screening and surveillance (CSS)", an interdisciplinary area I'm working to create. CSS has broad applications in many health conditions and diseases (e.g., smart aging, chronics/acute/psychological). I also work on data-centric wireless communication, edge computing, Internet-of-Things. In the past I worked on location based services, sensor networks, energy and power efficiency. My publications have been cited more than 15000 times according to Google Scholar, including over 30 granted/pending multi-national/US patents/applications.
My ongoing projects include: customized sensing and AI/ML algorithms, systems and infrastructure for health (computational screening and surveillance, smart aging); data-centric wireless communication, opportunistic learning in edge environments (e.g., vehicles, IoT and drones).
Some of the older projects include: data sharing in edge environments, enterprise-scale IoT management, indoor floor map construction, indoor vehicle/smartphone tracking and localization, mobile crowdsensing and its infrastructure, cloud based publish/subscribe service, wide area messaging, high availability and collaborative stream processing systems, sensor networks (robust data delivery, sensing coverage, and false data injection attacks).
Student/Postdoc Recruitment
(Sep 2023)I am looking for highly motivated students to join my group. Candidates should be proficient in programming, and have strong experiences in some of these areas (evidenced by papers published or systems/program developed): machine learnning algorithms and systems, sensing algorithms and systems, cloud infrastructure, signal processing (esp. wireless radios/radar), software define radios, operating systems, embedded/wireless/mobile systems. Please send your CV, transcripts, publications and software (if any). Or if you are passionate about research and willing to do whatever it takes to make a difference, you can also drop me a line.
Main ongoing projects
Computational Screening and Surveillance: develop learning algorithms, systems and infrastructure for analyzing multi-modality sensing data streams for detecting the onset and monitoring the progression of a broad set of individual and population health conditions.
Smart Aging: develop customized nontouch sensing technologies about the vital signs and physical activities of home-dwelling older adults to help manage their health conditions for improved quality of life. More background can be found on a news story by SBU.
Edge Learning: machine learning and stream processing on high volumes of data from diverse types and large numbers of sensors at edge devices, especially mobile environments (e.g., cameras, radars, lidars and sonars on connected vehicles).
Data Centric Wireless communication: a native pub/sub abstraction at radio level to offer high rate, low loss one-to-many communication capability, critically needed at the edge (e.g., vehicles, drones, IoT) but missing in all existing wireless communication technologies.
Recent Projects
Location Based Services (LBS)
I have multiple projects addressing scalable and accurate indoor map construction, and indoor localization/tracking.
BatTracker: Accurate, infrastructure-free 3D tracking of mobile devices without any external hardware. The paper will appear in ACM SenSys 2017.
BatMapper: We leverage acoustic signals to create floor maps in just a few minutes, with accuracy comparable to the state of the art, at two orders of magnitude less data and efforts. The paper appeared in ACM Mobisys 2017 with a short video
Knitter: a single, novice user can create accurate floor maps in just one hour, cutting down data amounts and user efforts by one order of magnitude compared to prior work. The paper appeared in IEEE Infocom 2017.
Jigsaw: We leverage crowdsensed data from mobile users to construct complete and accurate floor plans automatically. It's the first to create high accuracy and complete maps using commodity mobile devices. The paper appeared in ACM Mobicom 2014.
Garage Mapping: given vehicles' fragmented movement trajectories, we develop several algorithms to piece together them for complete garage maps. The paper appeared in IEEE ICC 2016.
VeTrack: we track the real time movements of vehicles in uninstrumented environments (e.g., underground parking structures) where no radio signal (e.g., WiFi, GPS, cellular) is available. The paper appeared in ACM Sensys 2015. A video demo shows the real time tracking in a garage.
Sextant: instead of WiFi signals, we leverage abundant physical objects as reference to triangulate the user location. The papers appeared in Infocom 2014, ICC 2014 and TMC.
Peer acoustic assisted localization: we exploit the more accurate acoustic ranging among peer phones to pose additional constraints on the location of a phone for much higher accuracy (~1m) compared to WiFi only methods. We have built a prototype and a demo video is available. The paper appeared in Mobicom 2012 and TMC.
Pervasive Edge Computing
We are developing a new mobile sensing/computing paradigm where edge devices (smartphones, vehicles, IoT)
communicate with each other directly to discovery data available in the surrounding environments.
They exchange, process data spontaneously using data centric primitives.
Pervasive Data Sharing: agile, light-weight peer data discovery and retrieval
among opportunitically congregated edge devices, specifically addressing short contact durations and uncertain data availability.
The paper appeared in ICDCS 2017. Fair Data Caching: data caching among edge devices of heterogeneous
ownership must achieve fairness to ensure and encourage participation.
The paper appeared in ICDCS 2017. Smart Environment We are developing a holistic hardware/software architecture for fine grained access,
command execution assurances and scalable management for enterprise Internet-of-Things (e.g.,
lights, door locks, window blinds, A/C etc. in tens of campus buildings of a university). There are potentially
hundreds of thousands of smart objects accessed by tens of thousands of people taking different roles and responsibilities.
Rechargeable Sensor Networks Using autonomous vehicles equipped with batteries, solar panels and wireless charging capabilities, we can replenish
the energy of sensor nodes to achieve perpetual operation.
Netwrap
: use named data based methods to collect recharging requests
and schedule the vehicle's charging activities. Multi-vehicle coordination:
coordinate the charging schedules of multiple vehicles for optimal network operation. Vehicle movement costs and capacity:
energy costs in vehicle movements and battery capacity are considered when
deciding the vehicle's charging schedule. Resonant repeaters:
a vehicle can recharge multiple sensor nodes at once when equipped with resonant repeaters. Solar energy harvesting:
combine the complementary strengths of solar panels and wireless charging capability.Email me if you have any questions about my work.