He, Suining

Suining He

Tenured Associate Professor, School of Computing

Email suining.he@uconn.edu
Phone (860) 486-2687
Mailing Address University of Connecticut 371 Fairfield Way, Unit 4155 Storrs, CT 06269-4155
Campus Storrs
Link Affiliation Website
Google Scholar Google Scholar Link

Brief Bio

Suining He is currently working as the Associate Professor (with Tenure) at the School of Computing, the University of Connecticut (UConn) since 08/2025. Before that, Suining He was working as a Tenure-Track Assistant Professor at the School of Computing, the University of Connecticut (UConn) since 09/2019. He leads the UConn's Ubiquitous and Urban Computing Lab. 

​Before joining UConn, he worked as a postdoctoral research fellow at the Real-Time Computing Lab (RTCL), at the Department of Electrical Engineering and Computer Science (EECS), The University of Michigan, Ann Arbor, MI during 11/2016 -- 08/2019. Suining He received the Ph.D. degree in Computer Science and Engineering, Department of Computer Science and Engineering (CSE), from the Hong Kong University of Science and Engineering (HKUST) in 08/2016, and the B.Eng degree (summa cum laude) in Mechanical Design, Manufacturing, and Automation, the School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST) in 06/2012. 

Suining He received the NSF CAREER Award in 2023, Google Research Scholar Program Award in 2021 and NVIDIA Applied Research Accelerator Program Award in 2021, and two UConn Research Excellence Program (REP) Awards in 2022 and 2020, and held the Google PhD Fellowship in Mobile Computing in 2015, HKUST School of Engineering (SENG) PhD Research Fellowship Award in 2015--2016, and Hong Kong Telecom Institute of Information Technology (HKTIIT) Post-Graduate Excellence Scholarship in 2016. His scholarly works appear in WWW, SenSys, UbiComp, INFOCOM, TKDE, and TMC, and received the IEEE MASS Best Paper Runner-up Award in 2020 and IEEE RTSS Outstanding Paper Award in 2021. He has been ranked among the Stanford's World's Top 2% Scientists since 2020. He serves as the Editor of IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN) since 2023 and the Human Dynamics Section Editor of Springer Computational Urban Science since 2025. He received the UConn Office of the Provost Excellence in Teaching Award in 2020. His core location-based service (LBS) technologies developed, patented, and transferred during his PhD studies have led to direct industrial and long-term societal impacts. He is the IEEE Senior Member and ACM Senior Member since 2024.

Suining's research is supported by National Science Foundation (NSF), United States Department of Agriculture (USDA), Google, NVIDIA, General Electric (GE), Cigna, Transportation Infrastructure Durability Center (TIDC), Department of Transportation (DOT), CT Division of Emergency Management and Homeland Security (DEMHS), SafeGraph, StreetLight Insight, and internal grants from UConn's Office of the Vice President for Research (OVPR). 

  • Cyber-Physical Systems (CPS): Learning-Enabled CPS, Embodied Artificial Intelligence
  • Smart & Connected Communities (S&CC): Mobility-on-Demand, Socio-Technical Systems
  • Human-Centered Computing (HCC): Human-(Micro)Mobility Interaction
  • Urban Computing Cyberinfrastructure:  Socially-Conscious AI, Location and Sensor Data Privacy-Preserving Learning
  • Artificial Intelligence (AI): Multimodal Large Language Model (MLLM)​, Cross-Modal Foundation Models, Foundation of Indoor Localization​

Our lab's research is supported by National Science Foundation (NSF), United States Department of Agriculture (USDA), Google, NVIDIA, General Electric (GE), Cigna, Transportation Infrastructure Durability Center (TIDC), Department of Transportation (DOT), CT Division of Emergency Management and Homeland Security (DEMHS), SafeGraph, StreetLight Insight, and internal grants from UConn's Office of the Vice President for Research (OVPR).

  • ENGR-5315: Capstone Project, Spring 2025
  • CSE-4997: Senior Thesis CSE, Spring 2025
  • CSE-4502/5717: Big Data Analytics, Spring 2025
  • CSE-4940: Computer Science and Engineering Design Project II, Spring 2025
  • CSE-4939W: Computer Science and Engineering Design Project I, Fall 2024
  • CSE-4820/5819: Introduction to Machine Learning, Fall 2024 (58 students; TAs: Jiahui Zhao, Niteesh Saravanan)
  • ENGR-5315: Capstone Project, Fall 2024
  • CSE-4997: Senior Thesis CSE, Spring 2024
  • CSE-4099: Independent Studies, Spring 2024
  • CSE-4940: Computer Science and Engineering Design Project II, Spring 2024 (24 students)
  • ENGR-5315: Capstone Project, Spring 2024
  • CSE-4939W: Computer Science and Engineering Design Project I, Fall 2023 (24 students)​
  • ENGR-5315: Capstone Project, Fall 2023
  • CSE-4099: Independent Studies, Fall 2023
  • ENGR-1000: Orientation to Engineering, Fall 2023
  • CSE-4820/5819: Introduction to Machine Learning, Fall 2023 (77 students; TAs: Rigel Mahmood, Abdul Wassay Qureshi)
  • CSE-4502/5717: Big Data Analytics, Spring 2023 (66 students; TAs: Yijue Wang, Shaoyi Huang)
  • CSE-4820/5819: Introduction to Machine Learning, Fall 2022 (58 students; TAs: Mahan Tabatabaie, Toan Nguyen)
  • ENGR-5315: Capstone Project, Summer 2022
  • CSE-4502/5717: Big Data Analytics, Spring 2022 (52 students; TA: Sara Wrotniak)
  • CSE-4940: Computer Science and Engineering Design Project II, Spring 2022 (15 students)
  • CSE-4939W: Computer Science and Engineering Design Project I, Fall 2021 (15 students)
  • CSE-4820/5819: Introduction to Machine Learning, Fall 2021 (52 students; TA: Aaron Palmer)
  • CSE-4502/5717: Big Data Analytics, Spring 2021 (60 students; TAs: Yijue Wang, Shariq Khan)
  • CSE-5095: Special Topics in Computer Science and Engineering, Fall 2020
  • CSE-4940: Computer Science and Engineering Design Project II, Spring 2020 (17 students)
  • CSE-4939W: Computer Science and Engineering Design Project I, Fall 2019 (17 students)
  • ENGR-1000: Orientation to Engineering, Fall 2019
  • CSE-5095: Special Topics in Computer Science and Engineering, Fall 2019

Journal Publication (underlined are UConn students mentored by me)

  • M. Tabatabaie, S. He, K. G. Shin, and H. Wang, "Toward Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation: Interaction Awareness and Hierarchical Explainability", ACM Journal on Autonomous Transportation Systems (J-AST), vol.2, no. 3, September 2025. [pdf][pdf2].​
  • X. Yang, S. He, K. G. Shin, M. Tabatabaie, and D. Dai, "Cross-Modality and Equity-Aware Graph Pooling Fusion: A Bike Mobility Prediction Study", IEEE Transactions on Big Data (TBD), vol.11, no. 1, January/February 2025. [pdf][pdf2].
  • M. Tabatabaie, S. He, H. Wang, and K. G. Shin, "Beyond Taming Electric Scooters: Disentangling Understandings of Micromobility Naturalistic Riding", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2024),  vol. 8, no. 129, pp. 1--24, August 2024. [pdf]. ​
  • B. Guo, S. Wang, Y. Ding, G. Wang, S. He, D. Zhang, and T. He, "Time-Constrained Actor-Critic Reinforcement Learning for Concurrent Order Dispatch in On-demand Delivery", IEEE Transactions on Mobile Computing (TMC), vol. 23, no. 8, pp. 8175--8192, August, 2024. [pdf]. 
  • H. Huang, X. Yang, S He, M. Tabatabaie, "Toward Ubiquitous Interaction-Attentive and Extreme-Aware Crowd Activity Level Prediction", ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 6, 2024. [pdf].
  • H. Huang, S. He, X. Yang, and M. Tabatabaie, "STICAP: Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction", ACM Transactions on Spatial Algorithms and Systems, vol. 10, no. 1, 2024. [pdf]
  • M. Tabatabaie and S. He, "Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2024), vol 7, no. 4, pp. 1--28, December, 2023. [pdf].
  • M. Tabatabaie, S. He, and K. G. Shin, "Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2023), vol. 7, no. 125, pp. 1--25, September, 2023. [pdf].
  • Q. Niu, K. Zhu, S. He, S. Cen, S.-H. Chan, and N. Liu,  "VILL: Towards Efficient and Automatic Visual Landmark Labeling", ACM Transactions on Sensor Networks (TOSN), vol. 19, no. 4, pp. 74:1--74:25, Nov. 2023. [pdf].
  • ​M. Hosseini, A. J. Sabet, S. He and D. Aguiar, "Interpretable Fake News Detection with Topic and Deep Variational Models Online Social Networks and Media", Elsevier Online Social Networks and Media, vol. 36, July 2023. [pdf].
  • W. Jiang, Q. Niu, S. He and N. Liu, "Adaptive Radio Map Reconstruction via Adversarial Wireless Fingerprint Learning", Springer Neural Computing and Applications, June 2023, [pdf].  
  • C. Xiang, Y. Zhou, H. Dai, Y. Qu, S. He, C. Chen and P. Yang, "​Reusing Delivery Drones for Urban Crowdsensing", IEEE Transactions on Mobile Computing (TMC), vol. 22, no. 5, pp. 2972--2988, May 2023. [pdf]
  • M. Tabatabaie and S. He, "Naturalistic E-Scooter Maneuver Recognition with Federated Contrastive Rider Interaction Learning", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2023), vol. 6, no. 3, pp. 1-27, December, 2022. [pdf][pdf2][video].
  • S. He and K. G. Shin, "Distribution Prediction for Reconfiguring Urban Dockless E-Scooter Sharing Systems", IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 34, no. 12, pp. 5722-5740, 1 Dec. 2022. [pdf][pdf2].
  • M. Tabatabaie, S. He and X. Yang, "Driver Maneuver Identification with Multi-Representation Learning and Meta Model Update Designs", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2022), vol. 6, no. 2, pp. 1-23, July 2022. [pdf]. ​
  • J. Luo', R. Bai, S. He and K. G. Shin, "Pervasive Pose Estimation for Fall Detection", ACM Transactions on Computing for Healthcare (HEALTH​), vol. 3, no. 27, pp. 1--23, July 2022. [pdf][pdf2]. 
  • ​S. He and K. G. Shin, "Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Coordination", IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 34, no. 3, pp. 1446--1461, Mar. 2022. [pdf][pdf2].
  • S. He and K. G. Shin, "Information Fusion for (Re)Configuring Bike Station Networks with Crowdsourcing", IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 34, no. 2, pp. 736--752, Feb. 2022. [pdf][pdf2].
  • X. Yang, S. He, B. Wang, and M. Tabatabaie, "Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions: A Wi-Fi-based Mobility Case Study", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2022), vol. 5, no. 187, pp 1–24, Dec. 2021. [pdf][pdf2].
  • N. Liu, T. He, S. He and Q. Niu', "Indoor Localization with Adaptive Signal Sequence Representations", IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 11, pp. 11678-11694, Nov. 2021. [pdf]. ​
  • H. Huang, X. Yang, and S. He, "Multi-Head Spatio-Temporal Attention Mechanism For Urban Anomaly Event Prediction", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2021), vol. 5, no. 104, pp 3:1–3:21, Sept. 2021. [pdf].
  • C. Xiang', S. He, K. G. Shin, Y. Qu and P. Yang, "Incentivizing Platform--User Interactions for Crowdsensing", IEEE Internet of Things Journal (IoT-J), vol. 8, no. 10, pp. 8314--8327, May 2021. [pdf].
  • Q. Niu', T. He, N. Liu, S. He, X. Luo and F. Zhou, "MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2020), vol. 4, no. 2, pp. 54:1-54:23, June 2020. [pdf].
  • S. He and K. G. Shin, "Spatio-Temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 4, pp. 39:1--39:28, July 2019. (IF: 3.19). [pdf][pdf2].
  • S. He, S.-H. Chan, L. Yu and N. Liu, "Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances," IEEE Transactions on Mobile Computing (TMC), vol. 18, no. 3,  pp. 602 - 617, March 1, 2019. (IF: 4.098), [pdf][pdf2].
  • K.-H. Chow', S. He*, J. Tan and S.-H. Chan, "Efficient Locality Classification for Indoor Fingerprint-based Systems," IEEE Transactions on Mobile Computing (TMC), vol. 18, no. 2, pp. 290-304, February 1, 2019. (IF: 4.098), [pdf].
  • H. Wu', Z. Mo, J. Tan, S. He and S.-H. Chan, "Efficient Indoor Localization Based on Geomagnetism," ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 4, pp. 42:1--42:25, 2019. (IF: 2.313). [pdf].
  • Q. Niu', N. Liu, J. Huang, Y. Luo, S. He, T. He, S.-H. Chan and X. Luo, “DeepNavi: A Deep Signal-Fusion Framework for Accurate and Applicable Indoor Navigation”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2019), vol. 3, No. 3, pp. 99:1-99:24, September 2019. [pdf]
  • Q. Niu', M. Li, S. He, C. Gao, S.-H. Chan, and X. Luo, "Resource-efficient and Automated Image-based Indoor Localization," ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 2, pp. 19:1 - 19:31, February, 2019. (IF: 2.313), [pdf]. 
  • S. He, S.-H. Chan, L. Yu', and N. Liu, "SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization," IEEE Transactions on Mobile Computing (TMC), vol. 17, no. 5, pp. 1176-1189, May 1, 2018. ​(IF: 4.098), [pdf][pdf2]. 
  • S. He and K. G. Shin, "Geomagnetism for Smartphone-based Indoor Localization: Challenges, Advances, and Comparison," ACM Computing Surveys (CSUR), vol. 50, no. 6, pp. 1-37, December 2017/January 2018. (IF: 6.748), [pdf][pdf2].
  • S. He, W. Lin', and S.-H. Chan, ​"Indoor Localization and Automatic Fingerprint Update with Altered AP Signals," IEEE Transactions on Mobile Computing (TMC), vol. 16, no. 7, pp. 1897-1910, July 1 2017. (IF: 4.098), [pdf][pdf2]. 
  • S. He and S.-H. Chan, ​"INTRI: Contour-based Trilateration for Indoor Fingerprint-based Localization," IEEE Transactions on Mobile Computing (TMC), vol. 16, no. 6, pp. 1676-1690, June 1 2017. (IF: 4.098), [pdf][pdf2]. 
  • S. He, T. Hu', and S.-H. Chan, "Towards Practical Deployment of Fingerprint-based Indoor Localization," IEEE Pervasive Computing Magazine, vol. 16, no. 2, pp. 76-83, April - June 2017. (IF: 3.022)​, [pdf].
  • S. He, B. Ji', and S.-H. Chan, ''Chameleon: Survey-free Updating of Fingerprint Database for Indoor Localization," IEEE Pervasive Computing Magazine, Vol. 15, pp. 66-75, October - December 2016. (IF: 3.022), [pdf][pdf2].
  • S. He and S.-H. Chan, "Tilejunction: Mitigating Signal Noise for Fingerprint-based Indoor Localization,'' IEEE Transactions on Mobile Computing (TMC), Vol. 15, No. 6, pp. 1554-1568, June 2016. (IF: 4.098), [pdf][pdf2].
  • S. He and S.-H. Chan, "Wi-Fi Fingerprint-based Indoor Positioning: Recent Advances and Comparisons,'' IEEE Communications Surveys and Tutorials, Vol. 18, pp. 466-490, First quarter 2016. (IF: 20.23), [pdf]​[pdf2].​​ (Highly-cited Paper!)

 

  • Peer-reviewed Conference Publications:
  1. C. Miao, S. He, Y. Li, and C. Zhang, "A Spatially Adapted SHAP Approach for Interpreting Deep Bike Usage Learning and Prediction" (short paper), in Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2025 (SIGSPATIAL 2025), (Minneapolis, MN), November 3-6, 2025, to appear. 
  2. H. Wang and S. He, "Vision-Language Modeling for Scene Understanding and Reasoning of Vehicle-to-X Interactions", in Proceedings of IEEE 22nd International Conference on Mobile Ad Hoc and Smart Systems (MASS), (Chicago, IL), October 6-8, to appear. 
  3. J. A. Mathew and S. He, "Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation", in Proceedings of The 14th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (Toronto, Canada), August 3, 2025, to appear. 
  4. F. Nooshi and S. He, "Multi-Agent Reinforcement Learning for Dynamic Mobility Resource Allocation with Hierarchical Adaptive Grouping", in Proceedings of The 14th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (Toronto, Canada), August 3, 2025, to appear.
  5. B. Wang, S. He, C. Zhang, A.-W. Qureshi, W. Li, S. Rajasekaran, W. Wei, and E. Howard, "Experience with an Interdisciplinary Competition-based Cybertraining Workshop", in ​Proceedings of ASEE Annual Conference & Exposition, June 22-25, 2025. [pdf].
  6. Y. Deng, S. He, and H. Wang, "Practical Self-Supervised Contrastive Driver Maneuver Interaction Learning via Augmenting Inertial Measurement Unit Signals", in Proceedings of 21st IEEE International Conference on Ubiquitous Intelligence and Computing (UIC), (Denarau Island, Fiji), December 2-7, 2024. [pdf].
  7. X. Yang, J. Wang, S. Han, and S. He, "Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network", in Proceedings of The 13th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (Barcelona, Spain), August 26, 2024, [pdf]. 
  8. X. Yang, S. He, and M. Tabatabaie, "Equity-Aware Cross-Graph Reinforcement Learning for Bike Station Network Expansion", in Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2023 (SIGSPATIAL 2023)​, (Hamburg, Germany), November 13-16, 2023. Acceptance Rate: 20.1% (38 out of 189), [pdf][pdf2][video]. 
  9. M. Tabatabaie, S. He, and K. G. Shin, "Interaction-Aware and Hierarchically-Explainable Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation", in Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), (Detroit, MI), [pdf]. ​
  10. H. Huang, S. He, and M. Tabatabaie, "Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning," in Proceedings of IEEE 39th International Conference on Data Engineering (ICDE), pp. 1059-1070, (Anaheim, CA), April 3-7, 2023, [pdf]. 
  11. S. He, B. Wang, K. G. Shin, and M. Tabatabaie, "Cross-Zone and Extreme-Aware Mobility Learning of Crowd Interactions with Built Environments", in Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2022), (Boston, MA), November 9-10, 2022, [pdf]. 
  12. X. Yang, S. He, M. Tabatabaie, and B. Wang, "Towards Dynamic Crowd Mobility Learning and Meta Model Updates for A Smart Connected Campus", in Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN 2022), (Linz, Austria), October 3-5, 2022, [pdf]. 
  13. S. He and K. G. Shin, "Socially-Equitable Interactive Graph Information Fusion-based Prediction for Urban Dockless E-Scooter Sharing," in Proceedings of The World Wide Web Conference (WWW 2022), (Lyon, France), pp.3269--3279, April 25--29, 2022. Acceptance Rate: 17.7% (323 out of 1822), [pdf][video].
  14. M. Tabatabaie, J. Maniscalco, C. Lynch, and S. He, "Towards Spatio-Temporal Cross-Platform Graph Embedding Fusion for Urban Traffic Flow Prediction," in Proceedings of the 11th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (Washington, DC), August 15, 2022, [pdf]. 
  15. ​C. Xiang, Y. Li, Y. Zhou, S. He, Y. Qu, Z. Li, L. Gong, and C. Chen, "A Comparative Approach to Resurrecting the Market of MOD Vehicular Crowdsensing", in Proceedings of IEEE Conference on Computer Communications (INFOCOM 2022), pp. 1479-1488. Acceptance Rate: 19.9%, (225 out of 1129), [pdf]. 
  16. B. Guo, S. Wang, Y. Ding, G. Wang, S. He, D. Zhang and T. He, "Concurrent Order Dispatch for Instant Delivery with Time-Constrained Actor-Critic Reinforcement Learning", in Proceedings of 42nd IEEE Real-Time Systems Symposium (RTSS 2021),  pp. 176-187, (Outstanding Paper Award & Best Paper Candidate), [pdf].
  17. M. Tabatabaie, S. He and X. Yang, "Reinforced Feature Extraction and Multi-Resolution Learning for Driver Mobility Fingerprint Identification", in Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2021 (SIGSPATIAL 2021), pp. 69--80, November 2-5, 2021. Acceptance Rate: 22.4% (34 out of 152), [pdf][pdf2][video].
  18. X. Yang, S. He and H. Huang, "Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction", in Proceedings of IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems (MASS 2020), (Delhi NCR, India), pp. 640--648, December 10--13, 2020, [pdf] (Best Paper Runner-Up Award; Finalist: 2/219). 
  19. X. Yang and S. He, "Towards Dynamic Urban Bike Usage Prediction for Station Network Reconfiguration",  in Proceedings of the 9th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (San Diego, CA), August 24, 2020, [pdf]. 
  20. S. He and K. G. Shin, "Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems" (oral presentation/full paper), in Proceedings of The World Wide Web Conference (WWW 2020), pp. 88--98, April 20-24, 2020, Acceptance Rate: 19.2%, (217 out of 1129), [pdf][pdf2][video].
  21. S. He and K. G. Shin, "Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration" (oral presentation/full paper), in Proceedings of The World Wide Web Conference (WWW 2020), pp. 133--143, April 20-24, 2020. Acceptance Rate: 19.2%, (217 out of 1129), [pdf][pdf2][video].
  22. S. He and K. G. Shin, "Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination" (short paper),  in Proceedings of The World Wide Web Conference (WWW 2019), (San Francisco, CA), pp. 2806--2813, May 13-17, 2019, Acceptance Rate: 19.9%, (72 out of 361), [pdf][pdf2].
  23. S. He and K. G. Shin, "Crowd-Flow Graph Construction and Identification with Spatio-Temporal Signal Feature Fusion," in Proceedings of The 38th Annual IEEE International Conference on Computer Communications (INFOCOM 2019), (Paris, France), pp. 757--765, April 29 - May 2, 2019,  Acceptance Rate: 19.7%, (288 out of 1464), [pdf].
  24. T. He, Q. Niu', S. He and N. Liu, “Indoor Localization with Spatial and Temporal Representations of Signal Sequences”, in Proceedings of IEEE Global Communications Conference: Wireless Communications (Globecom 2019 WC), Waikoloa, USA, pp. 1--7, December, 2019, [pdf].
  25. X. Xie, K. G. Shin, H. Yousefi and S. He, "Wireless CSI-Based Head Tracking in The Driver Seat," in Proceedings of ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2018), (Heraklion/Crete, Greece), pp. 112--125, December 4-7, 2018, Acceptance Rate: 17.3%, (32 out of 185), [pdf].
  26. S. He and K. G. Shin, "(Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization," in Proceedings of the Nineteenth International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2018), (Los Angeles, CA), pp. 1--10, June 25-28, 2018,  Acceptance Rate: 16.9%, (30 out of 178), [pdf][pdf2][video]. 
  27. Q. Niu', Y. Nie, S. He, N. Liu and X. Luo, "RecNet: A Convolutional Network for Efficient Radiomap Reconstruction," in Proceedings of IEEE International Conference on Communications - Selected Areas in Communications Symposium - Smart Cities Track (ICC'18 SAC-SC), (Kansas City, MO),  pp. 1--7, May 20-24, 2018, [pdf]. 
  28. S. He and K. G. Shin, "Steering Crowdsourced Signal Map Construction via Bayesian Compressive Sensing," in Proceedings of The  37th Annual IEEE International Conference on Computer Communications (INFOCOM 2018), (Honolulu, Hawaii), April 15-19, 2018, Acceptance Rate: 19.2%, (302 out of 1606), [pdf].
  29. H. Wu', S. He and S.-H. Chan, "A Graphical Model Approach for Efficient Geomagnetism-Pedometer Indoor Localization," in Proceedings of The 14th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2017), (Orlando, FL), pp. 371--379, 22-25 October 2017. [pdf]. 
  30. S. He and S.-H. Chan, "Towards Crowdsourced Signal Map Construction via Implicit Interaction of IoT Devices," in Proceedings of Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2017),  (San Diego, CA), pp. 145--153, 12-14 June, 2017, Acceptance Rate: 26.5% (45 out of 170), [pdf]. 
  31. H. Wu', S. He and S.-H. Chan, "Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization," in Proceedings of The International Conference on Embedded Wireless Systems and Networks (EWSN 2017),  (Uppsala, Sweden), pp. 156--167, 20-22 February 2017, Acceptance Rate: 36.7% (18 out of 49), [pdf]. 
  32. B. Yang', S. He and S.-H. Chan, "Updating Wireless Signal Map with Bayesian Compressive Sensing," in Proceedings of ​The 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM'16), (Malta), pp. 310--317, 13-17 November 2016, Acceptance Rate: 27% (36 out of 133), [pdf].
  33. S. He, J. Tan, and S.-H. Chan, "Towards Area Classification for Large-scale Fingerprint-based System,'' in Proceedings of The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), (Heidelberg, Germany), 12-16 September 2016, Acceptance Rate: 23.9% (115 out of 481), [pdf].
  34. S. He, T. Hu', and S.-H. Chan, "Contour-based Trilateration for Indoor Fingerprinting Localization," in Proceedings of The 13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), (Seoul, South Korea), pp. 225-238, 1-4 November, 2015, Acceptance Rate: 20.5% (27 out of 132), [pdf].
  35. S. He, S.-H. Chan, L. Yu', and N. Liu, "Calibration-free Fusion of Step Counter and Wireless Fingerprints for Indoor Localization," in Proceedings of The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), (Osaka, Japan), pp. 897-908, 7-11 September 2015, Acceptance Rate: 23.6% (93 out of 394), [pdf].
  36. S. He, S.-H. Chan, L. Yu', and N. Liu, "Fusing Noisy Fingerprints with Distance Bounds for Indoor Localization,'' in Proceedings of The 34th Annual IEEE International Conference on Computer Communications (INFOCOM 2015), (Hong Kong, P.R. China), pp. 2506-2514, 26 April - 1 May 2015,  Acceptance Rate: 19.2% (316 out of 1640), [pdf].
  37. S. He and S.-H. Chan, "Sectjunction: Wi-Fi Indoor Localization Based on Junction of Signal Sectors," in Proceedings of IEEE International Conference on Communications - Mobile and Wireless Networking Symposium (ICC'14 MWN), (Sydney, Australia), pp. 2611-2616, 10-14 June 2014, [pdf].      ​     
  • Book Chapters:
  1. S. He and K. G. Shin, "Urban Mobility-Driven Crowdsensing: Recent Advances in Machine Learning Designs and Ubiquitous Applications", in Mobile Crowdsourcing -- From Theory to Practice, Wireless Networks, pp. 33--58, Springer, 2023 [pdf]. 

 

  • Issued U.S. Patents:
  1. ​S.-H. Chan and S. He, "Facilitation of indoor localization and fingerprint updates of altered access point signals", US10383086B2, 2019. 
  2. S.-H. Chan and S. He, "Mitigating signal noise for fingerprint-based indoor localization", US9913092B2, 2018.

 

08/2025 Promoted to Associate Professor with Tenure

08/2025 One paper is accepted by ACM SIGSPATIAL 2025

07/2025 One paper is accepted by IEEE MASS 2025

07/2025 Two papers are accepted by ACM KDD UrbComp Workshop 2025

 

 

Selected Awards and Recognitions: ​

  • ACM Senior Member, 2024
  • IEEE Senior Member, 2024
  • NSF CAREER Award, 2023
  • Stanford’s World's Top 2% Scientists [link], 2024, 2023, 2022, 2021, 2020
  • UConn Research Excellence Program (REP) Award, 2022 
  • Google Research Scholar Program Award in Geo, 2021
  • NVIDIA Applied Research Accelerator Program Award, 2021
  • IEEE RTSS Outstanding Paper Award, 2021
  • UConn Office of the Provost Excellence in Teaching Award, 2020
  • IEEE MASS Best Paper Runner-Up Award, 2020
  • UConn Research Excellence Program (REP) Award, 2020 ​
  • ​HKTIIT (Hong Kong Telecom Institute of Information Technology) Post-graduate Excellence Scholarships, 2016
  • HKUST SENG (School of Engineering) PhD Fellowship Award, 2015
  • Google PhD Fellowship in Mobile Computing, 2015