Sanjay Ranka is a Distinguished Professor in the Department of Computer & Information Science & Engineering at the University of Florida. His research brings together data science, machine learning, applied algorithms, and high-performance computing to address problems in transportation, health care, and scientific computing. His work includes intelligent traffic intersections and digital twins, smartwatch-based health monitoring, GPU and exascale computing, and scientific data compression with error guarantees.

He is a Fellow of the IEEE, AAAS, AAIA, and AIIA. His honors include the 2020 Research Impact Award from the IEEE Technical Committee on Cloud Computing, the 2022 IIT Kanpur Distinguished Alumnus Award, and the 2024 UF College of Engineering Doctoral Dissertation Mentorship Award. He held University of Florida Foundation Research Professorships in 2018–2021 and 2023–2026.

He has authored or coauthored more than 360 refereed journal and conference publications and graduated more than 55 doctoral students. Since 2016, he has served as principal investigator on more than $16 million in funded research and as co-principal investigator on more than $18 million in funded research. His research has been supported by NIH, NSF, DOE, USDOT, and the Florida Department of Transportation.

From 1999 to 2002, he served as Chief Scientist and Chief Technology Officer and co-founded Paramark in Sunnyvale, California. He developed PILOT, a real-time advertising-optimization service that processed more than 10 million decisions per day with 99.99% uptime. Paramark raised more than $10 million in venture capital, was recognized as a Top 100 Internet technology company in 2001 and 2002, and was acquired in 2002.

RESEARCH AREAS

Scientific applications from high energy physics, nuclear physics, radio astronomy, and light sources generate large volumes of data at high velocity and are increasingly outpacing the growth of computing power, network, storage bandwidths and capacities. Furthermore, this growth is also seen in next-generation experimental and observational facilities, making data reduction or compression  an essential stage of future computing systems. Learn more

We are developing algorithms and software to fuse real-time feeds from video cameras and traffic sensor data to generate real-time detection, classification, and space-time trajectories of individual vehicles and pedestrians. This information is then transmitted to a cloud-based system and then synthesized to create a real-time city-wide traffic palette.  Learn more

Data mining and machine learning of large dimensional datasets is critical for understanding underlying relationships and ultimately improving healthcare outcomes.  Learn more

Hybrid multi-core processors (HMPs) – processors consisting of multiple cores and GPUs – are dominating the landscape of the next generation of computing from desktops to extreme-scale systems. We are developing algorithms and software that can exploit these architectures for a variety of computational science applications.  Learn more

BOOKS

RECENT FUNDING

Active awards listed in the July 2026 vitae:

  • Co-Core Leader, Data Science and Applied Technology Core, University of Florida Claude D. Pepper Older Americans Independence Center, National Institute of Aging (2022–2027), approx. $6,866,968 (total anticipated) (PI: Esser and Manini).
  • Co-PD/PI, Artificial Intelligence-based & Ethically-focused Multi-Modal Data Integration Framework for Screening, Diagnosing, and Caring for CKD Patients, NATL INST OF HLTH OD (2025–2026), approx. $2,084,185.
  • Principal Investigator, Computational Methods for Traffic Data Analytics and Digital Twins for Traffic Corridors, FL DEPT OF TRANSPORTATIO (2025–2027), $500,000.
  • Co-PD/PI, Phase 2 Evaluation of the Florida Keys Connecting Overseas to Advance Safe Travel (Keys COAST) Post-deployment Phase, FL DEPT OF TRANSPORTATION (2024–2027), approx. $236,894.
  • Co-PD/PI, Interstate 4 (I-4) Florida’s Regional Advanced Mobility Elements Stakeholder Coordination for Phase II: Comprehensive Assessment of Before and After Conditions, FL DEPT OF TRANSPORTATION (2023–2028), approx. $527,682.
  • Principal Investigator, Before and After Evaluation of Smart Bay Connected and Automated Vehicles (CAV) for Advanced Safety and Mobility Along US 98, FL DEPT OF TRANSPORTATION (2023–2027), approx. $75,000.
  • Principal Investigator, Using Trajectory Data and Ground Sensor Data for Traffic Signal Policy Optimization, FL DEPT OF TRANSPORTATION (2022–2026), approx. $407,584.
  • Principal Investigator. (December 2025 – August 2027). REDWOOD: Advancing Near Real-Time Resilient Workflows. US DEPT OF ENERGY – OAK RIDGE NATIONAL LABORATORY, $100K.
  • Co-Principal Investigator (PI: Anand Rangarajan). (2026 – 2030). MAGNET: MAthematics, ComputinG, and NETworking for Resource-Efficient Computational Science. US DEPT OF ENERGY – OAK RIDGE NATIONAL LABORATORY, 2026-2030, approx. $500,000.
  • Principal Investigator. RAPIDS3 Project: The RAPIDS3 Institute for Artificial Intelligence, Computer Science, and Data. US DEPT OF ENERGY – OAK RIDGE NATIONAL LABORATORY, 2026-2030, $600K.
  • Principal Investigator, G-CAR²E – Gerontology Centers united to Aid Remote Research in Elders (Todd Manini is the other PI). National Institutes of Health (2026-2031), approx. $3.5 million.

ANNOUNCEMENTS

Recent keynote and invited presentations:

  1. Keynote Speaker, “Artificial Intelligence for Intersection Traffic Safety,” International Conference on Intelligent Transportation and Smart Cities (ICITSC 2026), China (virtual), April 10, 2026.
  2. Keynote Speaker, “Leveraging AI for Smart Transportation” at the 2025 3rd International Conference on SmartRail, Traffic and Transportation Engineering (ICSTTE 2025), China, November 2025
  3. Invited Panelist, “From Gadgets to Care: Integrating Consumer and Regulated Devices,” MedTech Malta 2025, Valletta, Malta, November 12–14, 2025.
  4. Invited Speaker, “Data and Analytics Cross-Cutting Committee Showcase,” ITE Annual Meeting and Exhibit, Orlando, August 11, 2025.
  5. Invited Speaker, “CAESAR: A Unified Framework for Foundation and Generative Models for Spatio-Temporal Scientific Data Compression,” KDD 2025 AI for Science Workshop, Toronto, August 6, 2025.
  6. Invited Talk, Artificial Intelligence and Monitoring Systems for Aging, University of Texas Medical Branch as part of the Lefeber Hughes Winter Series on Aging, Feb 2025.
  1. M. Alpert, B. Sharma, E. Cenko, R. Zapata, Y. Karnati, R.B. Fillingim, T.M. Gill,  M. Marsiske, S. Ranka, T. M. Manini. Identifying barriers and facilitators for using a smartwatch to monitor health among older adults, Educational Gerontology, 2024, 50:4, 282-295.
  2. Gong, J. Chen, B. Whitney, X. Liang, V. Reshniak, T. Banerjee, J. Lee, A. Rangarajan, L. Wan, N. Vidal, Q. Liu, A. Gainaru, N. Podhorszki, R. Archibald, S. Ranka and S. Klasky. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX 24: 101590 (2023).
  3. He, P. Emami, S. Ranka and A. Rangarajan, “Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14872-14887, Dec. 2023.
  4. Smail, J. Alpert, M. Mardini, C. Kaufmann, C. Bai, T. Gill, R. Fillingim, E. Cenko, R. Zapata, Y. Karnati, M. Marsiske, S. Ranka, T. Manini, Feasibility of A Smartwatch Platform to Assess Ecological Mobility: Real-time Online Assessment and Mobility Monitor (ROAMM). The Journals of Gerontology: Series A, Volume 78, Issue 5, May 2023, Pages 821–830.
  5. Sengupta et al., “Computing Arterial Travel Time Distributions From Loop Detector and Probe Datasets,” in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 11, pp. 11607-11622, Nov. 2023.
  6. He, P. Emami, S. Ranka, A. Rangarajan, Learning Scene Dynamics from Point Cloud Sequences. International Journal of Computer Vision, 130(3), pp. 669-695 (2022).
  7. Lee, Q. Gong, J. Choi, T. Banerjee S. Klasky S. Ranka, A. Rangarajan, Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities. Applied Sciences. 2022; 12(13):6718.
  8. Mishra, K. Chen, S. Poddar, E. Posadas, A. Rangarajan, S. Ranka, “Using Video Analytics to Improve Traffic Intersection Safety and Performance,” VehiclesJournal, 2022, Vol. 4,  1288-1313.
  9. He, A. Wu, X. Huang, A. Rangarajan, and S. Ranka, Machine Learning-based Highway Truck Commodity Classification Using Logo Data, in Applied Sciences, 2022, 12, 2075.
  10. Huang, P. He, A. Rangarajan, and S. Ranka, Machine Learning based Real-time Multi-Camera Vehicle Tracking and Travel Time Estimation, in Journal of Imaging. 2022; 8(4):101.

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  1. Li, Q. Gong, J. Lee, S. Klasky, A. Rangarajan, and S. Ranka, Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications. Proceedings of DCC 2024, to appear.
  2. Lee, A. Rangarajan, and S. Ranka, Guaranteeing Error Bounds with Preservation of Derived Quantities in Compressive Autoencoders. Proceedings of DCC 2024, to appear.
  3. Lee, A. Rangarajan, and S. Ranka, Nonlinear-by-Linear: Guaranteeing Error Bounds in Compressive Autoencoders, Proceedings of 2023 International Conference on Contemporary computing, 2023, pp. 552-561.
  4. Lee, A. Rangarajan, and S. Ranka, Nonlinear constraint satisfaction for compressive autoencoders using instance-specific linear operators. IC3 2023: 562-571.
  5. Banerjee, J. Lee, J. Choi, Q. Gong, J. Chen, CS. Chang, S. Klasky, A. Rangarajan, and S. Ranka: Online and Scalable Data Compression Pipeline with Guarantees on Quantities of Interest. Proceedings of e-Science 2023, pp. 1-10.
  6. Sengupta, T. Banerjee, Y. Karnati, S. Ranka, A. Rangarajan:
    Using DSRC Road-Side Unit Data to Derive Braking Behavior. Proceedings of VEHITS 2023, pp. 420-427.
  7. Gong, C. Zhang, X. Liang, V. Reshniak, J. Chen, A. Rangarajan, S. Ranka, N. Vidal, L. Wan, P. Ullrich, N. Podhorszki, R. Jacob, S. Klasky,Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis. Proceedings of 2023 e-Science, 2023: pp. 1-10.
  8. Li, P. He, A. Wu, S. Ranka, A. Rangarajan, A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications. Proceedings of 26th IEEE International Conference on Intelligent Transportation (ITSC), 2023, pp. 1014-1021.
  9. Wu, T.  Banerjee, K. Chen, A. Rangarajan, S. Ranka,  A Multi-Sensor Video/LiDAR System for Analyzing Intersection Safety. Proceedings of 26th IEEE International Conference on Intelligent Transportation (ITSC), 2023, pp. 1158-1165.
  10. Sengupta, T. Banerjee, Y. Karnati, K. Chen, S. Ranka, A. Rangarajan: Towards Effective Traffic Signal Safety and Optimization Using Fisheye Video Proceedings of VEHITS 2023, pp. 54-63.

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  1. Keynote Speaker, AI for Intelligent Transportation, The International Association of Transportation Regulators (IATR) 37th Annual Conference, Fort Lauderdale, October 2024.
  2. Keynote Speaker, Machine Learning for Intelligent Transportation, 2024 International Conference on Smart Transportation and Future Mobility, Shanghai, October 2024.
  3. Keynote Speaker, Machine Learning for Scientific Data Reduction, IEEE ISCC, Paris, June 2024.
  4. Keynote Speaker, Machine Learning for Smart Transportation, International Conference on Civil Engineering Fundamentals and Applications (ICCEFA’23) , Lisbon, Dec. 2023.
  5. Keynote Speaker, Leveraging AI for Transportation, 2023 7th International Symposium on Computer Science and Intelligent Control, ISCSIC 2023 ISCSIC 2023, Oct. 2023.
  6. Keynote Speaker, Volumetric scientific data reduction with performance guarantees, International Conference on Contemporary Computing (IC3-2023), August 2023.
  7. Keynote Speaker, Machine Learning for Smart Transportation, Advances in Information Communication Technology & Computing, Bikaner, India, Dec. 2022.
  8. Keynote Speaker, Machine Learning for Smart Transportation, International Conference on Machine Learning, and Intelligent Systems (MLIS2022), Seoul, Republic of Korea, Nov. 2022.
  9. Keynote Speaker, Artificial Intelligence and Machine Learning for Smart Transportation, INFRAMEET 2022, Chicago, Nov. 2022.
  10. Keynote Speaker, Edge Based AI for Smart Transportation, International Conference on Computing Innovation and Applied Physics, Aug. 2022.
  11. Invited Speaker, A Multi-Sensor Edge Based System for Intelligent Traffic Intersections, IEEE MetroCAD, Detroit, Apr. 2022.
  12. Keynote Speaker, Edge Based Artificial Intelligence for Smart Transporation, International Conference on Informatics (ICI-2022), New Delhi, Apr. 2022.
  13. Plenary Speaker, Power and Performance tradeoffs for Exascale Computing, Global Summit on Power and Energy Engineering (GSPEE2022), Dubai, Mar. 2021.

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  1. United States Patent Number 8,805,715. Brian Jones and Sanjay Ranka. Method for Improving the Performance of Messages Including Internet Splash Pages. Aug. 12, 2014.
  2. United States Patent Number 8,386,315. Arvind Bala, Richard Chatwin, Brian Jones, Ahmet Nalcacioglu, and Sanjay Ranka. Yield Management System and Method for Advertising Inventory, Feb. 26, 2013.
  3. United States Patent Number 8,260,663. Sanjay Ranka, Diane Chang, and Daniel Veiner. Method, Algorithm, and Computer Program for Targeting Messages Including Advertisements in an Interactive Measurable Medium, Sept. 4, 2012.
  4. United States Patent Number 8,144,686. Sartaj Sahni, Nageshwar Sirikonda Venkata, Sanjay Ranka, Yan Li, Eun-sung Jung, and Narayana Kamath. Method and Systems for Bandwidth Scheduling and Path Computation for Connection-Oriented Networks, Mar. 27, 2012.
  5. United States Patent Number 7,573,978. Srijit Kamath, Sartaj Sahni, Jonathan Li, Jatinder Palta, and Sanjay Ranka. Variable Feathering Field Splitting for Intensity Modulated Fields of Large Size, Aug. 11, 2009.
  6. United States Patent Number 7,415,423. Sanjay Ranka, Jason Lenderman, and James Weisinger. Method, Algorithm, and Computer Program for Optimizing the Performance of Messages Including Advertisements in an Interactive Measurable Medium, Aug. 19, 2008.
  7. United States Patent Number 7,406,434. Diane Chang, Richard Chatwin, Sachin Kumar, Sanjay Ranka, James Weisinger, and Jason Lenderman. System and Method for Improving the Performance of Electronic Media Advertising Campaigns through Multi-attribute Analysis and Optimization, July 29, 2008.
  8. United States Patent Number 7,142,635. Srijit Kamath, Sartaj Sahni, Jonathan Li, Jatinder Palta, and Sanjay Ranka. Field Splitting for Intensity Modulated Fields of Large Size, 28, 2006.
  9. United States Patent Number 7,085,348. Srijit Kamath, Sartaj Sahni, Jonathan Li, Jatinder Palta, and Sanjay Ranka. Leaf Sequencing Method and System, Aug. 1, 2006.
  10. United States patent number 6,563,952. Anurag Srivastava, G. D. Ramkumar, Vineet Singh, and Sanjay Ranka. Method of High Dimensional Data and Apparatus for Classification, May 13, 2003.
  11. United States Patent Number 6,173,280. G. D. Ramkumar, Sanjay Ranka, and Shalom Tsur. Method and Apparatus for Generating Weighted Association Rules, Jan. 9, 2001.
  12. United States Patent Number 5,987,468. Vineet Singh, Khaled Alsabti, and Sanjay Ranka. Structure and Method for Efficient Parallel High-Dimensional Similarity Join, Nov. 16, 1999.
  13. United States Patent Number 5,983,224. Vineet Singh, Sanjay Ranka, and Khaled Alsabti. Method and Apparatus for Reducing the Computational Requirements of K-Means Data Clustering, Nov. 9, 1999.