📍 Location Delft, Netherlands 📧 Email address dewant.katare@gmail.com dewant.katare@ieee.org 📞 Contact Number +31 6 3357 8732 🔗 Links LinkedIn | Scholar | ORCiD | DBLP

ML/AI Research Engineer with interest in Processors, Accelerators, and Embedded Systems. PhD - Approximation at the Edge for Automated Driving Marie Skłodowska-Curie Actions Researcher | APROPOS Research Areas Approximate Computing for Vehicular Edge AI Energy-aware Machine Learning Sustainable Computing Continuum Edge AI and Systems

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Journals, Transactions and Magazines

• 12/2025 Adaptive Energy-aware Framework for Connected Vehicle Services: Approximate Computing for Vehicular Edge AI
  *Katare, D. Adaptive Energy-aware Framework for Connected Vehicle Services: Approximate Computing for Vehicular Edge AI. Research output: Dissertation (TU Delft)

• 05/2025 Rethinking Computing Systems in the Era of Climate Crisis: A Call for a Sustainable Computing Continuum
  Peltonen, E., Bayhan, S., Bermbach, D., Buschjager, S., Degeler, V., Ding, A. Y., Katare, D.,.. & van der Waaij, B. (2025). Rethinking computing systems in the era of climate crisis: A call for a sustainable computing continuum. IEEE Internet Computing.

• 04/2025 Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets
  Katare, D., Noguero, D. S., Park, S., Kourtellis, N., Janssen, M., & Ding, A. Y. (2025). Analyzing and mitigating bias for vulnerable road users by addressing class imbalance in datasets. IEEE Open Journal of Intelligent Transportation Systems.

• 02/2025 Approximating vision transformers for edge: variational inference and mixed-precision for multi-modal data
  Katare, D., Leroux, S., Janssen, M., & Ding, A. Y. (2025). Approximating vision transformers for edge: variational inference and mixed-precision for multi-modal data. Computing, 107(3), 71.

• 12/2024 ARASEC: Adaptive resource allocation and model training for serverless edge–cloud computing
  Katare, D., Marin, E., Kourtellis, N., Janssen, M., & Ding, A. Y. (2024). ARASEC: Adaptive resource allocation and model training for serverless edge–cloud computing. IEEE Internet Computing, 28(6), 17-27.

• 01/2024 Adaptive approximate computing in edge AI and IoT applications: A review
  Damsgaard, H. J., Grenier, A., Katare, D., Taufique, Z., Shakibhamedan, S., Troccoli, T., … & Nurmi, J. (2024). Adaptive approximate computing in edge AI and IoT applications: A review. Journal of Systems Architecture, 150, 103114.

• 08/2023 A survey on approximate edge AI for energy efficient autonomous driving services
  Katare, Dewant, Diego Perino, Jari Nurmi, Martijn Warnier, Marijn Janssen, and Aaron Yi Ding. “A survey on approximate edge AI for energy efficient autonomous driving services.” IEEE Communications Surveys & Tutorials 25, no. 4 (2023): 2714-2754.

Conference and Workshops

• 04/2025 Energy-aware vision model partitioning for edge AI
  Katare, D., Zhou, M., Chen, Y., Janssen, M., & Ding, A. Y. (2025, March). Energy-aware vision model partitioning for edge AI. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 671-678).

• 01/2025 Energy-Aware Adaptive Framework for CAV
  Katare, D., Janssen, M., & Ding, A. Y. (2025, January). Energy-Aware Adaptive Framework for CAV. In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 626-632). IEEE.

• 01/2025 Test-time Specialization of Dynamic Neural Networks
  Leroux, S., Katare, D., Ding, A. Y., & Simoens, P. (2024). Test-time Specialization of Dynamic Neural Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1048-1056).

• 03/2023 Energy-efficient edge approximation for connected vehicular services
  Katare, D., & Ding, A. Y. (2023, March). Energy-efficient edge approximation for connected vehicular services. In 2023 57th Annual Conference on Information Sciences and Systems (CISS) (pp. 1-6). IEEE.

• 02/2023 Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving
  Katare, D., Kourtellis, N., Park, S., Perino, D., Janssen, M., & Ding, A. Y. (2022, December). Bias detection and generalization in AI algorithms on edge for autonomous driving. In 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) (pp. 342-348). IEEE.

• 09/2019 Autonomous embedded system enabled 3-D object detector:(With point cloud and camera)
  Katare, D., & El-Sharkawy, M. (2019, September). Autonomous Embedded System Enabled 3-D Object Detector:(with Point Cloud and Camera). In 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES) (pp. 1-6). IEEE.

• 07/2019 Real-time 3-d segmentation on an autonomous embedded system: using point cloud and camera
  Katare, D., & El-Sharkawy, M. (2019, July). Real-time 3-d segmentation on an autonomous embedded system: using point cloud and camera. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 356-361). IEEE.

• 01/2019 Embedded system enabled vehicle collision detection: an ANN classifier
  Katare, D., & El-Sharkawy, M. (2019, January). Embedded system enabled vehicle collision detection: an ANN classifier. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC) (pp. 0284-0289). IEEE.

• 10/2018 Collision warning system: embedded enabled (RTMaps with NXP BLBX2)
  Katare, D., & El-Sharkawy, M. (2018, December). Collision warning system: embedded enabled (RTMaps with NXP BLBX2). In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 1-6). IEEE.

• 07/2018 IOT based solution for level detection using CNN and OpenCV
  Pathak, D., Sarangapani, R. N., Katare, D., Gaikwad, A. S., & El-Sharkawy, M. (2018). IOT based solution for level detection using CNN and OpenCV. In Proceedings on the International Conference on Internet Computing (ICOMP) (pp. 83-87). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).