Publications

For metrics and citations, please refer to Sandeep’s Google Scholar profile.

2024

[1] Narasimhan, S. S., Bhat, S., and Chinchali, S. P. “Safe Networked Robotics With Probabilistic Verification.” IEEE Robotics and Automation Letters, 2024.

2023

[1] Akcin, O., Unuvar, O., Ure, O., and Chinchali, S. P. “Fleet Active Learning: A Submodular Maximization Approach.” 7th Annual Conference on Robot Learning, 2023.

[2] Li, P.-han, Ankireddy, S. K., Zhao, R., Mahjoub, H. N., Pari, E. M., topcu, ufuk, Chinchali, S. P., and Kim, H. “Task-Aware Distributed Source Coding under Dynamic Bandwidth.” Thirty-seventh Conference on Neural Information Processing Systems, 2023.

[3] Yu, Y., Zhao, R., Chinchali, S., and Topcu, U. “Poisoning Attacks Against Data-Driven Predictive Control.” 2023 American Control Conference (ACC), 2023.

[4] Agarwal, S., Fridovich-Keil, D., and Chinchali, S. P. “Robust Forecasting for Robotic Control: A Game-Theoretic Approach.” 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023.

[5] Li, P.-han, Chinchali, S. P., and Topcu, U. “Differentially Private Timeseries Forecasts for Networked Control.” American Control Conference (ACC), 2023. pdf

2022

[1] Akcin, O., Streit, R. P., Oommen, B., Vishwanath, S., and Chinchali, S. “A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics.” arXiv preprint arXiv:2210.12881, 2022.

[2] Omama, M., Sundar, S. V. S., Chinchali, S., Singh, A. K., and Krishna, K. M. “Drift Reduced Navigation with Deep Explainable Features.” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.

[3] Akcin, O., Li, P.-han, Agarwal, S., and Chinchali, S. P. “Decentralized Data Collection for Robotic Fleet Learning: A Game-Theoretic Approach.” 6th Annual Conference on Robot Learning, 2022.

[4] You, C., Zhao, R., Liu, F., Dong, S., Chinchali, S., Topcu, U., Staib, L., and Duncan, J. “Class-Aware Adversarial Transformers for Medical Image Segmentation.” Advances in Neural Information Processing Systems, 2022.

[5] Li, P.-han, Topcu, U., and Chinchali, S. P. “Adversarial Examples for Model-Based Control: A Sensitivity Analysis.” 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2022, pp. 1–7. pdf

[6] Ghosh, B., Khan, M., Ashok, A., Chinchali, S., and Duggirala, P. S. “Dynamic Selection of Perception Models for Robotic Control.” arXiv preprint arXiv:2207.06390, 2022.

[7] Cheng, J., Tang, A., and Chinchali, S. “Task-Aware Privacy Preservation for Multi-Dimensional Data.” International Conference on Machine Learning, 2022, pp. 3835–3851.

[8] Omama, M., V. S., S. S., Chinchali, S., and Krishna, K. M. “Ladfn: Learning Actions for Drift-Free Navigation in Highly Dynamic Scenes.” 2022 American Control Conference (ACC), 2022, pp. 1200–1207.

[9] Qiu, H., Vavelidou, I., Li, J., Pergament, E., Warden, P., Chinchali, S., Asgar, Z., and Katti, S. “ML-EXray: Visibility into ML Deployment on the Edge.” Proceedings of Machine Learning and Systems, Vol. 4, 2022, pp. 337–351.

[10] Verginis, C., Koprulu, C., Chinchali, S., and Topcu, U. “Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics.” arXiv preprint arXiv:2204.11833, 2022.

[11] Agarwal, S., and Chinchali, S. P. “Synthesizing Adversarial Visual Scenarios for Model-Based Robotic Control.” 6th Annual Conference on Robot Learning, 2022.

[12] Omama, M., V. S., S. S., Chinchali, S., Singh, A. K., and Krishna, K. M. “Drift Reduced Navigation with Deep Explainable Features.” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 6316–6323.

[13] Cheng, J., Chinchali, S., and Tang, A. “Task-Aware Network Coding Over Butterfly Network.” arXiv preprint arXiv:2201.11917, 2022.

[14] You, C., Zhao, R., Liu, F., Chinchali, S., Topcu, U., Staib, L., and Duncan, J. S. “Class-Aware Generative Adversarial Transformers for Medical Image Segmentation.” arXiv preprint arXiv:2201.10737, 2022.

[15] Geng, Y., Zhang, D., Li, P.-han, Akcin, O., Tang, A., and Chinchali, S. P. “Decentralized Sharing and Valuation of Fleet Robotic Data.” Conference on Robot Learning, 2022, pp. 1795–1800.

2021

[1] Cheng, J., Pavone, M., Katti, S., Chinchali, S., and Tang, A. “Data Sharing and Compression for Cooperative Networked Control.” Advances in Neural Information Processing Systems, Vol. 34, 2021, pp. 5947–5958.

[2] Nakanoya, M., Im, J., Qiu, H., Katti, S., Pavone, M., and Chinchali, S. “Personalized Federated Learning of Driver Prediction Models for Autonomous Driving.” arXiv preprint arXiv:2112.00956, 2021.

[3] Chinchali, S., Sharma, A., Harrison, J., Elhafsi, A., Kang, D., Pergament, E., Cidon, E., Katti, S., and Pavone, M. “Network Offloading Policies for Cloud Robotics: a Learning-Based Approach.” Autonomous Robots, Vol. 45, No. 7, 2021, pp. 997–1012.

[4] Ghosh, B., Chinchali, S., and Duggirala, P. S. “Interpretable Trade-Offs between Robot Task Accuracy and Compute Efficiency.” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 5364–5371.

[5] Lubars, J., Gupta, H., Chinchali, S., Li, L., Raja, A., Srikant, R., and Wu, X. “Combining Reinforcement Learning with Model Predictive Control for on-Ramp Merging.” 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 942–947.

[6] Nakanoya, M., Chinchali, S., Anemogiannis, A., Datta, A., Katti, S., and Pavone, M. “Co-Design of Communication and Machine Inference for Cloud Robotics.” Robotics: Science and Systems, 2021.

2020

[1] Chinchali, S., Pergament, E., Nakanoya, M., Cidon, E., Zhang, E., Bharadia, D., Pavone, M., and Katti, S. “Sampling Training Data for Continual Learning between Robots and the Cloud.” International Symposium on Experimental Robotics, 2020, pp. 296–308.

[2] Chu, T., Chinchali, S., and Katti, S. “Multi-Agent Reinforcement Learning for Networked System Control.” arXiv preprint arXiv:2004.01339, 2020.

[3] Chu, T., Misra, R., Chinchali, S., Anemogiannis, A., Tandra, R., and Nagaraj, K. System and Method for Widescale Adaptive Bitrate Selection. Google Patents, Mar, 2020.

[4] Chinchali, S. P. Collaborative Perception and Learning Between Robots and the Cloud. Stanford University, 2020.

2019

[1] Chinchali, S. P., Livingston, S. C., Chen, M., and Pavone, M. “Multi-Objective Optimal Control for Proactive Decision Making with Temporal Logic Models.” The International Journal of Robotics Research, Vol. 38, Nos. 12-13, 2019, pp. 1490–1512.

2018

[1] Chinchali, S. P., Cidon, E., Pergament, E., Chu, T., and Katti, S. “Neural Networks Meet Physical Networks: Distributed Inference between Edge Devices and the Cloud.” Proceedings of the 17th ACM Workshop on Hot Topics in Networks, 2018, pp. 50–56.

[2] Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., and Katti, S. “Cellular Network Traffic Scheduling with Deep Reinforcement Learning.” Thirty-second AAAI conference on artificial intelligence, 2018.

2016

[1] Nagaraj, K., Bharadia, D., Mao, H., Chinchali, S., Alizadeh, M., and Katti, S. “Numfabric: Fast and Flexible Bandwidth Allocation in Datacenters.” Proceedings of the 2016 ACM SIGCOMM Conference, 2016, pp. 188–201.

[2] Chinchali, S. P., Livingston, S. C., Pavone, M., and Burdick, J. W. “Simultaneous Model Identification and Task Satisfaction in the Presence of Temporal Logic Constraints.” 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 3682–3689.

[3] Guturu, H., Chinchali, S., Clarke, S. L., and Bejerano, G. “Erosion of Conserved Binding Sites in Personal Genomes Points to Medical Histories.” PLoS computational biology, Vol. 12, No. 2, 2016, p. e1004711.

2013

[1] Yap, K.-K., Huang, T.-Y., Yiakoumis, Y., Chinchali, S., McKeown, N., and Katti, S. “Scheduling Packets over Multiple Interfaces While Respecting User Preferences.” Proceedings of the ninth ACM conference on Emerging networking experiments and technologies, 2013, pp. 109–120.

2012

[1] Chinchali, S., Livingston, S. C., Topcu, U., Burdick, J. W., and Murray, R. M. “Towards Formal Synthesis of Reactive Controllers for Dexterous Robotic Manipulation.” 2012 IEEE International Conference on Robotics and Automation, 2012, pp. 5183–5189.

[2] Chinchali, S., and Tandon, S. Deep Learning for Wireless Interference Segmentation and Prediction. , 2012.

2010

[1] Abad-Manterola, P., Burdick, J. W., Nesnas, I. A. D., Chinchali, S., Fuller, C., and Zhou, X. “Axel Rover Paddle Wheel Design, Efficiency, and Sinkage on Deformable Terrain.” 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 2821–2827.