Making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection

Chopra, Ayush, et al. "On the limits of agency in agent-based models." In Proceedings of Autonomous Agents and Multi-agent Systems (AAMAS) 2025

Singh, Abhishek, et al. "CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models." Proceedings of AAAI. 2025

Lu, Charles, et al. "Data Acquisition via Experimental Design for Data Markets." Advances in Neural Information Processing Systems (NeurIPS) 37 (2024): 118086-118118.

Lu, Charles, et al. "Data Acquisition via Experimental Design for Decentralized Data Markets." arXiv preprint arXiv:2403.13893 (2024).

Abhishek Singh, Split Inference - Metrics, Benchmarks and Algorithms, ECCV'24

Zaid Tasneem, DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images, ECCV'24

Garg, Aditi, and Ayush Chopra. "Distributed Calibration of Agent-based Models." epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery at KDD 2024. 2024.

Tsung-Han Lin, Connor Henley, Siddharth Somasundaram, Akshat Dave, Moshe Laifenfeld, and Ramesh Raskar. Handheld mapping of specular surfaces using consumer-grade flash lidar. In IEEE International Conference on Computational Photography (ICCP), 2024.

Whitepaper

Klinghoffer, Tzofi, et al. "PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar." IEEE Conference on Computer Vision and Pattern Recognition (2024).

Chopra, Ayush, et al. "flame: A Framework for Learning in Agent-based ModEls." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 2024.

Singh, Abhishek, et al. "Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release." Advances in Neural Information Processing Systems 36 (2024).

Chopra, Ayush, et al. "Private agent-based modeling." In Proceedings of Autonomous Agents and Multi-agent Systems (AAMAS) 2024

Gupta, G., Kapila, R., Chopra, A., & Raskar, R. (2024). First 100 days of pandemic; an interplay of pharmaceutical, behavioral and digital interventions--A study using agent based modeling. Proceedings of Autonomous Agents and Multi-agent Systems (AAMAS) 2024

Chopra, Quera-bofarull and Zhang. "Differentiable Agent-based Modeling: Systems, Methods and Applications". Tutorial at 23rd Autonomous Agents and Multi-agent Systems (AAMAS) 2024.

Singh, Abhishek, et al. "Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release." Thirty-seventh Conference on Neural Information Processing Systems. 2023.

Klinghoffer, Tzofi, et al. "DISeR: Designing Imaging Systems with Reinforcement Learning." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.

Chopra, Ayush, et al. "Using neural networks to calibrate agent based models enables improved regional evidence for vaccine strategy and policy." Vaccine 41.48 (2023): 7067-7071.

Lu, Charles, et al. "Federated conformal predictors for distributed uncertainty quantification." International Conference on Machine Learning (ICML). PMLR, 2023.

Kumar, B.*, Lu, C.*, Gupta, G., Palepu, A., Bellamy, D., Raskar, R., & Beam, A. "Conformal Prediction with Large Language Models for Multi-Choice Question Answering." Neural Conversational AI Workshop at ICML 2023.

Chopra, Ayush, et al. "Differentiable agent-based epidemiology." Proceedings of 22nd International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2023)

Quera-Bofarull, A., Chopra, A., Aylett-Bullock, J., Cuesta-Lazaro, C., Calinescu, A., Raskar, R., & Wooldridge, M. (2023). Don’t simulate twice: One-shot sensitivity analyses via automatic differentiation. Proceedings of Autonomous Agents and Multi-agent Systems (AAMAS 2023)

Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar. "ORCa: Glossy Objects as Radiance-Field Cameras." Conference on Computer Vision and Pattern Recognition.

Connor Henley, Siddharth Somasundaram, Joseph Hollmann, and Ramesh Raskar, "Detection and mapping of specular surfaces using multibounce LiDAR returns," Opt. Express 31, 6370-6388 (2023).

Formal Privacy Guarantees for Neural Network queries by estimating local Lipschitz constant

Private independence testing across two parties

Chopra, Ayush, et al. "Learning to censor by noisy sampling." European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022.

Suat Evren, Praneeth Vepakomma

Praneeth Vepakomma, Abhishek Singh, Emily Zhang, Otkrist Gupta, Ramesh Raskar, IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2021

Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar

NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training

Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy. "ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors". International Conference on Computer Vision (ICCV) 2021

Tristan Swedish, Connor Henley, Ramesh Raskar; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2593-2602

Chopra, Ayush, et al. "DeepABM: scalable, efficient and differentiable agent-based simulations via graph neural networks." Winter Simulation Conference 2021

IEEE Global Communications Conference (GLOBECOM), 2021

Romero-Brufau, S., Chopra, A., Ryu, A. J., Gel, E., Raskar, R., Kremers, W., ... & Kingsley, T. C. (2021). Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study. bmj, 373.

Vepakomma, Praneeth et al. Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. arXiv:2102.10802v1 [cs.LG] 22 Feb 2021

Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Xiao Zeng, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram and Salman Avestimehr. "FedML: A Research Library and Benchmark for Federated Machine Learning." NeurIPS-SpicyFL 2020. (Baidu Best Paper Award)

DAMS: Meta-estimation of private sketch data structures for differentially private COVID-19 contact tracing, Praneeth Vepakomma, Subha Nawer Pushpita and Ramesh Raskar, PPML (Privacy Preserving Machine Learning workshop) at NeurIPS

Abhishek Singh, Ayush Chopra, Praneeth Vepakomma, Ethan Z Garza, Vivek Sharma, , Ramesh Raskar. "DISCO: Dynamic and Invariant Sensitive Channel Obfuscation." CVPR 2021

Henley, C., Maeda, T., Swedish, T., & Raskar, R. (2020). Imaging Behind Occluders Using Two-Bounce Light. Computer Vision – ECCV 2020 Lecture Notes in Computer Science, 573-588. doi:10.1007/978-3-030-58526-6_34

Vepakomma, P., Balla, J., Raskar, R., "Splintering with distributions: A stochastic decoy scheme for private computation." 6 Jul 2020.

Peter Kairouz, H. Brendan McMahan, et al. "Advances and Open Problems in Federated Learning." arXiv:1912.04977 [cs.LG] 10 Dec 2019.

Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree KalpathyCramer, and Ramesh Raskar. In NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019

Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, GN Prasanna, Ramesh Raskar

Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar. In NeurIPS Workshop on Machine learning for the Developing World (ML4D), 2019

Praneeth Vepakomma, et al. "Detailed comparison of communication efficiency of split learning and federated learning." arXiv:1909.09145v1 [cs.LG] 18 Sep 2019.

Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm, Praneeth Vepakomma, Yulia Kempner

Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan. Data Markets to support AI for All: Pricing, Valuation and Governance, arXiv:1905.06462 (2019).

Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, Ramesh Raskar. Reducing Leakage in Distributed Deep Learning for Sensitive Health Data, ICLR 2019 AI for Social Good Workshop (2019).

Light-Field for RF. Manikanta Kotaru, Guy Satat, Ramesh Raskar, Sachin Katti, arXiv:1901.03953 (2019).

Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya. arXiv:1812.02428