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 This project is structured to create a three-tiered architecture consisting of the nomadic edge (in vehicles), the static edge (co-located with Radio Access Networks), and the cloud. To support diverse emerging applications across domains (e.g., smart transportation, urban planning, and more broadly across different aspects of smarter communities) which can issue queries spanning large spatio-temporal regions. The research agenda includes five complementary tasks: (i) Design of distributed and dynamic computer vision from the vehicular context, which involves collaborative and federated machine learning approaches to event and object detection under diverse conditions; (ii) Design of collaborative and distributed training and inference to address high level queries which will utilize various techniques of redundant and coded computing and communication to support efficiency and scalability; (iii) Estimation and prediction of the wireless network context to infer opportunities for communication between collaborating nomadic edge nodes, as well as between the nomadic and static edges; (iv) Design for privacy and security of both data and models; and (v) Systems integration and field trials to allow for technique refinement, evaluation, and reproducibility.
+{{:nautilus:nautilus-sys-diagram-apr2021.jpg?600|}}
 ===== Faculty =====
@@ Line 12: / Line 14: @@
 ===== Students =====
-* Shenghong Dai
+  * Shenghong Dai
-* Tuan Dinh
+  * Tuan Dinh
+  * Bhavya Goyal
 ----
 ===== Quarterly Updates =====
+==== May 2022 ====
+=== Papers accepted ===
+* Network Side Digital Contact Tracing on a Large University Campus
+Matthew Malloy, Lance Hartung, Steven Wangen, Suman Banerjee
+ACM MobiCom, 2022
+*Breaking Fair Binary Classification with Optimal Flipping Attacks
+C. Jo, J. Sohn, and K. Lee
+ISIT 2022
+*Debiasing Pre-Trained Language Models via Efficient Fine-tuning
+M. Gira, R. Zhang, and K. Lee
+ACL Workshop on Language Technology for Equality, Diversity, Inclusion 2022
+*Federated Unsupervised Clustering with Generative Models
+J. Chung, K. Lee, and K. Ramchandran
+AAAI Workshop on Federated Learning 2022
+*Improving Fairness via Federated Learning
+Y. Zeng, H. Chen, and K. Lee
+AAAI Workshop on Federated Learning 2022
+ Geometric Calibration of Single-Pixel Distance Sensors
+Carter Sifferman, Mohit Gupta, Michael Gleicher
+Robotics and Automation Letters (RAL);  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022
+* Single-Photon Structured Light
+Varun Sundar, A Sankaranarayanan, Mohit Gupta
+Proc. IEEE CVPR 2022
+* Compressive Single-Photon 3D Cameras
+Felipe Gutierrez-Barragan, Atul Ingle, T Seets, Mohit Gupta, Andreas Velten
+Proc. IEEE CVPR 2022
+* Single-Photon Camera Guided Extreme Dynamic Range Imaging
+Yuhao Liu, Felipe Gutierrez-Barragan, Atul Ingle, Mohit Gupta, Andreas Velten
+IEEE Winter Conference on Applications of Computer Vision (WACV 2022)
+==== Oct 2021 ====
+=== Papers accepted ===
+Sample Selection for Fair and Robust Training
+Y. Roh, K. Lee, S. Whang, and C. Suh, NeurIPS 2021.
+Gradient Inversion with Generative Image Prior
+J. Kim, J. Jeon, K. Lee, S. Oh, and J. Ok, NeurIPS 2021.
+A General Framework For Detecting Anomalous Inputs to DNN Classifiers
+J Raghuram, V Chandrasekaran, S Jha, S Banerjee,
+International Conference on Machine Learning, 2021.
+All Roads Lead to Rome: An MPTCP-Aware Layer-4 Load Balancer
+Y Zeng, M Buddhikot, S Banerjee
+IFIP Networking Conference (IFIP Networking), 2021.
+==== July 2021 ====
+=== Papers accepted ==
+*  S. Ahmed, I. Shumailov, N. Papernot, and K. Fawaz, Towards More Robust Keyword Spotting for Voice Assistants, in 31st USENIX Security Symposium  (USENIX Security 2022), Accepted.
+* Photon Starved Scene Inference using Single-Photon Cameras.
+Bhavya Goyal and Mohit Gupta.
+ Proc. IEEE International Conference on Computer Vision (ICCV 2021).
+* Coded-InvNet for Resilient Prediction Serving Systems
+T. Dinh and K. Lee
+ICML 2021 (long oral)
+* Powercut and obfuscator: An exploration of the design space for privacy-preserving interventions for smart speakers.
+V. Chandrasekaran, S. Banerjee, B. Mutlu, and K. Fawaz. In Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021). USENIX Association, Aug. 2021. [Online]. Available: https://www.usenix.org/conference/soups2021/presentation/chandrasekaran
+=== Presentations ===
+* (June 2021) Co-PI Lee gave an invited talk @ AI institute of POSTECH on FairBatch and Coded-InvNet
+* (June 2021) Co-PI Lee gave an invited talk at the Shannon meets Turing Colloquium @ Seoul National University on FairBatch and Coded-InvNet
 ==== Mar 2021 ====
 === Papers accepted ===
+* Anant Gupta, Atul Ingle, and Mohit Gupta. "Asynchronous
+single-photon 3D imaging." Proc. of the IEEE/CVF International
+Conference on Computer Vision. 2019.
+* Gupta, A., Ingle, A., Velten, A., & Gupta, M. (2019). Photon-flooded
+single-photon 3D cameras. Proc. of the IEEE/CVF Conference on Computer
+Vision and Pattern Recognition. 2019.
+* Ingle, A., Velten, A., & Gupta, M. (2019). High flux passive imaging
+with single-photon sensors. Proc. of the IEEE/CVF Conference on
+Computer Vision and Pattern Recognition. 2019.
 * FairBatch: Batch Selection for Model Fairness.
 Y. Roh, K. Lee, S. Whang, and C. Suh.
 ICLR 2021
+https://openreview.net/pdf?id=YNnpaAKeCfx
 * Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.
 S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos.
 MLSys 2021
+https://proceedings.mlsys.org/paper/2021/hash/1d7f7abc18fcb43975065399b0d1e48e-Abstract.html
 === Presentation slides ===
+N/A
+=== Presentations ===
+* (April 2021) Co-PI Lee's paper "Accordion" is presented at MLSys 2021
+* (May 2021) Co-PI Lee's paper "FairBatch" will be presented at ICLR 2021
+=== New preliminary results ===
+* Shenghong Dai is implementing the mobile learning agents in the vehicle simulator CARLA. Her simulator will play a key role in testing the realistic federated learning with mobile agents.
+* Tuan Dinh is working on finalizing the paper on coded computation + invertible neural networks. In particular, he is working on the use of his coded computation algorithm for normalized flows for generative learning.
+=== Awards for students or faculty ===
+N/A
+=== Invited talks ===
+* Co-PI Lee will give one invited talk on fairness issues in machine learning:
+i) (April 16th) FairBatch @ the special interest group (SIG) at the intersection of ethics and algorithms research composed of researchers from UC Santa Cruz, The University of Wisconsin at Madison, and The University of Washington, and the University of Chicago
+=== Additional comments ===
+Nothing noted.
-* 3D Time of Flight Cameras, Atul Ingle.
 ==== Jan 2021 Update ====

WiNGS Lab @ UW

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