Assignment 3: Read a paper

Before you begin

Before you begin: Go over the module Skill: How to read a paper if you did not attend the lecture on Week 3. Please also take time to look into the three paper-reading guides covered in class:
- S. Kechav. How to read a paper
- P.W.L. Fong. Reading a Computer Science Research Paper
- UCSD, ERSP, Guided reading form

Description

Your third assignment is to read an academic paper that you are interested in. We recommend that you choose the paper from the list given below. If you would like to read a different paper, please contact the teaching team to discuss. The paper should be a conference paper that describes original research (NOT a literature survey or vision paper, probably NOT a long journal paper). You can choose to read the paper you found in Assignment 1 if it is in the list, but it is also okay to choose a different one.

In your text submission for this assignment, please include:

- Title, authors, publication venue, year of the paper you chose, with link to a PDF of the paper (even if these are the same as Assignment 1, it is useful for us to have quick access to the paper when reading your response)
- What problem is addressed in the paper? Make sure you can state the problem without referring to the solution, i.e., a different paper could address the exact same problem but have different contributions.
- What are the key contributions of the paper?
- Which sections in the paper contributed to your understanding of the problem and contributions? (e.g., "they were listed in the introduction/conclusion", "I followed the term `proposed' in the methods section")
- Which paper-reading recommendations (e.g., Kechav, Fong, ERSP) did you follow while reading the paper? Did you find them useful? If yes, in what ways?
- What percentage of the paper text (including equations) did you not understand? What is an example of something you did not understand, if any?

Submission

This assignment is due on October 23, 2023 Monday 11:59pm.

Please submit a text entry with your answers to the five bullets above on GradeScope here.

LIST OF PAPERS

Artificial Intelligence

Jethani, N., Sudarshan, M., Covert, I. C., Lee, S. I., & Ranganath, R. (2021, September). FastSHAP: Real-Time Shapley Value Estimation. In International Conference on Learning Representations.

Computational Biology

Beebe-Wang, N., Celik, S., Weinberger, E., Sturmfels, P., De Jager, P. L., Mostafavi, S., & Lee, S. I. (2021). Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies. Nature communications, 12(1), 1-17.

Computer Architecture

Althoff, A., McMahan, J., Vega, L., Davidson, S., Sherwood, T., Taylor, M., & Kastner, R. (2018, June). Hiding intermittent information leakage with architectural support for blinking. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA) (pp. 638-649). IEEE.

Willsey, M., Stephenson, A. P., Takahashi, C., Vaid, P., Nguyen, B. H., Piszczek, M., ... & Ceze, L. (2019, April). Puddle: A dynamic, error-correcting, full-stack microfluidics platform. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 183-197).

Chen, T., Zheng, L., Yan, E., Jiang, Z., Moreau, T., Ceze, L., ... & Krishnamurthy, A. (2018). Learning to optimize tensor programs. Advances in Neural Information Processing Systems, 31.

Anderson, T. E., Canini, M., Kim, J., Kostić, D., Kwon, Y., Peter, S., ... & Witchel, E. (2020). Assise: Performance and Availability via Client-local {NVM} in a Distributed File System. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) (pp. 1011-1027).

Zhang, I., Sharma, N. K., Szekeres, A., Krishnamurthy, A., & Ports, D. R. (2018). Building consistent transactions with inconsistent replication. ACM Transactions on Computer Systems (TOCS), 35(4), 1-37.

Augmented & Virtual Reality, Computer Graphics

Kang, S., Shokeen, E., Byrne, V. L., Norooz, L., Bonsignore, E., Williams-Pierce, C., & Froehlich, J. E. (2020, April). ARMath: augmenting everyday life with math learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-15).

Park, K., Sinha, U., Barron, J. T., Bouaziz, S., Goldman, D. B., Seitz, S. M., & Martin-Brualla, R. (2021). Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5865-5874).

Weng, C. Y., Curless, B., Srinivasan, P. P., Barron, J. T., & Kemelmacher-Shlizerman, I. (2022). Humannerf: Free-viewpoint rendering of moving people from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16210-16220).

Computer Vision, Animation & Game Science

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., & Hajishirzi, H. (2018). Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation. In Proceedings of the european conference on computer vision (ECCV) (pp. 552-568).

Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016, October). Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference on computer vision (pp. 525-542). European Conference on Computer Vision (ECCV).

Sengupta, S., Curless, B., Kemelmacher-Shlizerman, I., & Seitz, S. M. (2021). A light stage on every desk. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2420-2429).

Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C. (2020). Action genome: Actions as compositions of spatio-temporal scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10236-10247).

Polozov, O., O'Rourke, E., Smith, A. M., Zettlemoyer, L., Gulwani, S., & Popović, Z. (2015, June). Personalized mathematical word problem generation. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Byravan, A., & Fox, D. (2017, May). Se3-nets: Learning rigid body motion using deep neural networks. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 173-180). IEEE.

Gecer, B., Aksoy, S., Mercan, E., Shapiro, L. G., Weaver, D. L., & Elmore, J. G. (2018). Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern recognition, 84, 345-356.

Computational Neuroscience

Khalvati, K., Kiani, R., & Rao, R. P. (2021). Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy. Nature communications, 12(1), 1-16.

Maheswaranathan, N., Williams, A., Golub, M., Ganguli, S., & Sussillo, D. (2019). Universality and individuality in neural dynamics across large populations of recurrent networks. Advances in neural information processing systems, 32.

Computing for Development

Vashistha, A., Sethi, P., & Anderson, R. (2018, April). BSpeak: An accessible voice-based crowdsourcing marketplace for low-income blind people. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

Ibtasam, S., Razaq, L., Ayub, M., Webster, J. R., Ahmed, S. I., & Anderson, R. (2019). " My cousin bought the phone for me. I never go to mobile shops." The Role of Family in Women's Technological Inclusion in Islamic Culture. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-33.

Keleher, N., Barela, M. C., Blumenstock, J., Festin, C., Podolsky, M., Troland, E., ... & Heimerl, K. (2020, June). Connecting Isolated Communities: Quantitative Evidence on the Adoption of Community Cellular Networks in the Philippines. In Proceedings of the 2020 International Conference on Information and Communication Technologies and Development (pp. 1-16).

Data Science

Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., & Althoff, T. (2021, April). Towards facilitating empathic conversations in online mental health support: A reinforcement learning approach. In Proceedings of the Web Conference 2021 (pp. 194-205).

Data Management & Visualization

Orr, L., Ainsworth, S., Cai, W., Jamieson, K., Balazinska, M., & Suciu, D. (2019). Mosaic: a sample-based database system for open world query processing. arXiv preprint arXiv:1912.07777.

Bao, C. S., Li, S., Flores, S. G., Correll, M., & Battle, L. (2022, April). Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health. In CHI Conference on Human Factors in Computing Systems (pp. 1-17).

Haynes, B., Daum, M., He, D., Mazumdar, A., Balazinska, M., Cheung, A., & Ceze, L. (2021, June). Vss: A storage system for video analytics. In Proceedings of the 2021 International Conference on Management of Data (pp. 685-696).

Liu, Y., Kale, A., Althoff, T., & Heer, J. (2020). Boba: Authoring and visualizing multiverse analyses. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1753-1763.

Jain, S., Moritz, D., Halperin, D., Howe, B., & Lazowska, E. (2016, June). Sqlshare: Results from a multi-year sql-as-a-service experiment. In Proceedings of the 2016 International Conference on Management of Data (pp. 281-293).

Fabrication

Leake, M., Bernstein, G., and Agrawala, M. "Sketch-Based Design of Foundation Paper Pieceable Quilts." (2022). ACM Symposium on User Interface Software and Technology (UIST).

Wu, C., Zhao, H., Nandi, C., Lipton, J. I., Tatlock, Z., & Schulz, A. (2019). Carpentry compiler. ACM Transactions on Graphics (TOG), 38(6), 1-14.

Human Computer Interaction & Accessible Technology

Saha, M., Patil, S., Cho, E., Cheng, E. Y. Y., Horng, C., Chauhan, D., ... & Froehlich, J. E. (2022, April). Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study. In CHI Conference on Human Factors in Computing Systems (pp. 1-14).

Thompson, R., Tanimoto, S., Lyman, R. D., Geselowitz, K., Begay, K. K., Nielsen, K., ... & Berninger, V. (2018). Effective instruction for persisting dyslexia in upper grades: Adding hope stories and computer coding to explicit literacy instruction. Education and information technologies, 23(3), 1043-1068.

Nordhoff, M., August, T., Oliveira, N. A., & Reinecke, K. (2018, April). A case for design localization: Diversity of website aesthetics in 44 countries. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12).

Chaudhuri, B., Perlmutter, L., Petelka, J., Garrison, P., Fogarty, J., Wobbrock, J. O., & Ladner, R. E. (2019, October). GestureCalc: an eyes-free calculator for touch screens. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility (pp. 112-123).

Weld, G., Zhang, A. X., & Althoff, T. (2022, May). What Makes Online Communities ‘Better’? Measuring Values, Consensus, and Conflict across Thousands of Subreddits. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 1121-1132).

Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016, August). " I Don't Code All Day" Fitting in Computer Science When the Stereotypes Don't Fit. In Proceedings of the 2016 ACM conference on international computing education research (pp. 23-32).

Swearngin, A., Wang, C., Oleson, A., Fogarty, J., & Ko, A. J. (2020, April). Scout: Rapid exploration of interface layout alternatives through high-level design constraints. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

Kong, J., Zhong, M., Fogarty, J., & Wobbrock, J. O. (2021, October). New Metrics for Understanding Touch by People with and without Limited Fine Motor Function. In The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-4).

Potluri, V., He, L., Chen, C., Froehlich, J. E., & Mankoff, J. (2019, October). A Multi-Modal Approach for Blind and Visually Impaired Developers to Edit Webpage Designs. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility (pp. 612-614).

Xu, X., Zou, T., Xiao, H., Li, Y., Wang, R., Yuan, T., Mankoff, J. & Dey, A. K. (2022, April). TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction. In CHI Conference on Human Factors in Computing Systems (pp. 1-17).

Zimmermann-Niefield, A., Polson, S., Moreno, C., & Shapiro, R. B. (2020, June). Youth making machine learning models for gesture-controlled interactive media. In Proceedings of the interaction design and children conference (pp. 63-74).

Machine Learning

Sagawa, S., Raghunathan, A., Koh, P. W., & Liang, P. (2020, November). An investigation of why overparameterization exacerbates spurious correlations. In International Conference on Machine Learning (pp. 8346-8356). PMLR.

Weinberger, E., Janizek, J., & Lee, S. I. (2020). Learning deep attribution priors based on prior knowledge. Advances in Neural Information Processing Systems, 33, 14034-14045.

Awasthi, P., Beutel, A., Kleindessner, M., Morgenstern, J., & Wang, X. (2021, March). Evaluating fairness of machine learning models under uncertain and incomplete information. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 206-214).

Chen, Y., Jamieson, K., & Du, S. (2022, June). Active Multi-Task Representation Learning. In International Conference on Machine Learning (pp. 3271-3298). PMLR.

Ndousse, K. K., Eck, D., Levine, S., & Jaques, N. (2021, July). Emergent social learning via multi-agent reinforcement learning. In International Conference on Machine Learning (pp. 7991-8004). PMLR.

Liu, X., Kong, W., Kakade, S., & Oh, S. (2021). Robust and differentially private mean estimation. Advances in Neural Information Processing Systems, 34, 3887-3901.

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., ... & Schmidt, L. (2022). Robust fine-tuning of zero-shot models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7959-7971).

Molecular Information Systems

Doroschak, K., Zhang, K., Queen, M., Mandyam, A., Strauss, K., Ceze, L., & Nivala, J. (2020). Rapid and robust assembly and decoding of molecular tags with DNA-based nanopore signatures. Nature communications, 11(1), 1-8.

Linder, J., La Fleur, A., Chen, Z., Ljubetič, A., Baker, D., Kannan, S., & Seelig, G. (2022). Interpreting neural networks for biological sequences by learning stochastic masks. Nature Machine Intelligence, 4(1), 41-54.

Shin, S. W., Thachuk, C., & Winfree, E. (2019). Verifying chemical reaction network implementations: a pathway decomposition approach. Theoretical Computer Science, 765, 67-96.

Natural Language Processing

Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011, June). Finding Deceptive Opinion Spam by Any Stretch of the Imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 309-319).

Manzini, T., Chong, L. Y., Black, A. W., & Tsvetkov, Y. (2019, June). Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 615-621).

Dou, Y., Forbes, M., Koncel-Kedziorski, R., Smith, N. A., & Choi, Y. (2022, May). Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 7250-7274).

Zellers, R., Lu, X., Hessel, J., Yu, Y., Park, J. S., Cao, J., ... & Choi, Y. (2021). Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34, 23634-23651.

Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020). Fact or fiction: Verifying scientific claims. In 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020.

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. Advances in neural information processing systems, 32.

Programming Languages & Software Engineering

Tate, R., Stepp, M., Tatlock, Z., & Lerner, S. (2009, January). Equality saturation: a new approach to optimization. In Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (pp. 264-276).

Nelson, L., Bornholt, J., Gu, R., Baumann, A., Torlak, E., & Wang, X. (2019, October). Scaling symbolic evaluation for automated verification of systems code with Serval. In Proceedings of the 27th ACM Symposium on Operating Systems Principles (pp. 225-242).

Just, R., Jalali, D., Inozemtseva, L., Ernst, M. D., Holmes, R., & Fraser, G. (2014, November). Are mutants a valid substitute for real faults in software testing?. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 654-665).

Wang, C., Feng, Y., Bodik, R., Dillig, I., Cheung, A., & Ko, A. J. (2021, May). Falx: Synthesis-powered visualization authoring. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).

Nandi, C., Willsey, M., Anderson, A., Wilcox, J. R., Darulova, E., Grossman, D., & Tatlock, Z. (2020, June). Synthesizing structured CAD models with equality saturation and inverse transformations. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (pp. 31-44).

Robotics

Lancaster, P. E., Smith, J. R., & Srinivasa, S. S. (2019, May). Improved proximity, contact, and force sensing via optimization of elastomer-air interface geometry. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 3797-3803). IEEE.

Shridhar, M., Manuelli, L., & Fox, D. (2022, January). Cliport: What and where pathways for robotic manipulation. In Conference on Robot Learning (pp. 894-906). PMLR.

Bhardwaj, M., Sundaralingam, B., Mousavian, A., Ratliff, N. D., Fox, D., Ramos, F., & Boots, B. (2022, January). Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation. In Conference on Robot Learning (pp. 750-759). PMLR.

Murray, M., Walker, N., Nanavati, A., Alves-Oliveira, P., Filippov, N., Sauppe, A., Mutlu, B. & Cakmak, M. (2022, January). Learning backchanneling behaviors for a social robot via data augmentation from human-human conversations. In Conference on Robot Learning (pp. 513-525). PMLR.

Bhattacharjee, T., Gordon, E. K., Scalise, R., Cabrera, M. E., Caspi, A., Cakmak, M., & Srinivasa, S. S. (2020, March). Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding. In 2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 181-190). IEEE.

Xie, C., Xiang, Y., Mousavian, A., & Fox, D. (2021). Unseen object instance segmentation for robotic environments. IEEE Transactions on Robotics, 37(5), 1343-1359.

Gupta, A., Yu, J., Zhao, T. Z., Kumar, V., Rovinsky, A., Xu, K., ... & Levine, S. (2021, May). Reset-free reinforcement learning via multi-task learning: Learning dexterous manipulation behaviors without human intervention. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6664-6671). IEEE.

Chen, Z., Kiami, S., Gupta, A., & Kumar, V. (2023). GenAug: Retargeting behaviors to unseen situations via Generative Augmentation. arXiv e-prints, arXiv-2302. Robotics: Science & Systems (RSS).

Security & Privacy

Owens, K., Alem, A., Roesner, F., & Kohno, T. (2022). Electronic Monitoring Smartphone Apps: An Analysis of Risks from Technical,{Human-Centered}, and Legal Perspectives. In 31st USENIX Security Symposium (USENIX Security 22) (pp. 4077-4094).

Loughlin, K., Neal, I., Ma, J., Tsai, E., Weisse, O., Narayanasamy, S., & Kasikci, B. (2021). {DOLMA}: Securing Speculation with the Principle of Transient {Non-Observability}. In 30th USENIX Security Symposium (USENIX Security 21) (pp. 1397-1414).

Kohlbrenner, D., & Shacham, H. (2016). Trusted browsers for uncertain times. In 25th USENIX Security Symposium (USENIX Security 16) (pp. 463-480).

Zeng, E., Kohno, T., & Roesner, F. (2021, May). What makes a “bad” ad? user perceptions of problematic online advertising. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-24).

Cobb, C., Simko, L., Kohno, T., & Hiniker, A. (2020). A Privacy-Focused Systematic Analysis of Online Status Indicators. Proceedings on Privacy Enhancing Technologies, 2020(3), 384-403.

Geeng, C., & Roesner, F. (2019, May). Who's in control? Interactions in multi-user smart homes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

Lin, H., & Tessaro, S. (2017, August). Indistinguishability obfuscation from trilinear maps and block-wise local PRGs. In Annual International Cryptology Conference (pp. 630-660). Springer, Cham.

Systems & Networking

Kwon, Y., Fingler, H., Hunt, T., Peter, S., Witchel, E., & Anderson, T. (2017, October). Strata: A cross media file system. In Proceedings of the 26th Symposium on Operating Systems Principles (pp. 460-477).

Lebeck, N., Krishnamurthy, A., Levy, H. M., & Zhang, I. (2020). End the senseless killing: Improving memory management for mobile operating systems. In 2020 USENIX Annual Technical Conference (USENIX ATC 20) (pp. 873-887).

Xu, X., Beckett, R., Jayaraman, K., Mahajan, R., & Walker, D. (2021, August). Test coverage metrics for the network. In Proceedings of the 2021 ACM SIGCOMM 2021 Conference (pp. 775-787).

Mazumdar, A., Haynes, B., Balazinska, M., Ceze, L., Cheung, A., & Oskin, M. (2019, November). Perceptual compression for video storage and processing systems. In Proceedings of the ACM Symposium on Cloud Computing (pp. 179-192).

Theory of Computation

Rao, A., & Sinha, M. (2018). Simplified separation of information and communication. Theory of Computing, 14(1), 1-29.

Beame, P., Har-Peled, S., Ramamoorthy, S. N., Rashtchian, C., & Sinha, M. (2020). Edge estimation with independent set oracles. ACM Transactions on Algorithms (TALG), 16(4), 1-27.

Coladangelo, A., Goldwasser, S., & Vazirani, U. (2022, June). Deniable encryption in a Quantum world. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing (pp. 1378-1391).

Karlin, A. R., Klein, N., & Gharan, S. O. (2021, June). A (slightly) improved approximation algorithm for metric TSP. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (pp. 32-45).

Ebrahimnejad, F., & Lee, J.R. (2021). Multiscale entropic regularization for MTS on general metric spaces. Innovations in Theoretical Computer Science (ITCS) 2022.

Cohen, M. B., Lee, Y.T., & Song, Z. (2021). Solving linear programs in the current matrix multiplication time. Journal of the ACM (JACM), 68(1), 1-39.

Jain, A., Lin, H., & Sahai, A. (2021, June). Indistinguishability obfuscation from well-founded assumptions. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (pp. 60-73).

Ubiquitous Computing

Wang, E. J., Li, W., Hawkins, D., Gernsheimer, T., Norby-Slycord, C., & Patel, S. N. (2016, September). HemaApp: noninvasive blood screening of hemoglobin using smartphone cameras. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 593-604).

Wang, Y., Ding, J., Chatterjee, I., Salemi Parizi, F., Zhuang, Y., Yan, Y., Patel, S. N. & Shi, Y. (2022, April). FaceOri: Tracking Head Position and Orientation Using Ultrasonic Ranging on Earphones. In CHI Conference on Human Factors in Computing Systems (pp. 1-12).

Wireless & Sensor Systems

Nandakumar, R., Gollakota, S., & Sunshine, J. E. (2019). Opioid overdose detection using smartphones. Science translational medicine, 11(474).

Daepp, M. I., Cabral, A., Ranganathan, V., Iyer, V., Counts, S., Johns, P., ... & Nguyen, B. H. (2022, May). Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. In 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) (pp. 28-40). IEEE.

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