•  Measure-Theoretic Reeb Graphs and Reeb Spaces. Qingsong Wang, Guanquan Ma, Raghavendra Sridharamurthy, Bei Wang. International Symposium on Computational Geometry (SOCG), 2024.
• Gallatin: A General-Purpose GPU Memory Manager Hunter McCoy, Prashant Pandey PPOPP 2024
• Overlapping and Robust Edge-Colored Clustering in Hypergraphs. A. Crane, B. Lavallee, B. D. Sullivan, N. Veldt WSDM 2024
• Algorithms for Covering Barrier Points by Mobile Sensors with Line Constraint. Princy Jain and Haitao Wang International Journal of Computational Geometry and Applications (IJCGA), 2024.
• Tight Bounds for Volumetric Spanners and Applications. Sepideh Mahabadi, Ali Vakilian, Aditya Bhaskara Advances in Neural Information Processing Systems NeurIPS 2023
• Interactive Visualization and Portable Image Blending of Massive Aerial Image Mosaics. Steve Petruzza, Brian Summa, Amy Gooch, Christine M Laney, Tristan Goulden, John Schreiner, Steven Callahan, Valerio Pascucci 2023 IEEE International Conference on Big Data
• New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries. A. Bhaskara, E. Evert, V. Srinivas, A. Vijayaraghavan. ACM Symposium on Theory of Computing (STOC) 2024
• Hypergraph Co-Optimal Transport: Metric and Categorical Properties. Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou. Journal of Applied and Computational Topology, 2023.
•  Sketching Multidimensional Time Series for Fast Discord Mining. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, and Eamonn Keogh. IEEE International Conference on Big Data. December 2023.

• Accelerators and VLSI: E. Brunvand, R. Balasubramonian
• Cache Coherence and Memory Consistency: V. Nagarajan
• Correct-by-Construction Hardware Design: V. Nagarajan
• Memory Systems: R. Balasubramonian
• Hardware Security: R. Balasubramonian

• Democratizing data-driven systems: This project focuses on three key aspects of data system democratization: enhancing usability of data systems for non-experts and experts, providing explanation frameworks to enable understanding of system behavior, and achieving trust and fairness in machine learning.
• Data structures for scalable computing: This project focuses on advancing the theory and practice of compact, dynamic, and scalable data structures to tackle the challenges of modern data analyses pipelines. We work on filters, hash tables, trees, succinct, and write-optimized data structures.
• Large-scale indexing raw genomics data: This project focuses on building scalable data processing pipelines for quickly indexing and searching through tera-bytes of raw genomic, transcriptomic, and metagenomics data.
• Efficient parallel graph processing: This project focuses on building highly parallel data structures and algorithms for efficiently processing static, streaming, and dynamic graphs. This project further explores using hardware accelerators such as GPUs for massively parallel processing of dynamic graphs.
• Persistent Data Summaries: This project builds summaries for massive data arriving over time, which are small space, efficient to build and query, and amenable to data analysis. Moreover, they can be queried with respect to a time window for retrospective analysis.
• Data Sketching:: We design and implement sketch data structures which are compressed representations of data with guaranteed trade-offs between the space and the accuracy of queries. Our group has designs sketches for quantiles, multi-dimensional data, frequent items, shape-fitting, trajectories data, and many more.
• Spatial Exposome Data: CEDaR is be an open exposomic data resource that can be used by researchers across disciplines to increase understanding of the environment and health. Sources of environmental exposure data are sparse, inconsistent, and rarely linked to individuals, making research complicated and difficult. Through CEDaR, we provide a single platform containing cleaned and standardized environmental exposure measures that can be used independently or to create holistic measures of the exposome.
• Data Systems on Modern Hardware: This project exploits modern compute hardware such as GPUs, FPGAs and storage hardware such as PMEMs, HBMs for accelerating data systems. Our group designs new algorithmic techniques to model the performance of new hardware and then analyzes data systems in the light of the new algorithmic models to accelerate them.
• Extreme-Scale Data Management: DataSpaces is a programming system targeted at current large-scale systems and designed to support dynamic interaction and coordination patterns between scientific applications. DataSpaces essentially provides a semantically specialized shared-space abstraction using a set of staging nodes. This abstraction derives from the tuple-space model and can be associatively accessed by the interacting applications of a simulation workflow.

• Prof. Prashant Pandey won the NSF CAREER Award (2024).
• IcebergHT paper from SIGMOD 2023 authored by Prof. Prashant Pandey featured in Quanta Magazine.
• Prof. Prashant Pandey will lead a tutorial on Advanced Filter Data Structures at SIGMOD 2024.
• Prof. Prashant Pandey won the 2023 IEEE-CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing.
• Prof. Jeff M. Phillips received an NSF Award on Integrating and Learning on Spatial Data via Multi-Agent Simulation.
• Prof. Anna Fariha was recognized as one of the distinguished PC members at SIGMOD 2023.
• Workshop proposal led by Prof. El Kindi Rezig has been accepted to VLDB 2023. The workshop (Poly) focuses on recent advances in polystore and data lake management systems. The workshop will take place on September 1st, 2023 at VLDB in Vancouver, Canada.
• Prof. Pandey organized a Workshop on Filter Data Structures at SPAA 2023 part of FCRC 2023.

2024

• WWW Tao Yang, Cuize Han, Chen Luo, Parth Gupta, Jeff M. Phillips, and Qingyao Ai: Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach.
• SIGMOD Prashant Pandey, Martin Farach-Colton, Niv Dayan, Huanchen Zhang: Beyond Bloom: A Tutorial on Future Feature-Rich Filters.
• FAST Yi Xu*, Henry Zhu*, Prashant Pandey, Alex Conway, Rob Johnson, Ramnatthan Alagappan, Aishwarya Ganesani: IONIA: High-Performance Replication for Modern Disk-based KV Stores.
• PPOPP Hunter McCoy, Prashant Pandey: Gallatin: A General-Purpose GPU Memory Manager.
• SIGCSE Anjali Singh, Anna Fariha, Christopher Brooks, Gustavo Soares, Austin Henley, Ashish Tiwari, Chethan M, Heeryung Choi, Sumit Gulwani: Investigating Student Mistakes in Introductory Data Science Programming.
• EMNLP Soohyeong Kim, Whanhee Cho, Minji Kim, Yong Suk Choi: Bidirectional Masked Self-attention and N-gram Span Attention for Constituency Parsing.

 2023

• IEEE BigData Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, and Eamonn Keogh: Sketching Multidimensional Time Series for Fast Discord Mining.
• SIGMOD Prashant Pandey, Michael A. Bender, Alex Conway, Martin Farach-Colton, William Kuszmaul, Guido Tagliavini, Rob Johnson: IcebergHT: High Performance Hash Tables Through Stability and Low Associativity.
• VLDB Helen Xu, Amanda Li, Brian Wheatman, Manoj Marneni, Prashant Pandey: BP-tree: Overcoming the Point-Range Operation Tradeoff for In-Memory B-trees.
• SIGMOD Bhavya Chopra, Anna Fariha, Sumit Gulwani, Austin Z. Henley, Daniel Perelman, Mohammad Raza, Sherry Shi, Danny Simmons, Ashish Tiwari: DemoCoWrangler: Recommender System for Data-Wrangling Scripts.
• PPoPP Hunter McCoy, Steven Hofmeyr, Katherine Yelick, Prashant Pandey: High-Performance Filters for GPUs.
• Knowledge and Information Systems Hasan Pourmahmood Aghababa, Jeff M. Phillips: An experimental study on classifying spatial trajectories.
• IPDPS Süreyya Emre Kurt, Jinghua Yan, Aravind Sukumaran-Rajam, Prashant Pandey, P. Sadayappan: Communication Optimization for Distributed Execution of Graph Neural Networks.
• APOCS Madhav Narayan Bhat, Paul Cesaretti, Mayank Goswami, Prashant Pandey: Distance and Time Sensitive Filters for Similarity Search in Trajectory Datasets.
• ACDA Hunter McCoy, Steven Hofmeyr, Katherine Yelick, Prashant Pandey: Singleton Sieving: Overcoming the Memory/Speed Trade-Off in Exascale k-mer Analysis.
• SIGCSE Rowan Hart, Brian Hays, Connor McMillin, El Kindi Rezig, Gustavo Rodriguez-Rivera, Jeffrey A. Turkstra: Eastwood-Tidy: C Linting for Automated Code Style Assessment in Programming Courses.

• LTE/5G Mobile Networking, Wireless Security, Spectrum Sharing, Wireless Testbeds: Kobus Van der Merwe, Sneha Kasera
• Systems (Networking, Scalable Systems); Security / Privacy (Systems Security): Eric Eide, Sneha Kasera, Robert Ricci, Anton Burtsev
• Operating Systems: Anton Burtsev, Ryan Stutsman
• Storage Systems: Prashant Pandey, Ryan Stutsman
• Federated Data Ecosystem: Manish Parashar

• Spring 24:  Paper Accepted at IEEE DySPAN 2024.
• Fall 23: Sirus Shahini defended his PhD.
• Fall 23: Paper Accepted at NSDI 2024!
• Summer 23: Hao (Harry) Jiang defended his PhD.

• Designing a framework for efficient, scalable, and performance-portable tensor applications (Saday Sadayappan)
• Developing effective performance models for compiler optimization by leveraging ML (Saday Sadayappan)
• Constructing a synthesis-based superoptimizer for vector intrinsics in LLVM intermediate representation (John Regehr)
• Better generative compiler fuzzing for loop optimizations in C++ and data-parallel languages (John Regehr)
• Designing exploratory compiler infrastructure for automating high-performance code generation (Mary Hall)
• Fully integrating data layout and data movement into compilers (Mary Hall)
• Devising a programmable approach to neural network compression with emphasis on correctness (Ganesh)
• Improving the performance and accuracy of numerical code on new platforms like GPUs, vector cores, and TPUs (Pavel Panchekha)
• Scaling web browsers to large web pages (Pavel Panchekha)
• Automatic synthesis of heterogeneous cache coherence protocols adhering to precise consistency models (Vijay Nagarajan)
• Enhancing the engineering of 5G networks through domain-specific languages (Eric Eide)
• Advancing the usability and effectiveness of compiler fuzzing through reusable tools for test-case generation. (Eric Eide)

• January 2024: Dr. Vsevolod Livinskii, advised by Prof. John Regehr, defended his Ph.D. thesis and joined NVIDIA.
• January 2024: Dr. Ian Briggs, advised by Prof. Pavel Panchekha, defended his Ph.D. and joined AWS. 
• January 2024: The MegaLibm project, led by Ian Briggs with help from Yash Lad and Prof. Pavel Panchekha, won a Distinguished Paper Award at POPL 2024.
• October 2023: Prof. Eide presented the first paper about Xsmith (a library for compiler test-case generation) at the GPCE ‘23 conference.
• August 2023: Profs. Vijay Nagarajan and Ben Greenman join the Kahlert School of Computing.
• May 2023: Guy Watson defended his MS thesis entitled “Random Testing of WebAssembly Implementations Using Semantically Valid Programs.”
• June 2023: Prof. Eide received the ACM SIGPLAN PLDI 2023 Distinguished Reviewer Award.
• September 2022: Dr. Tharindu Rusira, advised by Prof. Mary Hall, defended his Ph.D. thesis and joined Samsung Semiconductor, Inc.
• August 2022: Dr. Sureyya Emre Kurt, advised by Prof. Saday Sadayappan defended his Ph.D. thesis and joined Xantium.
• May 2022: Dr. Tuowen Zhao, advised by Prof. Mary Hall, defended his Ph.D. thesis and joined Sambanova Systems.

• Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots.Connor Mattson, Jeremy C. Clark, and Daniel S. Brown, 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 
• Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. Jerry Zhi-Yang He, Daniel S Brown, Zackory Erickson, Anca Dragan. Conference on Robot Learning, pp. 1865-1886. PMLR, 2023.
• Autonomous Medical Needle Steering In Vivo. Alan Kuntz, Maxwell Emerson, Tayfun Efe Ertop, Inbar Fried, Mengyu Fu, Janine Hoelscher, Margaret Rox, Jason Akulian, Erin A. Gillaspie, Yueh Z. Lee, Fabien Maldonado, Robert J. Webster III, and Ron Alterovitz. Science Robotics 2023
• Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model. Michael Bentley, Caleb Rucker, Chakravarthy Reddy, Oren Salzman, and Alan Kuntz. Journal of Medical Robotics Research 2023
• Asymptotically Optimal Inspection Planning via Efficient Near-Optimal Search on Sampled Roadmaps. Mengyu Fu, Alan Kuntz, Oren Salzman, and Ron Alterovitz. The International Journal of Robotics Research (IJRR) 2023

• Joshua Dawson, Eden Fisher, and Jason Wiese. 2024. Hospital Employee Experiences Caring for Patients in Smart Patient Rooms. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 16 pages. https://doi.org/10.1145/3613904.3642201
• Maxim Lisnic, Alexander Lex, Marina Kogan. ‘Yeah, this graph doesn't show that’: Analysis of Online Engagement with Misleading Data Visualizations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/3613904.3642448
• Sadia O. Khan, Tania Ghafourian, and Sameer Patil. 2024. Targets of Weaponized Islamophobia: The Impact of Misinformation on the Online Practices of Muslims in the United States. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 126 (April 2024), 38 pages. https://doi.org/10.1145/3637403
• Noelle Brown, Benjamin Xie, Ella Sarder, Casey Fiesler, and Eliane S. Wiese. 2024. Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education Publications. ACM Trans. Comput. Educ. 24, 1, Article 6 (March 2024), 36 pages. https://doi.org/10.1145/3634685

• Joshua Dawson, K. Jens Phanich, and Jason Wiese. 2024. Reenvisioning Patient Education with Smart Hospital Patient Rooms. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 4, Article 155 (December 2023), 23 pages. https://doi.org/10.1145/3631419
• Joshua Dawson, Thomas Kauffman, and Jason Wiese. 2023. It Made Me Feel So Much More at Home Here: Patient Perspectives on Smart Home Technology Deployed at Scale in a Rehabilitation Hospital. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 344, 1–15. https://doi.org/10.1145/3544548.3580757
• Johanna Cohoon, Kazi Sinthia Kabir, Tamanna Motahar, and Jason Wiese. 2023. Cultivating Altruism Around Computing Resources: Anticipation Work in a Scholarly Community. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 336 (October 2023), 22 pages. https://doi.org/10.1145/3610185
• Kazi Sinthia Kabir and Jason Wiese. 2023. A Meta-Synthesis of the Barriers and Facilitators for Personal Informatics Systems. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 3, Article 103 (September 2023), 35 pages. https://doi.org/10.1145/3610893
• Jason Wiese, John R. Lund, and Kazi Sinthia Kabir. 2023. Adding Domain-Specific Features to a Text-Editor to Support Diverse, Real-World Approaches to Time Management Planning. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 856, 1–13. https://doi.org/10.1145/3544548.3581536
• Tamanna Motahar, Noelle Brown, Eliane Stampfer Wiese, and Jason Wiese. 2023. Building “Design Empathy” for People with Disabilities: an Unsolved Challenge in HCI Education. In Proceedings of the 5th Annual Symposium on HCI Education (EduCHI '23). Association for Computing Machinery, New York, NY, USA, 68–71. https://doi.org/10.1145/3587399.3587409
• Noelle Brown, Koriann South, Suresh Venkatasubramanian, and Eliane S. Wiese. 2023. Designing Ethically-Integrated Assignments: It’s Harder Than it Looks. In Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1 (ICER '23), Vol. 1. Association for Computing Machinery, New York, NY, USA, 177–190. https://doi.org/10.1145/3568813.3600126
• Noelle Brown, Nidhi Patel, Xavier Davis, and Eliane S. Wiese. 2023. Students’ Self-Evaluations of Contextual Inquiry Techniques. In Proceedings of the 5th Annual Symposium on HCI Education (EduCHI '23). Association for Computing Machinery, New York, NY, USA, 96–100. https://doi.org/10.1145/3587399.3587411
• Derya Akbaba, Devin Lange, Michael Correll, Alexander Lex, and Miriah Meyer. 2023. Troubling Collaboration: Matters of Care for Visualization Design Study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 812, 1–15. https://doi.org/10.1145/3544548.3581168
• Maxim Lisnic, Cole Polychronis, Alexander Lex, and Marina Kogan. 2023. Misleading Beyond Visual Tricks: How People Actually Lie with Charts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 817, 1–21. https://doi.org/10.1145/3544548.3580910
• Nicole M Eklund, Jessey Ouillon, Vineet Pandey, Christopher D Stephen, Jeremy D Schmahmann, Jeremy Edgerton, Krzysztof Z Gajos, Anoopum S Gupta, Real-life ankle submovements and computer mouse use reflect patient-reported function in adult ataxias, Brain Communications, Volume 5, Issue 2, 2023, fcad064, https://doi.org/10.1093/braincomms/fcad064
• Vineet Pandey, Nergis C. Khan, Anoopum S. Gupta, and Krzysztof Z. Gajos. 2023. Accuracy and Reliability of At-Home Quantification of Motor Impairments Using a Computer-Based Pointing Task with Children with Ataxia-Telangiectasia. ACM Trans. Access. Comput. 16, 1, Article 10 (March 2023), 25 pages. https://doi.org/10.1145/3581790

• National Data Platform (NDP)
• DataSpaces
• R-Pulsar
• Compilers for sparse tensor contractions
• Sparse matrix/tensor algorithms on GPUs
• Code generation and optimization for GPUs
• Distributed training for NLP
• Autotuning and fusion for AI compilers
• Date layout transformations for sparse contractions.

• Computational Inverse Problems: C. Johnson, M. Kirby, H. Sundar
• Geometry and Mesh Generation: V. Pascucci, V. Shankar
• High-Performance Computing: M. Berzins, M. Hall, C. Johnson, M. Kirby, V. Pascucci, P. Sadayappan, V. Shankar, H. Sundar, M. Parashar
• Modeling Methods and Frameworks: M. Berzins, M. Kirby, V. Shankar, H. Sundar
• Extreme-Scale Data Management: M. Parashar

• Alberto Cattaneo
• Bo Zhang
• Budvin Edippuliarachchi
• David van Komen
• Gaurav Dhir
• Han Duc Tran
• LeAnn Lindsey
• Matthew Lowery
• Milena Belianovich
• Ramansh Sharma

• Cryptography: Pratik Soni
• Mobile Security: M. Zhang
• Security/Safety of IoT and Cyber-Physical Systems: L. Garcia
• Sociotechnical Aspects: S. Patil
• Systems Security: R. Balasubramonian, E. Eide, S. Kasera, S. Nagy, R. Ricci, M. Zhang, J. Xu

• Hyena: Balancing Packing, Reuse, and Rotations for Encrypted Inference. (S&P 2024) Sarabjeet Singh, Shreyas Singh, Sumanth Gudaparthi, Xiong Fan, Rajeev Balasubramonian
• Foundations of Adaptor Signatures. (EUROCRYPT 2024) Paul Gerhart, Dominique Schroder, Pratik Soni, Sri AravindaKrishnan Thyagarajan
• Targets of Weaponized Islamophobia: The Impact of Misinformation on the Online Practices of Muslims in the United States. (Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 126 (April 2024), 38 pages.) Sadia O. Khan, Tania Ghafourian, and Sameer Patil.
• VetEOS: Statically Vetting EOSIO Contracts for the “Groundhog Day” Vulnerabilities(NDSS’24) Levi Taiji Li, Ningyu He, Haoyu Wang, Mu Zhang
• Profile-guided System Optimizations for Accelerated Greybox Fuzzing (CCS 2023) Yunhang Zhang, Chengbin Pang, Stefan Nagy, Xun Chen, Jun Xu.
• SysPart: Automated Temporal System Call Filtering for Binaries (CCS 2023) Vidya Lakshmi Rajagopalan, Konstantinos Kleftogiorgos, Enes Göktaş, Jun Xu, Georgios Portokalidis
• Automated Generation of Security-Centric Descriptions for Smart Contract Bytecode (ISSTA'23) Yu Pan, Zhichao Xu, Levi Taiji Li, Yunhe Yang, Mu Zhang
• No Linux, No Problem: Fast and Correct Windows Binary Fuzzing via Target-embedded Snapshotting (USENIX'23) Leo Stone, Rishi Ranjan, Stefan Nagy, and Matthew Hicks.
• AEM: Facilitating Cross-Version Exploitability Assessment of Linux Kernel Vulnerabilities (S&P 2023) Zheyue Jiang, Yuan Zhang, Jun Xu, Xinqian Sun, Zhuang Liu, Min Yang
• Arvin: Greybox Fuzzing Using Approximate Dynamic CFG Analysis (AsiaCCS 2023) Sirus Shahini, Mu Zhang, Mathias Payer, Robert Ricci
• Distributed-Prover Interactive Proofs (TCC 2023) Sourav Das, Rex Fernando, Ilan Komargodsky, Elaine Shi, Pratik Soni
• Non-Interactive Anonymous Router with Quasi-Linear Router Computation (TCC 2023) Rex Fernando, Elaine Shi, Pratik Soni, Nikhil Vanjani, Brent Waters

• Visualization: Kate Isaacs, Christopher Johnson, Mike Kirby, Alexander Lex, Andrew Mcnutt, Valerio Pascucci, Paul Rosen, Bei Wang Phillips

• Computer Vision: Ziad Al-Halah, Shireen Elhabian, Ross Whitaker
• Computer Graphics: Eric Brunvand, Cem Yuksel, Yin Yang

• Visualization Design Lab
• CEDMAV