Funded Projects
The Jurist Center provides research support for the following projects:
2022
- Computational Epitome Prediction (Avik Bhattacharya, Ramgopal Mettu, Sam Landry): This project develops data-driven models to predict cancer mutations to target for therapy. The Jurist support for Summer 2022 enabled large scale data collection related to bladder cancer, and the implementation of predictive models.
publication: Bhattacharya et al. (2022). Incorporating antigen processing into CD4+ T cell epitope prediction with integer linear programming. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '22). - Understanding Emerging Issues in Public Schools from Online Reviews (Linsen Li, Aron Culotta, Nicholas Mattei): The goal of this project is to determine whether the language used in a school’s online reviews is a leading indicator of future changes at that school. For example, can we predict future changes in a school's demographics or test results based on how people describe the school online? Doing so has the potential to help us better understand how families select schools, what attributes are important to them, and how inequities may be exacerbated by online mechanisms. The work is in collaboration with Dr. Douglas Harris (Economics). The Jurist support for Summer 2022 enabled the collection and analysis of millions of online reviews from over 60K schools in the U.S.
publication: Li et al. (2023) “Online Reviews Are Leading Indicators of Changes in K-12 School Attributes” In the proceedings of the 2023 ACM Web Conference. - Robust Reinforcement Learning for Security (Xiaolin Sun, Zizhan Zheng): This project investigates how randomized smoothing can help obtain policies with certified adversarial robustness for (deep) reinforcement learning tasks subject to state perturbation attacks. The goal is to leverage state-of-the-art RL algorithms to learn smoothed policies more effectively, providing insights into deploying RL in security and safety-sensitive domains. The Jurist support for Summer 2022 enabled researchers to implement new RL algorithms and evaluate them on security domain benchmarks.
publication: Sun et al. (2023) "Pandering in a (Flexible) Representative Democracy," Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
Faculty Profiles
Our faculty lead projects across a broad array of artificial intelligence topics, including machine learning, multi-agent systems, computer vision, natural language processing, visualization, and ethics. Below is a sample of such projects:
- Learning to Secure Cooperative Multi-Agent Learning Systems: Advanced Attacks and Robust Defenses, Zizhan Zheng
- Fair Recommendation Through Social Choice, Nicholas Mattei
- Mechanisms and Algorithms for Improving Peer Selection, Nicholas Mattei
- Modeling and Learning Ethical Principles for Embedding into Group Decision Support Systems, Nicholas Mattei
- Machine Learning for Advanced Manufacturing, through the Louisiana Materials Design Alliance, Jihun Hamm
- Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms, Carola Wenk, J. Quincy Brown, Brian Summa
- Scalable, Content-Based, Domain-Agnostic Search of Scientific Data through Concise Topological Representations, Brian Summa
- Scalable Interactive Image Segmentation through Hierarchical, Query-Driven Processing, Brian Summa
- Predicting Real-time Population Behavior during Hurricanes Synthesizing Data from Transportation Systems and Social Media, Aron Culotta
- Quantifying Multifaceted Perception Dynamics in Online Social Networks, Aron Culotta
- Understanding the Relationship between Algorithmic Transparency and Filter Bubbles in Online Media, Aron Culotta
- Reducing Classifier Bias in Social Media Studies of Public Health, Aron Culotta
