Current Research Clusters | 2025-2026

The goal of the Ken Kennedy Institute's Research Cluster Initiative is to further strengthen the research identity of the Institute, develop distinct capabilities in the fields of AI and Computing, and support faculty in pursuing larger funding opportunities. Proposals for the Ken Kennedy Institute’s Research Cluster Initiative are solicited periodically to ensure that the clusters reflect the interests of the Institute's members. This year marks the second year for our Research Cluster Initiative to stimulate the pursuit of large-scale, cross-disciplinary research. We are excited to announce that efforts have doubled to support a total of twelve foundational and use-inspired research clusters to advance partnership, scholarship, impact and leadership among our exceptional faculty members at Rice University.

Our 2025-2026 research map will focus on the following areas:

• Human-AI Collaboration
• Trustworthy and Responsible AI
• Electromagnetics to Intelligence
• Generative AI 2.0
• Optimization for AI Training
• Quantum Theory, Algorithms and Systems
• Computer Vision
• Scientific Machine Learning
• AI and Computational Biology for Human Health
• AI for Genetic Design
• AI for Urban Resilience
• AI for Materials Science

View details about each Ken Kennedy Institute-supported group below.

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Foundational AI & Computing

Human-Centered Artificial Physical Intelligence (HAPI)
With rapid advances across sub-fields of artificial intelligence (AI), we are witnessing an un-precedented growth in the development and deployment of AI-enabled systems that interact with humans and the physical world. These physical AI systems—ranging from smart speakers to robotic assistants to autonomous vehicles—are designed for enhancing human capabilities across cognitive and physical tasks. In a variety of application domains (such as health, urban resilience, manufacturing, and outer space), these systems hold the promise of significantly enhancing human productivity as well as well-being. However, without a deliberative, human-centered approach in their development, there exists a substantial risk that these systems could cause unintended ad- verse consequences rather than serve as a net positive. To mitigate this risk, it is important to systematically study how Humans and Physical AI systems interact and develop enabling solutions to enhance this interaction.

• Led by Vaibhav Unhelkar (CS)
• Co-Is: Jing Chen (Psychological Sciences), Hanjie Chen (CS), Moshe Vardi (CS)
• Associates: Lydia Kavraki (CS), Vicente Ordonez (CS)

Trustworthy AI Privacy, Fairness, Security, and Societal Impact for Responsible AI (TRUST-AI)
The vision of this cluster is to build a rigorous, theory-driven foundation for Trustworthy and Responsible AI systems that are not only powerful but provably aligned with ethical, societal, and security expectations. To ensure security, fairness, and privacy under all operating conditions– including adversarial and edge-case scenarios–our research seeks to replace heuristic assurances with formal guarantees.

• Led by Maryam Aliakbarpour (CS)
• Co-Is: Nai-Hui Chia (CS), Alireze Fallah (CS)
• Associates: Jing Chen (Psychological Sciences), Santiago Segarra (ECE)

Rice Electromagnetics to Intelligence (E2I)
This cluster explores the relationship between AI methods and physical systems, aiming to advance the future of physical systems for communications, sensing, and imaging applications through AI-enhanced system design. The cluster covers every layer of the physical system stack, spanning from electromagnetic wave propagation to hardware implementation, signal processing, and intelligent information extraction. With this vision and interdisciplinary expertise, the E2I cluster has the potential to position Rice as a global leader in applications including 6G, digital health, defense, and beyond.

• Led by Taiyun Chi (ECE)
• Co-Is: Ashutosh Sabharwal (ECE), Eugene Ng (CS), Edward Knightly (ECE), Kaiyuan Yang (ECE)
• Associates: Ashok Veeraraghavan (ECE), Xia Hu (CS)

Mathematical Foundations of Unstructured Knowledge-Bases with LLMs (GenAI 2.0)
Currently, Large Language Models (LLMs) utilize an attention mechanism and its approximations to create a holistic understanding of context, but LLMs cannot effectively understand and manage very large knowledge bases. The goal of the GenAI 2.0 cluster is to research fundamental ideas in randomized algorithms and signal processing to design ‘outrageously long-range’ attention for LLMs that scale sub-linearly (nearly constant) with the size of the context. The cluster’s efforts will work towards an LLM that is trained holistically to learn linguistics, incorporate working knowledge, and access information from a large collection of stored knowledge without creating a compute or energy crisis. This effort can also advance solutions related to hallucinations and explainability.

• Led by Anshumali Shrivastava (CS)
• Co-Is: Co-Is: Richard Baraniuk (ECE)
• Associates: Todd Treangen (CS), Kenneth Evans (Baker Institute), Ankit Patel (ECE)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://www.cs.rice.edu/~as143/GenAI2.O/index.html

Advancing AI Training Paradigms Through Interdisciplinary Optimization
Through this research cluster, Rice University researchers aim to develop mathematical foundations and practical algorithms for continual learning, positioning Rice as a leader in state-of-the-art, open-source computational models and fostering greater collaboration across departments. At the core of this vision are Algorithms, Modeling, and Optimization—areas that define the current AI/ML landscape while remaining resilient to future engineering shifts, including quantum computing. The interdisciplinary team will strive to drive innovation in AI training paradigms by democratizing access to advanced AI capabilities, contributing to society through accessible and scalable AI.

• Led by Cesar Uribe (ECE)
• Co-Is: Shiqian Ma (CMOR), Sebastian Perez-Salazar (CMOR), Sasha Davydov (MECH), Shuvomoy Das Gupta (CMOR)
• Associates: Anastasios Kyrillidis (CS), Santiago Segarra (ECE), Arlei Silva (CS)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://akyrillidis.github.io/aiowls/

Quantum Theory, Algorithms and Systems (QuanTAS)
Quantum computing stands at a critical juncture where demonstrating practical “quantum utility” requires rigorous comparison with classical approaches. Our research vision centers on establishing a comprehensive framework to fairly evaluate when, how, and why quantum algorithms provide advantages over classical methods. The cluster’s interdisciplinary approach brings together expertise in algorithms, optimization theory, quantum computing, and systems design to address fundamental questions about the capabilities and limitations of quantum computation.

• Led by Anastasios Kyrillidis (CS)
• Co-Is: Tirthak Patel (CS)
• Associates: Shengxi Huang (ECE), Nai Hui-Chia (CS)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://akyrillidis.github.io/explore-quantum/

Reactive Closed-Loop Computer Vision
Computer vision is a fast-growing field with enormous potential in a wide-ranging array of applications and domains, and closed-loop computer vision is an emerging and exceedingly important sub-area without clear established leading institutions, where vision systems can dynamically incorporate feedback from the environment and improve their effectiveness in real time. This research cluster aims to establish a foundation that positions Rice as the preeminent research institution in closed-loop computer vision. The team will develop techniques for the adaptation of visual recognition models across disparate domains that depend on environmental factors such as weather conditions, illumination, and image sensor quality.

• Led by Vicente Ordonez (CS)
• Co-Is: Ashok Veeraraghavan (ECE), Guha Balakrishnan (ECE), Chen Wei (CS)
• Associates: Vivek Boominathan (ECE)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://vision.rice.edu

Scientific Machine Learning (SciML)
Scientific Machine Learning (SciML) is an emerging field that lies at the intersection of physical modeling and simulation and data-driven machine learning. By integrating these data-driven models with physics-driven models, the SciML cluster can produce novel computational methods and deliver reliable, predictive, and computationally efficient solutions to complex engineering and science problems. The cluster will focus on fundamental research while demonstrating algorithms and theories on applications in porous media, computational biomedicine and digital twins, and seismic imaging.

• Led by Beatrice Riviere (CMOR)
• Co-Is: Matthias Heinkenschloss (CMOR), Lu Zhang (CMOR)
• Associates: Santiago Segarra (ECE)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://sciml.rice.edu/home

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Use-Inspired Applications

AI and Computational Biology for Human Health
The AI2Health research cluster is geared towards fundamental and interdisciplinary research in AI and computational biology for human health. Numerous scientific advances have emerged in recent years that are specific to the application of AI to human health. The goal of AI2Health is to leverage this momentum and develop and deploy AI methods and tools to make advances in essential problems in human health through three research areas in Computational Biology: (i) Systems and Integrative Biology, (ii) Structural and Functional Biology, and (iii) Metagenomics and Microbiome Biology.

• Led by Todd Treangen (CS)
• Co-Is: Vicky Yao (CS), Santiago Segarra (ECE)
• Associates: Luay Nakhleh (CS), Lydia Kavraki (CS), Fritz Sedlazeck (BCM HGSC), Eric Chi (STAT), Ivan Coluzza (SCI)

This cluster has been supported since 2024. Updates will be posted to the following web page, managed by the cluster: https://treangenlab.github.io/ai2health/

Artificial Intelligence for Genetic Design (AI4GD)
AI for Genetic Design (AI4GD) is focused on developing computational and theoretical approaches with two broad goals: (1) developing ML/AI models that leverage existing and currently accumulating datasets to predict functional properties of genetic parts and their interactions, and (2) improving interpretability and generalizability of the AI models by incorporating biophysical knowledge into the model architectures. By enabling ML/AI researchers to work with experimental synthetic biologists—two groups of well-established research strengths at Rice—AI4GD will nucleate a cross-disciplinary team that develops innovative computational solutions to biotechnology challenges, further strengthening Rice as a leader in AI-enabled genetic engineering.

• Led by Oleg Igoshin (BIOE)
• Co-Is: Caleb Bashor (BIOE), Ankit Patel (ECE), Meng Li (STAT)
• Associates: James Chappell (BIOS), Cameron Glasscock (BIOS), Joff Silberg (BIOS), Todd Treangen (CS)

In collaboration with the Synthetic Biology Institute

Advancing AI for Climate Risk and Urban Resilience (AI4UrbanResilience)
The Advancing AI for Climate Risk and Urban Resilience (AI4UrbanResilience) research cluster unites climate scientists, engineers and AI experts to develop transformative solutions to address concerns with extreme weather modeling and disaster response, climate hazards, flood risk, and urban energy and transportation resilience. By integrating AI and machine learning with physics-based models, the AI4UrbanResilience cluster is able to produce open-source, high-resolution and computationally efficient tools for managing complex, interconnected systems under extreme weather stress and changing climate conditions. These efforts also serve as a catalyst for collaborations across the Rice research ecosystem, including collaborations with the Kinder Institute, SSPEED Center, Sustainability Institute, Water Institute, the School of Engineering’s Resilient and Adaptive Communities focus area, and other Ken Kennedy Institute research clusters.

• Led by James Doss-Gollin (CEVE)
• Co-Is: Sylvia Dee (EEPS), Avantika Gori (CEVE), Arlei Lopes da Silva (CS), Jamie Padgett (CEVE), Noemi Vergopolan (EEPS)
• Associates: Leonardo Dueñas-Osorio (CEVE), Kathy Ensor (STAT), Xinwu Qian (CEVE), Sang-Ri Yi (CEVE), Lu Zhang (CMOR)

This cluster has been supported since 2024. Updates will also be posted to the following web page, managed by the cluster: https://ai4climaterrr.rice.edu/

Transforming Material Defect Creation and Application via AI-Driven Computation and Experiment
Defects in materials, such as atomic vacancies, doping, substitutions, and their combinations, can be beneficial in many cases. Therefore, the ability to design, manufacture, and understand defects in materials is critical. This cluster aims to develop an AI-driven approach to enable defect-by-design and apply the AI framework to study quantum defects in two materials: silicon and hexagonal boron nitride (hBN). Through AI-driven computation and experiment, the cluster’s efforts can deepen our understanding of material atomic structures, thermodynamics, defect formation and evolution, as well as their relation to the electronic, optical, and spin properties.

• Led by Shengxi Huang (MSNE)
• Co-Is: Geoffroy Hautier (MSNE), Songtao Chen (ECE & Physics), Christopher Jermaine (CS)
• Associates: Anastasios Kyrillidis (CS)

Jointly funded by the Rice Advanced Materials Institute and Smalley-Curl Institute

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Faculty Member Involvement & Available Opportunities

Ken Kennedy Institute members are welcome to consider joining an existing research cluster during the 2025-2026 academic year, should their research align with the core focus of the cluster's activities. Anyone involved in a cluster as a co-I or associate will become affiliated with the Ken Kennedy Institute if not already. If interested, faculty are encouraged to coordinate with the cluster leads/PIs to determine which how to get involved.

Full-time tenure-track faculty members can also apply year-round for a Proposal Working Group. This funding category is geared for efforts towards a medium or large-size proposal (or proposals) to external funders. The application can be utilized for each funding opportunity that an individual/group seeks. Thus far, we are excited to join forces with RAMI, REINVENTS, the Department of Materials Science and NanoEngineering, and the Department of Computer Science to support the “AI-Enabled Autonomous Manufacturing of Next Generation Functional Electronic Materials” working group led by Jun Lou (MSNE). Note that proposal working groups that overlap with research clusters will not be supported. Please contact our Executive Director for Research Initiatives, David Pynadath, with any questions.

View information about last year's 2024-2025 Research Cluster Initiative here.