Artificial Intelligence & Machine Learning



Automata Reasoning Methodology
Dueñas-Osorio (CEE), Patel (ECE), Chaudhuri (CS), Hicks (CAAM), Vardi (CS)

Deep Learning Methodology
Heckel (ECE), Kyrillidis (CS), Baraniuk (ECE), Deem (BIOE), de Hoop (CAAM), Igoshin (BIOE), Merenyi (STAT), Patel (ECE), Segarra (ECE), Shrivastava (CS), Subramanian (CS)

Machine Learning Methodology
Heckel (ECE), Kyrillidis (CS), Segarra (ECE), Shrivastava (CS), Merényi (STAT), Allen (STAT), Brake (MECH), Hicks (CAAM), Kemere (ECE), Li (STAT), Lin (ECE), Nagarajaiah (CEE), Subramanian (CS)

Optimization and Large-Scale Machine Learning
Dueñas-Osorio (CEE), Heckel (ECE), Hicks (CAAM), Huchette (CAAM), Jermaine (CAAM), Kyrillidis (CS), Patel (ECE), Schaefer (CAAM), Shrivastava (CS)Brake (MECH), Deem (BIOE), Heinkenschloss (CAAM), Kemere (ECE),

Network Analytics and Graph Signal Processing
Segarra (ECE)

Quantum Computing and Algorithms
Dueñas-Osorio (CEE)


Traditional applications of AI research include reasoning, knowledge presentation, natural language processing, and object manipulation. Recent technological advances have broadened the range of AI applicability to potentially any intellectual task. Example applications include autonomous vehicles (e.g., drones, self-driving cars), medical diagnostics, artistic expression (e.g., poetry), advanced theoretical calculations, video gamer simulations (i.e., digital participants of video games), systematic search engines (e.g., Google search), digital assistance, image recognition, smart spam filtration, and judicial decision prediction.

AI Application areas at Rice include biomedicine, robotics, geoscience (e.g., oil and gas), computer vision, social science, and interdisciplinary areas. For example, Professor Pedram Hassanzadeh is working with Microsoft to use deep learning techniques, such as convolutional and recurrent neural networks, to identify and predict large-scale weather patterns. Once developed, such technology could predict extreme events such as heat waves, cold spells, downpours, and floods. Professor Caleb Kemere is developing machine-learning tools to understand how cognitive processes evolve across circadian timescales and over time. The sheer quantity of neurons involved raises the question of how these processes are implemented in neural circuits. The AI-based tools he is developing will enable researchers in neuroscience and neuroengineering to perform robust pre-processing and analysis of neural data.

Kemere (ECE), Patel (ECE), Sano (ECE), Schaefer (CAAM), Subramanian (CS), Allen (STAT), Deem (BIOE), Heckel (ECE), Huchette (CAAM), Igoshin (BIOE), Jermaine (CS), Kavraki (CS), Merényi (STAT), Nakhleh (CS), Shrivastava (CS), Segarra (ECE), Vannucci (STAT), Veeraraghavan (ECE)

Ghorbel (MECH), Kavraki (CS), Deem (BIOE), Huchette (CAAM), Shrivastava (CS), Veeraraghavan (ECE)

de Hoop (CAAM), ), Merényi (STAT), Subramanian (CS), Cartwright (CS), Hassanzadeh (MECH), Higgs (MECH), Jermaine (CAAMPalem (CS), Riviere (CAAM), Shahsavari (CEE)

Computer Vision
Patel (ECE), Veeraraghavan (ECE), Kemere (ECE), Nagarajaiah (CEE), Huchette (CAAM)

Engineering-wide Applications
Duenas-Osorio (CEE), Brake (MECH), Hassanzadeh (MECH), Heckel (ECE), Heinkenschloss (CAAM), Hicks (CAAM), Higgs (MECH), Jermaine (CAAM), Merényi (STAT), Nagarajaiah (CEE), Sano (ECE), Shahsavari (CEE), Shrivastava (CS), Subramanian (CS)

Computational Social Science
Subramanian (CS), Segarra (ECE)