Rice University empowers Houston professionals with hands-on AI and machine learning training

Rice University Empowers Houston Professionals with AI and Machine Learning Boot Camp

Artificial intelligence (AI) and machine learning are increasingly integral to many industries, supporting tasks like automation, trend analysis, and decision-making. As these technologies become more common, the need for effective training to ensure responsible and practical use grows.

Addressing this need, Rice University’s Ken Kennedy Institute hosted a focused three-day boot camp in Houston aimed at data science professionals and technical managers. The program, held May 7-9 at Rice’s BioScience Research Collaborative, provided 20 participants with hands-on instruction in key AI topics including machine learning, deep learning, natural language processing, reinforcement learning, and large language models.

Nine Rice faculty members led the training, sharing their expertise to help professionals build a solid foundation to apply AI techniques effectively in real-world settings.

Meet the AI Experts Leading the Training

• Hanjie Chen: Focuses on natural language processing and trustworthy AI, particularly neural language models for fields such as medicine, healthcare, and sports. Topics include AI ethics, explainable AI, human-in-the-loop systems, and AI applications in conservation, information security, and mental health.
• Xia (Ben) Hu: Specializes in machine learning algorithms for social and health informatics, plus information security. His interests cover AI ethics, explainable AI, and human-in-the-loop AI approaches.
• Christopher Jermaine: Develops systems for large-scale data processing tailored to machine learning and AI applications. His work touches on AI for materials science, data privacy, and generative AI.
• Anastasios (Tasos) Kyrillidis: Advances large-scale optimization techniques and open-source AI algorithms. His expertise includes AI efficiency, generative AI, quantum computing, and applications in chemistry and computational biology.
• Santiago Segarra: Creates mathematical tools for analyzing network-structured data, focusing on graph signal processing with applications in biology, social science, and wireless communications.
• Anshumali (Anshu) Shrivastava: Works on AI efficiency and next-generation large language models, with an emphasis on scalable and sustainable AI systems, generative AI, and data privacy.
• Arlei Silva: Develops algorithms for learning from complex datasets, especially graphs and networks, applying AI to humanities, climate forecasting, cybersecurity, misinformation detection, and environmental data analysis.
• Vaibhav Unhelkar: Expert in human-AI teaming, designing robotic assistants, intelligent tutors, and decision-support systems for healthcare, disaster response, and education.
• César A. Uribe: Researches distributed learning and optimization methods for decentralized machine learning, focusing on efficient training across large datasets and applications in health care, climate resilience, and public policy.

Professionals interested in connecting with these experts for interviews or insights can contact Silvia Cernea Clark, media relations specialist, at sc220@rice.edu.

For educators and professionals seeking further AI training opportunities, exploring Complete AI Training’s latest courses provides practical options to enhance skills in AI and machine learning.