By Kelly Peters,
Special to Rice News
The capabilities and widespread use of artificial intelligence continue to expand at an accelerated pace, transforming industries as workforces adopt generative technology and machine learning tools to increase efficiency, automate processes, identify trends and support decision-making. This broad adoption of AI underscores the need for enhanced technical education and training to ensure that responsible use remains central to its deployment.
To meet these demands, Rice University’s Ken Kennedy Institute offered an intensive three-day boot camp in Houston for data science practitioners and technical managers. The course was instructed by nine Rice faculty members with the goal of training industry professionals to gain the foundational knowledge, tools and hands-on experience needed to understand modern AI and machine learning for real-world application.
The boot camp took place May 7-9 at the university’s BioScience Research Collaborative, where 20 participants gained personalized instruction on topics including machine learning, deep learning, natural language processing, reinforcement learning and large language models. Experts who taught the class are available to address questions from the media related to their expertise.
Featured AI researchers:
● Hanjie Chen specializes in natural language processing, interpretable machine learning and trustworthy AI with a focus on the interpretability, analysis and optimization of neural language models to enable their collaboration with humans and applications in medicine, health care, sports and more.
- General interest topics: AI agents, AI ethics, explainable AI, generative AI, human-in-the-loop AI, human-robot interaction and AI for conservation, humanities, information security, detecting misinformation, chemistry, education, health care, medicine, psychology, sports science, autonomous vehicles, mental health, social informatics and social media
● Xia (Ben) Hu researches machine learning algorithms and automated deep learning systems relevant to applications in social informatics, health informatics and information security.
- General interest topics: AI agents, AI ethics, explainable AI, human-in-the-loop AI and AI for information security, health care, bioinformatics and social media
● Christopher Jermaine designs and builds systems for large-scale, computationally intensive data processing with a focus on system design and implementation for machine learning and AI applications.
- General interest topics: AI for materials science, computer systems for AI, data privacy and generative AI
● Anastasios (Tasos) Kyrillidis leads advancements in large-scale optimization, modeling, analysis and state-of-the-art, open-source machine learning and AI algorithms.
- General interest topics: AI agents, AI for optimization, efficiency in AI, generative AI, quantum computing and AI in chemistry, sports science, computational biology and data privacy
● Santiago Segarra’s research focuses on developing mathematical and computational tools for analyzing and learning from network-structured data with particular emphasis on graph signal processing, machine learning on graphs and their applications to domains such as biology, social science and wireless communications.
- General interest topics:AI agents, generative AI, social networks, network theory and AI for business, wireless communications, health care and computational biology
● Anshumali (Anshu) Shrivastava specializes in AI efficiency and sophisticated large language models with a technical focus on novel algorithms and systems for the design of next-generation scalable and sustainable AI ecosystems.
- General interest topics: Generative AI, human-in-the-loop AI, data privacy and AI for information security
● Arlei Silva’s research focuses on developing algorithms and models for mining and learning from complex datasets, especially for data represented as graphs or networks, to deploy machine learning, graph theory, network science and statistics to real-world applications.
- General interest topics: AI for humanities, climate forecasting, climate resilience, cybersecurity, detecting misinformation, environmental data analysis, flooding, social informatics, social media, social networks and team dynamics
● Vaibhav Unhelkar’s expertise in human-AI teaming spans the development of robotic assistants, intelligent tutors and decision-support systems aimed at enhancing human performance in domains ranging from health care to disaster response.
- General interest topics: AI agents, AI assistants, explainable AI, generative AI, human-in-the-loop AI, human-AI teaming, human-robot interaction and AI in education, health care, medicine, disaster response and emergency management, team dynamics and robotics
● César A. Uribe researches distributed learning and optimization, focusing on decentralized machine learning, algorithm analysis, distributed inference and social learning to develop efficient, scalable methods for training machine learning models across large datasets.
- General interest topics: AI agents, AI ethics and AI for conservation, climate forecasting, climate resilience, energy, health care, humanities, digital health monitoring, environmental data analysis, public policy and social networks
To arrange an interview with Rice experts, contact Silvia Cernea Clark, media relations specialist, at sc220@rice.edu.