AI has an exceptional range of uses in the research process including summarizing documents, finding similar papers, and identifying key points of a body of text. This workshop is for […]
Location: Blocker 220 and Zoom Dr. Li An is the Solon & Martha Dixon Endowed Professor at the College of Forestry, Wildlife and Environment and the Director of the Center […]
As AI and our understanding of it continues to evolve, how do you determine when it’s worth using in the academic environment? In this discussion-based, undergraduate-focused workshop, you’ll explore some […]
To use AI or to not use AI – that is the topic of this discussion-based workshop. AI is becoming increasingly hard to ignore and its use comes with many […]
As AI and our understanding of it continues to evolve, how do you determine when it’s worth using in the academic environment? In this discussion-based, undergraduate-focused workshop, you’ll explore some […]
JungHyun Han is a professor at Korea University, where he directs the Superintelligence Research Center. His research interests lie in computer graphics and virtual/augmented realities. Location: Blocker 220 and Zoom […]
Location: Rudder 302 Time: 8:30 am – 4:30 pm We are excited to invite you to our one-day Scientific Machine Learning Workshop, where you’ll have the opportunity to delve into […]
Dr. Sriraam Natarajan is a Professor and the Director of the Center for ML at the Department of Computer Science at the University of Texas, Dallas. His research interests include Artificial […]
Dr. Kim is an Assistant Professor in the Department of Construction Science (COSC) at Texas A&M University (TAMU), where he earned his Ph.D. in COSC. Prior to returning to TAMU, […]
Register Now Registration deadline is May 9th About the Workshop Discover the power of interdisciplinary collaboration at the upcoming Research in Artificial Intelligence for Science and Engineering (RAISE) workshop, supported […]
Location: Texas A&M University, Blocker Building, Room 102 This free five-day summer school is designed to introduce graduate students to the fundamentals of Physics-Informed Neural Networks (PINNs) and Scientific Machine […]
