At a young age, Howard Gersh ’91 knew what he wanted to do when he grew up — work on visual effects for “Star Wars.” By the time the prequel trilogy started production in the late 1990s, he had received his B.S. in computer science from UCI and worked his way up to becoming a senior technical director at Industrial Light + Magic. His dream became a reality as he helped create visual effects for “Star Wars” Episodes I, II and III, as well as for dozens of other movies, from “Forrest Gump” to “Harry Potter” and “Pirates of the Caribbean.” Now, the recent ICS Hall of Fame inductee teaches virtual reality, animation and digital visual effects to high school students at Marin School of the Arts as well as to kids in underserved communities through Enriching U, an organization he and his wife founded to help such kids pursue their dreams as Gersh once pursued his.
Computer Science Ph.D. student Jihyun Park is the lead author on a paper that recently won the best paper award at the 11th International Conference on Educational Data Mining Conference (EDM 2018) in Buffalo, N.Y. The paper, “Understanding Student Procrastination via Mixture Models,” proposes a new approach based on statistical machine learning techniques that can extract and quantify patterns of procrastination observed from student clickstream data in online college courses. In particular, persistent procrastination over the duration of a course was found to be strongly predictive of poorer student outcomes, providing strong evidence that time management is critical for success in online courses.
Computer Science Professor Magda El Zarki and History Professor Pat Seed were thrilled to see their game, “Sankofa,” first on the list of Bronze Medal winners for the 2018 International Serious Play Awards. The awards recognize excellence in serious games designed for use in K-12 or higher education, and “Sankofa” is designed to teach history and anthropology in a fun and engaging way by bringing 19th-century Ghana to life through gameplay.
Two algorithms developed by the researchers, collectively called Deep Cube, typically can solve the 3-D combination puzzle within 30 moves, which is less than or equal to systems that use human knowledge, according to the team’s research paper. Less than 5.8% of the world’s population can solve the Rubik’s Cube, according to the Rubik’s website.
Read the full story at the Los Angeles Times.
Although Stephen McAleer first became interested in artificial intelligence after reading the book Gödel, Escher, Bach: An Eternal Golden Braid, it wasn’t until Google’s AI program AlphaGo beat the world champion of the game Go that he decided to switch careers from finance to AI research. Now, as a Ph.D. student working with Chancellor’s Professor Pierre Baldi in the Donald Bren School of Information and Computer Sciences (ICS), he has helped tackle a new deep-learning challenge.
UCI Professor of Computer Science Aditi Majumder’s, Ph.D., journey into the virtual/augmented reality world stems back to a young girl growing up in Kolkata, India around her mother, a professional musician and her father, a civil engineer. Both parents unknowingly planted the hybrid seed of art and science, which led Majumder to pursue a blossoming career in virtual/augmented reality that intertwines with her deep appreciation for art and music.
Read the full story at UCI Applied Innovation Tech Currents.
In chess, by contrast, there is a relatively large search space but each move can be evaluated and rewarded accordingly. That just isn’t the case for the Rubik’s Cube.
Enter Stephen McAleer and colleagues from the University of California, Irvine. These guys have pioneered a new kind of deep-learning technique, called “autodidactic iteration,” that can teach itself to solve a Rubik’s Cube with no human assistance. The trick that McAleer and co have mastered is to find a way for the machine to create its own system of rewards.
Read the full story at MIT Technology Review.
Researchers from the University of California, Irvine developed a deep learning-based approach to accelerate drug discovery and cancer research.
“We have developed a convolutional neural network to improve the data analysis processes for high-throughput drug screening using our microphysiological system (MPS),” the researchers stated in their paper.
Read the full story at NVIDIA Developer News Center.