Tsuiwei (Lily) Wen. Credit: Shibo “Bobby” Yu
“Assistant Professor Wen exemplifies our commitment to building interdisciplinary scholarship and research in the new School of Computing, Information and Data Sciences,” said SCIDS Interim Dean Rajesh, who is also the founding director of HDSI. Mr.Gupta said. “We are creating new synergies through our talented faculty and researchers in emerging fields such as artificial intelligence and machine learning. We are grateful to Intel for recognizing Lilly’s excellence.”
The annual Rising Star Awards spotlight young academic researchers from around the world who are making breakthrough advances in technology research that demonstrate industry-disrupting potential. As this year’s RSA recipient, Wen joins seven other leading academic researchers from Carnegie Mellon University, Purdue University, Stanford University, Tel Aviv University, Ohio State University, University of Pennsylvania, and University of Southern California. They are praised for research that has found solutions to difficult problems in computer science, electrical engineering, and computer engineering.
“On behalf of UCSD, I am deeply honored to be selected as one of the top eight faculty members out of the top 30 universities in the world,” said Professor Wen. “I would like to thank HDSI for their strong support, the Jacobs School of Engineering, and UCSD for nominating me for the award. I would also like to extend my heartfelt thanks to my wonderful team, including the outstanding students and wonderful researchers in my lab. I would like to express my gratitude to all my collaborators.”
Wen’s research pushes the boundaries of machine learning. Specifically, her research efforts have made significant progress in improving the reliability and interpretability of deep neural networks (DNNs). These efforts include developing fast and provable robustness certificates, creating scalable and robust learning solutions in the area of robust ML, developing scalable automated interpretation tools to uncover the inner workings of DNNs, and the development of new algorithms for training inherently interpretable DNNs. Interpretable ML.
“Wen pioneered the field of robust machine learning by establishing the theoretical and algorithmic foundations for evaluating and improving the robustness of deep neural networks (DNNs),” Intel said in the announcement. Ta.
The research led by Weng and his students in the Trustworthy ML Lab also pioneered automatic interpretability algorithms in the visual and linguistic domains using innovative new approaches. These algorithms aim to describe DNN functionality using human-friendly concepts. These are also the first approaches to construct interpretable DNNs that can be extended to ImageNet without the need for carefully selected conceptual labels. Additionally, these advances address key challenges in AI, such as trust and transparency, which are important for applications in computer vision, language processing, self-driving technology, healthcare, and data security.
According to Wen, the social impact of this work is profound. “By pioneering more interpretable and robust deep neural networks, we address the urgent need for transparent, fair, and reliable AI systems in areas such as healthcare, transportation, and criminal justice.” says Wen. “For example, in the medical field, easier-to-interpret models can improve diagnostic tools, help healthcare professionals make more informed decisions, and ultimately improve patient outcomes. In autonomous driving, increasing the robustness and interpretability of AI will improve the safety of navigation systems and reduce accidents caused by AI errors. Additionally, it will identify and reduce bias in AI models. Initiatives to promote fairness and equity in automated decision-making processes and ensure that AI technologies benefit all members of society, regardless of race, gender, or socio-economic status. Masu.”
By addressing the key qualities essential to AI and deep learning systems, Wen’s research is developing the next generation of AI and deep learning systems, not only improving safety and effectiveness capabilities, but also improving system performance. It aims to increase explainability, robustness, and reliability. Dealing with major failures and biases.
In addition to being recognized for their research, Wen and other RSA awardees are recognized for their innovative teaching methods and for increasing the participation of women and underrepresented minorities in science and engineering. Selected. The award celebrates Wen’s research achievements, highlighting her role in advancing the field and fostering future innovation, and recognizes the impact her research has had on the scientific and research community, as well as beyond. It emphasizes the impact.
For more information about research and education at the University of California, San Diego, visit Artificial Intelligence.
