4 ICS Professors Among 7 UCI Researchers Named AAAS Fellows

On Jan. 26, 2022, UCI announced seven new faculty members had been named fellows of the American Association for the Advancement of Science (AAAS) — an association that “seeks to advance science, engineering, and innovation throughout the world for the benefit of all people.” AAAS Fellows are “a distinguished cadre of scientists, engineers and innovators who have been recognized for their achievements across disciplines, from research, teaching, and technology, to administration in academia, industry and government, to excellence in communicating and interpreting science to the public.”

Among the seven new inductees at UCI are four researchers from the Donald Bren School of Information and Computer Sciences (ICS): Rina Dechter of the Department of Computer Science, Paul Dourish of the Department of Informatics, Annie Qu of the Department of Statistics, and Padhraic Smyth of both the Department of Computer Science and Department of Statistics. Congratulations to these four esteemed researchers for achieving this lifetime honor.

Rina Dechter: Contributing to Automated Reasoning in AI
Distinguished Professor of Computer Science Rina Dechter was recognized for her contributions to computational aspects of automated reasoning and knowledge representation, including search, constraint processing and probabilistic reasoning, and for service to the computing community.

“I am very pleased to get this recognition from the Association for the Advancement of Science,” says Dechter, whose research is in the field of automated reasoning in artificial intelligence, with a focus on graphical models. Graphical models are used to accomplish many science, engineering and business tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. They are also instrumental for enhancing machine learning schemes.

“On the algorithmic side, we’re currently working on harnessing the power of neural networks for function approximation to advance inference algorithms, in the spirit of the emerging field of neurosymbolic AI. On the application side, we are applying our state-of-the-art algorithms for inference in graphical models to the computational protein design (CPD) problem,” says Dechter. “In particular, we’ve developed a framework for continued adaptation of existing state-of-the-art mixed inference schemes over AND/OR search spaces we developed, which should help address the problem of protein design.” Designing proteins to improve their interactions — for example, designing high-affinity monoclonal antibodies — is becoming increasingly important for advances in diagnosis and therapy options.

Paul Dourish: Creating Responsible IT
Chancellor’s Professor of Informatics Paul Dourish was recognized for his contributions to the field of human-computer interaction (HCI) and computer-supported cooperative work, particularly how historical and geographical contexts shape the design, production and use of information technologies. His research examines the social and cultural dimensions of data and digital practice, drawing on disciplines such as HCI, science and technology studies, media studies, and communication.

“I’m deeply honored to have been named as a fellow of AAAS, particularly at the moment when we have a pressing responsibility to bring scientific understandings and processes to a broad public audience,” says Dourish. He currently serves as director of UCI’s Steckler Center for Responsible, Ethical, and Accessible Technology (CREATE). The center promotes research and education focused on creating technological futures that produce positive change in the world, with an emphasis on principles of equity, accountability and care.

“I was especially delighted,” adds Dourish, “to realize that I’d been inducted alongside such an impressive number of ICS colleagues.”

Annie Qu: Integrating Statistics and Machine Learning
Chancellor’s Professor of Statistics Annie Qu was recognized for contributions to longitudinal data, high-dimensional statistics and machine learning, as well as for exceptional service to the profession.

“I am deeply honored and humbled by the AAAS fellow recognition,” says Qu. “I am also very pleased to contribute to ICS and UCI, and grateful to my students and my colleagues.”

Qu focuses on solving fundamental issues regarding structured or unstructured large-scale data; developing cutting-edge statistical methods and theory for machine learning; and extracting essential information from large volume high-dimensional data. “My research can be applied in many different fields such as biomedical studies, genomic research, public health research, and social and political sciences,” says Qu. “We are developing causal mediation analysis in heterogeneity settings that provide new understandings in discovering personalized, effective intervention strategies.” Such work could help identify, for example, early diagnostic epigenetic biomarkers to assess health disparities among populations exposed to traumatic stress.

Qu and her students are also developing dynamic treatment and data-integration schemes for mobile health. “We are working on estimating effective intervention regimes and real-time implementation to allocate limited resources to the most vulnerable individuals at optimal times for improving individual well-being and stress management.”

Padhraic Smyth: Building Foundations for Machine Learning
Chancellor’s Professor of Computer Science Padhraic Smyth was recognized for contributions to the field of machine learning, particularly the development of statistical foundations and methodologies.

“My research area of machine learning has been thrust into the spotlight in recent years in terms of now being widely applied in many different types of high-profile application areas,” says Smyth. “Along with my research group and collaborators, we are investigating different aspects of the robustness of machine learning models and asking questions such as do these models ‘know’ what they don’t know? Are they unbiased and fair in their predictions? Can humans and machine learning models collaborate effectively?” Smyth and his team are addressing these questions using a variety of different ideas from computer science, statistics, mathematics, and cognitive science.

“I’m delighted to receive this national recognition,” says Smyth. “Having been a professor at UCI for over 25 years, I’ve been very fortunate to work with terrific graduate students and great colleagues — this type of Fellow recognition would not be happening without all of their help.”

— Shani Murray