Professor Hanna Hajishirzi, a professor in the Natural Language Processing group and director of the Allen School’s H2Lab, and Yin Tat Lee, a professor in the Theory of Computation group, have been named 2020 Sloan Research Fellows by the Alfred P. Sloan Foundation. The program recognizes early-career scientists in the United States and Canada who are nominated and judged by their peers based on their creativity, leadership, and achievements in research.
“I am thrilled that the Sloan Foundation has honored Hanna and Yin Tat for their outstanding work on fundamental problems that have broad relevance and potential for impact,” said professor Magdalena Balazinska, director of the Allen School. “Hanna is working at the leading edge of artificial intelligence to transform the way we conceive of and build AI systems that touch people’s everyday lives, from education and media, to financial services and scientific documents. And Yin Tat is doing groundbreaking — even audacious — work that pushes past decades-old limits of computing to create faster, better solutions to a range of modern-day problems.”
Hajishirzi, who joined the Allen School faculty in 2018 and is also an AI research fellow at the Allen Institute for Artificial Intelligence (AI2), addresses foundational problems in natural language processing, artificial intelligence, and machine learning. Her goal is to develop general-purpose algorithms that can represent, comprehend, and reason about diverse forms of data efficiently and on a large scale. Hajishirzi’s research spans multiple domains, including representation learning, question answering, knowledge graphs, and applications such as conversational dialogue and knowledge extraction from unstructured text.
“Enormous amounts of information are available online in multiple forms across diverse resources; for example, in news articles, web pages, textbooks and technical documents,” explained Hajishirzi. “An important challenge in AI is how to represent and integrate diverse resources to facilitate further comprehension and reasoning. It is the right time to address this challenge at large scale and in real-world settings, using a unified representation that combines the best features of deep neural models and symbolic formalisms.”
Hajishirzi is among the pioneers in designing novel, end-to-end neural models for question answering and reading comprehension. One of her key contributions is Bi-Directional Attention Flow for Machine Comprehension, or BiDAF, which is a deep neural model for end-to-end question-answering about text and diagrams that has been widely adopted in academia and industry. Hajishirzi and her collaborators designed the system to be both scalable and modular, thus enabling its use with multiple modalities and knowledge bases. Hajishirzi is also among the first to address the problem of understanding scientific articles and data across multiple modalities, such as diagrams, math and geometry word problems. For example, she led the development of DyGIE, a system for enabling knowledge extraction from computer science and biomedical scientific papers. She also led the GeoS project, the first automated system for solving geometry word problems that can answer SAT geometry test questions on a par with the average American 11th grade student. More recently, Hajishirzi devised a new interpretable neural model for solving math problems, MathQA, that maps word problems to operation programs, and DenSPI and DecompRC, systems for real-time and multi-hop question answering that achieves state-of-the-art results by decomposing compositional questions into simpler sub-questions.
Hajishirzi has garnered numerous accolades for her research. She received an Allen Distinguished Investigator Award in AI for her work on the Spoon Feed Learning (SPEL) framework that combined principles of child education and machine learning to enable computers to interpret diagrams. Hajishirzi later earned a Google Faculty Research Award for her efforts to develop practical, scalable methods for open-domain question answering. She has also received an Amazon Research Award, a Bloomberg Data Science Award, and a Best Paper Award from the Special Interest Group on Discourse and Dialogue (SIGDIAL). Hajishirzi regularly publishes at the top conferences in the field, including the annual meeting of the Association for Computational Linguistics (ACL), the Conference on Empirical Methods in Natural Language Processing (EMNLP), and the Conference on Computer Vision and Pattern Recognition (CVPR).
Yin Tat Lee
Lee, who joined the Allen School faculty in 2017 and is also a visiting researcher at Microsoft Research AI, combines ideas from continuous and discrete mathematics to produce state-of-the-art algorithms for solving optimization problems that underpin the theory and practice of computing. His work encompasses multiple domains, including convex optimization, convex geometry, spectral graph theory, and online algorithms.
“From machine learning and experiment design, to route planning and medical imaging, convex optimization is used everywhere,” Lee said. “My group develops new techniques and algorithms to optimize faster, with the goal to design a universal optimization algorithm without compromising performance.”
Lee has already expanded convex optimization techniques to break long-standing running time barriers for a variety of problems. For example, he and his colleagues presented a new general interior point method that yielded the first significant improvement in linear programming in more than 20 years and a new algorithm for approximately solving maximum flow problems in near-linear time. Lee also has demonstrated the applicability of optimization techniques to an even broader class of problems than previously was considered feasible, devising a faster cutting plane method that improved the running time for solving classic problems in both continuous and combinatorial optimization. More recently, Lee contributed to a pair of new algorithms that achieve optimal convergence rates for optimizing non-smooth convex functions in distributed networks. That same year, Lee contributed to a total of six papers that appeared at the Symposium on Theory of Computing (STOC 2018) — a record high for an individual researcher at the conference.
Lee’s work has earned him multiple Best Paper and Best Student Paper awards at premier conferences in the field, including the IEEE Symposium on Foundations of Computer Science (FOCS), the ACM-SIAM Symposium on Discrete Algorithms (SODA), and the Conference on Neural Information Processing Systems (NeurIPS 2018). Last year, he earned a Microsoft Research Faculty Fellowship in recognition of his efforts to advance the field of theoretical computer science for real-world applications. In 2018, the Mathematical Optimization Society awarded Lee the A.W. Tucker Prize, which recognizes the best doctoral thesis in optimization in the prior three years, for his work on faster algorithms for convex and combinatorial optimization. That same year, he received a CAREER Award from the National Science Foundation to build upon that work and overcome multiple obstacles to optimization.
“To receive a Sloan Research Fellowship is to be told by your fellow scientists that you stand out among your peers,” Adam F. Falk, president of the Alfred P. Sloan Foundation, said in a press release. “A Sloan Research Fellow is someone whose drive, creativity, and insight makes them a researcher to watch.”
Hajishirzi and Lee are among four University of Washington researchers to watch in this latest group of Fellows, which includes Kyle Armour, a professor in the School of Oceanography and Department of Atmospheric Sciences, and Jacqueline Padilla-Gamiño, a professor in the School of Aquatic and Fishery Sciences.
A total of 37 current or former faculty members at the Allen School have been recognized through the Sloan Research Fellowship program. Recent honorees include Shayan Oveis Gharan, who was recognized last year for his work on solutions to fundamental NP-hard counting and optimization problems; Maya Cakmak, for her contributions to robotics; Ali Farhadi and Jon Froehlich, for their research in artificial intelligence and human-computer interaction, respectively; and Emina Torlak, for her work in computer-aided verification and synthesis.
View the complete list of 2020 Sloan Research Fellows here and read the Sloan Foundation press release here. Read a related UW News release here.