Nine faculty members from the Cornell Ann S. Bowers College of Computing and Information Science received 2024 Excellence Awards in recognition of their outstanding contributions to the college in research or teaching.
The selected faculty celebrated with their colleagues at a ceremony held Sept. 26 in the Statler Ballroom.
"There is no shortage of gifted teachers and brilliant researchers at Cornell Bowers CIS, and so this year – as always – it was a highly competitive field," said Thorsten Joachims, Jacob Gould Schurman Professor in the Departments of Computer Science and Information Science, who led the selection committee for the Ann S. Bowers ’59 Research Awards. He announced the following winners at the ceremony:
Ann S. Bowers ’59 Research Awards
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Yoav Artzi, associate professor of computer science at Cornell Tech, is a "top researcher of his generation" in the field of natural language processing (NLP). He is also a leading expert in situated language learning, a subfield of NLP focused on enabling computer systems to learn to understand spoken or written language when interacting with people. His group created a system that learned by itself how to control a drone via natural language instructions, and a game-like environment where humans collaborate with artificial agents. He also has research interests at the interface of cognitive science and NLP, with potential for advances in both fields.
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Florentina Bunea, professor of statistics and data science, is one of the leading researchers internationally in mathematical statistics. Her interests lie in the general area of high-dimensional statistical learning theory and methodology. She received an Institute of Mathematical Statistics fellowship for her earlier work on the foundations of model selection and aggregation in high dimensions. Most recently, she has worked on supervised and unsupervised learning in high-dimensional models with lower-dimensional latent structures. Not only do these models offer predictions, they also give insights into the underlying process, and quantify their uncertainty. Notably, her research collaborations have yielded powerful new methods for biological discovery.
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René Kizilcec, associate professor of information science, is a "rising star" and leading researcher on issues critical to the future of education. He works at the intersection of computing and education and aims to provide evidence-based best practices for applying new technologies. Kizilcec publishes prolifically and is widely cited, with expertise in areas such as learning at scale, learning analytics, and artificial intelligence (AI) in education. His work ranges from the behavioral and psychological implications of these technologies to the computational and technical aspects, with impacts for students of all ages.
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Adrian Sampson, associate professor of computer science, tackles one of the “grand challenges” in modern computing – improving computer performance – by combining techniques from architecture and programming languages to address problems that are unsolvable in one domain alone. He takes a comprehensive approach, developing new programming abstractions, new compilers, and new hardware designs that make it possible to map complex algorithms onto computer systems that include both general CPUs and hardware accelerators, cutting across hardware and software layers. He has received significant scientific recognition as a leading figure in the intersection of programming languages and computer architecture.
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Wen Sun, assistant professor of computer science, works on one of the most important problems in AI: ensuring that AI acts in a safe, principled, and ethical way. As one of the young leaders in this field, Sun is designing new reinforcement learning (RL) algorithms that will work in real-world environments. He has published highly influential papers, both on RL theory and more applied work, such as using RL to fine-tune self-driving cars’ detection systems and using RL from human feedback for large language models.
Claire Cardie, Joseph C. Ford Professor of Engineering in the Departments of Computer Science and Information Science, chaired the committee for the Teaching and Advising Excellence Awards. She announced the following recipients:
Teaching and Advising Excellence Awards
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Sumanta Basu, associate professor of statistics and data science, is an innovative teacher who has taught a broad range of introductory and advanced classes including: Data Science for All (STSCI 1380), Statistical Methods I (BTRY 6010), Computationally Intensive Statistical Methods (STSCI 6520), and Statistical Consulting courses (STSCI 4950/7950, 7951). He has also been instrumental in the development of a data science certificate program through eCornell and is a highly sought-after graduate advisor.
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Allison Koenecke, assistant professor of information science, is equally talented at teaching complicated material to a large lecture hall full of students as she is mentoring teaching assistants and coaching undergraduates through challenging problems. At the undergraduate level, she has taught Introduction to Data Science (INFO 2950) and Practical Principles for Designing Fair Algorithms (INFO 4930). To keep students engaged in 2950, a large and notoriously difficult class, she worked with members from the Active Learning Initiative to give students personal whiteboards – a highly successful effort that allowed students to answer questions in real-time. At the graduate level, she has taught Data Science for Global Development (INFO 6960).
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Noah Stephens-Davidowitz, assistant professor of computer science, is a gifted teacher who excels at teaching difficult material in an engaging way. Since joining the college in 2020, he has taught two graduate courses – Cryptography (CS 6830) and Lattices: Geometry, Cryptography, and Algorithms (CS 6802) – and two undergraduate courses – Introduction to Cryptography (CS 4830) and Mathematical Foundations of Computing (CS 2800). For three summers, he has also taught pre-2800 in the CSMore summer program. Stephens-Davidowitz designed CS 6802 and is in the process of writing a textbook for the class, which will enable other schools to adopt the course. One student remarked, "Prof. Noah is making it onto my Mount Rushmore of CS professors."
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Kilian Weinberger, professor of computer science, has long taught Introduction to Machine Learning (CS 3780) and consistently receives high ratings and enthusiastic student evaluations, both for his course design and engaging teaching style. During the COVID-19 pandemic, he created a series of videos for the class, which are still available on YouTube and widely used by students at Cornell and beyond. He has also mentored four junior faculty as a co-teacher for the course. Recently, he developed a new, much-needed course, Introduction to Deep Learning (CS 4782). He also teaches a popular graduate-level course, Advanced Topics in Machine Learning (CS 6784).
By Patricia Waldron, a writer for the Cornell Ann S. Bowers College of Computing and Information Science.