Each year, the MIT School of Engineering presents the Junior Bose Award to a junior faculty member who has made outstanding contributions as an educator. The award is given to a faculty member who is seeking promotion from the position of assistant professor to non-tenure-track associate professor. The 2023 Junior Bose Award was presented to two outstanding educators: Jacob Andreas, X-Window Consortium Professor in the Department of Electrical Engineering and Computer Science (EECS), and Mingda Li, Class of 1947 Career Development Professor in the Department of Engineering Nuclear Science and Engineering.
“Jacob and Mingda are incredibly talented educators who have made a lasting impact on their students,” says Anantha Chandrakasan, dean of MIT’s School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “The enthusiasm they have for the subjects they teach is contagious. They are both committed to finding new, engaging ways to teach students about incredibly complex ideas.”
Andreas and Li received the award in February at the MIT Engineering Council meeting. Both will be promoted to the position of non-tenure track associate professor with effect from July 1.
Jakub Andreas
During his second semester at MIT, Jacob Andreas, who studies machine learning for language understanding, was assigned to teach class 6.8610 (Natural Language Processing) (formerly 6.864). The course was most recently taught in 2017 by Regina Barzilay, Distinguished Professor of AI and Health in the School of Engineering at EECS. The intervening three years had a transformative impact on the field of natural processing, opening up up-to-date opportunities for teaching this course.
Language understanding problems that previously required specialized machine learning models can be solved using a set of standard neural network components. As a result, the range of topics that an introductory course could cover expanded dramatically. Andreas and his co-instructor Jim Glass, a senior research fellow at MIT’s Computer Science and Artificial Intelligence Laboratory, faced the challenge of finding a balance between teaching classic natural language processing methodologies while focusing on newer techniques. The challenge was exhilarating for Andreas.
“It was great fun – especially for me as a up-to-date faculty member – starting with Regina’s amazing notes from existing courses and rethinking how to describe this field from the ground up: which elements of the classical toolkit and the deep learning toolkit actually mattered and how a way to think through your relationship in the best possible way,” he says.
This bottom-up approach has influenced Andreas’ teaching in other subjects he teaches, including classes 6.3900 (Introduction to Machine Learning) and 6.1010 (Fundamentals of Programming).
“Instead of standing in front of the room and saying, ‘Here’s a great idea and three important special cases,’ I start by leading students to a deep enough understanding of special cases, such as programs or sentences, that they can use to connect the dots,” explains Andreas.
Andreas draws inspiration from the teaching style of his supervisor, Daniel Klein, a professor at the University of California, Berkeley. Klein remains one of Andreas’ primary sources for problem sets and exercises that aid students learn concepts and ideas on their own. His approach to teaching was also influenced by the overdue Professor William Theodore de Bary, who treated students as if they were colleagues rather than students.
“This model of the classroom as a place where teachers and students try to come to an understanding together has changed the way I think about what a professor should do. This was especially useful at MIT, where students constantly ask me questions that I don’t immediately know how to answer,” he adds.
Mingda Li
At first glance, the topic of class 22.12 (Interaction of radiation with matter) may seem discouraging. When Mingda Li started teaching this class, he was determined to fill the curriculum with excitement and fun.
“The name of the class may seem boring and even a little scary because it includes the word “radiation,” not to mention that it is a core class required for the Ph.D. qualifying exam. However, I decided to turn what some might consider monotonous into something fun, overturning a decades-old tradition of teaching classes,” explains Li.
Li managed to introduce dense, elaborate topics in a fun and engaging way without sacrificing the rigor of the course.
“I want to provide our students with a holistic yet rigorous understanding of the field. Rather than trying to cover all the content in an 800-page book on the intersection of matter and radiation, I focus on the important, relevant elements, with a rigorous and clear approach to how these topics relate to the field as a whole,” he explains.
After class, students consistently praise Li for his warmth, approachability, and enthusiasm for the subjects he teaches. Li demonstrated these characteristics throughout his academic career. Already as a newborn middle school student, he helped his peers learn challenging concepts. This carried over to his doctoral studies at MIT, where he won two teaching assistant awards.
“In a highly competitive academic environment, people sometimes focus more on competition than collaboration, and this competition can create tension. I strive to foster an environment in which difficult problems can be solved and solved through effective collaboration,” adds Li.
Li credits many of his teachers with shaping his approach to education. From his middle school math teacher, Ms. Cui, to MIT faculty including Professors Gang Chen, Mehran Kardar, Hong Liu, and the overdue Institute professor Mildred “Millie” Dresselhaus, Li learned to approach teaching with compassion and humor.