Professor Eric Braude Relishes Collaborations with Students and Alums
Associate Professor and Director of Digital Learning, Computer Science
PhD, Columbia University; MS, University of Miami; MS, University of Illinois; BSc, University of Natal
What is your area of expertise?
Artificial intelligence (which includes machine learning) and software engineering.
Tell us about your work—can you share any current research or recent publications?
My largest project over the past two years has been with a MET team tackling software design. The Unified Modeling Language (UML), the best-known way to show software designs, turns out not to be scalable, and is not used much for realistically sized applications. Our team includes part-time instructor Kuang-Jung (Michael) Huang, Aviral Srivastava (MET’21), and statistician Shiyu Zhang (CAS’20). We have developed a scalable version of UML called RUML, which shows multiple related views in the presence of hundreds of classes. It took us over a year to design and conduct an experiment comparing the use of classical UML and RUML. Our main paper will be submitted this summer.
A paper, “Generalizing Morley’s and Other Theorems with Automated Realization,” written by MS in Computer Science alum Satbek Abdyldayev (MET’17) and me, appeared in the Journal of Automated Reasoning. It automates holonomy to check plain geometry theorems. We have been called “champions” of this approach.
How does your work apply in practice? What is its application?
RUML should bring the analysis and documentation of software designs to a wider community of practitioners. I also have ongoing projects that apply technology to learning and evaluation, always of interest to universities.
What courses do you teach at MET?
My newest course is an online version of Artificial Intelligence (MET CS 644), coming in Fall 2021. Most recently, I have taught Machine Learning (MET CS 767), Artificial Intelligence (MET CS 664), Advanced Programming Techniques (MET CS 622), Analysis of Algorithms (MET CS 566), and Information Systems Analysis and Design (MET CS 682).
You are teaching Machine Learning (MET CS 767) online for Fall 2. For those interested in studying online, can you please talk about the online classroom experience?
Online learning can be very effective, as many have discovered during the pandemic. A surprising amount can be learned in seven weeks, and we ensure that our online courses are as demanding as our 14-week, on-campus versions. The value has much to do with concentration, students’ ability to replay recorded classes (including at high speed), and getting help via voice, video, or text in real time and asynchronously.
Please highlight a particular project within your courses that most interests your students.
In my Machine Learning, Artificial Intelligence (AI), and advanced programming courses, students select their own projects in addition to weekly labs. Their topics cover every field you can think of. In the last year, several have addressed the pandemic: prediction, vaccination, and trend recognition.
Are there opportunities for fruitful collaboration with faculty, or even students, in terms of marrying practice with research activities?
Yes, numerous students have been involved in our projects, in multiple roles.
From your previous work in the industry, what “real-life” exercises do you bring to class? And how does that inform your classroom?
I have been involved in AI and neural nets since the early 1980s, witnessing and participating in its growth—sometimes slow, sometimes dizzyingly fast—as a scientist and lab manager. I am working on a way to use fuzzy logic to learn from changing data in real time. Almost everything changes but curiosity should never be relinquished.
Is there anything else you would like to share with the MET Community?
I continue to be proud of the students who have contributed to research with me. They have gone on to doctorates, industry positions, entrepreneurial ventures, and university teaching.