About
Wayne Snyder joined CDS as a Lecturer during the summer of 2024, after retiring from full-time teaching in the CS Department at Boston University. Professor Snyder completed his PhD at the University of Pennsylvania in 1983 with a thesis in logic and artificial intelligence and over the next 36 years, did research with colleagues in France, Germany and the US; taught CS courses at all levels, with an emphasis on theory and programming courses; and served as department Chairman and Associate Dean for advising in the College of Arts and Sciences.
Can you share a little about your academic background and what led you to specialize in your current field?
I majored in Classical Studies at Dickinson College and was in graduate school for Classics when I became interested in using computers to analyze ancient texts. Long story short, this interest led me to a PhD program in Artificial Intelligence at UPenn. During my time at UPenn, my focus shifted to automated deduction and using computers for traditional mathematics. In the last decade, I have been drawn into machine learning, teaching probability and statistics, natural language processing, and more traditional AI courses. For the past two years, I have taught our graduate-level NLP class, emphasizing deep learning and the impressive results produced by large language models such as ChatGPT. It’s an exciting time to be in computer science!
What is your involvement with the OMDS program?
I am officially an Associate Professor Emeritus at BU and currently a Lecturer in CDS. But I have been friends and colleagues for many years with the faculty who created OMDS, and watched with great interest as the program took shape. I am thrilled to be working with so many old (and new) friends as I embark on this new chapter of my career.
Which courses do you teach at BU Computing & Data Sciences, and what do you enjoy most about teaching these subjects?
I am helping to develop and teach two machine learning classes for the OMDS, one an introduction to machine learning, emphasizing traditional (i.e., “non-deep”) algorithms; and the other an introduction to deep learning. What I most enjoy about machine learning is its combination of beautiful mathematics with really interesting programming, all in the service of finding robust solutions to very practical and important problems. It’s not often in one’s career as a teacher that the subject of the course is in the daily news practically every day, but that is where we are, and I’m thrilled to be sharing it with the next generation of data scientists.
Which recent developments in your field do you find particularly exciting? How have they influenced your work and teaching?
The MOST exciting thing to happen in computer science in the last decade is the the availability of vast processing power in the form of GPUs, and way that artificial neural networks have leveraged this power to create a new paradigm for computing: instead of telling the computer what to do to create a given outcome from a set of data, we tell the computer what outcome we want, give it the data, and let it figure out the method. This has resulted in a strong focus on data: how to gather it, format it, and explore what it means. Computer science has become data science. After decades of teaching the traditional way of thinking about computing, it is really refreshing and fascinating to look at all these familiar problems in a completely new light!
Because of this, I have focused my teaching in the last few years increasingly on deep learning, especially in applications to natural language processing.
