Cutting-edge research and a commitment to teaching distinguish the online Master of Science in Computer Science program at Case Western Reserve University’s Case School of Engineering—and Professor Michael Lewicki exemplifies both. Since earning his PhD from the California Institute of Technology, he’s spent the last 25 years at the frontier of computational perception. In the conversation that follows, Lewicki repeatedly returned to the importance of pedagogic skill in conveying this complex material via online learning.
Students considering an online degree in computer science would do well to explore Case Western Reserve’s part-time, non-thesis, 30 credit-hour graduate program. We recently spoke with Lewicki to ask about his field of specialization and his hopes for the online computer science master’s program at Case Western Reserve. Here’s what he had to say.
First, we have exceptional teachers with a high level of commitment to education and teaching as a craft. Several instructors have received teaching awards; Professor Harold Connamacher was recently made a chaired instructor, which is a first at the university.
Many of the faculty are also top experts in their field. We’re applying new techniques to new problems in new areas like data science, figuring out how to use developing knowledge. It’s really about the novelty; we push boundaries.
The same course can be taught by different people, and you’ll get a very different experience, depending on who’s teaching. They can bring their experience into the classroom and talk about current problems the field is facing, new techniques that they’ve learned about at conferences, things like that.
That’s especially important in computer science, a field that’s rapidly changing. There are always new things, new applications and new techniques, some of which haven’t even made it into the textbooks yet. I think it’s valuable to be part of a class where someone has that expertise. It just makes it more interesting, more current.
I work at the intersection of computation and perception. I’m interested in the information processing principles that underlie how we perceive, like computational and audition. Neuroscience factors in as well. I’m very interested in the fundamental principles that allow the brain to work.
There’s always a gap between what we can model with algorithms and what we see when we look at our own behavior. There’s a huge range of unsolved problems. Over the years, I’ve worked to understand what problems the brain solves when it’s doing perception, like sensory coding. I explore what goes on beyond that, looking at how neurons process information.
My work combines techniques in machine learning, probabilistic modeling, information theory, applied to speech recognition and computer vision. You’re really trying to develop theories that can predict or explain what’s going on in biological systems. Those principles apply to all systems at a fundamental level, whether you’re designing a visual system for a self-driving car or modeling how a bird flies in a forest.
That fits into the overall curriculum in the domain of artificial intelligence and machine learning. The course I’m developing for the computer science online master’s covers computational perception.
It applies to many different fields. Take sensors, for example; they’re huge now. The challenge is that the sensor is trying to pick up the signal, but there’s also noise in the signal, and somehow you have to process the signal in a way to get a reliable estimate. That’s the essence of what perception is doing. You’re taking noisy, incomplete information and trying to infer what’s out in the real world. Whether it’s a speaker’s voice or the waveform that’s present at this signal source they’re all applications of machine learning—but combined with different kinds of signal processing.
This is an area that computer science students don’t generally get to study. Usually, you would have that background in engineering, but we typically don’t encounter that in a computer science degree program. That’s one reason why I have this particular course in the program—and it’s something that distinguishes Case Western Reserve’s program from others.
One nice thing about Zoom is that all students can see well and hear well; that’s not always true in live classrooms. Students also like having the ability to go back and rewatch something or pause in a way you obviously can’t in a live classroom. It’s very valuable because you don’t have to worry so much about taking notes in an online program. You can pay more attention to the lecture and not worry about getting everything down. You know you might miss something, but you can always stop it to look up a definition or something like that.
Another benefit is that students can instantly share links and images, and they can draw on the virtual board. That makes it easier for students to contribute in class. In one class we discussed optical illusions and students were able to share examples immediately. That would be harder to do in a live classroom.
Our master’s program offers a high level of education. Think about it in contrast to undergraduate study, where you spend maybe the first two years completing general courses and deciding on your major. Then you pick a specific field—say, computer science. That’s a particularly challenging field at the undergraduate level because it’s grown so much; there’s so much to cover. There are so many different areas of computer science: artificial intelligence, programming languages, software engineering, theory and algorithms, operating systems, cloud computing, cybersecurity, the list just goes on and these fields all keep growing and getting deeper.
Undergraduates can’t take all the computer science courses because they have a relatively small number of elective slots. We designed a bachelor’s degree curriculum that ensures a core education based on the Association for Computing Machinery’s standardized computer science education curriculum, which defines what every computer science student needs to know. To that, we add a breadth degree requirement. Only after that do students get some flexibility to specialize in software engineering or artificial intelligence or some other related field.
That brings us to master’s-level education, where students can develop depth areas in algorithms and theory, artificial intelligence, bioinformatics, computer networks and systems, databases and data mining, network security and privacy or software engineering. At the undergraduate degree level, they maybe get to take one or two courses in any of those areas. At the graduate level, they can dig in and develop real proficiency. You get deep enough to learn how to apply your knowledge to whatever problems interest you.
The other unique opportunity offered by the master’s program is that it gives you a chance to do projects. It goes beyond textbook learning and taking exams. You’ll apply what you learn to a problem of your choice or read a bunch of papers to assess the state of the art in a particular field. It’s high-level, and it promotes genuine mastery.
In your opinion, how does online education fit into the broader framework of the university and higher education?
I think it broadens educational options and accessibility. The number of choices available to students right now is impressive. I have kids in high school, and they routinely use YouTube and online courses like Khan Academy and Brilliant. These supplements offer different ways of organizing information and different ways of trying to teach that information.
I don’t think online will supplant the university or live instruction, though. You know, there was a time when anyone could go to the library and get a textbook on whatever they were interested in, and some people did that. Universities didn’t go away because everyone had access to books, though. In the end, learning is still difficult. You still need teachers, and you still need to be with a group of people trying to learn the same thing
Education is, as it always has been, an unsolved problem. I’m not sure it’s really changed that much since the beginning, whenever that was. Education is about building your mental models and correcting the models that you have that are wrong, and that takes work on the student’s part and also expertise on the teacher’s part to understand it. Think about learning a musical instrument or a sport. It isn’t easy to do on your own. You need someone looking at what you’re doing and correcting you, giving you feedback about how to improve.
I think it goes back to the teaching. There are some really excellent instructors here, and there’s a high level of commitment to designing the course from modern educational principles. The instructors all share that desire.
Michael Lewicki is one of many award-winning computer scientists with whom you will study when you enroll in the Online MS in Computer Science program at Case Western Reserve University’s Case School of Engineering. The program is designed for computing professionals with some background in data structures, algorithms, and operating systems. Are you looking to bolster your computer science skills and techniques to address tomorrow’s computing challenges? If so, why not apply today?