The CMS PhD program

Back in January I wrote an excited post announcing our new Computing and Mathematical Sciences (CMS) PhD program.  Unfortunately, the approval did not happen in time for students to apply to it last year, but everything is up and running now, so we’re looking forward to seeing the first round of applicants this winter! (Though, we actually had 6 students who applied to other options last year switch into CMS for this fall — so the first class of CMS students is on-campus already!)

To prime the pump, I’m hoping that all the readers of this blog can spread the word about our exciting (& unique) new degree.  I know it’s what I wish I could’ve done when I was a grad student…  Please feel free to point prospective students to this post or to simply have them send me email.

Why did we start a new degree program?

We are in the midst of an unprecedented convergence of ideas & tools across disciplines.  This is happening among both traditionally “close” areas in the information sciences such as electrical engineering, computer science, and applied math as well as between the information sciences and seemingly disparate fields such as economics, biology, statistics, physics, and operations research.

As these fields collide, exciting new interdisciplinary topics are emerging.  The intersection of computer science and economics has yielded algorithmic game theory, privacy, social networks, and, more broadly, the field of network science.  The intersection of computer science, optimization, and statistics has yielded machine learning, big data, analytics, and, more broadly, the field of data science.  The intersection of electrical engineering, computer science, and biology has given us bioinformatics, molecular programming, and biomolecular circuits.  The intersection of physics and computer science has yielded quantum computing and quantum information theory.  Even the interaction of disciplines that are nearby each other yields exciting possibilities, e.g., computer science, controls, and electrical engineering have yielded the smart grid, smart buildings, and more generally the internet of things.

This convergence of areas has led both to new fields and to breakthroughs in existing fields — it is changing the landscape of the information sciences.  But, on the educational side, students are still taught in traditional, siloed fields.  As a result, no field alone is currently able to prepare its students for the emerging interdisciplinary areas the best and brightest are excited about pursuing today.  Instead, such students are forced to seek out a hodgepodge of courses from a variety of fields to prepare themselves for work in these new interdisciplinary areas.

However, it need not be this way.

There is a new intellectual core for “information sciences” surfacing as a result of the convergence of these traditional fields.

This core includes well-known topics such as algorithms, networks, learning, statistics, optimization, and their mathematical foundations.  But, each piece draws on perspectives from a variety of areas.  For example, though traditionally associated with computer science, the type of “algorithmic thinking” necessary today is more than just the traditional discrete & worst-case perspective of computer science, it must also include the continuous and average-case perspectives more typically associated with applied math and electrical engineering.  Similarly, perspectives on statistics and machine learning differ wildly across computer science, applied math, economics, and electrical engineering, and students today need a cross-cutting view.

The CMS PhD program is designed around this new intellectual core. 

The program builds from a unique, common core curriculum that includes classes on algorithms, networks, learning, statistics, optimization, and their mathematical foundations.  Beyond this core, however, the application areas pursued by students will be extensive and diverse, including problems from applied math, computer science, controls, electrical engineering, economics, and the physical sciences.  There are a few key tenets around which the program is built.

• Interpret “information” and “computation” broadly.  Information systems and computing can be found not only within traditional computer systems but also within biological systems, social systems, etc.  Studying the structures and mechanisms that communicate, store, and process information from this viewpoint — whether these structures are expressed in hardware and called machines, in software and called programs, in abstract notation and called mathematics, or in nature and society and called biological or social networks and markets — is crucial to pushing scientific boundaries.
• Data is central.  Data is being collected at an unprecedented speed and scale across fields, from the physical sciences to the social sciences to the information sciences.  Unlocking the potential of these massive data sets is crucial to all of these intellectual endeavors; however, in order to unlock this potential, we need new algorithms and tools for extracting actionable information.
• Algorithmic thinking is the foundation.  Simply put, it is almost impossible to do research in any scientific or engineering discipline without the ability to think algorithmically.  Algorithms are not just the basis for advanced technology; they are intrinsic components of diverse fields such as biology, physics, and economics.
• Seek rigor and relevance. The program unapologetically focuses on the theoretical foundations central to the newly emergent information sciences core.  This focus on rigorous foundations creates the opportunity for broad interdisciplinary impact across the variety of fields that are increasingly dependent on this core.  However, we seek more than just rigor, we also seek relevance.  So, students train not only in the foundations, but also in a range of interdisciplinary applications.

Given the breadth of the areas in the program, not surprisingly, there is broad faculty engagement in the new program, and students may choose advisers from anywhere within Applied Math, CS, EE, Control, Economics, Mech E, CNS,  Bioengineering, Physics, and beyond.

For more details on the courses, requirements, etc., see the program webpage.  And, if you’re thinking of applying, but have questions about the program, just send me email!