Stephen Wong

Rice University CS professor Stephen Wong.

Stephen Wong (Ph.D. ’89, MIT) is an experienced computer scientist, physicist and educator specializing in object-oriented programming, software engineering and computer science pedagogy. His current research includes the use design patterns in redesigning traditional algorithms, serious gaming systems, cloud-based enterprise information management systems, large-scale, high-fidelity simulation systems and technologies for massive open on-line courses.

Scott Rixner

Scott Rixner is a Professor of Computer Science at Rice University

Scott Rixner (Ph.D. ’01, MIT) is a Professor of Computer Science at Rice University. His research spans virtualization, operating systems, and computer architecture, with a specific focus on memory systems and networking. He is well versed in the internals of the Python programming language, as he has developed Python interpreters for both embedded systems and web browsers. He has been actively involved in curriculum development and oversight at Rice, having actively served on the curriculum committees for the University, School of Engineering, and Department of Computer Science. He has also taught or co-taught many of the introductory computer science courses at Rice, including Computational Thinking, Algorithmic Thinking, Introduction to Program Design, and Introduction to Computer Systems.

He has recently been named the Director for Rice’s Online Masters in Computer Science Program.

Moshe Vardi

Moshe Vardi.

Moshe Y. Vardi (Ph.D. ’81, Hebrew University of Jerusalem) is the Karen Ostrum George Distinguished Service Professor in Computational Engineering and was recently promoted to University Professor, Rice’s highest academic title.

His interests focus on automated reasoning, a branch of Artificial Intelligence with broad applications to computer science, including database theory, computational-complexity theory, knowledge in multi-agent systems, computer-aided verification, and teaching logic across the curriculum.

He is a member of the National Academy of Science and the National Academy of Engineering, an ACM Fellow, IEEE Fellow, Guggenheim Fellow, a Fellow of the American Academy of Arts and Sciences, and a recipient of the Fulbright Award.

At Rice, he is leading a new campuswide Initiative on Technology, Culture, and Society.

Follow him on Twitter at @vardi.




Luay Nakhleh

CS Department Chair Luay Nakhleh.

Computer Science professor and department chair Luay Nakhleh‘s passion for educating students was recognized in April 2019. The Brown Prize, Rice’s highest teaching award, is given annually based upon a survey of alumni who graduated within the past two to five years. He is the first professor from the Computer Science Department to win the prize.

Nakhleh became chair of the department in January 2017 and his priorities included growing the graduate student programs and improving their environment, launching an alumni initiative, and increasing the number of faculty members in what had become the largest academic department on campus.

He earned his M.S. in 1998 from Texas A&M University and his Ph.D. from the University of Texas in 2004.

Devika Subramanian

Rice University CS professor Devika Subramanian.

Devika Subramanian (Ph.D. ’89, Stanford) explores a wide range of research interests, including artificial intelligence and machine learning and their applications in computational systems biology, neuroscience of human learning, assessments of hurricane risks, network analysis of power grids, mortality prediction in cardiology, conflict forecasting and analysis of terrorist networks, and analysis of unstructured text data.

Some of her past projects include: designing an adaptive outdoor tour guide for the Rice campus (funded by Rice Engineering), reinforcement learning for non-stationary environments and applications to network routing (funded by Southwestern Bell), designing adaptive control systems for the Mars Bioplex (funded by NASA), designing experimentation strategies for protein crystallography (funded by NIH), adaptive compilers for power-sensitive applications (funded by Darpa and the Texas Advanced Technology Program), automating the conceptual design of opto-mechanical systems from specifications of behavior (funded by NSF), and dynamically learning models of humans acquiring a complex visualmotor task (funded by ONR).

Corky Cartwright

Rice University CS Professor Corky Cartwright.

Robert “Corky” Cartwright (Ph.D. ’77, Stanford) has devoted his career to elevating programming from a black art to a systematic discipline. To this end, he has: (i) conducted fundamental research on the mathematical principles governing the design and implementation of programming languages, (ii) helped found an outstanding academic Computer Science department at Rice University, and (iii) served as a professional leader in programming language research and computer science education.

His current research focuses on four topics:

• Developing extensions to Java, Scala, and Swift that foster developing parallel application programs that scale well as more cores are added to microprocessors. I an ardent advocate of a “mostly functional” approach to developing parallel programming applications.

• Developing “smart” programming environments that prove that type-safe programs are free of run-time errors. In essence, smart environments use static analysis to verify the preconditions for primitive program operations.

• Developing production-quality pedagogic programming environments for Java, Scala, and Swift using Rice undergraduates as the primary workforce. The DrJava and forthcoming DrScala environments are products of this research effort.

• Developing a programming language and supporting environment for developing implicit programs in which program parameters are dynamically adjusted by the language run-time in accord with an platform-dependent intent specification provided by the user. The intent typically focuses on minimizing or maximizing a platform-dependent measure such as energy usage or performance while meeting platform-independent accuracy or quality constraints. The results of some of this research are being integrated in the Rice undergraduate curriculum.