Chad Jenkins
Odest Chadwicke (Chad) Jenkins
Assistant Professor of Computer Science
If the blockbuster movie I, Robot accurately portrays a future where human-like robots serve as collaborators and contribute toward the needs of human society, then Odest Chadwicke (Chad) Jenkins, assistant professor of computer science, will help make the future happen. Jenkins' research interests include humanoid robotics, machine learning and computer animation - all areas that could make a cinematic fantasy a possibility.
Jenkins' work aims to leverage abilities demonstrated by humans in the real world to control robots and virtual characters. His approach involves addressing two major questions: How can human motion be collected in natural situations without instrumentation? And how can mechanisms for robot control be learned from human demonstration and motion?
While doing doctoral research at the University of Southern California, Jenkins realized that existing systems for capturing natural human motion were inadequate. He conceived a new method of computer vision that is capable of extracting both a person's motion and kinematic structure (i.e., bones and joints) using multiple cameras. His dissertation focused primarily on using machine learning to uncover behaviors underlying kinematic human motion data.
"We're creating new methods for capturing human behavior and building new robot architectures that will allow robots in the future to autonomously perform higher-level purposeful tasks," says Jenkins.
The Robonaut, a two-armed, ten-fingered, humanoid robot developed by NASA and DARPA, may be one near-term beneficiary of Jenkins' research. Since 2001, Jenkins has been among the scientists and researchers from a multi-university collaborative, including USC, the Massachusetts Institute of Technology, the University of Massachusetts and others, that has worked with NASA and DARPA on the Robonaut. Earlier this year, NASA announced it was considering using the Robonaut on a mission to service and repair the Hubble Space Telescope, which would require working outside the spacecraft.
In the fall semester of 2004, Jenkins will offer CS148, the Computer Science Department's course on building intelligent robots, which will explore the paradigms and problems of robot programming and will allow students to build their own mobile robots.
Jenkins, who comes to Brown after postdoctoral work in USC's Robotics Research Laboratory, notes the collegial atmosphere of Brown and the University's distinctive balance between teaching and research. He hopes to collaborate with computer vision faculty in engineering and computer science as well as with physical and life science researchers working on aspects of brain-machine interfaces.
Claire Kenyon
Professor of Computer Science
Claire Kenyon comes to Brown from the computer science laboratory at Ecole Polytechnique in France, where she has been a professor of computer science since 2002.
Her primary research area is the design and analysis of algorithms, but she has also worked in computational geometry, neural nets, DNA computing and computational statistical mechanics.
Kenyon had a number of offers from other institutions but said yes to Brown because "she liked the culture we have developed in this department," including its size and the potential to collaborate closely with faculty and students, according to Eli Upfal, professor of computer science and chair of the department.
Collaboration holds particular appeal for Kenyon, who is the first woman to be named a full professor in Brown's Department of Computer Science.
"I would rank few pleasures higher than the process of gaining new insights on a research problem, developed from the exchange of ideas during intensive, highly focused work sessions," she said. "For each of us, research stretches our possibilities to the limit in a joint effort toward the goal of gaining more understanding of the problem under study. In teaching, a similar pleasure comes from seeing a student understand and start to appreciate something new for him or her, particularly when it is some notion which I found exciting myself the first time I learned about it."
Kenyon's interest in computer science "was something of a chance event," she said. A mathematics major as an undergraduate at the Universite de Paris, "I had always been particularly interested in discrete mathematics. It so happened that during my senior year, I took a course in differential geometry, which I really disliked. I discovered programming and loved it; I was fascinated by the algorithmic sides of my programming and algorithms course."
She received the equivalent of a master's and Ph.D. in computer science from the Universite de Paris in 1985 and 1988, respectively. She conducted postdoctoral work at the French National Institute for Research in Computer Science and Control and at the Center for Discrete Mathematics and Theoretical Computer Science at Rutgers, then joined the French National Center for Scientific Research. She also has conducted research at the International Computer Science Institute in Berkeley, Calif. She has taught at the University of California-Berkeley and at Cornell. In 1991, she won a highly respected Prix IBM Jeune Chercheur; in 2002 she was named a junior member of Institut Universitaire de France (Center for French Universities).
Meinolf Sellmann
Assistant Professor of Computer Science
Meinolf Sellmann says he first approached computer science in hopes of finding an area of study where theory and practice combined in the solution of real-world problems. In fact, Sellmann, who arrives at Brown this fall as an assistant professor of computer science, once planned to become a medical doctor to use theoretical knowledge to diagnose cures.
"When I attended my first course on linear programming, I found my hopes fulfilled in computer science: Real-world problems are modeled mathematically and solved using sophisticated techniques from computer science," he says. Sellmann conducts research on the borders of operations research, algorithm theory and artificial intelligence.
"I am interested in combinatorial problems as they emerge from and cover a wide range of practical applications," he says. Combinatorial problems involve allocating limited resources - with a vast set of variables - to achieve desired objectives. Among those that Sellmann has worked on are airline crew scheduling, automatic recording of TV contents, resource management, graph bisection, network design, and the design of scientific experiments.
"These problems consist of finding a minimum over a finite set. For computer scientists, these tasks are very challenging due to the large magnitude of the sets under investigation. Frequently, the search spaces contain more elements than atoms in the universe," says Sellmann.
As a result, sophisticated methods that include linear programming, approximation, efficient data structures, and constraint programming are necessary to reduce the computational effort required to solve the problems these challenges present, he says.
In 2002, Sellmann received his computer science Ph.D. from the Department of Mathematics and Computer Science at the University of Paderborn in his native Germany. He notes that computer science at Brown has a great reputation for bridging theory and practice. He hopes to be among a working group of researchers whose interests include combinatorial optimization, and he looks forward to collaborating with colleagues who have expertise in theory, machine learning and constraint programming.
- Ricardo Howell and Tracie Sweeney
