P. S. Krishnaprasad received the Ph.D. degree from Harvard University in 1977. He was on the faculty of the Systems Engineering Department at Case Western Reserve University from 1977 to 1980. He has been with the University of Maryland since August 1980, where he has held the position of Professor of Electrical Engineering since 1987, and a joint appointment with the Institute for Systems Research since 1988. He is also a member of the Applied Mathematics Faculty.
Krishnaprasad has held visiting positions with Erasmus University (Rotterdam); the Department of Mathematics, University of California, Berkeley; the University of Groningen (the Netherlands); the Mathematical Sciences Institute at Cornell University; and the Mechanical and Aerospace Engineering Department at Princeton University.
Krishnaprasad's current focus is on collective behavior in biological systems and applications of related ideas to engineered systems (robots).  see downloadable articles available at the website
http://www.isr.umd.edu/~krishna/research.htm
Krishnaprasad's work is in the broad area of geometric control theory and its applications. His contributions include geometry of parametrization problems in linear systems, the Lie algebraic foundations of certain nonlinear filtering problems pertaining to system identification, the Lie theory and stability of interconnected mechanical systems (e.g., spacecraft with elastic attachments, spinning rotors, and fluidfilled cavities), and symmetry principles in nonlinear control theory. He has also investigated mathematical problems in the kinematics and control of robot manipulators, the realtime control of flexible robot arms with endpoint sensing, tactile perception, and the development of symbolic algebraic tools for design and control. In the last several years, his interests have drawn him to: problems of modeling, design, motion planning and control, arising in mobile robotics (legged and wheeled vehicles, autonomous underwater vehicles and autonomous aircraft); geometric methods in nonlinear dynamics; wavelet analysis for signals and systems; intelligent control architectures, in part inspired by biological paradigms such as central patterns generators and neural networks; the technology and theory of smart materials such as piezoelectric and magnetostrictive materials for use in actuation and sensing; problems of integration of actuators and sensors in control networks; and modeling, simulation, monitoring and control in semiconductor manufacturing processes, such as rapid thermal chemical vapor deposition.
Additionally, some of this work is also linked to the experimental efforts in the Intelligent Servosystems Laboratory which is equipped with a testbed for the study of collective behavior in robotics.
Degrees

PhDEngineering, Harvard University, 1977
I teach courses in the Department of Electrical and Computer Engineering with emphasis on control (nonlinear, optimal, adaptive, stochastic, and special topics  games, collectives etc.). For recent examples, available online, see:
Modeling, design, motion planning and control arising in mobile robotics; geometric methods in nonlinear dynamics; timefrequency analysis of acoustic signals and systems; intelligent control architectures, in part inspired by biological paradigms; technology and theory of smart materials for use in actuation and sensing; problems of high resolution optical wave front control; problems of integration of actuators and sensors in control networks; new types of particle filters for approximate solutions to nonlinear filtering problems; sensing and control in nature; control of collectives (communicating, networked, control systems)
I use methods from differential geometry, physics, and symmetry principles to investigate collective behavior. I work with data obtained by my collaborators through observations of large flocks of European starlings. I have collaborated on the study of flight behavior of echolocating bats. I work on associated inverse problems such as trajectory reconstruction and the extraction of sensorimotor feedback laws from behavioral data (flight, prey capture, flocking). I am also interested in the use of techniques from evolutionary game theory for modeling biological data.
In robotics, I study ways to employ marker based motion capture and tracking to evaluate feedback control strategies for robot collectives in the laboratory.