PROGRAM in ROBOTICS AND INTELLIGENT SYSTEMS

Princeton University

The Certificate Program in Robotics and Intelligent Systems is designed for Princeton undergraduate students who are interested in pursuing careers or graduate education in three general areas:

The analysis, design, and development of systems that automate manufacturing, transportation, health care, environmental stewardship, scientific research, and other activities;
The creation of systems for learning, adaptation, decision-making, identification, estimation, and control using concepts drawn from cognitive and biological sciences.
The understanding of human intelligence from the perspective of neuroscience and computation.

New industries and organizations depend increasingly on the interplay between engineering, computing, and the life sciences. Innovations and inventions require multi-disciplinary approaches and entrepreneurship, as well as grounding in theory and practice, in topics that may not be covered by a single department. The program offers an integrated set of core and elective courses, introducing students to fundamental concepts, providing depth in specific fields of interest, and setting the stage for further achievement. Students are encouraged to expand their experience through summer internships with companies, government agencies, and university laboratories.

Admission to the Program

The program is open to juniors and seniors at Princeton University who have a satisfactory background in mathematics, science, and computing. Students should have successfully completed:

Mathematics through MAT 202 or 204,
The AB Science and Technology Distributional Requirement or the BSE Freshman Science Requirement,
and COS 126, ORF 201, or an equivalent computing course.

A student planning to earn the program certificate should complete the Student Profile as early as possible and no later than the mid-point of the fall term of his or her senior year.

Program of Study

A concentrator in this program must satisfy both program and departmental requirements. The program for each student is worked out by the student and his or her departmental adviser. The program requirements are as follows:

1. All students must take six courses, including three core courses and three electives. To qualify for the certificate, a minimum grade average of B- in the six program courses is required. In some cases, a course can fulfill both a certificate program requirement and a regular departmental requirement (contact program director for details).

Core Courses (one from each group)

Laboratory (1)

ELE 201 Introduction to Electrical Signals and Systems
ELE 203 Electronic Circuits
ELE 206/COS 306 Introduction to Logic Design
MAE 224 Engineering Sciences Laboratory
PHY 210 Experimental Physics Seminar

Control Systems (1)

CHE 445 Process Control
ELE 483 Feedback Systems
MAE 345 Robotics and Intelligent Systems
MAE 433 Automatic Control Systems
MAE 434 Modern Control

Cognition, Language, and Decision-Making (1)

PSY 255 Cognitive Psychology
PSY 258 Introduction to Neuroscience: Brain and Behavior
PSY 259 Introduction to Cognitive Neuroscience
PSY 321/WWS 312 The Psychology of Decision Making and Judgment
PSY 322 Human-Machine Interaction
PSY 330 Introduction to Connectionist Models

Elective Courses (maximum of two from the same department to satisfy the requirement)

CEE 362 Structural Dynamics and Earthquake Engineering
CEE 460 Risk Assessment and Management
CEE 472 Hydrometeorology and Remote Sensing

CHE 442 Design, Synthesis, and Optimization of Chemical Processes
CHE 448 Introduction to Nonlinear Dynamics

COS 217 Introduction to Programming Systems
COS 323 Computing for the Physical and Social Sciences
COS 325 Transforming Reality By Computer
COS 340 Reasoning about Computation
COS 402 Artificial Intelligence
COS 426 Computer Graphics
COS 429 Computer Vision
COS 436 Human-Computer Interface Technology
COS 451 Computational Geometry
COS 461 Computer Networks
COS 487 Theory of Computation

ELE 211 Digital Systems and Microprocessors
ELE 302 System Design and Analysis
ELE 382 Distributed Algorithms and Optimization Methods for Engineering Applications
ELE 488 Image Processing and Transmission

LIN 280 Computational Linguistics
LIN 301 Phonetics & Phonology
LIN 303 Linguistic Semantics (also PHI 345)
LIN 306 The Structure & Meaning of Words
LIN 360 Linguistic Universals and Language Diversity
LIN 412 Advanced Syntax

MAE 206 Introduction to Engineering Dynamics
MAE 321 Engineering Design
MAE 322 Mechanical Design
MAE 331 Aircraft Flight Dynamics
MAE 332 Aircraft Design
MAE 341 Space Flight
MAE 342 Space System Design
MAE 412 Microprocessors for Measurement and Control

MOL 408 Cellular and Systems Neuroscience
MOL 437 Computational Neurobiology and Computing Networks

ORF 245 Fundamentals of Engineering Statistics
ORF 301 Elements of Interactive Computer Graphics
ORF 307 Optimization
ORF 309 Probability and Stochastic Systems
ORF 311 Optimization under Uncertainty
ORF 405 Regression and Applied Time Series
ORF 406 Statistical Design of Experiments
ORF 411 Operations and Information Engineering
ORF 467 Transportation

PHI/ELE 218 Learning Theory and Epistemology
PHI 312 Intermediate Logic
PHI 317 Philosophy of Language
PHI 322 Philosophy of the Cognitive Sciences
PHI 340 Philosophical Logic

PSY 306 Memory and Cognition
PSY 311 Rationality and Human Reason

2. A senior independent work project or thesis whose topic is relevant to the program and acceptable to the program committee must be completed and presented to the committee. A minimum grade of B- for the project or thesis is required to qualify for the certificate.

3. Close collaboration with faculty is expected. Program students are expected to demonstrate strong academic performance. Program courses may not be taken on a pass/D/fail basis unless that is the only grading alternative for the course.

4. Program students must fill out the Student Profile form at the beginning of each year in which they are members of the program. This is especially important during the senior year to assure that requirements for the certificate will be met by the end of the year.

Seminars on Robotics and Intelligent Systems

Seminars of interest to an undergraduate audience are announced to all interested students. Typical topic areas include:

Applications of Neural Networks
BioInformatics
Expert Systems in Engineering and Science
Computer Visualization
Neuroscience
Real-Time Systems
Robotic Devices and Vehicles
Micro-Mechanical Systems
Applications of Optimal Control

Advanced students are encouraged to attend regularly scheduled departmental seminars to further enrich their understanding of the field.

Typical Undergraduate Independent Research Projects

Undergraduate projects usually are undertaken for Independent Work or Senior Thesis credit, and opportunities exist for summer and work-study projects. These projects typically last for one or two academic terms, although they may extend over greater periods of time. Students work closely with faculty and staff members, and they have access to sophisticated computers and experimental facilities while conducting their independent research. Topics for past and future projects include the following:

Chemical Engineering

Optimal design of reactor systems
Robust control of chemical processes
Reaction networks to model immune response
Control based on microscopic/stochastic simulators

Civil and Environmental Engineering

Smart buildings
Active structural control
Control of water resources
Earthquake engineering
Remote sensing

Computer Science

The role of simplicity in reasoning by machines
Machine learning, including game playing, photo identification, and semantic identification
Using DNA for parallel computation

Electrical Engineering

Applications of neural networks and other classifiers to image signal processing problems, such as character recognition and image segmentation
Modeling and analysis of behavior of groups of animals (insects, birds, fish, etc.)
Modeling and analysis of complex control problems in materials processing applications
Algorithms for video analysis and manipulation
Analysis, simulation, and applications of algorithms for pattern recognition and machine learning

Mechanical and Aerospace Engineering

Autonomous sub-scale helicopter
Anthropomorphic robots
Autonomous undersea vehicles
Robots for space exploration and development
Neural network control of non-linear systems
Neural network analysis of DNA microarrays
Intelligent control systems
Biological Informatics: Modeling, Dynamics, and Control
System modeling and control of disease processes
Cooperating robots for manufacturing and assembly
Cooperative Control of Natural and Engineered Groups
Flight research with model aircraft
System Identification
Nonlinear Tracking Control
Computational Intelligence

Operations Research and Financial Engineering

Intelligent transportation systems
Financial management and risk analysis
Dynamic resource management
Optimal design
Signal and image processing

Philosophy

The role of simplicity in reasoning by machines
Machine learning
Default reasoning (or nonmonotonic reasoning) in artificial intelligence

Psychology

Inference, reasoning, problem solving
Neural network (connectionist) modeling of cognitive functions
In vivo imaging of brain function, using fMRI, EEG, and optical methods

Atypical Undergraduate Independent Research Project

The Princeton Autonomous Vehicle Engineering Team (PAVE) has developed autonomous vehicles for the DARPA Grand Challenge events and the Intelligent Ground Vehicle Competition. The PAVE team members are undergraduates drawn from several departments. Their lead faculty adviser is Alain Kornhauser, Professor of Operations Research and Financial Engineering and member of the Robotics and Intelligent Systems program committee.

CLICK HERE to see descriptions of THESIS AND INDEPENDENT WORK PROJECTS that have been suggested for the 2010-2011 academic year.

STUDENT PROFILE: Princeton University Students CLICK HERE to let us know of your interest in the Robotics and Intelligent Systems Program.

LINKS to RELATED SITES

Tools and Concepts

Libraries and Technical Report Servers

Simulation, Instrumentation, and Robot Kits

Programs, Organizations, and Contests

Robotics and Intelligent Systems Program Committee, 2010-2011

For more information, please contact one of the following faculty members:

Jonathan Cohen, Professor, Psychology, 258-2696; jdc at princeton.edu. Cognitive science, neuroscience and neural network modeling.
Bradley Dickinson, Professor, Electrical Engineering, 258-4644; bradley at princeton.edu. Systems theory, signal processing, neural networks, large-scale and distributed systems.
Gilbert Harman, Professor, Philosophy, 258-2823; harman at princeton.edu. Artificial intelligence, philosophy of cognitive science.
Yannis Kevrekidis, Professor, Chemical Engineering, 258-2818; igkevrek at princeton.edu. Nonlinear dynamical systems, chemical and biological processes.
Alain Kornhauser, Professor, Operations Research and Financial Engineering, 258-4657; alaink at soil.princeton.edu. Computer graphics, robotics, transportation system analysis.
Sanjeev Kulkarni, Professor, Electrical Engineering, 258-6727; kulkarni at princeton.edu. Machine learning, pattern recognition, statistical inference, image processing, information and communication theory.
Michael Littman, Professor, Mechanical and Aerospace Engineering, 258-5198; mlittman at princeton.edu. Human bi-pedal locomotion, parallel processing, distributed real-time control, rehabilitation engineering.
Stephen Lyon, Professor, Electrical Engineering, 258-3500; lyon at princeton.edu. Semiconductor devices.
Daniel Nosenchuck, Associate Professor, Mechanical and Aerospace Engineering, 258-5136; dan at princeton.edu. Mechanical design.
Daniel Osherson, Professor, Psychology, 258-1113; osherson at princeton.edu. Inductive logic, scalable algorithms, brains, machines, and minds.
Peter Ramadge, Professor, Electrical Engineering, 258-4645; ramadge at princeton.edu. System theory, discrete systems, adaptive and stochastic systems, control theory.
Szymon Rusinkiewicz, Assistant Professor, Computer Science, 258-7479; smr at princeton.edu. Computer graphics and vision.
Robert Schapire, Professor, Computer Science, 258-7726; schapire at cs.princeton.edu. Machine learning, artificial intelligence.
Robert F. Stengel, Director. Professor, Mechanical and Aerospace Engineering, 258-5103; stengel at princeton.edu. Intelligent control systems, aerospace dynamics.
Erik H. VanMarcke, Professor, Civil and Environmental Engineering, 258-5896; evm at princeton.edu. Stochastic systems and random media; risk assessment and management.