Master in Electrical and Computer Engineering
Develop & Apply Your Expertise
In most traditional engineering degrees, there is delayed satisfaction. First, you have to learn all the mathematics and then you’ll see the applications at the end. In the ECE program, for every advanced, sophisticated analytical tool we cover, we also cover highly relevant industrial applications almost at the same time. That way, you immediately see the relevance of what you’re learning.”
Engineering in 360°
Learn state-of-the-art topics in signal processing, data science, machine learning, and feedback control/reinforcement learning algorithms—as well as the most advanced mathematical methods to improve them.
Physical Computing Courses
Prototype working hardware components via lab work in embedded systems, fabrication, robotics, computer vision, laser cutting, and 3D printing.
Practice entrepreneurship, product design, startup management and other skills in cross-disciplinary teams with MBA, law, and engineering students in this required component of all Cornell Tech programs.
Computational Modeling Topics Covered
Adaptive Control & Reinforcement Learning
Applied Machine Learning
Big Data & Social Media
High Dimensional Signal Processing
Markov Decision Processes
Natural Language Processing (NLP)
Physical Computing Topics Covered
Autonomous Systems & Robotics
Human-Computer Interaction (HCI)
User-Centered Design and Prototyping
Studio Topics Covered
Challenges of Entrepreneurship
Global Leadership & Multicultural Awareness
Law for Non-Lawyers
Leadership for Digital Transformation
Marketing, Sales & Distribution
Startup Funding & Pitching
Signal and Data Processing
Vikram Krishnamurthy is a Professor at Cornell Tech and in the Electrical and Computer Engineering Department at Cornell University. Professor Krishnamurthy’s group works on statistical signal processing and controlled-sensing problems. The fundamental ideas revolve around Bayesian inference, stochastic optimization and game theory. The applications of the research are in three areas. The first application area is in smart adaptive radar tracking systems where the radar system can adapt its behavior in real time using feedback control, and where natural language processing models are used to determine anomalies in target trajectories. The second application area is in understanding how social sensors (human decision makers) interact and influence each other over a social network. This involves related ideas in behavioral economics and revealed preferences, information fusion and is backed up by real-world data from YouTube and psychometric experiments. The final application area is in modeling and controlling the dynamics of artificial cell membranes and nano-scale molecular machines/sensors built out of such membranes.
Daniel D. Lee
Tisch University Professor of Electrical and Computer Engineering
Daniel Dongyuel Lee is a Professor at Cornell Tech and in the Electrical and Computer Engineering Department at Cornell University. Until 2018, he was the UPS Foundation Chair Professor in the School of Engineering and Applied Science at the University of Pennsylvania. He received his A.B. summa cum laude in Physics from Harvard University and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology in 1995. Before coming to Penn, he was a researcher at AT&T and Lucent Bell Laboratories in the Theoretical Physics and Biological Computation departments. He is a Fellow of the IEEE and AAAI and has received the National Science Foundation CAREER award and the Lindback award for distinguished teaching. He was also a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, an affiliate of the Korea Advanced Institute of Science and Technology, and organized the US-Japan National Academy of Engineering Frontiers of Engineering Symposium and Neural Information Processing Systems (NIPS) conference. His group focuses on understanding general computational principles in biological systems and on applying that knowledge to build autonomous systems.
C. Richard Johnson
Professor, Fellow in Computational Arts and Humanities
C. Richard Johnson Jr. is the Fellow in Computational Arts and Humanities at the Jacobs Technion-Cornell Institute, as well as the Geoffrey S. M. Hedrick Senior Professor of Engineering, Electrical and Computer Engineering, Cornell University. He received a Ph.D. in Electrical Engineering from Stanford University in 1977 along with a PhD minor in Art History. He has been on the Cornell University faculty since 1981 and joined Cornell Tech in 2016.
Biosensors for Disease Detection
Vikram Krishnamurthy collaborated with Surgical Diagnostics to build and model devices out of artificial cell membranes. The ion channel biosensor, for example, is a fully functioning nanomachine that can detect ultra-low concentrations of target molecules such as HIV, Influenza viruses, and toxins.
Data & Modeling Research
The Data & Modeling Research Group at Cornell Tech includes faculty with backgrounds in computer science, electrical engineering, business, and operations research. Their research focuses on developing models for decision-making problems in a variety of areas including logistics, retail, marketing, defense, biotech, finance, and healthcare.
The Cornell Tech curriculum has been designed to address the needs of students seeking opportunities in exciting new areas such as AI, machine learning, and robotics.”
Who Should Apply?
We welcome applications who have a passion for applied technology–especially robotics and machine learning–and an entrepreneurial spirit. You should also have an academic background in electrical and computer engineering, mechanical engineering, computer science, physics, applied mathematics, or a related technical field. That background should include coursework in signals and systems, linear algebra, scientific programming, probability and statistics.
Unlike the Ithaca-based ECE Masters program, Cornell Tech does not offer a third-semester option for their ECE Masters students.
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