The Ph.D. program in Electrical and Systems Engineering (ESE) welcomes candidates with a strong background in science and engineering who are interested in pursuing an academic doctoral degree. The objective of this program is to help students develop skills needed to perform independent research and teaching in an exciting intellectual environment. Our students work with world-class faculty advisors and mentors, and our research laboratories provide ample opportunities for students to work with other students and faculty to develop cutting-edge theories and technologies in collaboration with other departments and schools within the University.
Penn ESE faculty drive progress in robotics, quantum engineering, and sustainable energy systems. Through pioneering research and personalized mentorship, they equip students to tackle complex challenges and thrive in roles that shape the future of technology and innovation.
Area of expertise: Networks and artificial intelligence
Students know me for: Hard work, excellence, and fun
I want to impact in: The emergence of intelligent autonomous networks and the education of my young fellow citizens
Area of expertise: Computer engineering
Students know me for: FPGAs
I want to impact in: How we design, realize, and secure computations
Area of expertise: Power electronics and circuits
Students know me for: Teaching a lab course in Detkin on how to build efficient power converters
I want to develop: Smaller and more efficient power solutions to enable new energy, medical, and robotic applications
Motivation, design, programming, optimization, and use of modern System-on-a-Chip (SoC) architectures. Hands-on coverage of the breadth of computer engineering within the context of SoC platforms from gates to application software, including on-chip memories and communication networks, I/O interfacing, RTL design of accelerators, processors, concurrency, firmware and OS/infrastructure software. Formulating parallel decompositions, hardware and software solutions, hardware/software trade offs, and hardware/software codesign. Attention to real-time requirements.
This rapidly moving course provides a rigorous development of fundamental ideas in probability theory and random processes. The course is suitable for students seeking a rigorous graduate level exposure to probabilistic ideas and principles with applications in diverse settings. The topics covered are drawn from: abstract probability spaces; combinatorial probabilities; conditional probability; Bayes’s rule and the theorem of total probability; independence; connections with the theory of numbers, Borel’s normal law; rare events, Poisson laws, and the Lovasz local lemma; arithmetic and lattice distributions arising from the Bernoulli scheme; limit laws and characterizations of the binomial and Poisson distributions; continuous distributions in one and more dimensions; the uniform, exponential, normal, and related distributions; random variables, distribution functions; orthogonal and stationary random processes; the Gaussian process, Brownian motion; random number generation and statistical tests of randomness; mathematical expectation and the Lebesgue theory; expectations of functions, moments, convolutions; operator methods and distributional convergence, the central limit theorem, selection principles; conditional expectation; tail inequalities, concentration convergence in probability and almost surely, the law of large numbers, the law of the iterated logarithm; Poisson approximation, Janson’s inequality, the Stein- Chen method; moment generating functions, renewal theory; characteristic functions.
This course reviews electrostatics, magnetostatics, electric and magnetic materials, induction, Maxwell’s equations, potentials and boundary-value problems. Topics selected from the areas of wave propagation, wave guidance, antennas, and diffraction will be explored with the goal of equipping students to read current research literature in electromagnetics, microwaves, and optics.
