Bio Page and Teaching Information

Bio

Prof. José M. F. Moura is a member of the US National Academy of Engineering, he is the Philip L. and Marsha Dowd University Professor at CMU. He is IEEE 2016 Vice President for Technical Activities. He was elected University Professor at Carnegie Mellon University to recognize his professional achievement as well as his breadth of interests and competence. This title is conferred on faculty members with exceptional national or international distinction. He joined CMU in 1986 as a Professor of Electrical and Computer Engineering. Currently he holds also a courtesy appointment as Professor with the Department of BioMedical Engineering. He is the founder and Director of the Information and Communications Technology Institute (ICTI) and co-founder and co-director of the Center for Sensed Critical Infrastructures Research (CenSCIR). Prior to joining CMU, he was on the faculty at Instituto Superior Técnico (IST), the Engineering School of the Technical University of Lisbon (Portugal). He has had visiting faculty appointments at MIT: in 1984-86 as Genrad Associate Professor of Electrical and Computer Engineering (visiting) and in 1999-2000 and 2006-2007 as visiting Professor of Electrical Engineering. He was also a visiting Research Scholar at the University of Southern California in the Summers of 1979-1981. He received his D.Sc. in Electrical Engineering and Computer Science from MIT where he also received his MSc. in Electrical Engineering and the Electrical Engineering degree. He holds a Licenciatura em Engenharia Electrotécnica from IST.

By Fall 2015, he graduated 41 PhD students and 33 master students. These students have gone to better and greater things in their lives, pursuing careers in academia, government and industrial labs, small and large companies, and live now in many corners of the US and of the World. Over the years, many undergraduates have carried out projects and Honor Theses in his research group.

His research interests are in the areas of data and network science, statistical and algebraic signal and image processing and network science. Projects he has worked on include distributed detection in sensor networks, time reversal imaging, bioimaging, SMART, and SPIRAL. In statistical signal and image processsing application areas where he has worked include: distributed decision making and estimation in sensor networks, detection and imaging in highly cluttered environments using time reversal, detection and estimation theory in digital communications and high density recording, design of good performance low density parity check (LDPC) codes, image and video representations, and medical imaging. In algebraic signal processing, he is interested in the concept of representations with applications in the design of fast algorithms and their fast implementations, and of invariance in image analysis, e.g., statistical shape theory.

José Moura has been involved as a volunteer in a number of positions with professional societies like IEEE. He is the IEEE 2016 Vice President for Technical Activities. He was the IEEE Division IX Director Elect (2011) that groups seven IEEE Societies, to serve as IEEE Division IX Director in 2012-13. He currently serves on the IEEE Publications Services and Products Board (PSPB). He was the President of the IEEE Signal Processing Society (SPS) (2008-09) and is currently Past President and Chair of the Nominations and Appointments Committee of the same Society (2010-11). He was a member of the IEEE Educations Activities Board (EAB) (2010) where he chaired the Society Education Outreach Committeee. He was Vice-Chair of the IEEE Publications Services and Products Board (PSPB) (2008). Read his editorials in the IEEE Signal Processsing Magazine (Jan 2008 through Jan 2010) he wrote as President of the Society. He was editor in chief (EIC) for the IEEE Transactions on Signal Processing and acting EIC for the IEEE Signal Processing Letters. He was Vice-President Publications for the IEEE Signal Processing Society (SPS) and Vice-President Publications for the IEEE Sensors Council. He was on the Board of Governors of the IEEE SPS. He chaired the IEEE Technical Activities Board (TAB) Transactions Committee that joins the eighty+ EICs for all IEEE Transactions and Journals and was a member of the TAB Publications Committee. He served on the TAB Publications Review Committee, and on the Board of several journals, including the IEEE Proceedings, the IEEE Signal Processing Magazine, and the ACM Sensors Network Journal. He serves presently on the Steering Committee of the ACM/IEEE Information Processing in Sensor Networks Conference (IPSN) and served on the Steering Committee of the IEEE International Symposium on BioImaging (ISBI) and on the steering committee of the IEEE Transactions on Multimedia.

José M. F. Moura is a Fellow of the IEEE, a Fellow of AAAS, and a corresponding member of the Academia das Ciências de Lisboa (Portugal). He received the 2010 IEEE Signal Processing Society Technical Achievement Award for fundamental contributions to statistical signal processing. He was awarded by CMU the 2008 Philip L. Dowd Fellowship Award for Contributions to Engineering Education and with Prof. Püschel the 2007 CIT Outstanding Research Award. He received in 2006 an IBM Faculty Award. He was awarded the IEEE Third Millenium Medal and the 2003 IEEE Signal Processing Society Meritorious Service Award.

Resume(pdf 362 KBy)

Teaching

Professor Moura has taught courses in digital communications, statistical signal processing, and detection estimation theories. He introduced a new course titled Algebraic Signal Processing in the spring of 2004, .

• 18-202 Mathematical Foundations of Electrical Engineering (Fall 2007, Fall 2008)
• 18-396 Signals and Systems (Spring 2006)
• 18-799 Network Science: Modeling and inference (Spring 2011)
• 18-899 Algebraic Signal Processing (Spring 2004)
• 18751 Applied Stochastic Processes (Fall 2004)
• 18752 Detection Estimation and Identification (Spring 2005, 2008)

18-799 H Adv. Topics in Signal Processing: Network Science: Modeling and inference

(12 Units, 1st offering Spring 2011, not taught regularly, graduate course, satisfies ECE coverage requirements) Do you ever wonder how seeming successfully ants forage for rich sources of food, bees move a beehive to more suitable locations, flocks of birds fly in formation? How come a tree falling in Ohio causes fifty five million people in the Northeast of the US and Canada to loose their electrical power? Why do the actions of a few in an office in the financial district in London impact so signicantly the World financial markets? Why do critical infrastructures, e.g., cellular and mobile networks, fail in times of crisis, when they are most needed? How do botnets spread and compromise millions of computers in the internet? Can companies understand the viral behavior of their three million (did you say one hundred million) (mobile) customers? These and others are background and motivational examples that guide us in this course whose goal is the study of relatively dumb agents that sense, process, and cooperate locally but whose collective, coordinated activity leads to the emergence of complex behaviors. Among others, the course will develop basic tools to understand: i) the modeling of these highly networked, large scale structures (e.g., colonies of agents, networks of physical systems, cyber physical systems;) ii) how to predict the behavior of these networked systems; and iii) how to derive and study the properties (e.g., convergence and performance) of distributed algorithms for inference and data assimilation. The course will develop graph representations and introduce tools from spectral graph theory, will cover the basics from queueing theory, Markov point processes, and stochastic networks to predict behaviors under several types of stress conditions and asymptotic regimens, and will explore consensus algorithms and several classes of distributed inference algorithms operating under infrastructure failures (intermittent random sensor and channel failures,) different resource constraints (e.g., power or bandwidth,) or random protocols (e.g., gossip.) The course is essentially self-contained. There will be a mix of homeworks, midterm exams, and projects. Students will take an active role by exploring examples of applications and applying network science concepts to fully develop the analysis of their preferred applications.

18-899 Adv. Topics in Signal Processing: Algebraic Signal Processing

(12 Units, 1st offering Spring 2004, not taught regularly, graduate course, satisfies ECE coverage requirements) Traditionally,

Digital Signal Processing (DSP) is taught and learned through formulas and summations. Fast algorithms, for example, the FFT, are introduced through intricate index manipulations. The underlying structure is lost with so many fast algorithms dispersed in the literature for the various linear transforms; no wonder it is more of an art of ingenious tricks to develop new fast algorithms. DSP can be understood in a different light, if only we ventured past Linear Algebra and Vector Spaces. Yes, Linear Algebra provides a beautiful geometric picture. But, going beyond illuminates much of the structure underlying many of the transforms and their fast algorithms, and provides a powerful systematization of many apparently disparate algorithms. This course will introduce basic concepts in Algebra with a focus on groups, representations, modules, algebras, and related topics. Applications covered in the course will include linear transforms, particular emphasis on the DFT and trigonometric transforms, their fast algorithms, but include others as well, possibly, in Statistics, for example, from Kendall's Shape Theory. Students are encouraged to explore their own preferred topics or other directions. On transforms, students will experiment with their fast implementations using SPIRAL. The course will mix formal lectures with discussions of research papers. This is the first time ever this course is offered at CMU, and it is most likely also a first at most other places, so there is ample opportunity for adapting to the students particular interests. Prerequisites: good understanding of Linear Algebra.

18-202 Mathematical Foundations of Electrical Engineering

(12 Units, taught every semester, undergraduate, sophomore level core course. I taught Fall 07 and teach Fall 08)

Basic mathematical fundamentals of Electrical Engineering, focus on complex numbers and complex analysis, ordinary differential equations, linear algebra. Co-prerequisit for 18-220.

18-396 Signals and Systems

(12 Units, taught every semester, undergraduate, sophomore/ junior level breadth course. Last time I taught was Spring 2006)

Basic introduction to signal transforms, linear systems, time and frequency descriptions, discrete and continuous time, emphasis on discrete time. Fourier, Laplace, Z-transforms. Sampling theorem.

18-751 Applied Stochastic Processes

(12 Units, taught every Fall, next offering Fall 2004, graduate course, satisfies ECE coverage requirements) We introduce random processes and their applications. Throughout the course, we emphasize the discrete-time point of view, although we also discuss the continuous-time case from time to time. Topics covered include: basic concepts of random variables, random vectors, random sequences, stochastic processes, and random fields; expectation and conditional expectation, moments, and characteristic functions; most common classes of random sequences and processes including white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields; second order descriptions for random sequences and random processes: mean, auto and cross correlation, auto and crosscovariance; 2nd order Fourier analysis, energy and power spectral and crossspectral densities, and random processes and linear systems. We also present elements of estimation theory and optimal filtering including Wiener and Kalman-Bucy filtering. Advanced topics in modern statistical signal processing such as linear prediction, linear models and spectrum estimation are discussed. 4 hrs. lec. Prerequisites: 36-217 and 18-396 is required for undergraduates, or permission of the instructor.

18-752 Detection, Estimation, and Identification

(12 Units, taught everyother spring semester, next offering Spring 2005, graduate course, satisfies ECE coverage requirements) Basic graduate course in detection and estimation theory. Decision theory: Binary hypothesis testing, M-ary testing, Bayes, Neyman-Pearson, Min-Max. Performance. Probability of error, ROC. Estimation theory: linear and nonlinear estimation, parameter estimation. Bayes, MAP, maximum likelihood, Cramér-Rao bounds. Bias, efficiency, consistency. Asymptotic properties of estimators. Orthogonal decomposition of random processes and harmonic representation. Waveform detection and estimation. Wiener filtering and Kalman-Bucy filtering. Elements of identification. Recursive algorithms. Spectral estimation. Topics may vary. 4 hrs. lec. Prerequisite: 18-751.