University of Pennsylvania
Center for Teaching and Learning
CTL Teaching Certificate (May 2018). Advanced training for teaching on the college or university level.
Department of Electrical and Systems Engineering (2014-2020)
Signal and Information Processing (ESE224; Spring 2016 - 2020): Discrete Time Signal Processing. Continuous Time Signal Processing. Signal processing applications in Electrical Engineering. Image processing. Principal Component Analysis. Graph Signal Processing.
Introduction to Research Methodology (ESE290; Spring 2016, 2017, 2020): The objective of this course is to mentor undergraduate students (typically, juniors) on a research project, guiding them through the basics of carrying out research and present it to a broader audience.
Stochastic Processes (ESE303; Fall 2015, 2019): Discrete Time Markov Chains. Continuous Time Markov Chains. Poisson Processes. Gaussian, Markov and Stationary Random Processes.
University of Buenos Aires
School of Engineering, Department of Mathematics (2010 - 2014)
Probability and Statistics: Probability and probability Spaces. Random variables. Moments. Transformations. Conditional expectation and prediction. Bernoulli trials. Poisson Processes. Central Limit Theorem. Statistics. Parameter estimation. Interval estimation. Hypothesis testing.
School of Engineering, Department of Electronics (2012 - 2014)
Signal Processing I and II (2013–2014): Digital filter design. Filter banks. Wiener filtering. Linear prediction. LS estimation. Kalman filtering. LMS filtering. Adaptive filtering.
Stochastic Processes (2012–2013): Random vectors. Wide sense stationary processes. Random processes and linear systems. White noise. AR, MA, ARMA. Matched filter. Bayes decision theory. Linear estimation. Markov processes. Queue theory.
School of Engineering, Department of Physics (2009 - 2012)
Physics II: Introduction to Electromagnetism: From Coulomb’s Law to the four complete Maxwell equations. Dielectric and ferromagnetic materials. Heat transmission and Thermodynamics.