Lectures
This course is running at Penn during Fall of 2022. This course is structured around recorded video lectures that students are expected to watch on Mondays followed by discussion sessions on Wednesdays and Fridays. Most video lectures last between 40 and 60 minutes, although some are a little longer. Follow this link to access lecture recordings and materials.
If you haven’t already realized this, please be aware that learning from a video is tough. This is why we have the discussion sessions but I also want you to make the most of the recorded lectures. To that end, you will see that individual lectures have been separated in modules that last between 3 and 10 minutes and that modules are aggregated in a playlist. This is intended for you to look at them separately or all together. Most people find that watching videos in short chunks is best.
Whatever your watching preferences, be aware that it is very easy to watch a video and not get anything out of it. To help you with that, in addition to recordings, you will see that each lecture comes with a handout and a script. If you put together the handout and the script, that’s the lecture for you. My idea is that you will be able to download the handout and the script and have them by your side as you watch the lecture. I am a big believer in associating space and concepts. I am hoping that the printed handout and script help you with that
Interactive Meetings
We meet on Wednesdays and Fridays at 8:30 am and 10:15 am Eastern Time. Meetings are dual mode in that you can attend live or remotely. If you choose to attend live, the meetings are in the active learning classroom in the 4th floor of Walnut 3401. If you choose to attend remotely, please access the ESE5140 zoom room. To access these meetings you need to have a password that has been provided by your instructors. These meetings are reserved for interactions. Come prepared to talk. Be ready to ask questions. Be ready to answer questions.
Please reserve 90 minutes of your time for these meetings. That is, reserve Wednesdays and Fridays from 8:30 am to 10 am or 10:15 am to 11:45 am Eastern Time, depending on your section. However, we will try to keep the discussion sessions to 60-70 minutes instead of 90.
On the same Monday when we start work on a set of lectures, we also start work on a set of questions associated with each lecture. You will be randomly assigned to groups of size 3-4 each, which will be announced at the beginning of the semester. Each group is required to prepare 1-3 slide(s) with their response to their assigned questions (group<->question assignments to be announced before the beginning of each week), as well as a speech lasting about 5 minutes. During the interactive sessions, you will share the slide(s) and speech with the rest of the class, and the instructors will provide feedback and open the floor for discussion. Half of the questions will be discussed on Wednesdays, and the other half will be discussed on Fridays. You can prepare your response using whatever software you choose. If you are a latex person, you can download a Penn-branded template.
The responses you will give during the interactive session are zero-stakes. Do your best and don’t stress. You are free to remember that I failed Programming 101 and Electronics 101 when I was in college.
Lab Assignments
On the Mondays of Weeks 2, 4, 6, 8, and 11, we will release Lab assignments. These assignments will be due on the Fridays of Weeks 3, 5, 7, 10, and 12. This means you have two work weeks to complete each assignment except for the one we will release on Week 8, which you will have to complete in 3 weeks. The grades for each lab will be announced on the Monday following the lab due date on Friday (i.e., on the same day the next lab is announced). You will then have two days (until the end of Wednesday) to submit corrections if some of your answers are incorrect. If you miss the original due date on Friday, you may make a late submission until the following Wednesday and receive partial credit, without a chance for resubmission. More details on the grading criteria can be found below.
You will work in groups of three. We will send information about pairings, and you will keep these pairings throughout the term. The labs are designed to be worked on your own. The purpose of having teams is to give you a sounding board. If you are stuck on a question, chances are your partners are not. If the three of you are stuck on a questions, it may be a good idea to submit a question to the discussion board for the TAs to answer.
Each of your lab groups is paired with an individual TA. This is your main point of reference when you need help. The TAs are also going to arrange two two-hour blocks per week where you can meet with them. Take advantage of these meetings. You don’t know your TAs yet, but I do. I can vouch that they are knowledgeable and hard working. They are also good people and will help you out as much as you need.
When you complete the assignment, you will have to submit a report for the group by having one group member submitting the report to Canvas. The labs will contain very concrete instructions on how to write the reports. I am interpreting the lab reports under the language of the Paperwork Reduction Act. They are intended for you to complete in less than one hour after you finish your work. No need for florid language, showing work, or code submission. For each question we will ask for a short deliverable. The goal is simply to check that your answers are correct. If you answers are note correct, we will get back to you to offer you a chance for corrections. As long as you implement these corrections, you will get full marks for the assignment. There will be a Canvas page for submitting responses.
The labs are designed to illustrate points I will be making in lectures. For example, Lab 1 will be about understanding the importance of choosing appropriate parametrizations when we are we solve a problem using learning techniques. I am telling you this because labs also have a heavy development component. You will have to write some code that is not difficult but not easy either. I don’t want you to loose focus on the fact that the objective of the lab is not to get the code working. Rather, the focus of the lab is to understand why your code works.
The labs will use the Python programming language, the PyTorch library for machine learning, and the Alelab Graph Neural Network library. In Lab 3 we are going to explain how to use the Alelab GNN library. However, we will assume from the start that you are familiar with Python and that you have succeeded at installing Pytorch. You don’t need to be familiar with Pytorch. We will go over the use of this library in Labs 1 and 2. For those of you that are not familiar with Python, and need to install Python and Pytorch, please refer to this guide that we have put together.
Discussion Forum
We are running a discussion Forum on Ed Discussion. Please be engaged. Submit questions. Answer questions. Offer comments. Offer help
Teaching Assistants
Juan Elenter (elenter@seas.upenn.edu) I’m a second year PhD. student at the University of Pennsylvania, where I mostly think about optimization theory, signal processing and machine learning with Prof. Ribeiro. Before graduate school, I spent some time at Stanford’s Kundaje Lab, working on chromatin accessibility prediction and genomic motif discovery. I was also a summer intern at CERN, contributing to the CMS Open Data initiative with Dr. Lassila-Perini. I’m originally from Montevideo, Uruguay, where I obtained a BSc. in Electrical Engineering from UdelaR. During my undergrad I was a software developer at IBM.
Ignacio Hounie (ihounie@seas.upenn.edu) is a second year PhD. student at the University of Pennsylvania, advised by Prof. Alejandro Ribeiro. He is broadly interested in machine learning, statistical signal processing and optimization. He is originally from Montevideo, Uruguay, where he received his BSc. in Electrical Engineering from UdelaR in 2020.
Sourajit Das (dassour@seas.upenn.edu) is one of the TAs for this course. Currently he’s a third year PhD student at Penn. Prior to his PhD, he completed his MSE in Electrical Engineering from Penn where he worked as a Graduate Research Assistant with Prof. Ribeiro and also as a Graduate Student Fellow in the Singh Centre of Nanotechnology. His research interests focus on the areas of signal processing, wireless communication, embedded systems, robotics and machine learning. He is currently working on robust routing in telecommunication networks using graph neural networks and joint design of control & communication in multiagent systems. You can reach out to him at dassour@seas.upenn.edu for any questions or research collaborations.
Raghu Arghal (rarghal@seas.upenn.edu) is a Ph.D. student in Electrical and Systems Engineering at the University of Pennsylvania. He received his Bachelor’s and Master’s degrees in 2019 and 2020 from the University of Michigan in Electrical and Computer Engineering with a concentration in signal and image processing and machine learning. His research interests include network science, optimal control, and game theory with a focus on information dynamics and decisions in networks.
Damian Owerko (owerko@seas.upenn.edu) is a 3rd year PhD student in ESE. He completed his bachelor’s degrees in System Engineering and Physics at the University of Pennsylvania. His research foci include machine leaning for distributed systems, graph/convolutional neural networks and constrained deep learning. He is a casual Formula 1 fan and loves water sports — both liquid and frozen.
Grading
There are 5 labs in this course, and each of them counts for 20 points. If all of the answers you submit are correct, you get 20 points for the lab. If some of your answers are incorrect, you get a chance to submit corrections. After submitting your corrections, we will grade your resubmission and give you a maximum of 18 points for the lab. In case you skip the main due date and make a late submission (by the end of the following Wednesday), we will grade your late submission and give you a maximum of 15 points. No resubmissions are allowed in this case.
This grading rubric is designed so that if you submit all of your assignments on time and also submit the corrections, you get an A- for the course. You will get an A for the course if at least 3 of your original reports are correct on the first submission. Your grade could go downhill quite quickly if you mostly submit your assignments late and/or do not submit the assignments at all.
Some of you are not getting credit for this course. We are inspired by your willingness to learn, but please keep us inspired. We just ask that you take the class as seriously as students that are officially registered. That is, watch the videos, show up to the interactive sessions, and complete the assignments.
Lecturers
Navid NaderiAlizadeh (nnaderi@seas.upenn.edu) is a postdoctoral scholar in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.S. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 2011, his M.S. degree in electrical and computer engineering from Cornell University, Ithaca, New York, in 2014, and his Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles, in 2016. Prior to UPenn, he spent more than four years as a research scientist at Intel Labs and HRL Laboratories. His research interests include various areas of machine learning, including multi-agent reinforcement learning, graph representation learning, and self-supervised learning, and the applications of information theory and machine learning for resource allocation in wireless networks. Navid co-organized a special session at IEEE SPAWC 2021 on “The Interplay between Machine Learning and Resource Management in Wireless Networks.” He has also served as an Associate Editor for the IEEE JSAC Series on Machine Learning in Communications and Networks.
Charilaos I. Kanatsoulis (kanac@seas.upenn.edu) is a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his Diploma (B.S. and M.E. degree) in electrical and computer engineering from the National Technical University of Athens, Greece, in 2014, and his Ph.D. degree in electrical and computer engineering from the University of Minnesota, Twin Cities, in 2020. His research interests lie in the intersection between machine learning and signal processing and include convolutional and graph neural network design, tensor analysis, representation learning and explainable artificial intelligence.
Alejandro Parada-Mayorga (alejopm@seas.upenn.edu) is a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.S. (5 years diploma) and a Master degree in electrical engineering from Industrial University of Santander (UIS), Colombia, in 2009 and 2011, respectively. During 2012 he was a lecturer at the Department of Electrical Engineering at UIS. In 2019 he received his Ph.D degree in electrical engineering from the University of Delaware (UD), Newark, DE. His research interests lie on the mathematical foundations of information processing and learning. In particular, he focuses on exploiting structure from data in principled ways borrowing tools from representation theory of algebras, graph limit theory, graph theory, algebraic geometry and category theory.
Course Creator
Alejandro Ribeiro (aribeiro@seas.upenn.edu) : I am an older version of the guy on the left and a Professor of Electrical and Systems Engineering (ESE) at the University of Pennsylvania (Penn). I was born in Montevideo, Uruguay where I studied and got an Electrical Engineering degree from the Universidad de la República in 1999. I worked as a systems engineer for a cellphone company until 2003 when I moved to the United States to study at the University of Minnesota (UoM) in Minneapolis. I received M. Sc. and Ph. D. degrees from the UoM in 2005 and 2008, respectively.
In case you need either, you can access my professional bio in this link and my academic cv in this link. These two documents are prepared by the serious person I am forced to impersonate every so often. To get a sense of my research and teaching activities please keep reading. For a more comprehensive discussion visit my lab’s website which has descriptions of my research vision and provides access to the sites for the courses I teach. I am hoping that you will find my lab’s blog to be illuminating and entertaining but if you are hard core, my list of papers is here.
I have had and continue to have the honor of working with remarkable doctoral students. Their papers have received a number of awards which I am happy to claim as my own. These awards are the 2020 Signal Processing Society young author pest paper award, the 2014 O. Hugo Schuck best paper award and paper awards at the 2020 International Conference on Acoustics, Speech and Signal Processing, the 2019 European Signal Processing Conference, the 2017 Conference on Decision and Control, the 2016 Workshop on Statistical Signal Processing, the 2016 Sensor Array, and Multichannel Signal Processing Workshop, the 2015 Asilomar Conference on Signals Systems and computers, the 2013 American Control Conference, and the 2005 and 2006 International Conferences on Acoustics, Speech, and Signal Processing. I also have had and continue to have the honor of working with remarkable undergraduate students. I am very proud of their recognition in the form of the 2012 S. Reid Warren, Jr. Award presented by the undergraduate student body of Penn Engineering for outstanding teaching as well as the recognition of my colleagues with the Penn-wide 2017 Lindback award for distinguished teaching. I have also been selected as a Fulbright Scholar class of 2003 and a Penn Fellow class of 2015.