Alejandro Ribeiro
Electrical and Systems Engineering
University of Pennsylvania
Email: aribeiro@seas.upenn.edu
Webpage: https://alelab.seas.upenn.edu/Alejandro-Ribeiro/
Blog: https://alelab.seas.upenn.edu/blog/
Group Members
Nacho Boero
Sourajit Das is currently a Ph.D. candidate in Electrical and Systems Engineering at the University of Pennsylvania, where he works under the mentorship of Prof. Alejandro Ribeiro. His research focuses on the intersection of graph neural networks, distributed optimization, and multi-agent systems, with applications to intelligent communication networks and real-time autonomous systems. As the lead researcher on the Intel Science and Technology Center (ISTC) project on Wireless Autonomous Systems, he worked on implementing joint design of control and communication for autonomous vehicles. He also contributed to the ARL-sponsored Distributed and Collaborative Intelligent Systems (DCIST) program, where he worked on mobile infrastructure on demand for autonomous aerial vehicles. In addition to current research, he has prior experience in nanofabrication as a Graduate Fellow at the Singh Center for Nanotechnology, where he designed and characterized Organic Thin Film Transistors and Solar Cells. Beyond research, he has held teaching assistantships in the courses on Graph Neural Networks, IoT Edge Computing, Information Theory and Signal Processing, and enjoys mentoring students in both coursework and research.
The name of his favorite dissertation paper is “Learning State-Augmented Policies for Information Routing in Communication Networks.”
Romina Garcia Camargo is a graduate student advised by Dr. Alejandro Ribeiro at the University of Pennsylvania. Her interests are the areas of wireless systems and signal processing. She has also participated in various projects involving robot swarms.
She completed her undergraduate studies at Universidad de la República, in Montevideo, Uruguay. During the last three years of her degree, she was fortunate to work at her school’s Institute for Electrical Engineering. She worked as an assistant teacher for several courses within the topics of data networks and wireless communications. She also participated in various research projects as part of Grupo ARTES.
Ignacio Hounie is a fourth year PhD student advised by Alejandro Ribeiro, broadly interested in machine learning, signal processing and optimization. The focus of his current research is on adapting pre-trained generative models under constraints. Previously he worked on constrained learning tackling problems ranging from time series forecasting, invariance and data augmentation to model quantization and federated learning.
He earned his BSc. in Electrical Engineering from Udelar in Montevideo, Uruguay, which is his hometown. During his time there, he worked on various ML applications including environmental sound monitoring, image restoration, and genome enabled prediction. he interned at Amazon in the Summer of 2024, where he worked on adapting large scale multi-modal classification systems under distribution shift.
You can reach him at ihounie@seas.upenn.edu.
Shervin Khalafi got his BSc in Electrical Engineering from Sharif University of Technology in Tehran, Iran. Currently he is a third-year PhD student at the University of Pennsylvania where he is advised by Prof. Alejandro Ribeiro. His research interests include Generative Models, Optimization Theory, and graph-based Machine Learning. He is developing constrained optimization frameworks for training generative models (diffusion models in particular) under requirements. He is also studying generative diffusion models for Graphs.
You can reach him at shervink@seas.upenn.edu.
Beiming Li is currently a Ph.D. candidate in Electrical and Systems Engineering at the University of Pennsylvania, where he works under the mentorship of Prof. Alejandro Ribeiro and Prof. Vijay Kumar. Prior to UPenn, he earned his B.S. in Computer Engineering from the University of Michigan. His research centers around artificial intelligence and robotics, with a specific focus on designing distributed multi-agent systems with reinforcement learning.
You can reach him at beimingl@seas.upenn.edu.
Antonio Pariente
Javier Porras-Valenzuela is a second year PhD student currently researching constrained optimization, neural network unrolling and theoretical properties of transformers on graphs. Other research interests include reinforcement learning, and the intersection of learning with combinatorial optimization. Originally from Heredia, Costa Rica, he holds a BSc in Software Engineering from Universidad Nacional de Costa Rica (2015) and an MSc in Computer Science from Tecnológico de Costa Rica (2022). In a past life, he was an ML Engineer. Now, he enjoys chess, hikes and not being cold.
You can reach him at jporras@seas.upenn.edu.
Yigit Berkay Uslu is a third year PhD student, co-advised by Shirin Saeedi Bidokhti and Alejandro Ribeiro. His research interests broadly cover constrained learning and optimization in networked systems. He is currently researching generative diffusion models for stochastic optimization in wireless networks and graph signal generation. He received his B.S. in Electrical and Electronics Engineering and B.A. in Mathematics from Bogazici University, Istanbul, Turkiye in 2022.
Frederic Vatnsdal’s aim is to create swarm systems. He splits his time between learning control policies for swarm navigation and establishing the tooling necessary to deploy these policies in the real world. Before joining Alelab, he was a vehicle engineer at Nvidia. He obtained his BASc in Electrical Engineering from the University of Waterloo. Hobbies include climbing and woodworking.
You can reach him at vatnsdal@seas.upenn.edu.
Visiting Students
Claudio Battiloro is a postdoctoral fellow at the Harvard T.H. Chan School of Public Health in the NSAPH group supervised by Prof. Francesca Dominici. He is a former Visiting Associate at the University of Pennsylvania School of Engineering and Applied Science in the AleLab group supervised by Prof. Alejandro Ribeiro. He received a M.Sc. cum laude (and recognized as a top 1.5
His favorite dissertation paper is “Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back.”
Sergio Rozada holds a Ph.D. in Signal Theory and Communications from King Juan Carlos University, during which he completed a research stay at the University of Pennsylvania. He also holds a Master’s degree in Data Science from City, University of London, and a Bachelor’s degree in Electronic and Automation Engineering from the University of Oviedo. His research interests lie in dynamical systems, sequential optimization, and reinforcement learning, with a broader focus on decision-making problems. In addition to his academic work, Sergio has extensive experience developing data-based products in industry.
His PhD dissertation was on “Low-Rank Methods in Reinforcement Learning,” and his favorite paper is “Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning.”