René Vidal
· Rachleff and Penn Integrates Knowledge University Professor, Director of the Center for Innovation in Data Engineering and Science (IDEAS)University of Pennsylvania · Statistics and Data Science
Active 1988–2025
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About
René Vidal is the Rachleff University Professor at the University of Pennsylvania, with joint appointments in the Department of Radiology in the Perelman School of Medicine and the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science. He is recognized as a global pioneer of data science and has been named a Penn Integrates Knowledge University Professor. Dr. Vidal received his B.S. degree in Electrical Engineering with highest honors from the Pontificia Universidad Catolica de Chile in 1997, and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. His research areas include computer vision and perception, dynamical systems and control, and machine learning/AI and autonomous systems. He has held research positions at National ICT Australia and has been a faculty member at Johns Hopkins University in the Department of Biomedical Engineering and the Center for Imaging Science.
Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Data Mining
- Algorithm
- Neuroscience
- Mathematical analysis
- Mathematical optimization
- Applied mathematics
- Mathematics
Selected publications
Tutorial on Recommendation with Generative Models (Gen-RecSys)
2025-02-26 · 15 citations
articleThis intermediate-level tutorial, titled "Gen-RecSys", merges both industrial and academic perspectives on recent advances in Generative AI for recommender systems (beyond LLMs). It aims to highlight the transformative role of generative models in modern recommender systems, which have significantly impacted the AI field-particularly with the rise of large language models (LLMs) like ChatGPT-and have contributed to a rapid convergence of the fields of search, data mining, and recommendation. By…
Recommendation with Generative Models
arXiv (Cornell University) · 2024-09-18 · 5 citations
preprintOpen accessGenerative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures such as GPT. These models have applications across various domains, such as image generation, text synthesis, and music compo…
bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-22 · 3 citations
preprintOpen accessAbstract Background Autism spectrum disorder (ASD), a condition defined by deficits in social communication, restricted interests, and repetitive behaviors, is associated with early impairments in motor imitation that persist through childhood and into adulthood. Alterations in the mirror neuron system (MNS), crucial for interpreting and imitating actions, may underlie these ASD-associated differences in motor imitation. High-density diffuse optical tomography (HD-DOT) overcomes logistical chall…
The British Journal of Psychiatry · 2025-01-28 · 3 citations
articleOpen accessBackground Reliable and specific biomarkers that can distinguish autism spectrum disorders (ASDs) from commonly co-occurring attention-deficit/hyperactivity disorder (ADHD) are lacking, causing misses and delays in diagnosis, and reducing access to interventions and quality of life. Aims To examine whether an innovative, brief (1-min), videogame method called Computerised Assessment of Motor Imitation (CAMI), can identify ASD-specific imitation differences compared with neurotypical children and…
Imaging Neuroscience · 2025-01-01 · 3 citations
articleOpen accessAutism spectrum disorder (ASD), a condition defined by deficits in social communication, restricted interests, and repetitive behaviors, is associated with early impairments in motor imitation that persist through childhood and into adulthood. Alterations in the mirror neuron system (MNS), crucial for interpreting and imitating actions, may underlie these ASD-associated differences in motor imitation. High-density diffuse optical tomography (HD-DOT) overcomes logistical challenges of functional…
Recent grants
CRS--EHS: Collaborative Research: An Algebraic Geometric Approach to Hybrid Systems Identification
NSF · $200k · 2005–2008
NSF · $391k · 2013–2016
NSF · $493k · 2010–2012
Frequent coauthors
- 47 shared
Benjamin D. Haeffele
Johns Hopkins University
- 41 shared
Daniel P. Robinson
Lehigh University
- 37 shared
S. Shankar Sastry
- 30 shared
Manolis C. Tsakiris
University of Chinese Academy of Sciences
- 28 shared
Yi Ma
Shaoyang University
- 26 shared
Chong You
Universiti Tunku Abdul Rahman
- 24 shared
Roberto Tron
- 22 shared
Gregory D. Hager
Johns Hopkins University
Labs
Education
- 1998
Ph.D., Electrical and Computer Engineering
University of California, San Diego
- 1995
M.S., Electrical and Computer Engineering
University of California, San Diego
- 1993
B.S., Electrical and Computer Engineering
University of California, San Diego
Awards & honors
- Penn Integrates Knowledge University Professor
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