Inaugural Lectures - Prof. Tanja Käser and Prof. Amir Zamir
Event details
| Date | 23.04.2024 |
| Hour | 18:00 › 19:30 |
| Speaker | Prof. Tanja Käser, Prof. Amir Zamir |
| Location | |
| Category | Inaugural lectures - Honorary Lecture |
| Event Language | English |
Date: Tuesday 23 April 2024
Program:
Registration: Click here
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Prof. Tanja Käser
Generalizable and Interpretable Models of Human Learning
Abstract
Technology empowered by artificial intelligence has the potential to transform education by providing scalable and automated personalized tutoring to students and, at the same time, support teachers in classroom orchestration. However, current methods are limited: they are either defined for specific learning domains or lack interpretability and a foundation in learning theory. My group works on modeling human behavior and learning, with the goal to create models that are generalizable and explainable. In this talk, I will first discuss the key challenges in machine learning for education. I will then discuss recent results from our lab, highlighting use cases spanning a diverse range of applications and complex data sets.
About the speaker
Tanja Käser is a tenure-track assistant professor in computer science at EPFL, heading the Machine Learning for Education Lab. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning. Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich and a postdoctoral researcher at the Graduate School of Education at Stanford University. Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award in 2015.
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Prof. Amir Zamir
Multimodality and Embodiment in Vision
Abstract
The remarkable progress in Computer Vision and Machine Learning now enables us to automatically detect the objects in images, caption them, or estimate the 3D structure. But are we close to sophisticated visual capabilities, such as those that even simple biological organisms exhibit? I will discuss two related directions as steps toward that goal: multimodality and embodiment.
About the speaker
Amir Zamir is an Assistant Professor of computer science at EPFL. His research is in computer vision, machine learning, and perception-for-robotics. Before joining EPFL in 2020, he was with UC Berkeley, Stanford, and UCF. He has received paper awards at SIGGRAPH 2022, CVPR 2020, CVPR 2018, CVPR 2016, and the NVIDIA Pioneering Research Award 2018, PAMI Everingham Prize 2022, and ECCV/ECVA Young Researcher Award 2022. His research has been covered by press outlets, such as The New York Times and Forbes. He was the computer vision and machine learning chief scientist of Aurora Solar, a Forbes AI 50 company, from 2015 to 2022.
Publications, project pages, code: amirzamir.com
Program:
- 18:00-18:05: Introduction by Prof. Rüdiger Urbanke, Dean of the IC School
- 18:05-18:35: Inaugural Lecture Prof. Tanja Käser
- 18:35-18:45: Q & A
- 18:45-18:50: Introduction by Prof. Rüdiger Urbanke, Dean of the IC School
- 18:50-19:20: Inaugural Lecture Prof. Amir Zamir
- 19:20-19:30: Q & A
- 19:30-21:00: Apéritif in the FoodLab Alpine restaurant
Registration: Click here
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Prof. Tanja Käser
Generalizable and Interpretable Models of Human Learning
Abstract
Technology empowered by artificial intelligence has the potential to transform education by providing scalable and automated personalized tutoring to students and, at the same time, support teachers in classroom orchestration. However, current methods are limited: they are either defined for specific learning domains or lack interpretability and a foundation in learning theory. My group works on modeling human behavior and learning, with the goal to create models that are generalizable and explainable. In this talk, I will first discuss the key challenges in machine learning for education. I will then discuss recent results from our lab, highlighting use cases spanning a diverse range of applications and complex data sets.
About the speaker
Tanja Käser is a tenure-track assistant professor in computer science at EPFL, heading the Machine Learning for Education Lab. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning. Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich and a postdoctoral researcher at the Graduate School of Education at Stanford University. Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award in 2015.
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Prof. Amir Zamir
Multimodality and Embodiment in Vision
Abstract
The remarkable progress in Computer Vision and Machine Learning now enables us to automatically detect the objects in images, caption them, or estimate the 3D structure. But are we close to sophisticated visual capabilities, such as those that even simple biological organisms exhibit? I will discuss two related directions as steps toward that goal: multimodality and embodiment.
About the speaker
Amir Zamir is an Assistant Professor of computer science at EPFL. His research is in computer vision, machine learning, and perception-for-robotics. Before joining EPFL in 2020, he was with UC Berkeley, Stanford, and UCF. He has received paper awards at SIGGRAPH 2022, CVPR 2020, CVPR 2018, CVPR 2016, and the NVIDIA Pioneering Research Award 2018, PAMI Everingham Prize 2022, and ECCV/ECVA Young Researcher Award 2022. His research has been covered by press outlets, such as The New York Times and Forbes. He was the computer vision and machine learning chief scientist of Aurora Solar, a Forbes AI 50 company, from 2015 to 2022.
Publications, project pages, code: amirzamir.com
Practical information
- Informed public
- Registration required