Ginkgo Biloba brain as a herbal medicine
Screen Shot 2019-11-11 at 11.32.06 AM.pn

What: Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions

Why: To reduce academia’s carbon footprint, accommodate the schedules of the world’s top scientists and maintain social distancing

Who: All are welcome!

Where: Zoom Webinar

Recordings of previous talks can be found here

Screen Shot 2022-11-16 at 12.45.00 PM.png
Weinan Sun, PhD - Research Scientist, Janelia Research Campus
When: December 9th, 2022, 10:00-11:00 am EST
Title: Longitudinal imaging of thousands of hippocampal neurons reveals emergence of internal models that parallel changes in behavioral strategy 

Abstract: The hippocampal formation is essential for an animal’s ability to navigate and forage effectively in complex environments. It contributes by forming structured representations of the environment, often called cognitive maps. While many experimental and theoretical aspects of learned hippocampal cognitive maps are well established, the exact learning trajectories for their formation and usage remain unknown. We performed large-scale 2-photon calcium imaging of more than 5,000 neurons in mouse CA1 and tracked neural activity of the same neurons over 30 days, while the animals learned multiple versions of a linear two-alternative choice task in virtual reality (VR) environment. We used various manifold discovery techniques to visualize the high-dimensional neural data over the entire learning period and found that each animal went through a stereotyped transition of learning stages, demarcated by distinct low-dimensional embeddings and decorrelations of neural activity at key positions along the VR track, which correlated with task performance. Our results indicate that the evolution of hippocampal representations during learning reflects the extraction of task-related features that correlate temporally with the animal’s evolving performance. Furthermore, the learned structures appear to be reused in novel tasks, suggestive of transfer learning. By designing and simulating artificial agents based on reinforcement learning, we found that some architectures reproduced key features of both animal behavior and neural activity. The ability to monitor the formation of cognitive maps over weeks-long periods of learning provides a platform for developing and testing hypotheses regarding the underlying plasticity mechanisms, cell types, circuits, and computational rules responsible for adaptive learning.

Bio: With expertise in cellular and systems neuroscience as well as machine learning, Dr. Sun aims to: (1) better understand the biological underpinnings of animal cognition and intelligent behavior, and (2) use this understanding to improve AI systems. His ongoing work involves using deep learning theories to understand how declarative memories are transformed over time as well as using large-scale 2-photon mesoscopic imaging methods to record thousands of neurons in rodents engaged in learning tasks. 

Screen Shot 2022-11-16 at 12.41.13 PM.png
Gitta Kutyniok, PhD - Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians Universität München
When: December 2nd, 2022, 10:00-11:00 am EST
Title: Reliable AI in Medical Imaging: Successes, Challenges, and Limitations

Abstract: Deep neural networks as the current work horse of artificial intelligence have already been tremendously successful in real-world applications, ranging from science to public life. The area of (medical) imaging sciences has been particularly impacted by deep learning-based approaches, which sometimes by far outperform classical approaches for particular problem classes. However, one current major drawback is the lack of reliability of such methodologies.

In this lecture we will first provide an introduction into this vibrant research area. We will then present some recent advances, in particular, concerning optimal combinations of traditional model-based methods with deep learning-based approaches in the sense of true hybrid algorithms. Due to the importance of explainability for reliability, we will also touch upon this area by highlighting an approach which is itself reliable due to its mathematical foundation. Finally, we will discuss fundamental limitations of deep neural networks and related approaches in terms of computability, and how these can be circumvented in the future, which brings us in the world of quantum computing

Bio: Gitta Kutyniok currently holds a Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians Universität München. She received her Diploma in Mathematics and Computer Science as well as her Ph.D. degree from the Universität Paderborn in Germany, and her Habilitation in Mathematics in 2006 at the Justus-Liebig Universität Gießen. From 2001 to 2008 she held visiting positions at several US institutions, including Princeton University, Stanford University, Yale University, Georgia Institute of Technology, and Washington University in St. Louis, and was a Nachdiplomslecturer at ETH Zurich in 2014. In 2008, she became a full professor of mathematics at the Universität Osnabrück, and moved to Berlin three years later, where she held an Einstein Chair in the Institute of Mathematics at the Technische Universität Berlin and a courtesy appointment in the Department of Computer Science and Engineering until 2020. In addition, Gitta Kutyniok holds an Adjunct Professorship in Machine Learning at the University of Tromso since 2019. Gitta Kutyniok has received various awards for her research such as an award from the Universität Paderborn in 2003, the Research Prize of the Justus-Liebig Universität Gießen and a Heisenberg-Fellowship in 2006, and the von Kaven Prize by the DFG in 2007. She was invited as the Noether Lecturer at the ÖMG-DMV Congress in 2013, a plenary lecturer at the 8th European Congress of Mathematics (8ECM) in 2021, the lecturer of the London Mathematical Society (LMS) Invited Lecture Series in 2022, and an invited lecturer at both the International Congress of Mathematicians 2022 (ICM 2022) and the International Congress on Industrial and Applied Mathematics 2023 (ICIAM 2023). Moreover, she became a member of the Berlin-Brandenburg Academy of Sciences and Humanities in 2017, a SIAM Fellow in 2019, and a member of the European Academy of Sciences in 2022. In addition, she was honored by a Francqui Chair of the Belgian Francqui Foundation in 2020. She was Chair of the SIAM Activity Group on Imaging Sciences from 2018-2019 and Vice Chair of the new SIAM Activity Group on Data Science in 2021, and currently serves as Vice President-at-Large of SIAM. She is also the spokesperson of the Research Focus "Next Generation AI" at the Center for Advanced Studies at LMU, and serves as LMU-Director of the Konrad Zuse School of Excellence in Reliable AI. Gitta Kutyniok's research work covers, in particular, the areas of applied and computational harmonic analysis, artificial intelligence, compressed sensing, deep learning, imaging sciences, inverse problems, and applications to life sciences, robotics, and telecommunication.