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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

Emily S. Finn, PhD - Assistant Professor, Department of Psychological and Brain Sciences, Dartmouth College
When: October 8, 9:45-11:00 am
Title: Is it time to put rest to rest?

Abstract: TBA

Biography: Dr. Emily S. Finn is an assistant professor in the Department of Psychological and Brain Sciences at Dartmouth College, where she directs the Functional Imaging and Naturalistic Neuroscience (FINN) Lab. Her work focuses on individual variability in brain activity and behavior, especially as it relates to appraisal of ambiguous information under naturalistic conditions. She received a PhD in Neuroscience from Yale, and completed a postdoc at the National Institute of Mental Health.

Achuta Kadambi, PhD - Assistant Professor of ECE and CS, UCLA
When: October 15th, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: Achuta Kadambi received the PhD degree from MIT and joined UCLA as an Assistant Professor. His publications have been presented as Orals at CVPR, ICCV, ICCP and SIGGRAPH. He is currently co-authoring a textbook ("Computational Imaging", MIT Press 2021) and is also a co-founder in a robotic imaging startup (http://akasha.im). Achuta's research has received several recognitions including the NSF CAREER Award, Forbes 30 under 30 (Science), Google Faculty Award, Sony Imaging Young Faculty Award, and Army Young Investigator Award (ARO-YIP).

Michel Thiebaut de Schotten, PhD - Head of Brain Connectivity and Behaviour Laboratory (BCBLab), Sorbonne Universities and Head of Neurofunctional Imaging Group (GIN), University of Bordeaux
When: November 5th, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography: With over ten years’ experience in neuropsychology and brain connectivity neuroimaging, Michel Thiebaut de Schotten benefits from an established scientific track record and have made solid contributions to the field of neuroscience. His work, published in Science (2005), revealed that spatial neglect is a consequence of the disruption of communication between the frontal and the parietal lobes, and thus should be considered as a disconnection syndrome. Moreover, he mapped, for the first time, the organization of white matter anatomy in the healthy living human brain (Nature Neuroscience 2011 as well as in the Atlas of the Human Brain Connections published with Marco Catani in 2012). He have also pursued work concerning brain connectivity in stroke populations by identifying new brain-behavior association and was recently published in Cerebral Cortex (2014-2015-2016). He is co-founder of the NatBrainLab , founder of the BCBlab and plays a key role as treasurer in the facilitation and in the organization of the Human Brain Mapping annual conference. In 2014, he was awarded the prestigious British Neuropsychological Society’s Early Career Award, The Elizabeth Warrington Prize as well as the European Society for Neuropsychology Cortex prize. At present, he is associate professor in Paris, head of the Brain Connectivity and Behaviour group (www.bcblab.com). Overall, Michel enjoys writing and sharing discoveries and new hypotheses about the human brain.

Andreas Maier, PhD - Head of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg
When: November 12th, 9:45-11:00 am
Title: Known Operator Learning - An Approach to Unite Machine Learning, Physics, and Signal Processing

Abstract: We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from computed tomography image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such, the concept is widely applicable for many researchers in physics, imaging and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging and signal processing.

Biography: Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool – PEAKS – that has been used to analyze over 4.000 patient and control subjects so far. From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit.
In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant “4D nanoscope”.  Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

Archana Venkataraman - John C. Malone Assistant Professor, Johns Hopkins Whiting School of Engineering
When: November 19th, 9:45-11:00 am
Title: TBA

Abstract: TBA

Biography:  Archana Venkataraman develops new mathematical models to characterize complex processes within the brain. She is core faculty in the Malone Center for Engineering in Healthcare, which aims to improve the quality and efficacy of clinical interventions, and she is affiliated with the Mathematical Institute for Data Science. Venkataraman’s lab, the Neural Systems Analysis Laboratory (NSA Lab), concentrates on building a comprehensive and system-level understanding of the brain by strategically integrating computational methods, such as machine learning, signal processing and network theory, with application-driven hypotheses about brain functionality. Based on this approach, Venkataraman and her team aim toward a greater understanding of debilitating neurological disorders, with the long-term goal of improving patient care. 

Mihaela van der Schaar, PhD - Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge
When: December 3rd, 9:45-11:00am
Title: Revolutionizing Healthcare: Understanding and guiding clinical decision making using machine learning

Abstract: TBA

Biography: Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA. Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI. Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).