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Ginkgo Biloba brain as a herbal medicine
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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

We are currently inviting speakers for Fall 2024! If you are interested or would like to nominate a speaker, please fill out this nomination form

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Paul Sajda, PhD
When: Friday, March 15th, 2024, 10am-11am EST
Title:  Deep Learning for Fusion and Inference in Multimodal Neuroimaging

Abstract: Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that combines the advantages of both modalities, offering insights into the spatial and temporal dynamics of neural activity. In this presentation, we address the inference problem inherent in this technique by employing a transcoding framework. Transcoding refers to mapping from a specific encoding (modality) to decoding (the latent source space) and subsequently encoding the latent source space back to the original modality. Our proposed method focuses on developing a symmetric approach involving a cyclic
convolutional transcoder capable of transcoding EEG to fMRI and vice versa. Importantly, our method does not rely on prior knowledge of the hemodynamic response function or lead field matrix. Instead, it leverages the temporal and spatial relationships between the modalities and latent source spaces to learn these mappings. By applying our method to real EEG-fMRI data, we demonstrate its efficacy in accurately transcoding the modalities from one to another and recovering the underlying source spaces. It is worth noting that these results are obtained on previously unseen data, further emphasizing the robustness and generalizability of our approach. Furthermore, apart from its ability to enable symmetric inference of a latent source space, our method can also be viewed as low-cost computational neuroimaging. Specifically, it allows for generating an ‘expensive fMRI BOLD image using low-cost EEG data. This aspect highlights our approach’s potential practical significance and affordability for research and clinical applications
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Bio: Paul Sajda is the Vikram S. Pandit Professor of Biomedical Engineering and Professor of Electrical Engineering and Radiology (Physics) at Columbia University. He is also a Member of Columbia’s Data Science Institute and an Affiliate of the Zuckerman Institute of Mind, Brain, and Behavior. He received a BS in electrical engineering from MIT in 1989 and an MSE and Ph.D. in bioengineering from the University of Pennsylvania in 1992 and 1994, respectively. Professor Sajda is interested in what happens in our brains when we make a rapid decision and, conversely, what neural processes and representations drive our underlying preferences and choices, mainly when we are under time pressure. His work in understanding the basic principles of rapid decision-making in the human brain relies on measuring human subject behavior simultaneously with cognitive and physiological state. Professor Sajda applies the basic principles he uncovers to construct real-time brain-computer interfaces that improve interactions between humans and machines. He is also using his methodology to understand how deficits in rapid decision-making may underlie and be diagnostic of many types of psychiatric diseases and mental illnesses. Professor Sajda is a co-founder of several neurotechnology companies and works closely with various scientists and engineers, including neuroscientists, psychologists, computer scientists, and clinicians. He is a fellow of the IEEE, AIMBE, and AAAS. He also received the Vannevar Bush Faculty Fellowship (VBFF), the DoD’s most prestigious single-investigator award. Professor Sajda is also the current President of IEEE EMBS.

April Khademi, PhD
When: Friday, March 22nd, 2023, 10am-11am EST
Title:  TBD

Abstract: TBD

BioTBD

David Ouyang, PhD
When: Friday, April 12th, 2023, 10am-11am EST
Title:  TBD

Abstract: TBD

BioTBD

Stephanie Hyland, PhD
When: Friday, September 13th, 2024, 10am-11am EST
Title:  TBD

Abstract: TBD

BioTBD

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Anant Madabhushi, PhD
When: Friday, September 27th, 2024, 10am-11am EST
Title:  Sequence-based deep learning models for understanding gene regulation and disease genetics

Abstract: 

Traditional biology generally looks at only a few aspects of an organism at a time and

attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation

for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and

microenvironment, as seen in its histological image features, its 3 dimensional tissue

architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its

metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission

Tomography (PET), result in emergence of definable phenotypes. Within our group has been

developing novel computerized knowledge alignment, representation, and fusion tools for

integrating and correlating heterogeneous biological data spanning different spatial and

temporal scales, modalities, and functionalities. These tools include computerized feature

analysis methods for extracting subvisual attributes for characterizing disease appearance and

behavior on radiographic (radiomics) and digitized pathology images (pathomics). In this talk I

will discuss the development work in our group on new radiomic and pathomic approaches for

capturing intra-tumoral heterogeneity and modeling tumor appearance. I will also focus my talk on how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and response prediction models.

Bio:

Dr. Anant Madabhushi is the Robert W Woodruff Professor of Biomedical Engineering; and on the faculty in the Departments of Pathology, Biomedical Informatics, and Radiology and Imaging Sciences at Emory University. He is also a Research Health Scientist at the Atlanta Veterans Administration Medical Center. Dr. Madabhushi has authored more than 475 peer-reviewed publications and more than 100 patents issued or pending. He is a, Fellow of the American Institute of Medical and Biological Engineering (AIMBE), and the Institute for Electrical and Electronic Engineers (IEEE) and the National Academy of Inventors (NAI). His work on "Smart Imaging Computers for Identifying lung cancer patients who need chemotherapy" was called out by Prevention Magazine as one of the top 10 medical breakthroughs of 2018. In 2019, Nature Magazine hailed him as one of 5 scientists developing "offbeat and innovative approaches for cancer research". Dr. Madabhushi was named to The Pathologist’s Power List in 2019, 2020, 2021 and 2022.

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