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6 p.m. Networking reception
6:30 p.m. Welcome and discussion
7:30 p.m. Cocktail reception
Pérez Art Museum Miami
1103 Biscayne Boulevard
Miami, Florida 33132
On November 16, you can see what few people anywhere have seen: a demonstration of Hopkins’ thought-controlled prosthetic arm, the most advanced in the world. Join us to speak with the people developing this amazing technology, hear a patient share his incredible story, and learn about the Summer Program in Undergraduate Research that enables our students to work on this exciting, life-changing advance. Learn more at rising.jhu.edu/miami. Questions? Email firstname.lastname@example.org
The Hopkins Robotics Cup, hosted by the Center for Educational Outreach at Johns Hopkins University’s Whiting School of Engineering, has been rescheduled from January 30 on the JHU Homewood campus to Saturday, Feb. 6 at Highlandtown Elementary/Middle School, due to the recent winter weather.
The event is a competition open to all Baltimore City Public Schools with VEX Robotics teams. The game for 2016, Nothing But Net, challenges teams to build robots designed to score points by launching balls into nets with various target zones.
Viktor Jirsa, Director of the Inserm Institut de Neurosciences des Systèmes at Aix-Marseille-Université and Director of Research at the Centre National de la Recherche Scientifique (CNRS) in Marseille, France, will present “Translational Medicine: From Bifurcations to Epilepsy Surgery.”
Abstract: Over the past decade we have demonstrated that constraining computational brain network models by structural information obtained from human brain imaging (anatomical MRI, diffusion tensor imaging (DTI)) allows patient specific predictions, beyond the explanatory power of neuroimaging alone. This fusion of an individual’s brain structure with mathematical modelling allows creating one model per patient, systematically assessing the modeled parameters that relate to individual functional differences. The functions of the brain model are governed by realistic neuroelectric and neurovascular processes and allow executing dynamic neuroelectric simulation; further modeling features include refined geometry in 3D physical space; detailed personalized brain connectivity (Connectome); large repertoire of mathematical representations of brain region models, and a complete set of physical forward solutions mimicking commonly used in non-invasive brain mapping including functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) Electro-encephalography (EEG) and StereoElectroEncephalography (SEEG). So far our large-scale brain modeling approach has been successfully applied to the modeling of the resting state dynamics of individual human brains, as well as aging and clinical questions in stroke and epilepsy. In this talk I will focus on the example of epilepsy and systematically demonstrate the individual steps towards the creation of a personalized epileptic patient brain model.
Those unable to attend on the Homewood campus may view a simulcast in Traylor 709 on the Johns Hopkins School of Medicine campus. The lecture will also be streamed through Panopto. Click here to view the webcast.
Archana Venkataraman, assistant professor of electrical and computer engineering at Johns Hopkins University, will present “An Adaptable Framework to Extract Abnormal Brain Networks” as part of the Institute for Computational Medicine‘s Distinguished Seminar Series. The seminar begins at 11 a.m. in 110 Clark Hall on March 7.
There is increasing evidence that complex neurological disorders reflect distributed impairments across multiple brain systems. These findings underscore the importance of network-based approaches for functional data. However, network analyses in clinical neuroimaging is largely limited to aggregate measures, which do not pinpoint a concrete etiological mechanism. In contrast, I will present a novel Bayesian framework that captures the underlying topology of the altered functional pathways.
In the first part of this talk, I will introduce our core framework to extract abnormal network foci from functional MRI data. This model relies on a latent structure, which captures hidden interactions within the brain; the latent variables are complemented by an intuitive likelihood model for the observed neuroimaging measures. The resulting variational EM algorithm produces clinically meaningful results by simultaneously localizing the centers of abnormal activity and the network of altered connectivity. Next, I will address three technical challenges: flexible network topology, multimodal integration and patient-specific analysis. I will demonstrate that our core framework can elegantly be adapted to each of these scenarios and yields novel insights into autism, schizophrenia and epilepsy, respectively. Finally, I will highlight some exciting future directions for our methodology that revolve around clinical understanding and interventions.
The Laboratory for Computational Sensing and Robotics will highlight its elite robotics students and showcase cutting-edge research projects in areas that include Medical Robotics, Extreme Environments Robotics, Human-Machine Systems for Manufacturing, BioRobotics and more. JHU Robotics Industry Day will take place from 8 a.m. to 3 p.m. in Hackerman Hall on the Homewood Campus at Johns Hopkins University.
Robotics Industry Day will provide top companies and organizations in the private and public sectors with access to the LCSR’s forward-thinking, solution-driven students. The event will also serve as an informal opportunity to explore university-industry partnerships.
You will experience dynamic presentations and discussions, observe live demonstrations, and participate in speed networking sessions that afford you the opportunity to meet Johns Hopkins most talented robotics students before they graduate.
Registration is free, but required. Register here.Click here to view the schedule
Collin M Stultz MD, PhD
Principal Investigator, Research Laboratory of Electronics
Massachusetts Institute of Technology
“Computational Chemistry and Machine Learning in Molecular Medicine and Healthcare: Applications from the Molecule to the Patient”
New computational methods coupled with increasing computing power are revolutionizing medicine and biomedical research. At the molecular scale, computational models allow us to probe fundamental biophysical processes at an unprecedented level of detail, garnering insights that would have been difficult to obtain from experiment alone. At the patient or population scale, computation (and machine learning in particular) provides a platform for learning models that can help physicians choose the best therapies for their patients. In this seminar I will attempt to illustrate these concepts using work from our own research group.
In the first half of this seminar I describe a new method, which is based on Bayesian statistics, for modeling the structure of flexible proteins like Aβ42 – an intrinsically disordered protein (IDP) that plays a role in the pathogenesis of Alzheimer’s disease. While disordered and monomeric in solution, Aβ42 forms neurotoxic soluble aggregates in the brains of patients with Alzheimer’s disease. Through a series of computational and experimental studies we deduced fundamental steps that are important in the aggregation process. These data provide a detailed mechanistic description of the amyloid-β aggregation pathway and suggest a structural link between the disordered free monomer and the growth of amyloid fibrils and soluble oligomers. In the second half of this seminar I demonstrate how new insights, which can be used to guide clinical decision-making, may be obtained with machine learning. We focus on the problem of identifying patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) – a clinical condition characterized by reduced blood flow to the heart. Using a database of over 5000 patients who have suffered an ACS, we developed a neural network model that is able to identify high-risk patient subgroups. Such models provide clinicians with a rich source of information that can be exploited to ensure that patients receive therapies that are appropriate for their level of risk.
Visit the Institute for Computational Medicine website for more information.
The Institute for NanoBioTechnology invites you to celebrate National Nanotechnology Day. Bring your friends and colleagues and join us in celebrating the nanometer scale with food and beverages, a raffle prize, a selfie station, and practice your building skills with Nanoblocks.