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September 13, 2017

  

Wednesday, September 13, 2017

Thesis Defense Seminar
10:00 AM - 11:00 AM

Bloomberg School of Public Health

Does Hospital Preparedness Work? An Analysis of Hospital Preparedness and its Effectiveness during Disaster Response Hyo-Jeong Kim, DrPH Candidate Department of Health Policy and Management
Biostatistics Help: Faculty, Staff, Pre and Post Doc Walk-In Clinic
11:00 AM - 12:00 PM

Biostatistics consulting is available to all Johns Hopkins University faculty, staff, pre and post docs conducting clinical and translational research. 11:00 AM – 12:00 PM Wolfe Street Building Room: E3142 Contact Information: Nita James | jhbc@jhu.edu
Michael Lipnowski " Growth of the smallest 1-form eigenvalue on hyperbolic manifolds"
11:00 AM - 12:00 PM

Homewood

Speaker: Michael Lipnowski (Toronto) Title: Growth of the smallest 1-form eigenvalue on hyperbolic manifolds and applications to torsion homology growth Abstract: Joint work with Mark Stern. We relate small 1-form eigenvalues to relative cycle complexity on hyperbolic manifolds: small eigenvalues correspond to closed geodesics no multiple of which bounds a surface of small genus. We describe potential applications of this equivalence principle toward proving optimal torsion homology growth in families of congruence, arithmetic hyperbolic 3-manifolds.
Faculty Candidate Series
12:15 PM - 1:20 PM

Bloomberg School of Public Health

Causal Inference in Early Life and Life Course Research:
A Case for Etiologic and Public Health Relevance


Jonathan Huang, PhD, MPH
Postdoctoral Researcher,  Chevrier Research Group; Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine; McGill University

For more information, please contact Rachel Reid.
Public Health: Bridging Science and Politics
12:15 PM - 1:20 PM

Bloomberg School of Public Health

Office of Public Health Practice and Training

Ruben F. del Prado, MD, MPH ‘88
Visiting Guest Lecturer
UNAIDS Country Director and Representative to Nepal, Bhutan and Bangladesh
 
Reception to follow in the Gallery.

For more information, please contact publichealthpractice@jhu.edu. 
Event Image  CTL Toolkit:Introduction to Active Learning
2:00 PM - 3:00 PM

Bloomberg School of Public Health

Please join the Center for Teaching and Learning for a Teaching Toolkit Workshop on an Introduction to Active Learning.  We encourage onsite attendance, but the workshop will also be streamed via Adobe Connect at http://connect.johnshopkins.edu/ctl-toolkit-workshop. Recordings of the workshop will be posted to our Toolkit events page at a later date.

During this session, we will

  • provide an overview of active learning
  • introduce & explore active learning techniques which engage students in meaningful ways, whether online or on-campus
  • discuss obstacles and solutions to issues that sometimes arise with active learning

 If you have any questions, please contact JHSPH_Toolkit@jhu.edu or your Instructional Designer. 

Yannis Kevrekidis "Data and the computational modeling of complex/multiscale systems"
3:00 PM - 4:00 PM

Homewood

Speaker: Yannis Kevrekidis, Johns Hopkins University Title:No equations, no variables, no parameters, no space, no time: Data and the computational modeling of complex/multiscale systems Abstract: Obtaining predictive dynamical equations from data lies at the heart of science and engineering modeling, and is the linchpin of our technology. In mathematical modeling one typically progresses from observations of the world (and some serious thinking!) first to equations for a model, and then to the analysis of the model to make predictions. Good mathematical models give good predictions (and inaccurate ones do not) - but the computational tools for analyzing them are the same: algorithms that are typically based on closed form equations. While the skeleton of the process remains the same, today we witness the development of mathematical techniques that operate directly on observations -data-, and appear to circumvent the serious thinking that goes into selecting variables and parameters and deriving accurate equations. The process then may appear to the user a little like making predictions by "looking in a crystal ball". Yet the "serious thinking" is still there and uses the same -and some new- mathematics: it goes into building algorithms that "jump directly" from data to the analysis of the model (which is now not available in closed form) so as to make predictions. Our work here presents a couple of efforts that illustrate this ``new” path from data to predictions. It really is the same old path, but it is travelled by new means.

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