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September 13, 2017

Highlighted Events

Monday, September 25, 2017

“Big Hunger” Book Talk and Signing
12:00 PM - 1:30 PM

Bloomberg School of Public Health

Andy Fisher is a leading national expert on community food security. In 1994, he co-founded the Community Food Security Coalition, a national alliance of groups working on food access and local food, and he led that organization until 2011. During this time, he created and publicized the concept of community food security, and played a key role in building the food movement. He has worked on a wide variety of food system topics, including food policy councils, community food assessments, healthy corner stores, coalition building, and farm-to-cafeteria. He has taught at various universities in Oregon, and is an adjunct instructor in the public health department at Portland State University.



Room W5030
Johns Hopkins Bloomberg School of Public Health
615 N. Wolfe Street
Baltimore, MD


About Big Hunger:

In Big Hunger,” Andy Fisher takes a critical look at the business of hunger and offers a new vision for the anti-hunger movement. From one perspective, anti-hunger leaders have been extraordinarily effective. Food charity is embedded in American civil society, and federal food programs have remained intact while other anti-poverty programs have been eliminated or slashed. But anti-hunger advocates – reliant on corporations for donations of food and money – are leaving out an essential element of the problem: economic inequality driven by the low wages that corporations pay.

Register Now!

*Register early for a chance to win a free copy of the book.

For more information about the event contact Alicia Carter.

[multiple] Biostatistics Seminar
12:15 PM - 1:15 PM

Bloomberg School of Public Health

Biostatistics Seminar "Linked Component Models for Integrative Analysis of Heterogeneous Multi-view Data" Dr. Irina Gaynanova Texas A&M University

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 |
Michael Lipnowski " Growth of the smallest 1-form eigenvalue on hyperbolic manifolds"
11:00 AM - 12:00 PM


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 
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 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 or your Instructional Designer. 

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


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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