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November 29, 2017

  

Wednesday, November 29, 2017

SOURCE Power Bar Drive: Mon, Nov 27 - Fri, Dec 8 (Multi-Day Event)
All Day

East Baltimore

Help local populations who are experiencing homelessness by donating power bars, granola bars and energy bars! It’s an easy, nutritious and delicious way to help the homeless. Bars will be donated to SOURCE partner organizations. Donation bin locations: JHSPH Student Affairs (E1002) and the first floor student lounge; SON main lobby entrance; SOM AMEB lobby (by stairs). You can also drop your donation off at the SOURCE office located at JHSPH, W1600.
(Cancelled) Thesis Defense Serminar
9:00 AM - 10:00 AM

Bloomberg School of Public Health

Evaluating the Impact of Meningococcal Disease Outbreaks in Universities and Local, State, and Federal Public Health Authorities Elizabeth Chmielewski, PhD Candidate Department of International Health
Biostatistics Help: Faculty, Staff, Pre-MD and Post Doc Walk-In Clinic
11:00 AM - 12:00 PM

Biostatistics consulting is available to all Johns Hopkins University faculty, staff, pre-MD 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
Community-based primary health care: A core strategy for achieving SGDs
12:00 PM - 1:15 PM

Bloomberg School of Public Health

International Center for Maternal and Newborn Health Lunchtime Seminar 
International Health

Community-based primary health care: A core strategy for achieving SGDs for Maternal and Neonatal Health

Panel: Robert Black, MD, MPH; Henry Perry, MD, PhD, MPH; Mary Carol Jennings, MD, MPH; Emma Sacks, PhD
Webcast:  https://www.jhsph.edu/research/centers-and-institutes/international-center-for-maternal-and-newborn-health/news-and-events/ICMNH%20seminar
Lunch will be provided
[multiple] DMH Wednesday Noon Seminar - Emma Beth McGinty, PhD
12:15 PM - 1:00 PM

Bloomberg School of Public Health

DMH Wednesday Noon Seminar Series

Emma Beth McGinty, PhD
Assistant Professor
Department of Health Policy and Management
Bloomberg School of Public Health

Mental Health and Addiction Stigma: Implications for Public Policy

Continuing discussion with students from 1 - 1:30 PM in HH188.
Population, Family and Reproductive Health - Wednesday Seminar Series
12:15 PM - 1:20 PM

Bloomberg School of Public Health

Gender-Based Violence, Cancer, and Facebook: Using Behavioral Design to Improve Women's Health in Latin America
Jana Smith, MPH
Adjunct Faculty
New York University’s Center for Global Affairs
Vice President, ideas42


For more information, please contact Janay Willliams.
Thesis Defense Seminar
2:30 PM - 3:30 PM

Bloomberg School of Public Health

Kosakonia & Chromobacterium vs. Malaria & Dengue - Insights into Anti-pathogen Strategies of Mosquito Midgut Bacteria Raul Saraiva, PhD Candidate Department of Molecular Microbiology and Immunology
Yingzhou Li "Kernel functions and their fast evaluations."
3:00 PM - 4:00 PM

Homewood

Speaker: Yingzhou Li,Duke University Abstract: Kernel matrices are popular in machine learning and scientific computing, but they are limited by their quadratic complexity in both construction and storage. It is well-known that as one varies the kernel parameter, e.g., the width parameter in radial basis function kernels, the kernel matrix changes from a smooth low-rank kernel to a diagonally-dominant and then fully-diagonal kernel. Low-rank approximation methods have been widely-studied, mostly in the first case, to reduce the memory storage and the cost of computing matrix-vector products. Here, we use ideas from scientific computing to propose an extension of these methods to situations where the matrix is not well-approximated by a low-rank matrix. In particular, we construct an efficient block low-rank approximation method -- which we call the Block Basis Factorization -- and we show that it has O(n) complexity in both time and memory. Our method works for a wide range of kernel parameters, extending the domain of applicability of low-rank approximation methods, and our empirical results demonstrate the stability (small standard deviation in error) and superiority over current state-of-art kernel approximation algorithms.

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