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March 28, 2018


Wednesday, March 28, 2018

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-MDs and post-docs conducting clinical and translational research. 11:00 AM – 12:00 PM Wolfe Street Building Room: E3531 Contact Information Name: Nita James Phone: 410-614-2661 Email:
Burn Injuries: A Neglected Public Health Issue
12:00 PM - 1:30 PM

Bloomberg School of Public Health

Department of International Health
Johns Hopkins International Injury Research Unit

Part of Johns Hopkins International Injury Research Unit’s 10th anniversary schedule, this event will present a conversation on a critical injury topic, burn injuries. Speakers to include Dr. Asad Latif and Dr. Hadley Wesson. A facilitated discussion and light reception will follow.
Thesis Defense Seminar
12:00 PM - 1:00 PM

Bloomberg School of Public Health

Comorbidity of Mental Disorders and its Association with the Serotonin Transporter Gene Polymorphism in a Community Sample Ruben Miozzo, PhD Candidate Graduate Training Program in Clinical Investigation
Thesis Defense Seminar
12:00 PM - 1:00 PM

Bullets and Booze: Alcohol Outlet Access and Violent Crime in Baltimore City

Pamela Trangenstein, PhD Candidate
Department of Health, Behavior and Society

Bloomberg School of Public Health
624 N. Broadway St
Baltimore, MD 21205
Hampton House
Room 250
[multiple] DMH Wednesday Noon Seminar - Calliope Holingue, MPH
12:15 PM - 1:00 PM

Bloomberg School of Public Health

Calliope Holingue, MPH
PhD Candidate
Department of Mental Health
Johns Hopkins School of Public Health

The Gastrointestinal System and Gut Microbiome in Autism Spectrum Disorders

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

Biological Mechanisms of Perinatal Mood and Anxiety Disorders

Lauren M. Osborne, MD
Assistant Professor of Psychiatry and Behavioral Sciences
Assistant Director, Women’s Mood Disorders Center
Fellowship Director, Reproductive Psychiatry
Johns Hopkins School of Medicine

Contact: Rachel Reid

Rama Chellappa "Learning Along the Edge of Deep Networks."
3:00 PM - 4:00 PM


Speaker: Rama Chellappa, University of Maryland, College Park Abstract: While Deep Convolutional Neural Networks (DCNNs) have achieved impressive results on many detection and classification tasks (for example, unconstrained face detection, verification and recognition), it is still unclear why they perform so well and how to properly design them. It is widely recognized that while training deep networks, an abundance of training samples is required. These training samples need to be lossless, perfectly labeled, and spanning various classes in a balanced way. The generalization performance of designed networks and their robustness to adversarial examples needs to be improved too. In this talk, we analyze each of these individual conditions to understand their effects on the performance of deep networks and present mitigation strategies when the ideal conditions are not met. First, we investigate the relationship between the performance of a convolutional neural network (CNN), its depth, and the size of its training set and present performance bounds on CNNs with respect to the network parameters and the size of the available training dataset. Next, we consider the task of adaptively finding optimal training subsets which will be iteratively presented to the DCNN. We present convex optimization methods, based on an objective criterion and a quantitative measure of the current performance of the classifier, to efficiently identify informative samples to train on. Then we present Defense-GAN, a new strategy that leverages the expressive capability of generative models to defend DCNNs against adversarial attacks. The Defense-GAN can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. An approach for training a DCNN using compressed data will also be presented by employing the GAN framework. Finally, to address generalization to unlabeled test data and robustness to adversarial samples, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. This is accomplished by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. We demonstrate the impact of the analyses discussed above on a variety of reconstruction and classification problems.
Yoshio Tsutsumi "Well-posedness and smoothing effect for nonlinear dispersive equations."
4:30 PM - 6:00 PM


Speaker: Yoshio Tsutsumi, Kyoto Abstract: Visit the following website for information

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