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January 25, 2018

  

Thursday, January 25, 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: E3142
Xiaofeng (Felix) Ye “Stochastic dynamics: Markov chains and random transformations”
11:00 AM - 12:00 PM

Homewood

Speaker: Xiaofeng (Felix) Ye, University of Washington Abstract: The theory of stochastic dynamics has two different mathematical representations: stochastic processes and random dynamical system (RDS). RDS actually is a more refined mathematical description of the reality; it provides not only stochastic trajectories following one initial condition, but also describes how the entire phase space, with all initial conditions, changes with time. Stochastic processes represent the stochastic movements of individual systems. RDS, however, describes the motions of many systems that experience a common deterministic law that is changing with time due to extrinsic noises, which represent a fluctuating environment or complex external singles. The RDS is often a good framework to study a quite counterintuitive phenomenon called noise-induced synchronization: the stochastic motions of noninteracting systems under a common noise synchronize; their trajectories become close to each other, while individual one remains stochastic. I first established some elementary contradistinctions between Markov chain theory and RDS descriptions of a stochastic dynamical system under discrete time, discrete state (dtds) setting. It was shown that a given Markov chain is compatible with many possible RDS, and I particularly studied the corresponding RDS with maximum metric entropy. I then proved the sufficient and necessary conditions for synchronization in general dtds-RDS and in dtds-RDS with maximum metric entropy. The work is based on the observation that under certain mild conditions, the forward probability in a hidden Markov model exhibits synchronization, which yields an efficient estimation with subsequences. Here I developed a mini-batch gradient descent algorithm for parameter inference in the hidden Markov model. I first efficiently estimated the rate of synchronization, which was proven as the gap of top Lyapunov exponents, and then fully utilized it to approximate the length of subsequences in the mini-batch algorithm. I theoretically validated the algorithm and numerically demonstrated the effectiveness.
LunchLearnLink
12:00 PM - 1:00 PM

Bloomberg School of Public Health

Unlocking the Potential of Patient-reported Outcomes Claire Snyder, PhD Professor of Medicine Johns Hopkins School of Medicine
Medically Unnecessary Surgeries on Intersex Children in the U.S.
12:00 PM - 1:30 PM

Bloomberg School of Public Health

Katherine B. Dalke, MD, MBE Assistant Professor of Psychiatry Penn State College of Medicine Attending Psychiatrist Pennsylvania Psychiatric Institute Kyle Knight Researcher Lesbian, Gay, Bisexual and Transgender Rights Program Human Rights Watch
Thesis Defense Seminar
2:00 PM - 3:00 PM

Bloomberg School of Public Health

The Dynamic Interplay of Socio-emotional and Academic Functions from Elementary through High School: An Analysis of Cascade Effects Lauren Okano, PhD Candidate Department of Population, Family and Reproductive Health
Cognitive Science Department Colloquium on Scheduled Thursdays. See details for dates.
3:45 PM - 5:00 PM

Homewood

Cognitive Science Department Colloquium Presentation on Scheduled Thursdays. Please see http://web.jhu.edu/cogsci/events/Colloquia for schedule and full details.

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