NISS-CANSSI Collaborative Data Science webinar: TBD

Overview:

The National Institute of Statistical Sciences (NISS) and the Canadian Statistical Sciences Institute (CANSSI) are pleased to present a collaborative webinar exploring... .

Intro paragraph TBD.

Speakers

Dr. Anup Katheria, Associate Professor of Pediatrics at Drexel University College of Medicine, and Director of the Neonatal Research institute at Sharp Mary Birch Hospital for Women & Newborns

Dr. Abhik Das, Distinguished Fellow, Biostatistics at RTI International

Moderator

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About the Speakers

Dr. Anup Katheria is an Associate Professor of Pediatrics at Drexel University College of Medicine, and the Director of the Neonatal Research institute at Sharp Mary Birch Hospital for Women & Newborns. Interests are in functional echocardiography, point of care ultrasound, and conducting clinical trials. I am currently conducting several large multi center trials: 1. comparing cord milking to early cord clamping in term non-vigorous infants, and 2. comparing delayed cord clamping to umbilical cord milking in preterm infants, 3. Comparing empiric antibiotic therapy to placebo in extremely low birthweight infants. 4. comparing early CPAP to early caffeine plus less invasive surfactant administration (LISA), 5. Evaluating the use of Cromolyn Sodium therapy to reduce BPD 6. Comparing the effectiveness of nasal high flow cannula to CPAP . Dr. Katheria earned his BS in Biology from the University of California, Los Angeles, his MD from Drexel University College of Medicine, completed his pediatric residency at Children’s Hospital of Orange County, and his Neonatal-Perinatal Fellowship at the University of California, San Diego. See Profile

Dr. Abhik Das is a Distinguished Fellow in Biostatistics. Having led the Data Coordination Center for the NICHD Neonatal Research Network (NRN) for 16 years, Dr. Das is an expert in modeling, analyzing, and interpreting public health data. He has a wealth of experience in the design of intervention studies, including randomized clinical trials, and he also provides statistical expertise in neonatology, substance abuse, health insurance coverage, diabetes, and maternal and child health. Since 1999, Dr. Das has been providing biostatistical leaderships for multicenter clinical studies in neonates. As the Principal Investigator for the Data Coordinating Center for the NRN, he helped design, implement, monitor, analyze and publish 30 plus multicenter randomized controlled trials, 20 plus observational studies, and 200 plus publications in perinatology that have informed clinical practice. Dr. Das has designed and analyzed studies in perinatal settings spanning a variety of designs, including Bayesian, pragmatic comparative effectiveness, comprehensive cohort, factorial, cluster randomized, phase II and pharmacokinetic studies. He has a wide range of experiences in a variety of areas, including studying near-term and neurodevelopmental morbidities, fetal alcohol effects, effects of prenatal substance use on development, pharmacologic interventions under investigational new drug application, and international trials in maternal-infant nutrition. Dr. Das is a member of the Society for Pediatric Research. He serves as the Associate Editor for the American Journal of Perinatology. Additionally, he is a reviewer on several journals, including JAMA Pediatrics and the Journal of Pediatrics. Dr. Das is also a reviewer on several study sections and data and safety monitoring committees associated with the National Institutes of Health (NIH), the National Science Foundation, and more. See Profile

About the Moderator

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About the NISS-CANSSI
Collaborative Data Science Web Series:

The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

Goals of the Initiative

The goal is to foster progress in:

• Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
• Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
• Establishing new practices for scientific reproducibility and replicability through Data Science.

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