Tenure-Track Faculty Positions
The Department of Statistics of Rutgers University seeks outstanding applicants for tenure-track positions of assistant and associate professors to start fall 2019. Applicants must have a Ph.D. in statistics or a related field by September 1, 2019. Responsibilities of the position include: teaching and supervising both undergraduate and graduate programs in statistics, and conducting original research in broad areas of statistics, particular strength in data science, Bayesian statistics and spatial statistics are preferred. Pursuit of external research funding is expected.
Interested individuals should apply online through Rutgers online link http://jobs.rutgers.edu/postings/75153 by providing a curriculum vitae, research statement and teaching statement, and arranging for submission of at least three confidential letters of reference.
Rutgers, The State University of New Jersey, is an Equal Opportunity / Affirmative Action Employer. Qualified applicants will be considered for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, genetic information, protected veteran status, military service or any other category protected by law. As an institution, we value diversity of background and opinion, and prohibit discrimination or harassment on the basis of any legally protected class in the areas of hiring, recruitment, promotion, transfer, demotion, training, compensation, pay, fringe benefits, layoff, termination or any other terms and conditions of employment.
MS Data Science Program
MS Data Science (Statistics Track). The Department of Statistics and Biostatistics is pleased to offer this new master's in Data Science program.
Recent Awards And Honors
Elynn Chen, Ph.D. Candidate, has been awarded the prestigious Postdoctoral Fellowship from National Science Foundation. Congratulations!
Sai Li - Distinguished Written Paper Award, 2017 Western North American Region of the International Biometric Society (WNAR)
Congratulations to our PhD candidate on this award!
Yiwei Shen is the recipient of the 2017 Award for the best performance in the PhD Qualifying Exam. Congratulations!
Anthony Rosa is a finalist for the Student Partner of the Year award offered by WileyPLUS Student Partner program. Congratulations!
Dr. Han Xiao - has been awarded the "2018 Excellence in Teaching and Mentoring Award" from the Rutgers School of Graduate Studies
Dr. Robin Gong - Recipient of the 2017-2018 Arthur P. Dempster Award
PI - Dr. Rong Chen - with co-PI's Drs. Fred Roberts of Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) and Minge Xie
of Statistics and Biostatistics Department.
NSF; "I-Group Learning for Dynamic Real Time Abnormality Detection with Applications in Maritime Threat Detection"
PI - Dr. Rong Chen - with co PI's Drs. Dan Yang and Cun-Hui Zhang of Statistics and Biostatistics Department
NSF: "BIGDATA: F: Statistical Learning with Large Dynamic Tensor Data"
Dr. Tirthankar Dasgupta - NSF: "Design and Analysis of Optimization Experiments with Internal Noise to Maximize Alignment of Carbon Nanotubes"
Dr. Ying Hung - NSF: "Collaborative Research: Statistical Modeling of Mechanosensing by Cell Surface Receptors"
Dr. John Kolassa - NSF: "Collaborative Research: Higher-Order Asymptotics and Accurate inference for Post-Selection"
Dr. Zhiqiang Tan - Patient Centered Outcomes Research Institute (pcori): "Improving casual inference methods via statistical learning with high-dimensional data"
Dr. Cun-Hui Zhang - NSF: "Collaborative Research: Statistical Methods, Algorithms and Theory for Large Tensors"
Prof. Minge Xie: Repro sampling method: a transformative artificial-sample-based inferential framework with applications to discrete parameter, high-dimensional data and rare events inferences
Prof. Michael Stein: Multifaceted Mathematics for Rare, High Impact Events in Complex Energy and Environment Systems
Prof. Harry Crane: Modeling and Inference for Dynamic Network Analysis
Prof. Linjun Zhang: Statistical Properties of Privacy-Preserving Algorithms: Optimality, Adaptivity, and Stability