– Masters/ PhD Qualifying Exam (2020 May) has been cancelled

The Department has come to the decision to cancel the May 11, 2020 Qualifying Exam. At this time we do not have a make-up exam scheduled due to overall uncertainty of how this all will pan out.

For now if there are any Masters in STAT or Masters in BIST students who were planning to take the MS Exam this May to Graduate either this May or Summer 2020 please e-mail Anthony Luis ( right away by April 15th so we can get a headcount of students effected by this. Again this only applies to those MS students wishing to graduate THIS MAY or THIS SUMMER.

Those of you who have signed up for the PhD qualifying exam scheduled in May, 2020. Due to the current extraordinary circumstance, the Department has decided to cancel the exam, however, if you are an MS student and have applied for admission to our PhD program, you will be admitted regardless of your record of the qualifying exam. Please note that if you have not cleared the PhD qualifying exam, you are still required to do so in your remaining attempts in order to be eligible for continuing study in the PhD program. Please send Tracy Burke an email once you submit your application.

The next exam is currently scheduled for January 2021.

– Coronavirus status update – Department of statistics, UConn

Coronavirus status update – Department of statistics, UConn


Please refer to UConn’s information page regarding the overall updates and policies concerning the Coronavirus situation.


Per the university’s policy, teaching will be done via the web for the remainder of the spring 2020 semester.


All department events are currently suspended until the university deems it safe to resume public gatherings.


The Department of Statistics Spring 2020 PASS of Fail policies are given in this link.  We strongly advise our majors to discuss their plans of converting a required course for their major to P/F grading with their academic advisor.


The Department has come to the decision to cancel the May 11, 2020 Qualifying Exam. The next exam is currently scheduled for January 2021.


The main office will be functioning during this time but we ask that you communicate with us via e-mail or by phone to limit foot traffic in the main office.


UConn Storrs buildings will not be unlocked during their regularly scheduled hours. If you must come to campus, please coordinate it with our staff. In general, we ask everyone to keep visiting the department to absolutely essential matters.


We are hopeful that this unpleasant situation will not last long, but until then we ask everyone to follow the recommendations and guidelines of the University, State, and Centers for Disease Control and Prevention.

– Chaoran Hu has received the 2020 Student Paper Award in the Statistical Computing and Statistical Graphics Sections of the ASA

We are pleased to inform you that Chaoran’s paper on animal movement has been selected as one of the winners of this year’s student paper competition from the Section on Statistical Computing and the Section on Statistical Graphics of ASA. The same paper has also been chosen as honorable mention for 2020 JSM ENVR section student paper competition award.

More info about the Award of ASA can be found at the following links: Click here


– Zhe Sun has received 2020 ENAR Distinguished Student Paper Award and John Van Ryzin Award

We are delighted to report that Zhe’s manuscript entitled Sparse Log-Contrast Regression with Functional Compositional Predictors: Linking Gut Microbiome Trajectory in Early Postnatal Period to Neurobehavioral Development of Preterm Infants has been selected as the best paper submitted this year(2020) to the International Biometric Society Eastern North American Region’s (ENAR) Distinguished Student Paper Award Competition! This paper was co-authored by Zhe Sun, Wanli Xu, Xiaomei Cong, Gen Li and Kun Chen. This award for the top paper is called the John Van Ryzin Award, and is awarded to Zhe in addition to the Distinguished Student Paper Award.

This is an unprecedented achievement in the department of Statistics. Congratulations!

– Yan Li has received 2020 ENAR Distinguished Student Paper Award

We are happy to report that Yan Li has been selected to receive one of the International Biometric Society Eastern North American Region’s (ENAR) Distinguished Student Paper Awards for the 2020 ENAR Spring Meeting in Nashville, TN. He is co-advised by Dr. Jun Yan and Dr. Kun Chen. The award recognizes his paper entitled “Pursuing Sources of Heterogeneity in Mixture Regression”, co-authored by Yan Li, Chun Yu, Yize Zhao, Robert Aseltine, Weixin Yao and Kun Chen.

Once again, the quality of manuscripts submitted was high and the competition was keen. It was difficult for the committee to choose among so many deserving papers. Congratulations on his accomplishment!

– NESS Colloquium @ BU – Nov. 14 – David Dunson (Duke University) – Learning & Exploiting Low-dimensional Structure In High-Dimensional Data

Hi All,
The NESS Colloquium series will continue on Thursday, November 14th at Boston University with a talk Professor David Dunson from Duke University. This colloquium is open to the general public and should be accessible to those interested in statistics at all levels. The event is sponsored by the New England Statistics Society (NESS) and the Department of Mathematics and Statistics at Boston University.

Please share this email widely and please post the attached flyer. See below for more information.

Daniel Sussman
Assistant Professor, Department of Mathematics and Statistics
Boston University


Thursday, November 14, 2019
Starting at 4 pm (with refreshments served starting at 3:25 pm)


College of General Studies Building
871 Commonwealth Avenue, Boston, MA, 02215
Room CGS 129


Professor David Dunson is the Arts and Sciences Professor of Statistical Science at Duke University, has been given numerous awards including the COPSS Presidents’ Award, and is a Fellow in the Institute of Mathematical Statistics and the American Statistical Association.

Professor Dunson’s research spans numerous areas of statistics with a focus on scalable procedures with provable guarantees that can be applied to complex data structures. His work has had broad impact within the statistics community and in many other fields including biomedical research, genomics, ecology, criminal justice, and neuroscience.


Learning & Exploiting Low-dimensional Structure In High-Dimensional Data

This talk will focus on the problem of learning low-dimensional geometric structure in high-dimensional data. We allow the lower-dimensional subspace to be non-linear. There are a variety of algorithms available for “manifold learning” and non-linear dimensionality reduction, mostly relying on locally linear approximations and not providing a likelihood-based approach for inferences. We propose a new class of simple geometric dictionaries for characterizing the subspace, along with a simple optimization algorithm and a model-based approach to inference. We provide strong theory support, in terms of tight bounds on covering numbers, showing advantages of our approach relative to local linear dictionaries. These advantages are shown to carry over to practical performance in a variety of settings including manifold learning, manifold de-noising, data visualization (providing a competitor to the popular tSNE), classification (providing a competitor to deep neural networks that requires fewer training examples), and geodesic distance estimation. We additionally provide a Bayesian nonparametric methodology for inference, using a new class of kernels, which is shown to outperform current methods, such as mixtures of multivariate Gaussians.