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Month: April 2018

Latest paper on detailed radiation modeling of droplets online!

Posted on April 8, 2018 by Zhao, Xinyu

Many factors contribute to the strength of radiative absorption and scattering for a single droplet. Predictive calculations of radiation in a multiphase environment requires detailed modeling. Why and how? See below.

https://authors.elsevier.com/a/1Wr41564SFsR8

The following link will be available till May 26, 2018. Feel free to read about our latest progress and findings. Email us with your questions and comments!

Good job @Bifen WU.

CTF@UCONN is featured in UCONN Today.

Posted on April 8, 2018 by Zhao, Xinyu

We are exploring the exciting interactions between intense turbulence and combustion!

https://today.uconn.edu/school-stories/professor-xinyu-zhao-awarded-450000-grant-conduct-aeronautical-combustion-research/

11/20 Statistics Colloquium: Tumulesh Solanky, University of New Orleans, Distinguished Alumni Award

Statistics Colloquium: Tumulesh Solanky, University of New Orleans, Distinguished Alumni Award

Wednesday, November 20th, 2024

04:00 PM

Austin Building

Some issues related to implementation and generalization of the partition problem formulations for normal populations

Presented by Tumulesh Solanky, Chair and Professor, Department of Mathematics, The University of Louisiana System Foundation and Michael and Judith Russel Professor in Data Science/Computational Sciences, University of New Orleans

Wednesday, November 20, 2024, 4:00 PM, AUST 202
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
Webex Meeting Link
Abstract: In the area of selection and ranking, partitioning of treatments by comparing them to a control treatment is an important statistical problem. For over eighty years this problem has been investigated by a number of researchers via various statistical designs to specify the partitioning criteria and optimal strategies for data collection. We have studied various aspects of several key formulation available in literature from a practitioner’s point of view. The focus of the presentation would be on the design proposed in Tong (1969) which had utilized Bechhofer’s (1954) indifference-zone concept to partition treatments with respect to the control population. This formulation, had united a number of other formulations available in the statistical literature, and has since seen considerable interest from researchers. Focusing on the versatility of the Tong’s formulation and its applications, a generalization of the Tong’s formulation is presented to partition the treatments in the indifference zone as a separate identifiable group without altering basic idea of indifference zone. It is shown that the Tong’s formulation is a limiting case of the proposed generalized formulation. For the proposed generalization, a purely sequential procedure and a two-stage procedures are presented along with their theoretical properties. An example is provided to illustrate the proposed generalization and several other procedures available in the literature.
Bio: Tumulesh Solanky is a professor and chair of mathematics at the University of New Orleans (UNO), where he has been teaching for over three decades. For the past 16 years, he has also served as the department chair. Additionally, he holds the Michael and Judith Russell Professorship in Data/Computational Sciences. Throughout his career, Dr. Solanky has secured several hundred thousand dollars in sponsored research and has contributed extensively to academic literature, publishing books, book-chapters, and journal articles. Apart from his research and teaching, Dr. Solanky has served on various university committees and professional organizations. In recognition of his exceptional teaching and service, he received the Seraphia D. Leyda University Teaching Fellow Award in 2009 and the Cooper Mackin Medallion in 2018, the latter awarded to a faculty or staff member for outstanding contributions in support of the University’s mission.

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Phone:	860-486-0241
E-mail:	xinyu.zhao@uconn.edu
Address:	United Technology Engineering Building 378