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Suvra Pal

Name

[Pal, Suvra]
  • Assistant Professor, Department of Mathematics

Biography

I am currently an Assistant Professor of Statistics in the Department of Mathematics, University of Texas at Arlington. Prior to joining UT Arlington, I was a Lecturer (research track) in the School of Statistics and Actuarial Science at University of the Witwatersrand, South Africa. I earned my Ph.D. in Statistics from the Department of Mathematics and Statistics at McMaster University, Canada. Before joining McMaster University, I worked with Tata Consultancy Services in India as an Assistant Systems Engineer. 

Professional Preparation

    • 2014 Ph.D. in StatisticsDepartment of Mathematics and Statistics, McMaster University, Canada
    • 2008 M.Sc. in StatisticsDepartment of Statistics, University of Calcutta, India
    • 2006 B.Sc. in StatisticsDepartment of Statistics, University of Calcutta, India

Appointments

    • Aug 2016 to Present Assistant Professor
      Department of Mathematics, University of Texas at Arlington
    • Aug 2015 to Aug 2016 Visiting Assistant Professor
      Department of Mathematics, University of Texas at Arlington
    • Dec 2013 to July 2015 Lecturer (research track)
      School of Statistics and Actuarial Science, University of the Witwatersrand, South Africa
    • Sept 2010 to Nov 2013 Graduate Teaching Assistant
      Department of Mathematics and Statistics, McMaster University, Canada
    • Aug 2008 to Aug 2010 Assistant Systems Engineer
      Tata Consultancy Services, Kolkata, India

Memberships

  • Life Member
    • June 2015 to Present International Indian Statistical Association
  • Annual Membership
    • Aug 2013 to Present American Statistical Association (ASA)
    • Jan 2013 to May 2015 International Indian Statistical Association
    • Mar 2014 to Mar 2015 South African Statistical Association

Awards and Honors

    • Dec  2017 UTA Faculty Senate Travel Award sponsored by UTArlington
      Achievements: Paper presentation at the International Indian Statistical Association Conference
    • Jan  2015 Research Grant sponsored by Faculty of Science, University of the Witwatersrand, South Africa
      Achievements:

      Excellence in Research

    • Feb  2014 Dean's SPARC Funding sponsored by Faculty of Science, University of the Witwatersrand, South Africa
      Achievements:

      Research Excellence

    • Jan  2013 Best Student Paper Presentation in Applications sponsored by International Indian Statistical Association
      Achievements:

      Paper presentation at the International Indian Statistical Association conference

    • Oct  2012 Travel Award sponsored by NSF
      Achievements:

      Present a talk in International Conference on Advances in Interdisciplinary Statistics and Combinatorics

    • Sep  2012 Clifton W. Sherman Prestige Scholarship sponsored by McMaster University, Canada
      Achievements:

      Excellence in Doctoral study

    • Dec  2011 Travel Award sponsored by Graduate Student Association, McMaster University, Canada
      Achievements:

      Participate in International Conference on Advances in Probability and Statistics

    • Sep  2010 Entrance and Research Scholarships sponsored by Department of Mathematics and Statistics, McMaster University, Canada
      Achievements:

      Excellence in Doctoral Study

Research and Expertise

  • Survival Analysis, Cure Rate Modeling, Statistical Inference, Statistical Computing, Model Discrimination

    Cure rate models are models for lifetime or survival data consisting of a surviving fraction. Due to huge improvements in the treatment of cancer and some other disease, cure rate models have become increasingly popular in the analysis of data from clinical trials. For certain types of cancer, including breast cancer, leukemia, melanoma, and prostate cancer, a substantial proportion of patients may be cured by treatment, i.e., show no recurrence of the disease. These patients who are cured are called immunes or long-term survivors, while the remaining patients who develop a recurrence of the disease are called susceptibles. The population of interest may thus be regarded as a mixture of these two types of patients. Traditional methods of survival analysis assumes that all patients remain at risk of death or relapse and as such do not accommodate cure. However, estimation of a treatment-specific cure rate provides valuable information that is not only of use to the investigator but is also of primary interest to the patient at the time of diagnosis. The cure rate is also an important and useful measure in seeing the trends in the survival of patients suffering from cancer. 

    In my research, I consider a competing cause scenario, i.e., there can be more than one competing cause related to the occurrence of an event of interest (e.g. death due to cancer). Since the number of competing causes produced by nature is unobserved (latent variable), we model the number of competing causes by a discrete distribution. On the other hand, the lifetime (time to event) associated with each competing cause is modelled by a continuous distribution. Noting that the data in a cure rate setup can be proved to be missing and that the expectation maximization (EM) algorithm is a well-known tool to handle such missing data, the main objective is to develop the steps of the EM algorithm for the detrmination of the maximum likelihood estimates of the model parameters after taking into account the role of covariates that might affect the cure rate of patients. Instead of assuming particular distributions for the competing cause and lifetime, the focus is on assuming generalized family of distributions which contain some of the well-known distributions as its sub-family. This would allow us to carry out a formal test of hypothesis to select a parsimonious competing cause distribution together with a suitable lifetime distribution, within the chosen families of distributions, that would jointly provide the best fit to a data.

    To bring in a more practical scenario, we can think of a situation where out of the initial number of competing causes, a certain number of competing causes get destroyed by treatment. For instance, some of the initial malignant cells, competing to give rise to a tumor, get destroyed after a patient undergoes a chemotherapy or a radiation. This gives a practical and realistic interpretation of the biological mechanism of the occurrence of an event of interest as what is recorded is only from the undamaged portion of the initial number of competing causes.

    Although right censoring is the most common form of censoring encountered in practice, the proposed estimation technique via the EM algorithm can also be developed for interval censored data where patients are monitored at certain time points only (not continuous over time). In this case, if the event of interest is observed, its exact occurrence time is not known, but, is known to have occurred between the previous and present inspection times. In this case, a careful thought needs to be given for the contruction of the observed  likelihood function, i.e., the probability of the observed data.

    Instead of looking at a particular parametric distribution that provides the basic description of the lifetime, it is also possible to consider modeling the lifetime through the hazard function, where we do not assume any functional form for the baseline hazard function and carry out the necessary analysis. This would lead to a semi-parametric approach, which is considered to be a more general form of  analysis.  Furthermore, the problem can also be attacked from a Bayesian point of view, which would require choosing proper prior distributions for the model parameters.

Publications

      Journal Article Under Review
      • Wiangnak, P. and Pal, S. (2017). Inference for interval censored long-term survivor model with COM-Poisson competing cause and lognormal lifetimes. Annals of the Institute of Statistical Mathematics

        {Peer Reviewed }
      Under Review
      • Otsuka, Y., Ito, A., Takeuchi, M., Pal, S. and Tanaka, H. (2017). Predictive evaluation of powder X-ray diffractogram of pharmaceutical formulation powders based on infrared spectroscopy and chemometrics. Journal of Pharmaceutical Sciences

        {Journal Article }

      Journal Article 2018
      • Pal, S., Majakwara, J. and Balakrishnan, N. (2018). An EM algorithm for the destructive COM-Poisson regression cure rate model. Metrika 81 143-171

        {Peer Reviewed }
      2018
      • Pal, S. and Balakrishnan, N. (2018). Expectation maximization algorithm for Box-Cox transformation cure rate model and assessment of model mis-specification under Weibull lifetimes. IEEE Journal of Biomedical and Health Informatics 22 926 - 934

        {Journal Article }
      2018
      • Wiangnak, P. and Pal, S. (2018). Gamma lifetimes and associated inference for interval censored cure rate model with COM-Poisson competing cause. Communications in Statistics - Theory and Methods 47 1491-1509

        {Journal Article }

      Journal Article 2017
      • Pal, S. and Balakrishnan, N. (2017). Likelihood inference for the destructive exponentially
        weighted Poisson cure rate model with Weibull lifetime and an application to melanoma
        data. Computational Statistics 32 429-449

        {Peer Reviewed }
      2017
      • Wang, X., Reddy, D. D., Nalawade, S. S., Pal, S., Lima, F. G. and Liu, H. (2017). Impact of heat on metabolic and hemodynamic changes in transcranial infrared laser stimulation measured by broadband near-infrared spectroscopy. Neurophotonics DOI: 10.1117/1.NPh.5.1.011004. Published online 19 September 2017

        {Journal Article }
      2017
      • Pal, S. and Balakrishnan, N. (2017). Likelihood inference for COM-Poisson cure rate
        model with interval-censored data and Weibull lifetimes. Statistical Methods in Medical
        Research 26
        2093-2113

        {Peer Reviewed }
      2017
      • Pal, S. and Balakrishnan, N. (2017). An EM type estimation procedure for the destructive exponentially weighted Poisson regression cure model under generalized gamma lifetime. Journal of Statistical Computation and Simulation 87 1107-1129

        {Peer Reviewed }

      Journal Article 2016
      • Otsuka, Y., Ito, A., Matsumura, S., Takeuchi, M., Pal, S. and Tanaka, H. (2016). Quantification of pharmaceutical compounds based on powder X-ray diffraction with chemometrics. Chemical and Pharmaceutical Bulletin 64 (8) 1129-1135

        {Peer Reviewed }
      2016
      • Balakrishnan, N., Koutras, M. V., Milienos, F. and Pal, S. (2016). Piecewise linear
        approximations for cure rate models and associated inferential issues. Methodology and
        Computing in Applied Probability
        18 937-966

        {Peer Reviewed }
      2016
      • Balakrishnan, N. and Pal, S. (2016). Expectation maximization-based likelihood inference for 
        flexible cure rate models with Weibull lifetimes. Statistical Methods in Medical
        Research
        25 (4) 1535-1563

        {Journal Article }
      2016
      • Pal, S. and Balakrishnan, N. (2016). Destructive negative binomial cure rate model and
        EM-based likelihood inference under Weibull lifetime. Statistics & Probability Letters 116
        9-20

        {Peer Reviewed }

      Journal Article 2015
      • Pal, S. and Balakrishnan, N. (2015). Likelihood inference based on EM algorithm for the
        destructive length-biased Poisson cure rate model with Weibull lifetime. Communications
        in Statistics-Simulation and Computation
        DOI: 10.1080/03610918.2015.1053918. Published online 24 June 2015

        {Peer Reviewed }
      2015
      • Balakrishnan, N. and Pal, S. (2015). Likelihood inference for flexible cure rate models
        with gamma lifetimes. Communications in Statistics-Theory and Methods 44 (19) 4007-4048

        {Peer Reviewed }
      2015
      • Balakrishnan, N. and Pal, S. (2015). An EM algorithm for the estimation of 
        flexible cure rate model parameters with generalized gamma lifetime and model discrimination using
        likelihood- and information-based methods. Computational Statistics 30 (1) 151-189

        {Peer Reviewed }

      Book Chapter 2014
      • Balakrishnan, N. and Pal, S. (2014). COM-Poisson cure rate models and associated
        likelihood-based inference with exponential and Weibull lifetimes. In: Applied Reliability
        Engineering and Risk Analysis: Probabilistic Models and Statistical Inference
        , Chapter
        22 (Eds., I. Frenkel, A. Karagrigoriou, A. Lisnianski and A. Kleyner), pp. 308-348. John
        Wiley & Sons, Chichester, U.K.

        {Peer Reviewed }

      Journal Article 2013
      • Balakrishnan, N. and Pal, S. (2013). Lognormal lifetimes and likelihood-based inference
        for  flexible cure rate models based on COM-Poisson family. Computational Statistics &
        Data Analysis
        67 41-67

        {Peer Reviewed }

      Journal Article 2012
      • Balakrishnan, N. and Pal, S. (2012). EM algorithm-based likelihood estimation for some
        cure rate models. Journal of Statistical Theory and Practice 6 (4) 698-724

        {Peer Reviewed }

Presentations

    • December  2017
      Destructive Cure Rate Model and Associated Inference
      Presented in the International Indian Statistical Association Conference, Hyderabad, India, December 28-30, 2017 (Invited)
    • August  2017
      Inference for COM-Poisson Cure Rate Model with Interval Censored Data
      Presented in the Joint Statistical Meeting, Baltimore, Maryland, USA, July 29-August 03, 2017
    • July  2017
      Destructive COM-Poisson Cure Rate Model and Associated Likelihood Inference
      Presented in the 10th International Conference on Mathematical Methods in Reliability, Grenoble, France, July 03-06, 2017 (Invited)
    • March  2017
      EM-Based Likelihood Inference for Destructive COM-Poisson Regression Cure Rate Model with Weibull Lifetime
      Presented in the Conference of Texas Statisticians (organized by Department of Statistical Science, Southern Methodist University), Dallas, Texas, USA, March 24-25, 2017 (Invited)
    • March  2016
      EM-Based Likelihood Inference for Destructive Cure Rate Models
      Presented in the Statistics seminar series, Department of Statistical Science, Southern Methodist University, Texas, USA, March 18, 2016 (Invited)
    • February  2015
      EM-Based Likelihood Inference for the Destructive Exponentially Weighted Poisson Cure Rate Model with Generalized Gamma Lifetime and Model Discrimination
      Presented in the Mathematical Statistics Seminar Series, School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, South Africa, February 19, 2015 (Invited)
    • January  2015
      EM-Based Likelihood Inference for the Destructive Exponentially Weighted Poisson Cure Rate Model with Generalized Gamma Lifetime and Model Discrimination
      Presented in the Statistics Seminar Series, Department of Mathematics and Statistics, McMaster University, Canada, January 20, 2015 (Invited)
    • July  2014
      Destructive Negative Binomial Cure Rate Model and EM-Based Likelihood Inference with Weibull Lifetime
      Presented in the International Indian Statistical Association Conference, Riverside, California, USA, July 11-13, 2014 (Invited)
    • July  2014
      On Some Likelihood Inference for the Destructive Negative Binomial Cure Rate Model
      Presented in the Summer Statistics Seminar Series, Department of Mathematics and Statistics, McMaster University, Canada, July 02, 2014 (Invited)
    • February  2014
      COM-Poisson family of cure rate models and exact likelihood inference based on EM algorithm
      Presented in the Mathematical Statistics Seminar Series, School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, South Africa, February 27, 2014 (Invited)
    • August  2013
      Likelihood-Based Inferential Aspects for Flexible Cure Rate Models

      Presented in the Joint Statistical Meeting, Montreal, Canada, August 3-8, 2013

    • March  2013
      Weibull Lifetimes and Likelihood-Based Inferential Aspects for Flexible Cure Rate Models
      Presented in the Statistics Seminar Series, Department of Mathematics and Statistics, McMaster University, Canada, March 26, 2013 (Invited)
    • January  2013
      EM Algorithm based Likelihood Estimation for Some Cure Rate Models

      Presented as a student paper competition in the International Indian Statistical Association Conference, Chennai, India, January 2-5, 2013

    • October  2012
      EM Algorithm based Likelihood Estimation for Some Cure Rate Models

      Presented in the International Conference on Advances in Interdisciplinary Statistics and Combinatorics, University of North Carolina-Greensboro, USA, October 5-7, 2012

    • March  2012
      EM Algorithm based Likelihood Esti- mation for Some Cure Rate Models
      Presented in the Statistics Seminar Series, Department of Mathematics and Statistics, McMaster University, Canada, March 13, 2012 (Invited)
    • December  2011
      On Some Likelihood Inferential Aspects for Different Cure Rate Models
      Presented as a special student talk in the International Conference on Advances in Probability and Statistics - Theory and Applications: A Celebration of N. Balakrishnans 30 years of Contributions to Statistics, Chinese University of Hong Kong, Hong Kong SAR, China, December 28-31, 2011 (Invited)

Students Supervised

  • Doctoral
    • Present
      thumbnail
      Thesis topic in discussion stage
    • Present

      Currently working on his thesis entitled "On Some Likelihood Inference for the Destructive COM-Poisson Regression Cure Rate Model with Generalized Gamma Lifetime", School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, South Africa

    • Nov 2016
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      Expectation Maximization-Based Likelihood Inference for COM-Poisson Cure Rate Model with Interval Censored Data

  • Master's
    • Apr 2018
      An Extension of Long-Term Survival Model: Illustration with Geometric Competing Causes
    • Apr 2018
      Demonstration of the Flexibility of the Extended Generalized Gamma Distribution
    • Nov 2017
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      Comparison of EM and Stochastic EM Algorithms for Bernoulli Regression Cure Rate Model
    • Apr 2017
      Use of Generalized Gamma Distribution in Modeling Lifetime Data
    • Nov 2016
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      Cure Rate Estimation In Survival Analysis Using Box-Cox Cure Rate Model and Generalized Gamma Lifetime

    • Nov 2015
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      Likelihood Inference for Mixture Cure Rate Model

  • Undergraduate Honor's Thesis

Collaborators

    • thumbnail
      Duration : Sept 2010 to Present

      Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada

    • thumbnail
      Duration : July 2013 to Present
      Department of Philosophy, Education, Psychology, University of Ioannina, Greece
    • thumbnail
      Duration : July 2013 to Present
      Department of Statistics and Insurance Science, University of Piraeus, Greece
    • thumbnail
      Duration : Jan 2017 to Present
      Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA
    • thumbnail
      Duration : June 2018 to Present
      Department of Statistics, University of Manitoba, Winnipeg, Canada

Courses

      • MATH 5305-001 STATISTICAL METHODS

        A comprehensive study of basic statistical methods. Topics include descriptive statistics, basic
        probability, statistical distributions, inference, experiments with one factor, regression analysis,
        and extensive use of R statistical software.

        Fall - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • MATH 3302-001 MULTIVARIATE STATISTICAL METHODS

        Organization of multivariate data and some basic descriptive statistics; Elements of matrix theory; Random vectors and matrices; Multivariate normal distribution; Inferences about a mean vector; Comparisons of several multivariate means; Multiple regression; Non-linear regression; Applications of multivariate data analysis in other areas of interest; Use of R statistical software.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • HONR-SC 1426-001 HONORS CALCULUS I

        Concepts of limit, continuity, differentiation and integration; applications of these concepts

        Fall - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • MATH 1426-271 CALCULUS I

        Concepts of limit, continuity, differentiation and integration; applications of these concepts

        Fall - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • MATH 5305-001 STATISTICAL METHODS

        A comprehensive study of basic statistical methods. Topics include descriptive statistics, basic
        probability, statistical distributions, inference, experiments with one factor, regression analysis,
        and extensive use of R statistical software

        Fall - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • MATH 5359-001 SURVIVAL ANALYSIS

        A comprehensive study of basic statistical methods in the analysis of lifetime data with applications in engineering and biomedical research, including clinical trials, epidemiological studies, and animal studies. Topics include basic statistical concepts; some common descriptive statistics; introduction to some basic quantities - survival function, hazard function, cumulative hazard function, and mean residual life; some common parametric distributions to model lifetime data; censoring and different forms of censoring; Kaplan-Meier estimator of the survival function and confidence bands; testing for the survival times for two or more groups; parametric regression models; Cox proportional hazards model (both fixed and time dependent covariate); regression diagnostics; accelerated failure time models; sample size determination for survival studies, extensive use of R statistical software

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • MATH 5392-003 Introduction to Commonly Used Statistical Methods in Clinical Trials

        A comprehensive study of basic statistical methods in clinical research. Topics include basic
        statistical concepts, testing of hypothesis (one and two sample t-tests), analysis of variance,
        analysis of covariance, repeated measures analysis, linear regression, non-linear regression, sur-
        vival analysis and Cox proportional hazards model, some common non-parametric tests, use of
        R statistical software, and introduction to SAS

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • MATH 5392-005 STATISTICAL METHODS IN CLINICAL TRIALS

        A comprehensive study of basic statistical methods in clinical research. Topics include basic statistical concepts, testing of hypothesis (one and two sample t-tests), analysis of variance, analysis of covariance, repeated measures analysis, linear regression, non-linear regression, survival analysis and Cox proportional hazards model, some common non-parametric tests, and use of R statistical software

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • MATH 3302-001 MULTIVARIATE STATISTICAL METHODS

        Organization of multivariate data and some basic descriptive statistics, Elements of matrix the-
        ory, Random vectors and matrices, Multivariate normal distribution, Inferences about a mean
        vector, Comparisons of several multivariate means, Multiple regression, Non-linear regression,
        Applications of multivariate data analysis in other areas of interest, and use of R statistical
        software

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • MATH 3316-001 STATISTICAL INFERENCE

        A comprehensive study of basic statistical methods. Topics include descriptive statistics, numeracy, basic probability, basic principles of experimental design, inference, regression analysis, and use of R statistical package

        Fall - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours1 Document
      • MATH 5305-001 STATISTICAL METHODS

        A comprehensive study of basic statistical methods. Topics include descriptive statistics, numeracy, basic probability, basic principles of experimental design, inference, regression analysis, and use of R statistical software

        Fall - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours1 Document

Service to the Community

  • Appointed
    • Feb 2015 to  July 2015 Course coordinator

      Served as a coordinator for Mathematical Statistics 3, a third year cluster of courses consisting of Risk Theory, Stochastic Process, Regression Analysis, Generalized Linear Models, and Non Parametric Statistics at School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, South Africa from February 2015 to July 2015

    • Jan 2014 to  Dec 2014 External examiner for masters thesis

      Served as an external examiner for a Masters thesis, titled as ``Estimation of Discretely Sampled Continuous Diffusion Processes with Application to Short-Term Interest Rate Models", from University of Johannesburg, South Africa, 2014

    • Sept 2012 to  Sept 2013 Organizer of graduate student seminar

      Organized graduate student seminar for the 2012-2013 academic year at Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada

Service to the Profession

  • Appointed
    • Sept 2011 to  Present Referee for journal articles

      Served/Serving as a referee for manuscripts submitted to the following journals: Communications in Statistics-Simulation and Computation (5); Communications in Statistics-Theory and Methods (1); Journal of the Royal Statistical Society Series C (1); Computational Statistics & Data Analysis (1); South African Statistical Journal (1); Journal of Statistical Theory and Practice (1); Journal of Statistical Computation and Simulation (5); Biometrics (1); Statistical Methods in Medical Research (1); Hacettepe Journal of Mathematics and Statistics (1); REVSTAT Statistical Journal (1)

    • Aug 2016 to  Aug 2016 Organizer and chair of an invited session of a conference

      Organized and chaired an invited session on "Survival Analysis" in Ordered Data and their Applications in Reliability and Survival Analysis: An International Conference in Honour of N. Balakrishnan for his 60th Birthday, McMaster University, Hamilton, Canada, August 07-10, 2016

    • Mar 2017 to  Mar 2017 Judge for poster presentations

      Served as a judge for 12 poster presentations at masters level in Conference of Texas Statisticians (organized by Department of Statistical Science, Southern Methodist University), Dallas, Texas, USA, March 24-25, 2017

Service to the University

  • Appointed
    • Aug 2015 to  Present Member of committee for masters defense

      Served as a member of committee for masters project defense of the following students:

      1) Souad Sosa, Department of Mathematics, November 23, 2015

      2) Lauren Savage, Department of Mathematics, November 24, 2015

      3) David Bagwell, Department of Mathematics, November 25, 2015

      4) Maryam Moghimi, Department of Mathematics, April 24, 2017

      5) Dhivya Srinivasan, Department of Bioengineering, May 04, 2017

      6) Yudhajit Das, Department of Bioengineering, May 13, 2017

      7) Mario Garza, DEpartment of Mathematics, November 16, 2017

    • Feb 2017 to  Present Member of committee for BS degree creation in data science

      Serving as a member of committee for BS degree creation in statistics, informatics, and data science, February 2017 to present

    • Feb 2017 to  Present Member of committee for masters degree creation in data science

      Serving as a member of committee for masters degree creation in data science, February 2017 to present

    • Apr 2017 to  Apr 2017 Member of Math Chair Review Committee

      Served as a member of Math Chair Review Committee, April 2017.

    • Apr 2017 to  Present Member of Doctoral dissertation committee

      Serving as a member of Doctoral dissertation committee for the following students:

      1) Yi Liu, Department of Mathematics, October 11, 2017

      2) Geoffrey Schuette, Department of Mathematics

  • Volunteered
    • Feb 2017 to  Present Member of Graduate Affairs Committee

      Serving as a member of Graduate Affairs Committee, February 2017 to present