My academic experience, working as an Assistant Professor with a solid research background and teaching various online and face to face courses. I am interested in developing a career that combines teaching and research while maintaining my research interest in health data analysis, data science with computer applications and commitment to excellent pedagogical practises, and a demonstrated interest in teaching-related scholarly activities.

**Mailing Address:**Department of Statistical Sciences, University of Toronto, 100 St. George St., Toronto ON, M5S 3G3**Office:**TBD**Office Hours:****STA457H1/STA2202H**- Monday: 9 - 10
- Tuesday: 9 - 10

**STA452H1**- Monday: 10 - 11
- Tuesday: 10 - 11

**Teaching Fall 2020:**- Time Series Analysis - STA457H1/STA2202H
- Mathematical Statistics I - STA452H1

*Mathematical Statistics I - STA452H***Academic Year:**Fall 2020**Calendar Description:**Statistical theory and its applications at an advanced mathematical level. Topics include: Probability and distribution theory as it specifically pertains to the statistical analysis of data; linear models and the geometry of data; least squares and the connection to conditional expectation; the basic concept of inference and the likelihood function.*Time Series Analysis – STA457H1/STA2202H***Academic Year:**Fall 2020**Calendar Description:**An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods; filtering and smoothing time series; theory of stationary processes; identification and estimation of time series models; forecasting; seasonal adjustment; spectral estimation; bivariate time series models.

*MATH 101:10 - Mathematical Concepts I***Academic Year:**Winter 2020, Fall 2019, Winter 2019, and Fall 2018**Calendar Description:**This course surveys topics from diverse areas of mathematics, including problem solving, set theory, logic, historical numeration systems, and number theory. Students will solve problems using processes such as abstraction, pattern recognition, deduction and generalization. Acceptable for credit only in the Faculties of Arts and Business and the Departments of Human Kinetics, Human Nutrition and Nursing. Prerequisite: Grade 12 math or equivalent. Three credits and two seventy-five-minute lectures per week.*ENGR 224/STAT 231/STAT 224 - Probability and Statistics for Engineers***Academic Year:**Winter 2020**Calendar Description:**This course covers probability laws and the interpretation of numerical data, probability distributions and probability densities, functions of random variables, joint distributions, characteristic functions, inferences concerning mean and variance, tests of hypotheses, linear regression, and time series analysis. Engineering applications are emphasized and statistical computer packages are used extensively. Three credits, three fifty-minute lectures, and two-hour problem session per week. Prerequisite: Grade 12 math or equivalent.*STAT 101(12H/13H) - Introductory Statistics***Academic Year:**Winter 2020, Fall 2019 and Fall 2018**Calendar Description:**The course cover the following topics: Introduction to Data; Picturing Variation with Graphs; Numerical Summaries of Center and Variation; Modeling Variation with Probability; Modeling Random Events: The Normal and Binomial Models; Survey Sampling and Inference; Hypothesis Testing for Population Proportions; Inferring Population Means; Regression Analysis: Exploring Associations between Variables. Two seventy-five-minutes lectures per week.*STAT 101(66/67) - Introductory Statistics***Academic Year:**Fall 2019, Summer 2019, Spring 2019, Summer 2018, Spring 2018, Summer 2017, and Spring 2017**Calendar Description:**The course cover the following topics: Introduction to Data; Picturing Variation with Graphs; Numerical Summaries of Center and Variation; Modeling Variation with Probability; Modeling Random Events: The Normal and Binomial Models; Survey Sampling and Inference; Hypothesis Testing for Population Proportions; Inferring Population Means; Regression Analysis: Exploring Associations between Variables.*STAT 331/BIOL 331 - Statistical Methods***Academic Year:**Winter 2020 and Winter 2019**Calendar Description:**An investigation of statistics and experimental design in the context of biological and health science issues. Topics include analysis of variance, categorical data; distribution-free tests; linear and multiple regression. Students will learn to analyze data and interpret conclusions using a statistical software package. Recommended strongly for all major, advanced major, and honours students. Credit will be granted for only one of STAT 331, PSYC 394, PSYC 390. Cross-listed as BIOL 331. Prerequisite: STAT 101(201) or 224 or 231.*STAT 311 - Survey Sampling Design***Academic Year:**Winter 2019 and Fall 2016**Calendar Description:**The course cover the following topics: Introduction to Survey Sampling; Simple Random Sampling; Confidence Intervals; Sample Size Estimation; Estimating Proportions, Ratios, and Sub-population Means; Unequal Probability Sampling; Ratio Estimation; Regression Estimation; Stratified Sampling; Cluster and Systematic Sampling; Multi-stage Designs.*STAT 101(21B/23B) - Introductory Statistics***Academic Year:**Winter 2018 and Winter 2017**Calendar Description:**The course cover the following topics: Introduction to Data; Picturing Variation with Graphs; Numerical Summaries of Center and Variation; Modeling Variation with Probability; Modeling Random Events: The Normal and Binomial Models; Survey Sampling and Inference; Hypothesis Testing for Population Proportions; Inferring Population Means; Regression Analysis: Exploring Associations between Variables.*CSCI 223 - Introduction to Data Science***Academic Year:**Winter 2018**Calendar Description:**The course cover the following topics: Introduction to Data Science; Data; Exploring Data; Classification: Basic Concepts, Decision Trees, and Model Evaluation; Classification: Alternative Techniques; Association Analysis: Basic Concepts and Algorithms; Association Analysis: Advanced Concepts; Cluster Analysis: Basic Concepts and Algorithms; Cluster Analysis: Additional Issues and Algorithms; Anomaly Detection.

*CSCI 125/ENGR 144-Engineering Problem Solving with C***Academic Year:**Fall 2017 and Fall 2016**Calendar Description:**The course cover the following topics: Problem solving methodology; Data types; Constants and variables; Arithmetic operators; Standard Input/Output; Mathematical functions; Structured programming; Conditional expressions; Selection statements (if, if/else, switch); Loop structures (while, do/while, for loops); File Input/ Output; User-defined functions; Arrays and matrices; Pointers.*CS 135 - Computer Application Technology***Academic Year:**Fall 2017 and Fall 2016**Calendar Description:**This is a hands-on course in which you will learn to use a computer to practice the four most popular programs within the Microsoft Office Suite (Word, Excel, Access, and PowerPoint). You will learn to be an intermediate level user of the Microsoft Offic Suite. Within the Microsoft Office Suite, you will use Word, Excel, Access, and PowerPoint. Microsoft Word is a word processing program with which you can create common business and personal documents. Microsoft Excel is a spreadsheet program that organizes and calculates accounting-type information. Microsoft Access is a database program that organizes large amounts of information in a useful manner. Finally, Microsoft PowerPoint is a presentation graphics program with which you can develop slides to accompany an oral presentation.*CS 128 - Computing Literacy and Coding for Problem Solving using Python***Academic Year:**Fall 2017**Calendar Description:**This course introduces coding for everyday problem solving. Coding is introduced through multimedia computing including manipulation of images, sound and video. Intuitive programming languages, constructs and environment are used to introduce basic coding structures. The prevalence of computing in modern society is discussed. Students from all disciplines can develop their powers of coding for problem solving.*STAT 472 - Computational Statistics***Academic Year:**Winter 2017**Calendar Description:**An introduction to computational methods in applied statistics. The objective of this course is to teach students important computational technique used in scientific programming (using R or RStudio) and to introduce the stochastic modelling. After learning this course, students are expected to be able to write codes for your statistical analysis, be able to draw simple and informative graphs to display your findings, be able to develop codes for simulating the data for a specific scenario (or model) and test the adequacy of the model, and be able to analyze large data sets using modern statistical tools.*STAT 333 - Introductory Probability Theory***Academic Year:**Fall 2016**Calendar Description:**The course cover the following topics: Combinatorial Analysis; Axioms of Probability; Conditional Probability and Independence; Discrete Random Variables and Discrete Distributions; Continuous Random Variables and Continuous Distributions; Transformations; Joint Distributed Random Variables; Properties of Expectation; Moment Generating Function; Central Limit Theorems.

*STAT 2550-Statistics (Section 056)***Academic Year:**Fall 2015**Calendar Description:**This is an introduction to basic statistics methods with an emphasis on applications to the sciences. Material includes descriptive statistics, elementary probability, binomial distribution, Poisson distribution, normal distribution, sampling distribution, estimation and hypothesis testing (both one and two sample cases), chi-square test, one way analysis of variance, correlation and simple linear regression.

The majority of the research I have completed and continue to pursue fall under the broad umbrella of empirical-likelihood-based variable selection, especially in generalized linear models, survival analysis, and longitudinal studies. I became interested in this work as a doctoral candidate, where I sought to find efficient and consistent inference for the parameter of interest when the model is unspecified.

I'm working on expanding my expertise in these areas of data mining techniques regard high-dimensional inference and include exploratory data analysis (EDA), dimensionality reduction, visualization, loss-based estimation with cross-validation (e.g., density estimation, classification, regression, model selection), cluster analysis, and multiple hypothesis testing.

Penalized Empirical Likelihood based Variable Selection for Longitudinal Data Analysis. Tharshanna Nadarajah, Asokan Mulayath Variyath, & J Concepción Loredo-Osti, American Journal of Mathematical and Management Sciences, 2020.

Empirical Likelihood Based Longitudinal Data Analysis. Tharshanna Nadarajah, Asokan Mulayath Variyath, & J Concepción Loredo-Osti, Open Journal of Statistics, 2020, 10, 611-639.

Penalized Generalized Quasi-Likelihood based Variable Selection for Longitudinal Data. Tharshanna Nadarajah, Asokan Mulayath Variyath, & J Concepción Loredo-Osti, Springer Lecture Notes in Statistics, 2016.

Improving the Students’ Learning Process Through the Use of Statistical Applets. Asokan Mulayath Variyath & Tharshanna Nadarajah, Under Review.

Technology can be used to improve the understanding of statistical principles by providing students with visual and dynamic presentations of ideas and making it possible for students to investigate the methodologies. We have constructed various interactive statistical applets for these purposes under Dr. Variyath's supervision during my graduate program. These can be very effective in teaching a wide range of statistical concepts. These applets are most appropriate for lower-level undergraduate statistics courses, and they facilitate the investigation of statistical concepts rather than calculation or analysis. We selected a few statistical techniques for developing applets. The factors considered for selecting methods are the importance of frequently used techniques, their difficulty to understand, and their usability in practice. A set of specific techniques identified are summary statistics, histograms, sampling techniques, probability, normal distribution, scatter plot, and regression.

These applets can be found at https://www.math.mun.ca/~variyath/Stat_Applets/lrngHome.html

I was part of Statistical Consulting Service (2011-2016) at Memorial University of Newfoundland and StFX (2016-date). Statistical help for companies, faculty, graduate, and undergraduate students.

I advised clients with an extensive background in statistics and biostatistics of practical experience in quantitative methods.

Inputting, organizing, and cleaning the data.

Implemented statistical analysis such as t-test, ANOVA, traditional regression, logistic regression, MANOVA, factor analysis, sampling techniques, longitudinal data analysis, survival analysis, time series analysis, discriminant analysis, and more advanced statistical methods such as variable selection and empirical likelihood methods.

Used statistical programming languages such as SAS, SPSS, STATA, Minitab, R, and Access.

Explaining the results.

I completed a short-term external project *Statistical Analysis of the Vessel Movement* sponsored by Suncore Energy Inc. under
the supervision of Dr. Variyath and Dr. Oyet in 2014.

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