Supti Biswas Email & Phone Number

Cedar Rapids, Ia, Us

South Kingstown, Ri, Us

Kingston, Rhode Island, United States

• Conducted evaluation of finite sample estimators in a cluster-randomized trial for HIV prevention.• Utilized data simulation procedure modeled after the Treatment as Prevention (TasP) framework.• Analyzed and interpreted results to provide insights for future research.

Kingston, Rhode Island, United States

▪ Leveraged R to preprocess and clean raw time series data, ensuring data integrity and consistency. This involved tasks such as handling missing values, outlier detection, and data normalization to prepare the data for analysis. Then I conducted comprehensive exploratory data analysis using statistical techniques and visualization tools in both R and Python. This involved plotting time series data, identifying trends, seasonality, and detecting any anomalies or patterns that could inform further analysis. ▪ Employed a variety of time series models, including but not limited to ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and Exponential Smoothing methods. Utilized R libraries such as forecast and Python libraries such as statsmodels to fit these models to the data.▪ Applied seasonal decomposition techniques, such as Seasonal Decomposition of Time Series (STL) in R and Seasonal-Trend decomposition using LOESS (STL) in Python, to separate time series data into its trend, seasonal, and residual components for deeper analysis. ▪ Engineered relevant features from the time series data to improve model performance including creating lag features, rolling statistics, or incorporating external variables that may influence the time series behavior.

Kingston, Rhode Island, United States

• Course: DSP 552: Computer-based Data Exploration• Responsibilities: Weekly one lecture in laboratory class, Grading assignments• Topics covered: Methods for data acquisition, cleaning, and aggregation. Also, taught computer-basedsystems and methods for data storage, retrieval, manipulation, and visualization. I showed exploratoryanalysis using the R language to create tables, plots, and simple models.• Tools used: RStudio — visualization for big datasets (library: tidyverse, ggplot2), data transformation(library: nycflights13, dplyr), tidy operations (library: tidyr), string manipulation (library: stringr), creatingdates/times (library: lubridate), etc.• Project Supervision: I guided 15 students through their individual projects, providing mentorship and support at every step of the process. From conceptualization to execution, I worked closely with each student to ensure their projects were meticulously crafted and academically sound. Additionally, I played a pivotal role in the assessment process, meticulously grading each project with a keen eye for detail and providing constructive feedback to facilitate their growth and development.

Kingston, Rhode Island, United States

• Responsibilities: Weekly lecture (1.5 hours) on R programming language and its hands on exercises to the undergraduate and graduate students and faculties from different disciplines

Kingston, Rhode Island, United States

▪ Course: STA 308: Introductory Statistics▪ Responsibilities: Weekly one lecture in each of two sections every week, Grading homeworks and exams, Creating exam questions▪ Topics Covered: Through this course, I taught descriptive statistics, basic probability theory and elements of inferential techniques, point and interval estimates, hypothesis testing, and regression.▪ Tools used: I presented the materials from an applied point of view, with many examples and solved problems using MS Excel and Minitab

Master Of Science - Ms, Statistics

Courses Taken: Neural Networks & Deep Learning, Bayesian Statistics, Time Series Analysis, Applied Regression Analysis, Categorical Data.

Master Of Science - Ms, Statistics

Courses Taken: Applied Multivariate Analysis, Theory of Inference, Advanced Biostatistics, Advanced Sampling Methods, Advanced.

Bachelor Of Science - Bs, Statistics

Courses Taken: ANOVA and Linear Inference, Artificial Intelligence, Stochastic Processes, Biostatistics, Ordinary Differential Equation.