Surbhi Rathore

3+ years of ML Research Experience. I am a Data Science/ Machine Learning practitioner who tackles real world problems with the help of analytics and ML libraries. I design algorithms and frameworks to provides actionable insights from the data.

Getting Started with Deep Learning NVIDIA, March 2021

• Information Theoretic Framework for Evaluation of Task Level Fairness- EAI-KDD22 - https://charliezhaoyinpeng.github.io/EAI-KDD22/camera_ready/information.pdf
• Use of neurocognitive data to evaluate text summarization of science content - presented at NARST 2024
• Model-agnostic feature selection for fairness - 2022 - https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=3264&context=theses
• Integrating Neuroscience Principles into the Development of a Tailored Daily Report: A Universal Design Approach (Article in International Journal of Psychology and Neuroscience · August 2023 - https://neuropsyjournal.wordpress.com/articles/psycho-tailored-daily-reports/)

• Time Series Analysis; R, Python, statsmodels, forecast, TSA, Executed a demand forecasting project by implementing a Linear Regression model to evaluate the interdependencies among variables. Applied ARIMA and SARIMA models to perform comparative analysis, accounting for the seasonality and upward trend in product demand.
• Big Data Analysis; R, Python, Matplotlib, ggplot, Developed predictive models, including the Susceptible-Infectious-Recovered (SIR) model and both simple and multiple linear regression models, to quantitatively analyze the impact of COVID-19.
• Neural Networks & Deep Learning; Python, ML, NumPy, PyTorch, Keras, pandas, Matplotlib, Image classification using CNN, MLP and Multiclass Logistic Regression to compare accuracies and losses.
• Big Data Algorithms; Apache Spark- PySpark, DynamoDB, Kafka, Implemented Lambda Architecture (Big Data pipeline) to process real time and batch data. Implemented customer segmentation using K means clustering.