Software Engineer Machine Learning

NISHEE AGRAWAL

****************@*****.*** 619-***-**** https://www.linkedin.com/in/nisheeagrawal EDUCATION

San Diego State University, San Diego, CA, USA Aug 2021 - May 2023 Masters of Science, Computational Science with Emphasis in Data Science Medicaps University, Indore, MP, India Aug 2017 - May 2021 Bachelors of Technology, Information Technology with Specialization in Machine Learning TECHNICAL SKILLS

Programming Languages: Python, C, C++, SQL

Tools and Utilities: Django, Git, Flask, Jupyter, PyCharm, VSCode, R Studio, Excel, Microsoft Office Data Science/Statistics: NumPy, Pandas, Seaborn, Exploratory Data Analysis, Sentiment Analysis, Scikit-learn, Pytorch, Spark EXPERIENCE

Graduate Research Assistant, SDSU, California, USA Aug 2023 - Present Fraudulent Transaction Detection in Blockchain Platforms

● Working on detecting scams on public blockchain data through thorough analysis of transaction data.

● Developing specialized tools to identify and characterize suspicious transactions within decentralized digital ledgers.

● Identifying Distinguish legitimate transactions from potential fraudulent activities by identifying irregular patterns and behaviors. Graduate Research Assistant and Final Project, Research Foundation, California, USA May 2022 - May 2023 Graph Convolutional Network-based Dynamic Task Scheduling for Mobile Ad-Hoc Computing

● Utilized Graph Convolutional Networks to develop a dynamic task scheduler for Mobile Ad Hoc computing systems.

● Developed communication delay constraint-solving algorithms using python libraries including pytorch.

● Implemented a system that updates in real-time, resulting in improved system efficiency and optimized resource utilization.

● Designed optimized Task Scheduling in real-life applications such as smart transportation and IoT devices. Software Engineer Intern, Tata Consultancy Services (iON), India Jan 2020 - Sep 2020 Automate Handwritten Extraction of text from an image

● Accomplished “Automate Handwritten Extraction of text from an image” using KNN and NN classifiers based on zoning feature extraction technique using python.

● Achieved an accuracy rate of 70.07% for KNN and 72% for NN, with the highest accuracy of 72% achieved for an 80% training and 20% test set split.

INDEPENDENT PROJECTS

Portfolio Website [Python, Django, Html, CSS] May 2023

● Developed and Showcased projects, skills, and achievements in an organized and visually appealing manner.

● Incorporated responsive design, clean interface, and customizable layout to enhance user experience. Fraudulent Transaction Detection [Python, ML, Numpy, Pandas] May 2022

● Developed Machine Learning models, including Bagging Classifier, Decision Tree Classifier, and Random Forest Classifier.

● Achieved reliable prediction results by identifying over 99.50% of fraudulent transactions.

● Acknowledged the trade-off between precision and recall and emphasized the importance of selecting the appropriate strategy for each unique circumstance.

Covid -19 Data Visualization Dashboard [Python, Plotly, Pandas] May 2022

● Conducted an analysis of the impact of the pandemic on demographic factors, including age, race, ethnicity, and gender.

● Developed a Stream lit dashboard to showcase the results of the analysis using Plotly libraries, including a county-level map in a chloropleth format and a line graph displaying the effects of weekly new cases and fatalities.

● Compared the reporting schedules used by various US states information to provide a comprehensive view of the pandemic's impact. Drowsiness Detection System [Python, Keras, Numpy, Pandas, Matplotlib, Haar Cascade] May 2021

● Programmed a cost-effective Drowsiness Detection System/Alert System to monitor the driver’s Drowsiness using Eye Aspect Ratio and Mouth Aspect Ratio.

● The experimental results demonstrate the contour of the face with different eye states and mouth conditions and generates an alarm in case of inactivity when it reaches a threshold. The accuracy of the algorithm is 98.67% . PUBLICATIONS

Drowsiness Detection System (Strad Research Journal of Science and Technology)

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