First & Last Name Address (123)-123-1234 email@address.com Education Masters of Science in Data Science Aug 2020 University, City, State ● Cumulative GPA: 4.0/4.0 Bachelor of Science in Computer Science Dec 2018 University, City, State Relevant Experience Graduate Research Assistant, SEECS May 2019 - Aug 2020 University, City, State ● Developed regression, classification, and clustering machine learning models used for object detection and temperature analysis ● Facilitated creation of annotated thermal datasets for complex deep learning supervised learning algorithms ● Used Python, OpenCV, and Tensorflow to write machine learning code to train and validate models ● Helped set standards for large scale data collection using drones, from IR camera types and times for optimal flying to the types of data that should be collected ● Communicated insights to sources of funding using tools like Excel and Tableau ● Explored forecasting and economic dispatching methods on time-series wind power generation using LSTM recurrent neural network. Publications ● Co-Author: Name, "Conference poster presentation on image classification," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA. ● Co-Author: Name, “Survey paper on quantifying heat loss with thermal images,” Journal of Building Engineering, 2020, ● Co-Author: Sai Peri, “Paper on deep learning and instance segmentation”, (Sent for revision) Conferences IEEE Big Data 2019 Conference Dec 9 - Dec 12, 2019 Los Angeles, California ● Presented research topic on machine learning and quantifying heat loss at one of the largest big data conferences of the year ● Communicated an emerging concept of using thermal cameras, drones, and machine learning to automate detection and analysis of key points ● Represented the University Computer Science and Data Science program ● Presented project to the top researchers and engineers within the data science field Projects Heat Loss Project (Instance Segmentation) Jul 2019 - Aug 2020 University, City, State ● Trained and validated deep learning model for the purpose of classification using Python, TensorFlow, and scikit-learn ● Implement hotspot detection using several clustering algorithm approaches ● Analyzed large temperature datasets using Python, Tableau, and Excel to select models and communicate insight ● Tuned model performance and parameters to combat issues like long inference times and overfitting ● In conjunction with managing a local UAS team, created guidelines for dataset creation and data capturing ● Heavily relied on experimentation methodology, like hypothesis testing, during development process ● Created automated and scalable architecture and front-end for execution on the host machines along with headless implementation for services like AWS and Azure Hybrid Forecasting Methods with Long Short-term Memory (LSTM) Dec 2019 - Aug 2020 University, City, State ● Exploring hybrid regression and machine learning algorithms in order to create an accurate model for predicting power generated for different wind farms ● Implemented ARIMA/LSTM and SVM/LSTM hybrid models and tested on four wind farms for short/medium/long-term forecasting using Python, Pandas, Matplotlib, TensorFlow and R ● Used visualization frameworks, Matplotlib and ggplot2, to communicate results and process data ● Used the University’s high-performance computer (HPC) and Slurm scripts in order to train and validate, and tune complex models ● From predictions, explored economic dispatching for effective power delivery/load Skills ● Tools: Github, Object-Oriented Programming, R Studio, Jupyter Notebook, AMPL, STATA, Microsoft Windows, macOS, Linux, TensorFlow, Pandas, Matplotlib, OpenCV, Slurm, Anaconda, ggplot2, scikit-learn, Tableau, Pytorch, sklearn ● Database Management: MySQL, Oracle, Microsoft Access ● Languages: Python, Php, Perl, Javascript, HTML/CSS, Java, XML, Android, C
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