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 regressio n , 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 ● Us ed Python, OpenCV, and Tensorflow to write machine l earning 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 ● C ommunicated insight s to sources of funding using tools like Excel and Tableau ● Explore d forecasting and economic dispatching methods on time - series wind power generation using LSTM recurrent neural network. Publications ● Co - Autho r: Name , " Conference poster pres entation 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 r evision ) Conferences IEEE Big Data 2019 Conference Dec 9 - Dec 12, 201 9 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 ● T rained and validated deep learning model for the purpose of classification us ing Python , Tensor F low , and scikit - learn ● I mplement 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 hypoth esis testing, during development process ● Created automated and scalable architecture and front - end for execution on the host machine s 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 ● E xploring 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, TensorFlo w and R ● Used visualization frameworks, Matplotlib and ggplot 2 , 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 pre dictions, 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, ggplot 2 , scikit - learn, Tableau, Pytorch , sklearn ● Database Management: MySQL, Oracle, Microsoft Access ● Languages: Python, Php, Perl, Javascript, HTML/CSS, Java, XML, Android, C