I’m Suyash Sorte, a graduate student at the Rochester Institute of Technology’s Computer Science program. I am looking forward to fully immerse myself in a professional world working with those organizations and professionals with the same passion for creative, reliable, and scalable applications that have brought me so far in my educational endeavors.
My internship and graduate studies projects have furthered my intellectual curiosities in and around software development. It is those same curiosities that brought me to the U.S. from India to continue my studies and the same that motivates me to get in touch with people like you to discuss collaboration. From work in machine learning and big data to customer-focused web dashboard creation, I am so enthusiastic to bring my skills and experiences to building real, usable products for customers and for society.
KKR & Co., Inc., New York, NY
Tools/Technologies: FLASK, SQL, Python, IIS, Redis, SSRS, SSMS, Agile, JIRA
• Devised permission-driven business user request processes on position and transaction data, integrating reporting and business logic by generating SQL queries dynamically based on user input using python.
• Improved web product data architecture via data grid testing and performance by implementing Redis cache, increasing data retrieval speeds by 70%.
• Overhauled business logic in SQL stored procedures to fetch accurate data from the server.
Data Quality Database Design and Implementation
Developed a tool to upload new data containing information of android devices to automatically add to the database while maintaining the same relationships among tables.
Tools/Technologies: HTML5, CSS3, Bootstrap, PHP, SQLite
Designed full-stack car rental website with searching, sorting and filtering functionality by generating SQL queries dynamically based on car brand, style, and price.
Google NSynth Dataset Taxonomy
Tools/Technologies: Python, Torch, NumPy, Scikit-learn, pandas
Audited accuracy percentages of the Google NSynth machine learning model to correctly classify ten musical instrument recordings. Benchmarked train, test, and validation data of convolutional neural network (60%), BSLTM (58%), and LSTM models (56%).
Satellite Mapping Route Finder
Tools/Technologies: U Net Architecture, A* Search Algorithm
Built route-finding algorithmic model based on raw satellite images sourced from the Space Net dataset. Incorporated machine learning convolution/ deconvolution concepts into route discovery using A* pathfinding algorithm.