Machine Learning Archive

This project showcases an analysis on the research output at the University of Rochester.

We designed an ML-Driven Wirelessly Controlled Robot which uses machine learning to detect objects of a given class and automatically navigate towards the closest one within its camera view. The robot is able to detect over 80 different classes of objects, and can also be controlled via three modes: Physical Joystick, ML Object Detection, and Web‑Based Virtual Joystick.

The Pluck & Dispense System (PDSys™) automates the precise transfer of cells from microbubbles to wells, enhancing accuracy and efficiency in laboratory research. This system addresses key challenges such as reducing human error and increasing throughput, making it a valuable tool for small pharmaceutical companies or academic researchers working with microbubbles.

DiscSense is aiming to advance athletes' throwing skills through the development of a gyroscopic sensor that tracks the end conditions of throws. Throughout our capstone project, we concentrated on building a classification model that will aid athletes in recognizing patterns of successful throws and pinpoint prevalent errors.

Author Sponsor Dr. Zidian Xie Instructor Professor Ajay Anand Professor Cantay Caliskan Abstract In the digital age, Twitter has emerged as ancentral arena for public health issues, particularly those involv-ing…

ML Based classification model to detect triage level for patients arriving at trauma centre, and thus allocate appropriate resources. This was achieved using patients' data from URMC (Department of Paediatrics).

Team Yihe Chen Harry Huang Junting Chen Kehan Yu Mentor Cantay Caliskan Abstract Predictive Analytics for Demand Responsive Para- transportation Vision & Goal ● Create a productive schedule for Demand…

This project uses the luminescence of the nighttime sky as a predictive features for economic activity.

This project aims to observe, visualize, and model the trends in which COVID-19 patients at the University of Medical Center were allocated ventilators. Descriptive analyses are performed to investigate the relationships between variables such as but not limited to recovery rate and length of ventilator allocation and gender, race, and age.

This project aims to build a model which detects features such as crosswalks and curb ramps at intersections in the city of Rochester.

Team Qianqian Gu (Project Manager) Wei Wu Chen Yao Hanyang Zhang Mentor Ajay Anand Abstract The Goergen Institute for Data Science (GIDS) masters admission office wants to better understand applicants’…

Team Steven Dai Zachary Mustin Uzoma Ohajekwe Duy Pham Sponsor Vnomics Corporation Matt Mayo Mentor Prof. Ajay Anand Abstract Our task is to predict imminent failures in Diesel Particulate Filters…