Posts categorized DSC Archive

In order to help vineyards in the Finger Lakes region with frost prediction, we built a Hidden Markov Model matching algorithm that computes log-likelihood scores between seasons. We implemented seasonal grouping in this approach.

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…

Our new approach uses Major Code, lessening the total codes from 850 to 23. In Major Classification, we used input layer and embedding as well as 2 hidden layers. For Job Title Classification by Major, we used Random Forest to output based on Majority Voting.

Our hierarchical approach uses Major Code, lessening the total codes from 850 to 23. In Major Classification, we used input layer and embedding as well as 2 hidden layers. For Job Title Classification by Major, we used Random Forest to output based on Majority Voting.

Team Team Member Major Amanda Pignataro B.S. in Data Science Avery Girksy B.S. in Data Science Ryan Hilton B.S. in Data Science Vaarya Srivastava B.S. in Data Science Mentor Prof.…

Our project is to aid in the development of a pickleball analytics platform by improving ball detection and tracking. The baseline model used is a TrackNetV2 (Sun et. al. 2020) model trained on badminton, and the purpose of this project is to adapt the model by using transfer learning techniques to improve its performance in pickleball.

1. Team 2. Mentor  Georgen Institute for Data Science (GIDS) 3. Sponsor Lisa Altman 4. Abstract Due to the continuously increased demand for Data Science degrees, our school will open…

Magnetic Couple Study collected data and information from heterosexual couples who are of mixed HIV-status and recorded their prevention methods, including condom use, viral load, and new method-PrEP. This project focused on using unsupervised learning algorithms to examine the main predictors associated with protection strategies.

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).

Virufy has created machine learning models that analyze coughs in order to provide a COVID-19 diagnosis. Training these models requires an even balance between COVID-positive and COVID-negative data, but they unfortunately have very little positive data. In order to combat this issue, the team hoped to generate synthetic coughs that closely resemble real coughs.

Team Academic Advisor Prof. Ajay Anand and Prof. Cantay Caliskan Project Sponsor Dr. Erika Ramsdale and the URMC Geriatric Oncology Team Introduction Studies in recent years have shown that cancer…

Team Mentor Professor Ajay Anand Dr. Preston Countryman Sponsor Corning Inc. – Data Science & Intelligence (DSI) Team Abstract Corning wants to develop a deep-time-series model to perform accurate customer-level…

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…

Team Xiaoen Ding Jiecheng Gu Sung Beom Park Joseph Smith Mentor Ajay Anand Sponsor Lisa Altman Gretchen Briscoe Abstract The Goergen Institute for Data Science wants to understand the types…

Team Yihan Shao Chuqin Wu Melanie Xue Zihe Zheng Mentor Cantay Çalışkan Abstract The goal of this project is to forecast the pest pressure of Grape Powdery Mildew at a…

This project investigates the associations between geriatric assessment based features and relative dose intensity of chemotherapy. It is at the first few phases of Wilmot Cancer Institute’s Ger Oncology Research team at University of Rochester Medical Center. The team refined the data preprocessing pipeline, built predictive models and employed feature selection on the dataset, providing insightful suggestions for future work in cancer studies.

Successfully built autoencoder models with ML Flow and Keras to predict truck failures given sensor data for a fuel optimization startup called Vnomics. The model is optimized by comprehensive time series feature engineering with TS Fresh to achieve a high recall score of 56% on unseen data.

The City of Rochester wants to understand if physical proximity to a convenience store or liquor store affects the likelihood of different types of part 1 crimes.