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 Responsive Para-transportation by predicting the customers’ cancellation. ● Provide executable Python code and classification model. ● Discover best performance metrics. ● Generate well-organized supporting Data […]
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’ decisions and the overall application cycle from 2015 to 2021. The goal of this project is to generate meaningful insights and helpful suggestions on future […]
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 (DPFs) of truck trailers up to fourteen days before breakdown occurs and to identify critical indicators of DPF failures. Upon extracting daily trip records fourteen […]
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 of institutions and programs that students are choosing to attend. Thus, the goal of this project is to better understand our applicant pool and the […]
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 specific location to allow growers to treat this plant disease in time. We will experiment with various Time Series Forecasting (Index: 0 to 100) and […]
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.
The Geriatric Oncology Research Team at URMC wants to better understand chemotherapy tolerability in vulnerable older adults.
The goal of the project was to identify upsell opportunities for Paychex’s 401(k) service products to their existing clients.
Rochester Monroe Anti-Poverty Initiative (RMAPI) launched a new survey to better understand the impact of COVID- 19 on community member’s income and basic needs as well as what community members need to be safe and financially secure. The goal of the project was to analyze the survey and responses to inform United Way which kind of assistance needs to be provided, and what features of living necessities are more important for the respondents.
Identify scenarios where DPF (Diesel Particulate Filter) failure is likely to happen so that the trucking customer can be alerted in advance to avoid costly roadside breakdowns.
The main objective is to identify the maximum water flow tolerance of the Moses-Saunders Dam in order not to exceed the permissible limits of Lake St. Louis.
The project supported the goal of UR Utilities and Energy Management deparment to improve the efficiency of chilled water production through predictive modeling
The goal of the project was to explore public perception on COVID-19 vaccine by analyzing social media platform data (Twitter).
The project aim was: 1) Understand how the degree of mental health issues changed over time and space during COVID-19; 2) Find out what topics are people concerned about, and 3) Infer what group of people are more likely to have mental health issues.
To spur museum membership growth, encourage donations from members, and increase overall museum revenue
The Caldwell-Fay equation (2002) attempts to model what Lake Ontario’s current water level would be if dam construction had never taken place along the St. Lawrence Seaway (i.e. the natural hydraulic state of the lake).
Newly unearthed Lake Ontario data going back to the 1860s has been discovered, and we had the rare opportunity to be the first to digitize and publicly analyze it.
Since this data set predates any dam construction it actually captures the lake’s natural state. Therefore it can be used to verify Caldwell-Fey’s equation which is being used to govern the lake’s inflow and outflow rate on a daily basis.
We were given a patient reported symptoms dataset PRO-CTCAE and applied a variety of clustering methods. The clusters were then statistically tested for associations with a selection of outcomes such as hospitalization. We found significant associations with clusters and outcomes and compared it to linear regression results.
RTS is a regional transportation authority established by New York State and the goal of the project is to find the potential reasons for preventable accidents caused by bus operators. First, descriptive and exploratory analysis is performed on all the data provided and driver-related variables and environmental-related variables. Then, frequent pattern mining is applied and conditional probabilities are calculated for the accident history of operators with high risk of accidents to extract accident patterns.
Wegmans grocery stores experience changes in consumer demand due to weather-related events which may result in item shortages. Our goal was to generate a list of items that are expected to have a huge increase in sales which would allow Wegmans to prepare beforehand. We correlated the change in consumer demand over time with weather warning data and detected anomalous behaviors in item sales.
In this project, we want to apply DSC and machine learning techniques to identify and analyze group communication and interaction patterns from the data collected, e.g. “Who interacts with whom” and “Who attended which breakout sessions”, which can function as an indicator of team performance, group intelligence and meeting efficiency. We can further use the information to increase the productivity of Un-meetings by modifying related elements.