Predicting 850 Job Title Codes Using Hierarchical Classification
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.
Pairs Trading Algorithm Development for FLXAI
1. Introduction Investment, based on the definition of Robinhood (one famous online brokerage platform), is the attempt to buy assets (stocks, real estate, etc.) with own resources (money or credit)…
Sentiment Analysis on Twitter Data Regarding Dental Issues associated with Opioid Consumption
DSCC383 Group I Team Youssef Ouenniche, Ian Kaplan, Michael Kingsley, Goutham Swaminathan, Shiva Rahul Edara Advisor: Professor Ajay Anand | Sponsor: Dr. Zidian Xie Analysis & Modeling Background: Opioid Use…
Public perception of marijuana/cannabis on Twitter in the US
Team Members Runtao Zhou, Qihao Yun, Jiahang Wu, Zhengyuan Wang, Mengmeng Yu Project Sponsor Dr. Zidian Xie Project descriptions and motivation Our project aims to explore the public’s perception of…
University of Rochester: Corporate Purchasing Non-Clinical Spend Analysis
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.…
Pickleball Analytics
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.
A Comparison of MS and Ph.D. Programs for Three University of Rochester Departments between 2015-2022
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…
Clustering Analysis of HIV Prevention Strategies on Magnetic Couples Study
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.
Machine Learning Decision Support Tool For Trauma Activation Level
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).
Mitigating Class Imbalance by Generating Synthetic Coughs Using WaveGAN
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.
Classifying Patient Perceptions of Tolerability of Cancer Treatment
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…
Revenue Forecast Using Time Series-Based Deep Learning Model
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…
Rochester Transit Service
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…
MacroX-Nightlights
This project uses the luminescence of the nighttime sky as a predictive features for economic activity.
URMC-COVID Resource Allocation
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.
City of Rochester
This project aims to build a model which detects features such as crosswalks and curb ramps at intersections in the city of Rochester.
GIDS-2
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’…
Vnomics
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…
GIDS-1: Masters Admissions
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…
Benchmark Labs – Powdery Mildew Prediction
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…
URMC Geriatric Oncology
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.
Vnomics 1
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.
City of Rochester Crime & Convenience Stores
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.
URMC Geriatric Oncology
The Geriatric Oncology Research Team at URMC wants to better understand chemotherapy tolerability in vulnerable older adults.
A Model to Predict Paychex 401(k) Services’ Potential Clients and Explainers for Analysis
The goal of the project was to identify upsell opportunities for Paychex’s 401(k) service products to their existing clients.
COVID-19 Survey Analysis to Understand the Community’s Socioeconomic Needs
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.
Predictive Maintainence for Trucks
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.
Modeling of Lake St. Louis Water Levels
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.
Improve Efficiency of Chilled Water Production
The project supported the goal of UR Utilities and Energy Management deparment to improve the efficiency of chilled water production through predictive modeling
Public Perception on COVID-19 Vaccines
The goal of the project was to explore public perception on COVID-19 vaccine by analyzing social media platform data (Twitter).