Virginia and Charlottesville have seen a blossoming of exciting machine learning work emerging from academia, startups and government endeavours. This one-day event will highlight novel and applied machine learning research that brings together this growing community.
The conference’s emphasis is on advancing the understanding of practical issues of applying data science and machine learning techniques to real world problems. Presentations will describe the implementations of solutions and systems for practical tasks in data science and applied machine learning.
Doug Bryan, VP of Foundational Data Products, Merkle: 1:05PM Looking for a Boost? XGBoost Performance for Customer Look-alike Models
Taryn Price and Courtney Shindeldecker, Data Scientists, CCRi: 3PM Learn about Your Location (Using ALL Your Data)
A series of rapid fire talks will highlight the strides being made in data science and specifically with machine learning.
1:40PM – Andrew Montalenti, CTO, Parse.ly Predicting Internet Attention
2:00PM – Colin Cassady, M.S. Student, UVA Data Science Institute Leveraging Machine Vision to Predict Entrée Composition
2:20PM – Dr. Andrew Fast, Chief Scientist, Elder Research Using Data Science to Ask Why: A brief introduction to Causal Modeling
3:30PM – Dr. Hongning Wang, Assistant Professor, UVA Modeling Social Norms Evolution for Personalized Sentiment Classification
3:50PM – Eileen Krepkovich, Biomedical Engineer, Barron Associates Helping People Walk: Modeling Energy Expenditure with Prosthetic-Integrated Sensors