Projects in 2012–2013
E. & J. Gallo Winery: Livingston Cooperage Problem
- Susan Martonosi
E. & J. Gallo Winery has requested a program to evaluate the size, type, and quantity of new tanks required to accommodate increased volume and variety of wine production at their Livingston winery facility. Our team focused on modeling the allocation of storage tanks to a full harvest of different wine types, with a target solve time of only a few minutes. Our program recommends new tanks to purchase based on the projected harvest, costs of varying tanks, and quality constraints.
- Eric Autry
- Corinne McElwain
- Stephanie Porter
- Elizabeth Schofield (Project Manager)
InstaMed Communications LLC: Estimating Health Care Prices Using Historical Data
- Talithia Williams
The 2012–2013 InstaMed Clinic Project seeks to implement and evaluate algorithms that use past healthcare claims in order to provide prompt cost estimates of medical services for doctors and patients. The team approaches the project using a simple statistical, heuristic approach as well as more advanced machine learning archetypes of decision trees and neural networks. The team also intends to provide InstaMed with a platform suitable to evaluate the accuracy, precision, and versatility of algorithms.
- Peter Loftus (fall) (Project Manager)
- Matt Hin (spring) (Project Manager)
- Leverett Morgan
- Lisbeth Santana
Jane Street Capital: Analyzing the Effects of Market Legislation on Market Behavior
- Weiqing Gu
Recent events have prompted the Securities and Exchange Commission to institute new regulations to prevent extreme market behavior. These regulations take effect for short periods of time when individual securities rapidly change in price. However, the impact of these regulations on market behavior has not yet been extensively studied. Our project is to apply quantitative techniques on high-frequency market data to determine the impact of these rules on securities.
- Devin Bowers (Project Manager)
- Matt Johnson
- Jeehyun Kim
- Matt Toal
Shell Exploration and Production: Optimizing Drilling Rate With Machine Learning
- Rachel Levy
Shell E&P spends millions of dollars every day on offshore drilling operations. To monitor this process, a substantial amount of drilling data is collected and transmitted to an onshore operation center in real time. The team worked to find a method to analyze the aggregated data to determine for similar rigs when drilling is not proceeding at an optimal rate. The algorithm implemented identifies these suboptimal conditions and then proposes changes of drilling parameters to optimize drilling speed.
- Kyle Chakos
- Sam Gray
- Xanda Schofield (Project Manager)
- John Wentworth
Southern California Gas Company: Manual Meter Reading Cost Minimization
- Lisette de Pillis
Southern California Gas (SCG) Company is converting manually read meters to automated meters, obviating the need for employees to visit and read gas meters in person. As clusters of meters throughout Southern California become automated over time, the number of remaining manually read meters will diminish until all meters have become automated. Our team is developing a tool that uses the locations of the remaining manually read meters to generate new cost-minimizing meter reading routes.
- Ben Gross
- Katarina Hoeger (Project Manager)
- Kevin Varela O'Hara
- Tim Yee