skip navigation

This page looks better in modern browsers. Please upgrade.

Brown Home Brown Home Brown Home Brown CS

Laboratory for Computation in Finance and Commerce

Stock Traders

Faculty

Graduate Students


More Information:   Welcome   News   Projects   Publications

Projects

COMMODITY TRADING This project studies computation problems related to inventory management, and commodity trading. The focus is on efficient algorithms for evaluating and ranking various decision options, and for estimating portfolio valuation and risk.
(Project Lead: Eli Upfal)
TRANSPORTATION NETWORKS Unreliable transportation networks pose a serious challenge for the reliable supply of resources. This projects investigates robust strategies for trading commodities such as electricity and natural gas which are provided through transportation networks.
(Project Lead: Eli Upfal and Thomas Hofmann)
MULTI AGENT LEARNING This project is concerned with multi-agent learning among Internet agents engaged in game-theoretic (i.e., strategic) activities, such as bidding in on-line auctions and dynamic pricing among shopbots and pricebots
(Project Lead: Amy Greenwald)
RECOMMENDER SYSTEMS Recommender systems are used in electronic commerce to help people to find products and information they are looking for. Our research has been focused on statistical methods for collaborative or social filtering that make recommendations based on known user profiles.
(Project Lead: Thomas Hofmann)
CONFIGURATION SYSTEMS Configuration systems are used in electronic commerce to help design a complex product (e.g., a personal computer system) from components (e.g., RAM, network cards) given constraints on its functionality (e.g., 3-D graphics, high-speed communication) and, possibly, multiple decision criteria. Our research focuses on the design of algorithmic techniques and languages to support the design of configuration systems. (Project Lead: Pascal Van Hentenryck)

Enabling Technologies

PLANING UNDER UNCERTAINTY The focus of this research project is on planning under uncertainty using Markov decision processes. The main application areas is the design of automated planning and scheduling systems for stochastic domains. The theoretical emphasis is on algorithms for solving Markov decision processes with very large state and action spaces.
(Project Lead: Tom Dean)
STOCHASTIC OPTIMIZATION In combinatorial optimization problems such as optimal scheduling and resource allocation, the variables that define the problem instance are typically not known to arbitrary precision in advance due to measurement noise and system failures. This project aims at developing optimization techniques and languages to compute robust and alterable solutions to stochastic optimization problems.
(Project Lead: Pascal Van Hentenryck)
Page Owner: Thomas Hofmann

Page Owner: Thomas Hofmann Last Modified: Tue Oct 14 19:01:38 2008