Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

This course introduces fundamental concepts and computations for statistical analysis of business data and real-world problems with an emphasis on understanding and interpreting statistical information, and using it to form sound judgments in business situations. The course covers basic descriptive statistical methods, sampling methodology, how to draw inferences from samples to larger populations and how to make predictions based upon historical relationships between variables.

This course introduces fundamental concepts and computations for statistical analysis of business data and real-world problems with an emphasis on understanding and interpreting statistical information, and using it to form sound judgments in business situations. The course covers basic descriptive statistical methods, sampling methodology, how to draw inferences from samples to larger populations and how to make predictions based upon historical relationships between variables.

This course provides a rigorous introduction to mathematical logic from an algorithmic perspective. Topics include Propositional Logic: Horn Clause Satisfiability and SAT Solvers; First Order Logic: Soundness and Completeness of Resolution; Compactness Theorem; Automatic Theorem Proving; Model Checking. Students will learn the theory behind and use the following systems, languages, and tools: the Coq theorem prover, Datalog, the NuSMV Model Checker, and SAT and SMT solvers.

Introduction to mathematical logic: propositional logic, first-order logic, completemess, emphasizing applications to computer science including SAT solvers, model checking, and resolution theorem proving.

Introduction to mathematical logic: propositional logic, first-order logic, completemess, emphasizing applications to computer science including SAT solvers, model checking, and resolution theorem proving.

Embedded Computational Systems is a hands-on laboratory for building integrated systems. We develop analytical skills and discuss sensors, motor drivers, I/O, control logic, and operating systems for embedded applications. Students build several individual projects and invent one integrated, semester-long team project (robot insects and stigmergy, Trinity fire fighting challenge).

This course provides a broad overview of modern image synthesis techniques. Specific topics include photorealistic rendering, global illumination techniques,
Monte Carlo ray tracing, texture synthesis, image editing and compositing.