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Many areas of science and information technology are producing tremendous amounts of data at dramatically higher rates than ever before, as a result of innovations in observational equipment, the advent of the World Wide Web, and the continued exponential growth of computational capabilities. The avalanche of data has created the exciting opportunity for new scientific discovery and insights through data analysis, which is quickly joining simulation as a key component of the third, computational, pillar of science. It has also driven the development of novel information services with powerful societal impact. However, truly taking advantage of these opportunities requires an interdisciplinary approach, bringing together data analysis methods, dynamic simulation models, applications of the methods to real-world problems, scalable systems, and their interplay.

Bringing together faculty with relevant expertise from several different departments, this cross-disciplinary course provides an introduction to many of the key modern methods for data analysis, and their specializations to and applications across several disciplines ranging from biology to astrophysics to analysis of text, audio and video. It also discusses the applicability of key approaches to different application areas, exposing students to applications of the methods in a variety of disciplines, to foster shared learning and expose new challenges. And it discusses the interplay of data analysis with dynamic simulation and model analysis, which is increasingly critical in many areas, as well as with scalable computing as a vehicle for performing sophisticated analyses on large data sets.

The course is appropriate for students from many departments who want to learn about methods and their applications, including Computer Science students as well as other students from science and engineering disciplines. Audits are welcome. 

Schedule/Classroom Assignment:
Mondays 1:30 pm - 4:00 pm
Computer Science 105 (Small Auditorium)

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Syllabus and Course Materials

COS 598C
Advanced Topics in Computer Science: Graphical Models
David M. Blei

Description/Objectives:
Graphical models are an indispensable tool to machine learning, with applications in information retrieval, computer vision, natural language processing, and bioinformatics. Following a brief introduction to this formalism, we will study advanced research papers focusing on approximate inference. Topics include Monte Carlo Markov chain sampling, variational methods, nonparametric Bayesian models, and relational data. Reading will be from several fields, including computer science, statistics, and various application areas.

Schedule/Classroom Assignment:
Class Number: 42740 - Seminar S01 : 1:30 pm - 4:20 pm M
    Computer Science Building (COMPU) 301

CHE 537/COS 554
Computational Analysis of Biological Networks
Stanislav Y. Shvartsman

Description/Objectives:

Analysis of biological networks requires a combination of tools from all branches of applied mathematics and engineering. The course will introduce computational techniques for the analysis of structure and function of genetic, biochemical, and cellular networks. The topics covered include dynamics of intracellular networks, network identification from gene expression data, deterministic and probabilistic tools for networks inference and simulation.

Schedule/Classroom Assignment:
Class Number: 40545 - Lecture L01 : 9:00 am - 12:00 pm W
    Carl Icahn Laboratory (ICAHN) 280  

ORF 523
Nonlinear Optimization
Alexandre W. d'Aspremont

Description/Objectives:
An introduction to the central concepts needed for studying the theory, algorithms, and applications
of nonlinear optimization problems. Topics covered include first- and second-order optimality conditions; unconstrained methods, including steepest descent, conjugate gradient, and quasi-Newtonian methods; constrained active-set methods; and duality theory and Lagrangian methods. Prerequisite: linear optimization.

Schedule/Classroom Assignment:
Class Number: 41739 - Lecture L01 : 11:00 am - 12:20 pm M W
    Computer Science Building (COMPU) 102

ORF 569
Nonparametric Modeling and Functional Data Analysis
David Lando

Description/Objectives:
Driven by many sophisticated applications and fueled by modern computing power, many useful data-analytic modeling techniques have been proposed to relax traditional parametric models and to exploit possible hidden structure. The techniques are also called semiparametric and nonparametric regression. The course will cover many powerful ideas in the data-analytic modeling with emphasis on the analysis of functional data. The course will emphasize on the underlying theory and methodology that are driven by many applications.

Schedule/Classroom Assignment:
Class Number: 43380 - Seminar S01 : 11:00 am - 12:20 pm M W
    Engineering Quad E-Wing (EQUAE) E223  

MAE 557
Simulation and Modeling of Fluid Flows
Maria P. Martin


Description/Objectives:
Numerical methods are applied to solve the equations that govern fluid motion. Fluid flow problems involve convection, diffusion, and source terms. The governing equations are non-linear and coupled. Finite-difference and finite volume methods are considered, together with concepts of accuracy, consistency, stability, convergence, conservation, and shock capturing. A range of current methods is reviewed with emphasis on multidimensional steady and unsteady compressible flows. Homework topics include writing codes to solve the conservation equation for a scalar, boundary layer flow, shock tube flow, application to curvilinear coordinates.

Schedule/Classroom Assignment:
Class Number: 42820 - Lecture L01 : 10:30 am - 11:50 am T Th
    Engineering Quad A-Wing (EQUAA) A124

APC 518/ORF 518
Applied Stochastic Analysis and Methods Weinan E

Description/Objectives:
An introduction to stochastic models in the physical sciences with emphasis on numerical methods, asymptotics and connection with partial differential equations. After a brief introduction of the basics of probability theory, Markov process and stochastic differential equations, the course concentrates on Fokker-Planck equations, invariant distributions, path integrals, large deviation and rare events. Numerical methods for computing transition pathways and transition rates, kinetic Monte Carlo methods will be discussed. Prerequisite: Elementary differential equations.

Schedule/Classroom Assignment:
Class Number: 42574 - Lecture L01 : 1:30 pm - 3:30 pm M
    Fine Hall (FINEH) 224

CHM 512
Chemical Kinetics
Steven L. Bernasek

Description/Objectives:
A survey of chemical kinetics. Kinetic measurements and experimental methods, reaction rate theory, molecular dynamics experiment and theory will be discussed. Both gas phase and condensed phase kinetic studies will be considered.

Schedule/Classroom Assignment:
Class Number: 42248 - Lecture L01 : 9:00 am - 9:50 am M W F
    Frick Chemistry Laboratory (FRICK) 118  

ORF 542
Controlled Markov Processes
Savas Dayanik

Description/Objectives:
Sequential stochastic optimization including optimal stopping and impulse control. Applications to finance (pricing American-type contingent claims, portfolio optimization, optimal dividend policies). Applications in statistics and operations research (sequential hypothesis testing, sequential change detection, dynamic resource allocation, multi-armed bandit problems).

Schedule/Classroom Assignment:
Class Number: 42170 - Lecture L01 : 3:00 pm - 4:20 pm M W
    Computer Science Building (COMPU) 102

MAE 546
Optimal Control and Estimation
Robert F. Stengel

Schedule/Classroom Assignment:
Class Number: 41532 - Lecture L01 : 1:30 pm - 2:50 pm T Th
    Engineering Quad J-Wing (EQUAJ) J201  

CEE 525
Applied Numerical Methods
Yin L. Young

Description/Objectives:
The goal of this course is to introduce students to a broad spectrum of numerical methods for the analysis
of typical mathematics, physics, or engineering problems. Topics covered include: error analysis,
interpolation and polynomial approximation, numerical differentiation and integration,
ordinary differential equations, and partial differential equations.

Schedule/Classroom Assignment:
Class Number: 41563 - Lecture L01 : 1:30 pm - 2:50 pm M W
    Friend Center (FRIEN) 203

COS 557
Analysis & Visualization of Large-Scale Genomic Data Sets
Olga G. Troyanskaya

MAT 594/APC 584
Wavelets and Time-Frequency Analysis
Radu Balan

Description/Objectives:
This course will cover topics of wavelet and time-frequency analysis, with special emphasis on wavelet basis construction and filterbanks. It aims at building a bridge between the mathematics of harmonic analysis and its applications in engineering sciences. Two-thirds of the time will be spent on theory, with remaining one-third to be devoted to applications.
(See APC 584)

Schedule/Classroom Assignment:
Class Number: 42852 - Class C01 : 7:00 pm - 8:20 pm T Th
  Fine Hall (FINEH) 401  

APC 523/AST 523
Scientific Computation in Astrophysics
Professor(s): James M. Stone, Robert H. Lupton

Description/Objectives:
A broad introduction to scientific computation using examples drawn from astrophysics. From computer science, practical topics including processor architecture, parallel systems, structured programming, and scientific visualization will be presented in tutorial style. Basic principles of numerical analysis, including sources of error, stability, and convergence of algorithms. The theory and implementation of techniques for linear and nonlinear systems of equations, ordinary and partial differential equations will be demonstrated with problems in stellar structure and evolution, stellar and galactic dynamics, and cosmology.

Schedule/Classroom Assignment:
Class Number: 22543 - Lecture L01: 1:30 pm - 2:50 pm T Th

CHE 442
Design, Synthesis, and Optimization of Chemical Processes
Professor: Christodoulos A. Floudas

Introduction to chemical process flow-sheeting; process simulation design, sizing and cost estimation of total processes; process economics; introduction to optimization, linear programming, integer programming, and nonlinear programming; heat integration methods, minimum utility cost, minimum number of units, network optimization.

Schedule/Classroom Assignment:
Class Number: 20430 - Laboratory B01 : 7:30 pm - 9:20 pm W
Class Number: 20431 - Lecture L01 : 11:00 am - 11:50 am M W F

CHE 502/APC 502
Mathematical Methods of Engineering Analysis II
Professor: Dudley A. Saville

Solutions of ordinary differential, partial differential and finite difference equations with emphasis on second order linear partial differential equations and their applications. Topics include special functions, eigenvalues and eigenfunctions, Sturm-Liouville analysis, Green's functions, explicit and implicit finite difference methods, stability analysis, transform methods, asymptotic analysis.

Schedule/Classroom Assignment:
Class Number: 20438 - Lecture L01: 1:30 pm - 2:20 pm M W F

CHE 530
Systems Engineering
Professor: Yannis G. Kevrekidis

The purpose of this course is to introduce an integrated approach, combining
fundamental modeling, applied mathematics, numerical algorithms and scientific
computation, to address complex engineering systems. The course examines
basic modeling and simulation issues, advanced linear algebra, steady and dynamic
computation, optimization and applications in process synthesis, operations,
dynamics and control.

Schedule/Classroom Assignment:
Class Number: 23104 - Lecture L01
3:00 pm - 5:50 pm T

CHE 554/APC 544
Topics in Computational Nonlinear Dynamics
Professor: Yannis G. Kevrekidis

This is a Special Topics course in computational nonlinear dynamics. We are going
to study methods of numerical bifurcation theory (stability, continuation, singularity
detection) with special emphasis on the "large scale computing" aspects of the
algorithms. In addition to the basic techniques, iterative linear algebra, Krylov
subspaces and time-stepper based bufurcation methods will be presented and
illustrated through examples from reaction and transport phenomena modeled
by ODEs and Partial Differential Equations.

Schedule/Classroom Assignment:
Class Number: 20443 - Lecture L01
2:00 pm - 4:50 pm F

COS 551/MOL 551
Introduction to Genomics and Computational Molecular Biology
Professor(s): Mona Singh, Saeed Tavazoie

Introduction to basic computational methods used for problems arising in
molecular biology. Topics include computational approaches to: sequence
similarity and alignment, phylogenic inference, gene recognition, gene expression
analysis, structure prediction, and whole- and cross-genome analysis.

Schedule/Classroom Assignment:
Class Number: 22960 - Seminar S01
3:00 pm - 4:20 pm T Th

COS 597A
Advanced Topics in Computer Science: Structural Bioinformatics
Professor: Thomas A. Funkhouser

Introduction to structural bioinformatics, focusing on geometric analysis of
three-dimensional protein structures. Course assumes no prior background in
biology; will start with a review of protein fundamentals and then move into
computational techniques for analysis of protein structures. Topics include
protein folding, binding site detection, and small molecule docking. Mix of
lectures and recent papers. Coursework includes readings and a final course project.

Schedule/Classroom Assignment:
Class Number: 22965 - Seminar S01
1:30 pm - 2:50 pm M W

COS 597C
Advanced Topics in Computer Science: Scalable Systems & Applications
Professor: Jaswinder P. Singh

Focuses on effective design and development of applications for scalable computer
systems, i.e., parallel and distributed systems. Programming models and key
architectural elements of the systems will be discussed to understand the issues
in exploiting the systems to develop effective, high-performance algorithms and
applications. Applications may be from computational science as well as scalable
information services. Course is open to grads and advanced undergrads from all
departments, including those who want to better understand scalable systems and
algorithms from an applications perspective and vice versa.

Schedule/Classroom Assignment:
Class Number: 23000 - Seminar S01
1:30 pm - 4:20 pm F

ELE 382
Distributed Algorithms and Optimization Methods for Engineering Applications
Professor: Mung Chiang

Introduces distributed algorithms to optimize networked systems in electronic, mechanical,
or biochemical substrates, and other methodologies of optimization, both structures
and numerical algorithms, for a variety of engineering applications. Applications will be
selectively drawn from the following: computer networking, Internet protocols,
communication systems, signal processing, circuit design, controlled dynamic systems,
computational geometry, and financial engineering.

Schedule/Classroom Assignment:
Class Number: 21831 - Lecture L01
3:00 pm - 4:20 pm M W

ELE 525
Random Processes in Information Systems
Professor: Sergio Verdú

This course presents the fundamentals of applied random processes needed by
students in communications, computer engineering, controls, and signal processing.
Probability, random variables (discrete ad continuous), random number generation
for simulation, random processes, stationarity and ergodicity, spectral analysis,
Gausian processes, Brownian motion and diffusion processes, Poisson processes
and brith-and-death processes, Large deviation approximation, and efficient simulation
techniques.

Schedule/Classroom Assignment:
Class Number: 21592 - Lecture L01
9:00 am - 10:20 am T Th

ELE 573/CHE 573
Cellular and Biochemical Computing Systems
Professor: Ron Weiss

A discussion of computational issues in modeling cellular systems and the engineering
of synthetic biochemical computing systems. Topics include modeling of genetic regulatory
networks using continuous and stochastic methods, construction of synthetic gene networks,
metabolic networks, signal transduction pathways, cell-to-cell signaling, molecular and DNA
computing, molecular self-assembly, directed molecular evolution, transcriptional and
translational regulation, oscillation and circadian clocks, cell differentiation and pattern
formation, chemotaxis, molecular switches and molecular electronics, theory of chemical
computation.

Schedule/Classroom Assignment:
Class Number: 21598 - Lecture L01
3:00 pm - 4:20 pm M W am T Th