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Course Listing

Advanced Scientific Computing: Numerical Methods

APMTH 205
2019 Fall

Christopher Rycroft
Tuesday, Thursday
10:30am to 11:45am

An examination of the mathematical foundations of a range of well-established numerical algorithms, exploring their use through practical examples drawn from a range of scientific and engineering disciplines. Emphasizes theory and numerical analysis to elucidate the concepts that underpin each algorithm. There will be a significant programming component. Students will be expected to implement a range of numerical methods through individual and group-based project work to get hands-on experience with modern scientific computing.

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Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization

APMTH 207
2019 Fall

Weiwei Pan
Monday, Wednesday
12:00pm to 01:15pm

Develops skills for computational research with focus on stochastic approaches, emphasizing implementation and examples. Stochastic methods make it feasible to tackle very diverse problems when the solution space is too large to explore systematically, or when microscopic rules are known, but not the macroscopic behavior of a complex system. Methods will be illustrated with examples from a wide variety of fields, like biology, finance, and physics.

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Computing Foundations for Computational Science

COMPSCI 205
2020 Spring

David Sondak
Tuesday, Thursday
01:30pm to 02:45pm

Computational science has become a third partner, together with theory and experimentation, in advancing scientific knowledge and practice, and an essential tool for product and process development and manufacturing in industry. Big data science adds the ‘fourth pillar’ to scientific advancements, providing the methods and algorithms to extract knowledge or insights from data. The course is a journey into the foundations of Parallel Computing at the intersection of large-scale computational science and big data analytics. Many science communities are combining high performance computing and high-end data analysis platforms and methods in workflows that orchestrate large-scale simulations or incorporate them into the stages of large-scale analysis pipelines for data generated by simulations, experiments, or observations. This is an applications course highlighting the use of modern computing platforms in solving computational and data science problems, enabling simulation, modeling and real-time analysis of complex natural and social phenomena at unprecedented scales. The class emphasizes on making effective use of the diverse landscape of programming models, platforms, open-source tools, computing architectures and cloud services for high performance computing and high-end data analytics.

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Systems Development for Computational Science

COMPSCI 207
2019 Fall

David Sondak
Tuesday, Thursday
12:00pm to 01:15pm

This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to fit tools and approaches to the problem being solved.

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Data Science 1: Introduction to Data Science

APCOMP 209A
2019 Fall

Pavlos Protopapas, Kevin A. Rader, Christopher Tanner, Maddy Nakada
Monday, Wednesday
01:30pm to 02:45pm

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part one of a two part series.

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Data Science 2: Advanced Topics in Data Science

APCOMP 209B
2020 Spring

Pavlos Protopapas, Mark Glickman, Christopher Tanner
Monday, Wednesday
01:30pm to 02:45pm

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning. Part two of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part two of a two part series.

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Critical Thinking in Data Science

APCOMP 221
2020 Spring

James Waldo, Michael Smith
Tuesday, Thursday
12:00pm to 01:15pm

This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include algorithmic accountability and discriminatory algorithms, black box algorithms, data privacy and security, ethical frameworks; and experimental and product design. We will work through case studies in a variety of contexts including media, tech and sharing economy platforms; medicine and public health; data science for social good, and politics. We will look at the underlying machine learning algorithms, statistical models, code and data. Threads of history, philosophy, business models and strategy; and regulatory and policy issues will be woven throughout the course.

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Computational Methods in the Physical Sciences

APCOMP 227
2019 Fall

Sauro Succi
Wednesday, Friday
10:30am to 11:45am

In this Course, we shall familiarize with the main computational methods which permit to simulate and analyze the behavior of a wide range of problems involving fluids, solids, soft matter, electromagnetic and quantum systems, as well as the dynamics of (some) biological and social systems. The course consists of three main parts,

Part I  : Classical and Quantum Fields on Grids

Part II : Mesoscale Methods

Part III: Statistical Data Analysis and Learning

In Part I, we shall discuss the fundamentals of grid discretization and present concrete applications to a broad variety of problems from classical and quantum physics, such as Advection-Diffusion Reaction transport, Navier-Stokes fluid-dynamics, nonlinear classical and quantum wave propagation. Both regular and complex geometrical grids will be discussed through Finite Differences, Volumes and Elements, respectively.

In Part II we shall discuss mesoscale technique based on the two basic mesoscale descriptions: probability distribution functions, as governed by Boltzmann and Fokker-Planck kinetic equations, and stochastic particle dynamics (Langevin equations). The lattice Boltzmann method will be discussed in great detail, with applications to fluids and soft matter problems.

In addition, we shall provide the opportunity of hands-on on a multi scale codes for X (extreme) simulations at the interface between physics and molecular biology.

Finally, in Part III, we shall present data analysis & learning tools of particular relevance to complex systems with non-gaussian statistics, such as turbulence, fractional transport and extreme events in general. An introduction to Physics-Aware Machine Learning will also be presented.

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Computational Design of Materials

APCOMP 275
2020 Spring

Boris Kozinsky
Tuesday, Thursday
12:00pm to 01:15pm

This course will teach theoretical background and practical applications of modern computational methods used to understand and design properties of advanced functional materials. Topics will include classical potentials and quantum first-principles energy models, density functional theory methods, Monte Carlo sampling and molecular dynamics simulations of phase transitions and free energies, fluctuations and transport properties, and machine learning approaches. Examples will be based on rational design of industrially relevant materials for energy conversion and storage, electronic and magnetic devices, and nanotechnology.

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Topics in Applied Computation: Advanced Practical Data Science

APCOMP 295
2020 Spring

Pavlos Protopapas
Tuesday, Thursday
04:30pm to 05:45pm

In this course we explore advanced practical data science practices. The course will be divided into three major topics:
1) How to scale a model from a prototype (often in jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about microservices and Kupernetes. Along the way, students will be exposed to Dask.
2) How to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This can be very important, given the vast compute and time resources required to develop neural network models on these problems and given the huge jumps in skill that these models can provide to related problems. In this part of the course we will examine various pre-existing models and techniques in transfer learning.
3) In the third part we will be introducing a number of intuitive visualization tools for investigating properties and diagnosing issues of models. We will be demonstrating a number of visualization tools ranging from the well established (like saliency maps) to recent ones that have appeared in https://distill.pub.

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Data Science Capstone Research Project Course

APCOMP 297R
2019 Fall

Pavlos Protopapas, Christopher Tanner
Tuesday
03:00pm to 05:45pm

The data science master's capstone project is intended to integrate and apply the skills and ideas data science master's students acquire in their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no homework or lectures. Students will be dealing with real-world problems, messy data sets, and the chance to work on an end-to-end solution to a problem using computational methods.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2020 Spring

Christopher Tanner
Thursday
03:00pm to 05:45pm

The CSE capstone project is intended to integrate and apply the skills and ideas CSE students acquire in their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no homework or lectures. Students will be dealing with real-world problems, messy data sets, and the chance to work on an end-to-end solution to a problem using computational methods.

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Interdisciplinary Seminar in Computational Science & Engineering

APCOMP 298R
2019 Fall

Daniel Weinstock
Wednesday, Friday
03:00pm to 04:15pm

This course, centered on the Institute for Applied Computation Science (IACS) seminar series, will provide broad exposure to cutting-edge topics, applications, and unifying concepts in Computational Science & Engineering. Students will read, present and discuss journal articles related to IACS talks, attend the seminars and meet with visiting speakers. Possible topics to be covered include scientific visualization, computational approaches to disease, mathematical neuroscience, computational archeology, and computational finance.

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Special Topics in Applied Computation

APCOMP 299R
2019 Fall

Pavlos Protopapas

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

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Special Topics in Applied Computation

APCOMP 299R
2020 Spring

Pavlos Protopapas

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

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