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Course Listing

Systems Development for Computational Science

APCOMP 207
2021 Fall

Fabian Wermelinger
Tuesday, Thursday
12:45pm to 2:00pm

This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing and data sciences. 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 adapt basic tools and approaches to solve computational problems in academic or industrial environments.

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Data Science 1: Introduction to Data Science

APCOMP 209A
2021 Fall

Pavlos Protopapas, Natesh Pillai
Monday, Wednesday
9:45am to 11:00am

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
2022 Spring

Pavlos Protopapas, Mark Glickman
Monday, Wednesday, Friday
9:00am to 10:15am

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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Models, Algorithms and Data

APCOMP 211
2021 Fall

Petros Koumoutsakos
Tuesday, Thursday
12:45pm to 2:00pm

The class presents fundamental computing concepts bridging models algorithms and data.  The course will present a unifying approach to stochastic methods for modeling, search, optimization and data driven uncertainty quantification. Class projects will emphasize the steps necessary to transfer algorithms  to software in multi- and many-core computer architectures.

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Advanced Practical Data Science

APCOMP 215
2021 Fall

Pavlos Protopapas
Tuesday, Thursday
2:15pm to 3:30pm

This course aims to review existing Deep Learning flow while applying it to a real-world problem. Then we will build and deploy an application that uses the deep learning model to understand how to productionize models. This course follows the CS109 model of balancing between concept, theory, and implementation. Split into three parts; the course starts with the review of Deep Learning concepts for data and modeling and how to apply them to different tasks, including vision and language tasks. The next part will be Development, where you use the models you trained in part 1 and incorporate them into real-world applications. Finally, you will Deploy the application in Google Cloud Platform (GCP). The three parts will cover in detail topics such as Transfer learning, Containerization using Docker, and Scaling deployments using Kubernetes. At the end of this module, you will build efficient deep learning models and design, build and deploy applications that scale.

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Critical Thinking in Data Science

APCOMP 221
2022 Spring

Michael Smith

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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Extreme Computing: Project-based High Performance Distributed and Parallel Systems

APCOMP 290R
2022 Spring

Fabian Wermelinger

Computer simulations are recognized as an essential part of scientific and engineering pursuits.  Their predictive power will play an ever more important role in scientific discoveries, national competitiveness, and in solving societal problems. For predictions of real-world problems, the ability to scale solution techniques, algorithms, and software to large-scale is of utmost importance. This course will explore the techniques, infrastructure, and algorithms used for extreme computing.  The course will be organized into two modules, each focusing on a different aspect of fluid mechanics. The first module will focus on simulating turbulence in incompressible fluids using the finite element method, while the second module will focus on hemodynamic simulations using the Lattice Boltzmann Method. Both topics have important scientific and societal relevance and benefit enormously from large scale computing.  The faculty from Harvard, in collaboration with visiting researchers from Sandia National Laboratories, Sapienza University, and the Institute for Calculus Applications in Italy, will conduct the lectures and lab sessions. Computing resources will be provided for the class projects.

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Deep Learning for NLP

APCOMP 295
2021 Fall

Christopher Tanner
Tuesday, Thursday
9:45am to 11:00am

How can computers understand and leverage text data and human language? Natural language processing (NLP) addresses this question, and in this course students study the current, best approaches to it. No prior NLP experience is needed, but it is welcomed. This course provides students with a foundation of advanced concepts and requires students to conduct significant research on an NLP topic of their choosing. The aim is to produce a short paper worthy of submitting to an NLP conference. Assessment also includes pop quizzes, homework assignments, and an exam. The course starts with language representations and modelling, followed by machine translation (converting text from one language to another). Next, students learn about transformers (e.g., BERT and GPT-2), which are incredibly powerful deep learning models that currently yield state-of-the-art results in nearly every NLP task. We end the semester by covering tasks concerning bias and fairness, adversarial approaches, coreference resolution, and commonsense reasoning.

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Data Science Capstone Project

APCOMP 297R
2021 Fall

Christopher Tanner
Thursday
12:45pm to 3:30pm

The capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from 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 additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2022 Spring

Christopher Tanner

The capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from 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 additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Interdisciplinary Seminar in Applied Computation

APCOMP 298R
2021 Fall

Daniel Weinstock
Wednesday, Friday
2:15pm to 3:30pm

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
2021 Fall

Daniel Weinstock

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
2022 Spring

Daniel Weinstock

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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