Courses of Study 2017-2018 [ARCHIVED CATALOG]
Course Descriptions
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CS—Computer Science |
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CS 6241 - Numerical Methods for Data Science
Spring. 3 credits. Student option grading.
Prerequiste: Strong background in linear algebra, prior exposure to numerical methods.
D. Bindel.
A discussion of numerical methods (particularly iterative methods for linear algebra and optimization) in the context of machine learning and data analysis problems. The course will particularly focus on sparsity, rank structure, and spectral behavior of underlying linear algebra problems; convergence behavior and “regularization via iteration” effects for standard solvers; and comparisons between numerical methods for data analysis with large-scale numerical methods used in other areas of science and engineering.
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CS 6320 - Advanced Database Systems
Spring. 4 credits. Student option grading.
Prerequisite: CS 4320 or permission of instructor.
Staff.
Covers a variety of advanced issues ranging from transaction management to query processing to data mining. Involves extensive paper reading and discussion.
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CS 6360 - Educational Technology (crosslisted) INFO 6360
Spring. 3 credits. Letter grades only.
Prerequisite: CS 3110 or equivalent, or permission of instructor.
Staff.
An introduction to research in educational technology – an interdisciplinary field that draws from human-computer interaction, design, artificial intelligence, and video games. Potential topics include learning science, instructional scaffolding, knowledge representations, diagnosis of misconceptions, adaptation, intelligent tutoring systems, Massive Open Online Courses (MOOCs), games for learning, automation, user studies, data analysis, and large-scale experimentation. Combines lectures, group activities, paper reading, and a semester-long team project. Particular emphasis is placed on the design, implementation, and release of research artifacts that achieve real-world impact.
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CS 6410 - Advanced Systems
Fall. 4 credits. Student option grading.
Prerequisite: CS 4410 or permission of instructor.
Staff.
Advanced course in systems, emphasizing contemporary research in distributed systems. Topics may include communication protocols, consistency in distributed systems, faulttolerance, knowledge and knowledge-based protocols, performance, scheduling, concurrency control, and authentication and security issues.
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CS 6411 - Systems Principles
Spring. 4 credits. Letter grades only.
Prerequisite: CS 4410
A. Lorenzo.
The design of computer systems is driven by a small number of principles that are leveraged to achieve both functionality and performance. These principles, and the key techniques used to instantiate them, are the subject of this course. We will study them both in isolation and in the context of systems in which they have been applied. Examples will be drawn from computer architecture and organization, operating systems, database management systems, computer networks, and distributed systems. A final paper or project will be due on which the course grade will be based. It will be either an independent study with final paper and presentation, or a software project and presentation, at the students’ choice.
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CS 6432 - Distributed Consensus and Blockchains
Fall. 3 credits. Letter grades only.
Prerequisite: undergraduate discrete math, CS 2800 or equivalent.
E. Shi.
Distributed consensus protocols have been widely adopted in replicated databases and decentralized cryptocurrencies. In this course, we will explore how to design formally secure protocols that allow nodes to reach consensus in a distributed system. Both classical consensus and modern blockchains will be discussed.
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CS 6452 - [Datacenter Networks and Services]
Spring. Not offered 2017-2018. 3 credits. Student option grading.
Prerequisite: CS 4410 or equivalent.
Staff.
This course examines novel network architectures, networking protocols and software services to support high-performance datacenter applications. It covers physical network design, addressing and naming, transport protocols, consensus protocols, lock management, key-value stores, NoSQL, virtualization and green datacenters. Students are expected to undertake a significant class project.
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CS 6453 - [Big Data Systems: Trends and Challenges]
Spring. Not offered 2017-2018. 4 credits. Letter grades only.
Prerequisite: CS 6410 or permission of instructor.
R. Agarwal.
Introduces the critical technology trends, the state-of-the-art systems, and the key challenges that make the big data research exciting, both from academic and industrial perspectives.
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CS 6455 - Advanced Computer Networking
Fall. 4 credits. Letter grades only.
Prerequisite: CS 4410 or permission of instructor required.
R. Agarwal.
This course explores state-of-the-art network architectures and protocols through a review of recent research literature, discussions during lectures and class projects. Students will complete a semester-long research project based on one of the topics covered in the class.
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CS 6465 - Emerging Cloud Technologies and Systems Challenges
Fall. 3 credits. Student option grading.
Prerequisite: CS 6410 . Enrollment limited to: Ph.D. students.
K. Birman.
Course explores new hardware and software technologies that are likely to have a major impact on large-scale computer systems, with a focus on understanding the research challenges that will arise as we start to integrate them into existing cloud infrastructures. The class will center on a mix of readings, discussion, and individualized research activities. The technologies to be discussed may include, for example, RDMA networking, network interfaces with novel capabilities, new memory options such as 3D-XPoint and high-density multi-layer NAND/SSD, battery-backed SSD, NetFPGA and GPU co-processors, and the new Intel SGX enclave security technology.
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CS 6466 - Cryptocurrencies and Smart Contracts
Spring. 3 credits. Letter grades only.
Prerequisite: CS 2110 .
E. G. Sirer.
This paper-reading course will examine the design and implementation of cryptocurrencies, digital coin systems and smart tokens. Starting with the initial virtual currency proposals from the 90’s, we will cover the landmark papers that provide the foundation for today’s cryptocurrencies and smart contract platforms, with some emphasis on public key cryptography, consensus protocols, and other foundational building blocks. The course will also cover smart contract construction, including Digital Autonomous Organizations and other application areas.
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CS 6630 - [Realistic Image Synthesis]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: CS 4620 /CS 5620 or equivalent and undergraduate-level understanding of algorithms, programming, and vector calculus.
Staff.
Advanced course in realistic image synthesis, focusing on the computation of physically accurate images.
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CS 6640 - [Computational Photography]
Fall or spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: CS 3110 or equivalent, and MATH 2210 and MATH 2220 or equivalent. Some background in computer graphics or vision at the undergraduate level is helpful.
Staff.
A course on the emerging applications of computation in photography. Likely topics include digital photography, unconventional cameras and optics, light field cameras, image processing for photography, techniques for combining multiple images, advanced image editing algorithms, and projector-camera systems. Course work includes implementing several algorithms and a final project.
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CS 6644 - [Modeling the World]
Fall. Not offered 2017-2018. 3 credits. Letter grades only.
Prerequisite: CS 4620 /CS 5620 or CS 4670 /CS 5670 , or equivalent course and permission of instructor.
Staff.
In recent years, there has been an explosion of visual images and video content captured by novices and professionals alike, and shared on community photo collections. These images capture the rich range of shapes, materials, and lighting in the world. We study how this content is being used to build comprehensive visual models of the world. We cover image-based modeling, lighting, and rendering algorithms; 3D reconstruction algorithms; and acquisition and capture of geometry, material, and lighting.
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CS 6650 - [Computational Motion]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: undergraduate-level understanding of algorithms, and some scientific computing.
Staff.
Covers computational aspects of motion, broadly construed. Topics include the computer representation, modeling, analysis, and simulation of motion. Students implement several of the algorithms covered in the course and complete a final project.
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CS 6670 - Computer Vision
Fall. 4 credits. Student option grading.
Prerequisite: undergraduate-level understanding of algorithms and MATH 2210 or equivalent.
Staff.
This course will introduce the core problems of computer vision and discuss both classical approaches based on the geometry and physics of image formation as well as current approaches based on recent advances in deep learning. Topics include stereopsis and 3D reconstruction, optical flow, image segmentation, object recognition, feature representations of images and patches, and convolutional networks.
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CS 6700 - Advanced Artificial Intelligence
Spring. 4 credits. Student option grading.
Prerequisite: CS 4700 or permission of instructor.
Staff.
Covers a variety of areas in AI, including knowledge representation, automated reasoning, learning, game-playing, and planning, with an emphasis on computational issues.
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CS 6702 - [Topics in Computational Sustainability] (crosslisted) INFO 6702
Fall. 4 credits. Student option grading.
Enrollment limited to: graduate standing or permission of instructor.
Staff.
For description, see INFO 6702 .
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CS 6740 - Advanced Language Technologies (crosslisted) INFO 6300
Spring. 3 credits. Student option grading.
Permission of instructor required.
C. Cardie.
Graduate-level introduction to technologies for the computational treatment of information in human-language form, covering modern natural-language processing (NLP) and/or information retrieval (IR). Possible topics include latent semantic analysis (LSI), clickthrough data for web search, language modeling, text categorization and clustering, information extraction, computational syntactic and semantic formalisms, grammar induction, and machine translation.
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CS 6741 - Structured Prediction for Natural Language Processing
Fall. 3 credits. Letter grades only.
Prerequisite: CS 2110 or equivalent programming experience, a course in machine learning (CS 4780 /CS 5780 , CS 6780 or equivalent). Enrollment limited to: Ph.D. students. Offered at Cornell Tech in New York City.
Y. Artzi.
Robust language understanding has the potential to transform how we interact with computers, extract information from text and study language on large scale. However, to accurately recover the meaning of language, automated systems must learn to reason about the meaning of words and the intricate structures they combine to. This research-oriented course examines machine learning and inference methods for recovering structured representations of language meaning. Possible topics include formalisms, inference and learning for: sequence models (tagging, named-entity recognition), tree models (constituency and dependency parsing), mapping sentences to logical form representations and alignment models (machine translation).
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CS 6742 - Natural Language Processing and Social Interaction (crosslisted) INFO 6742
Fall. 3 credits. Student option grading.
Prerequisite: CS 2110 or equivalent programming experience, a course in artificial intelligence or any relevant subfield (e.g., NLP, information retrieval, machine learning). Enrollment limited to: Ph.D./MS students.
Staff.
More and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This research-oriented course examines the opportunities for natural language processing to contribute to the analysis and facilitation of socially embedded processes. Possible topics include sentiment analysis, learning social-network structure, analysis of text in political or legal domains, review aggregation systems, analysis of online conversations, and text categorization with respect to psychological categories.
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CS 6746 - [Language Processing for Computational Social Science] (crosslisted) INFO 6030
Fall. 3 credits. Letter grades only (no audit).
Prerequisite: Machine Learning, Linear Algebra, Advanced Programming. Permission of instructor required.
C. Danescu-Niculescu-Mizil.
For description and learning outcomes, see INFO 6030 .
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CS 6751 - Introduction to Robotic Mobile Manipulation (crosslisted) MAE 6730
Spring. 4 credits. Letter grades only.
Prerequisite: graduate standing or a previous robotics course such as MAE 4180 or CS 4758 . A background in mathematics is required, especially linear algebra and probability. A strong programming ability in C++ or Python is required.
Staff.
Mobile manipulation is the ability for a robot to interact physically with versatility in the world. As robots become integrated into complex human environments, mobile manipulation is increasingly necessary. Robotic mobile manipulation will enable such applications as personal assistant robots in the home and factory worker in advanced manufacturing. This course covers the fundamental theory, concepts, and systems of mobile manipulation, both software and hardware. It addresses the topics of kinematics, dynamics, controls, grasping, planning, mapping, dealing with uncertainty, and human-robot interaction.
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CS 6756 - [Advanced Topics in Robot Learning: 3D Perception]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Staff.
This course focuses on learning techniques for 3D perception for robots with inexpensive RGBD cameras such as Kinect that give 3D data in addition to an image. We will study machine-learning algorithms (such as graphical models and sampling based methods) for perceiving the environment, which includes performing object detection, semantic labeling of the environment and human activity recognition. Particular robotic applications include navigation, manipulation, human-robot interaction and assistive robotics. Undergraduates and masters students need permission of the instructor to take this course.
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CS 6758 - [Robot Learning]
Spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program (e.g., CS 1114 or CS 2110 or CS 3110 or equivalent); any one of the following courses in probability/statistics or signal processing: CS 2800 or ECE 2200 or ECE 3100 or ENGRD 2700 (or equivalent). Co-meets with CS 4758 .
Staff.
Studies the problem of how an agent can learn to perceive its world well enough to act in it, to make reliable plans, and to learn from its own experience. The focus is on algorithms and machine learning techniques for autonomous operation of robots. Topics include filtering and state estimation (Kalman filters, particle filters); Markov decision process; learning (reinforcement and supervised learning); planning and control; perception (vision, sensing). The course has a term project involving physical robots; no final exam.
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CS 6764 - [Reasoning about Knowledge]
Spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: mathematical maturity and acquaintance with propositional logic.
Staff.
Knowledge plays a crucial role in distributed systems, game theory, and artificial intelligence. Material examines formalizing reasoning about knowledge and the extent to which knowledge is applicable to those areas. Issues include common knowledge, knowledge-based programs, applying knowledge to analyzing distributed systems, attainable states of knowledge, modeling resource-bounded reasoning, and connections to game theory.
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CS 6766 - [Reasoning about Uncertainty]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: mathematical maturity and acquaintance with propositional logic.
Staff.
Examines formalizing reasoning about and representing uncertainty, using formal logical approaches as a basis. Topics: logics of probability, combining knowledge and probability, probability and adversaries, conditional logics of normality, Bayesian networks, qualitative approaches to uncertainty, going from statistical information to degrees of belief, and decision theory.
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CS 6780 - [Advanced Machine Learning]
Spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: programming skills (at the level of CS 2110 ) and basic knowledge of linear algebra (at the level of MATH 2940 ) and probability theory (at the level of MATH 4710 ) and multivariable calculus (at the level of MATH 1920 ). Enrollment limited to: Ph.D. students or permission of instructor. Students who have already taken CS 4780 /CS 5780 should not take this class.
Staff.
Gives a graduate-level introduction to machine learning and statistical pattern recognition and in-depth coverage of new and advanced methods in machine learning, as well as their underlying theory. Emphasizes approaches with practical relevance and discusses a number of recent applications of machine learning, such as data mining, computer vision, robotics, text and web data processing. An open research project is a major part of the course.
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CS 6782 - [Probabilistic Graphical Models] (crosslisted) BTRY 6790
Fall. 4 credits. Student option grading.
Prerequisite: probability theory (BTRY 3080 or equivalent), programming and data structures (CS 2110 or equivalent); a course in statistical methods is recommended but not required (BTRY 4090 or equivalent).
Staff.
For description, see BTRY 6790 .
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CS 6783 - Machine Learning Theory
Spring. 4 credits. Student option grading.
Prerequisite: CS 4780 /CS 5780 or CS 4786 /CS 5786 or CS 6780 or equivalent, or permission of instructor.
Staff.
This course on machine learning theory introduces basic results, tools, and techniques used in analysis of statistical and online learning algorithms. The course also introduces the basics of computational learning theory.
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CS 6784 - Advanced Topics in Machine Learning
Fall. 4 credits. Student option grading.
Prerequisite: CS 4780 or equivalent, or CS 5780 or equivalent, or permission of instructor.
Staff.
Extends and complements CS 4780 and CS 5780 , giving in-depth coverage of new and advanced methods in machine learning.
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CS 6787 - Advanced Machine Learning Systems
Fall. 4 credits. Student option grading.
Prerequisite: CS 4780 or CS 4786 .
C. De Sa.
Graduate-level introduction to system-focused aspects of machine learning, covering guiding principles and commonly used techniques for scaling up to large data sets. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing metaparameters, parallelization within a chip and across a cluster, and innovations in hardware architectures. An open-ended project in which students apply these techniques is a major part of the course.
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CS 6788 - [Advanced Topic Modeling] (crosslisted) INFO 6150
Fall. 3 credits. Letter grades only.
Prerequisite: familiarity with Bayesian statistics and probabilistic modeling. Permission of instructor required. Enrollment limited to: graduate students or seniors.
D. Mimno.
For description, see INFO 6150 .
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CS 6810 - [Theory of Computing]
Spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: CS 4810 and CS 4820 or CS 6820 or permission of instructor.
Staff.
Advanced treatment of theory of computation, computational-complexity theory, and other topics in computing theory.
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CS 6820 - Analysis of Algorithms
Fall. 4 credits. Student option grading.
Prerequisite: CS 4820 or graduate standing.
Staff.
Methodology for developing and analyzing efficient algorithms. Understanding the inherent complexity of natural problems via polynomial-time algorithms, advanced data structures, randomized algorithms, approximation algorithms, and NP-completeness. Additional topics may include algebraic and number theoretic algorithms, circuit lower bounds, online algorithms, or algorithmic game theory.
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CS 6825 - [The Science Base for the Information Age]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Staff.
Covers the evolving science base that supports modern computer science. Topics include high dimensional space, singular valued decomposition, random graphs, Markov chains, learning theory, massive data, topic models, and deep learning.
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CS 6830 - [Cryptography]
Spring. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: General ease with algorithms and elementary probability theory, maturity with mathematical proofs (ability to read and write mathematical proofs). Enrollment limited to: Cornell Tech students. Limited enrollment for Ithaca students. Offered at Cornell Tech in New York City. Distance learning for Ithaca students.
Staff.
Graduate introduction to cryptography. Topics include encryption, digital signatures, pseudo-random number generation, zeroknowledge, and basic protocols. Emphasizes fundamental concepts and proof techniques.
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CS 6832 - [Applied Cryptography]
Fall. Not offered 2017-2018. 3 credits. Letter grades only.
Prerequisite: undergraduate security and cryptography recommended but not required.
E. Shi.
We will explore how to program and adopt cryptography in secure systems design, including applications such as cloud computing and cryptocurrencies. We will also explore interesting research questions and how to do research in applied cryptography. Background recommended but not required: undergraduate-level security or cryptography. We welcome students from non-cryptography areas such as programming languages and systems to participate and contribute to our discussions.
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CS 6850 - The Structure of Information Networks (crosslisted) INFO 6850
Spring. 4 credits. Student option grading.
Prerequisite: CS 4820 .
J. Kleinberg.
For description, see INFO 6850 .
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CS 6860 - [Logics of Programs]
Fall. Not offered 2017-2018. 4 credits. Student option grading.
Prerequisite: CS 6110 , CS 4810 , and MATH 4810.
Staff.
Topics in type theory as a foundation for programming languages and computing theory. The key prerequisites are CS 6110 and CS 4810 . Topics will include: semantics of programming languages and logics, constructive and intuitionistic logics, the propositions-as-types principle, extracting proofs from programs, Hoare logics and Kleene algebras for reasoning about programs, computational complexity in type theory, formal relationships among type theories and programming logics, inductive and co-inductive types.
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CS 6861 - Introduction to Kleene Algebra
Spring. 4 credits. Student option grading.
Prerequisite: CS 6860 or permission of instructor.
D. Kozen.
Kleene algebra is the algebra of regular expressions and finite automata, structures of fundamental importance in computer science. Kleene algebra is the algebraic theory of these objects, although it has many other natural and useful interpretations: relational algebra, programming language semantics, program logics, automata and formal languages, network programming, computational geometry, and the design and analysis of algorithms. In this course we will explore the theory and applications of this system, including models, deductive systems, completeness and complexity results, and applications in the areas mentioned above. A final paper or project will be due on which the course grade will be based. It will be either an independent study with final paper and presentation, or a software project and presentation, at the students’ choice.
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CS 7090 - Computer Science Colloquium
Fall, spring. 1 credit. S/U grades only.
For staff, visitors, and graduate students interested in computer science.
Staff.
Weekly meeting for the discussion and study of important topics in the field.
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CS 7190 - Seminar in Programming Languages
Fall, spring. 1 credit. S/U grades only.
Prerequisite: CS 6110 or permission of instructor.
Staff.
The Programming Languages Discussion Group meets weekly to discuss papers in the area of programming languages, program analysis, and compilers. The goal is to encourage interactions and discussions between students, researchers, and faculty with interests in this area. The seminar is open to everybody interested in languages and compilers. First-year and second-year students are especially encouraged to participate.
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CS 7192 - [Seminar in Programming Refinement Logics]
Fall, spring. 1 credit. Student option grading.
Permission of instructor required.
Staff.
Topics in programming logics, possibly including type theory, constructive logic, decision procedures, heuristic methods, extraction of code from proofs, and the design of proof-development and problem-solving systems.
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CS 7290 - Seminar on Scientific Computing and Numerics (crosslisted) MATH 7290
Fall, spring. 1 credit. Student option grading.
Staff.
Talks on various methods in scientific computing, the analysis of their convergence properties and computational efficiency, and their adaptation to specific applications.
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CS 7390 - Database Seminar
Fall, spring. 1 credit. S/U grades only.
Permission of instructor required.
Staff.
The database seminar is the weekly meeting of students and faculty interested in data management and data mining at Cornell. We typically discuss one or two papers on related topics per session. We focus on recent and seminal papers of general interest.
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CS 7490 - Systems Research Seminar
Fall, spring. 1 credit. S/U grades only.
Enrollment limited to: Ph.D. students; others require permission of instructor.
Staff.
The Systems Research Seminar discusses recent, interesting papers in the systems area, broadly defined to span operating systems, distributed systems, networking, architecture, databases, security, and programming languages. The goal is to foster technical discussions among the Cornell systems research community.
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CS 7493 - Computer Security Seminar
Fall, spring. 1 credit. S/U grades only.
Prerequisite: CS 4410 . Permission of instructor required for non-PhD students. Enrollment limited to: Ph.D. standing.
A. Myers.
This is a graduate seminar primarily aimed at Ph.D. students. Students will read, present, and discuss recent and classic papers in the computer security area. Outside speakers will also be invited to present current research.
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CS 7594 - [Seminar on Computational Issues in Health and Medicine]
Fall. 1 credit. Student option grading.
Staff.
An overview of computational issues that arise in the clinical practice of medicine. Topics include the role of IT in clinical practice; medical imaging problems in CT and MR; data mining; clinical decision support; workflow optimization; electronic medical records and health care IT standards. Lectures are given primarily by attending physicians from the Department of Radiology at Weill Cornell Medical College. Open to students at all levels.
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CS 7670 - [Special Topics in Computer Vision]
Fall, spring. Not offered 2017-2018. 1 credit. Student option grading.
Staff.
Informal weekly seminar in which current topics in computer vision are discussed.
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CS 7690 - [Computer Graphics Seminar]
Spring. 1 credit. Student option grading.
Staff.
The Graphics/Vision Research Seminar discusses recent research in the areas of computer graphics and computer vision. The goal is to foster technical discussions and collaboration among the Cornell graphics and vision research community.
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CS 7790 - Seminar in Artificial Intelligence
Fall, spring. 1 credit. S/U grades only.
Permission of instructor required.
Staff.
The AI seminar will meet weekly for lectures by graduate students, faculty, and researchers emphasizing work-in-progress and recent results in AI research.
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CS 7792 - Special Topics in Machine Learning
Fall, spring. 1 credit. S/U grades only.
Permission of instructor required.
Staff.
Reading group on advanced topics in machine learning.
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CS 7794 - Seminar in Natural Language Understanding
Fall, spring. 1 credit. Student option grading.
Staff.
This course, the NLP seminar, is a weekly meeting for people currently or soon to be actively doing research in NLP. (Students simply looking to learn more about NLP should not enroll, but should take one of our lecture courses instead.) One participant leads discussion each week, either of a recently published paper or of their own work in progress. Attendance at all sessions is mandatory.
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CS 7796 - Robotics Seminar
Fall, spring. 1 credit. S/U grades only.
Intended for: students and faculty actively involved in robotics research.
R. Knepper.
Informal seminar in which current topics in robotics are discussed.
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CS 7890 - Seminar in Theory of Algorithms and Computing
Fall, spring. 1 credit. S/U grades only.
Permission of instructor required.
Staff.
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CS 7893 - Cryptography Seminar
Fall, spring. 1 credit. Student option grading.
Staff.
Seminar for discussing recent or classical papers in cryptography.
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CS 7999 - Independent Research
Fall, spring. 1-15 credits, variable. Student option grading.
Permission of Computer Science advisor required.
Staff.
Independent research for CS PhD students who have not yet passed their A-exam.
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CS 9999 - Thesis Research
Fall, spring. 1-15 credits, variable. S/U grades only.
Permission of Computer Science advisor required.
Staff.
Doctoral research.
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CZECH—Czech |
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CZECH 1131 - Elementary Czech I
Fall. 4 credits. Student option grading.
Distance learning from Columbia University.
Staff.
This course aims to expand basic proficiency in understanding, reading, speaking, and writing the Czech language. The course works through a selection of dialogues, texts and exercises to develop mastery of the most essential idiomatic vocabulary and grammatical structures necessary for basic communications in Czech and for laying a solid foundation for further study of the language.
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CZECH 1132 - Elementary Czech II
Spring. 4 credits. Student option grading.
Prerequisite: CZECH I or permission of instructor. Distance learning from Columbia University.
Staff.
This course aims to expand basic proficiency in understanding, reading, speaking, and writing the Czech language. The course works through a selection of dialogues, texts and exercises to develop mastery of the most essential idiomatic vocabulary and grammatical structures necessary for basic communications in Czech and for laying a solid foundation for further study of the language.
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CZECH 3300 - Directed Studies
Fall. 1-4 credits, variable. Student option grading.
Permission of instructor required.
W. Browne.
Taught on a specialized basis to address particular student needs.
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DEA—Design & Environmental Analysis |
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DEA 1050 - Career Explorations
Spring. 1 credit. S/U grades only.
Y. Hua.
Survey course for students interested in careers that influence habitat and human behavior. Careers may include employment in the fields of design (user experience design, interaction design, interior architecture, etc. ), design strategy and consulting, ergonomics, facility planning, business, and real estate. Experts and young alumni representing these disciplines discuss their work while addressing current issues, trends, key skill sets, and career opportunities (especially those favoring the knowledge and skill sets that the curriculum of Design and Environmental Analysis supports to develop).
Outcome 1: Become familiar with the fields of academic study, research and career opportunities related to the design, planning, management and assessment of the built environment and the human experience within.
Outcome 2: Explore their own academic interests, skills and expectations.
Outcome 3: Expand their awareness of career opportunities related to their academic interests, and use the information to help plan curriculum and extra-curriculum experience more effectively.
Outcome 4: Participate in dynamic dialogue with experts and alumni invited to guest lecture in the class.
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DEA 1100 - Design Generation(s)
Summer. 3 credits. Student option grading.
Staff.
How do designers think, create, solve problems, and help humans interact with everything in our world, from spaces and places to skateboards and web sites? During this course, students interested in a career in design will learn how the creative problem-solving process is similar in a variety of design disciplines and how they can make a difference as a design professional.
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DEA 1101 - Visual Literacy and Design Studio (crosslisted) VISST 1101
(LAD-HE)
Fall. Only offered Fall 2017. Will return to Fall and Spring in 2018-2019. 4 credits. Letter grades only.
Cost of materials: approximately $200. Permission of instructor required for non-DEA majors. Enrollment limited to: DEA majors. DEA majors must earn a B- or higher in DEA 1101 to enroll in subsequent studios.
J. Elliott.
This course is an introductory design studio. The primary course objective is to introduce principles of visual literacy as it pertains to two-dimensional and three-dimensional issues in design at all scales. Concepts about representation, expression, composition, color, form, light, structure, and function will be explored through project based learning. The emphasis will be on learning explicit compositional concepts, visualization skills, and media techniques as well as implicit design sensitivities to serve the student throughout the rest of his or her DEA experience and beyond.
Outcome 1: Develop grounding in the field through the learning 2D and 3D design principles both in theory and in practice (comprehend discipline and field).
Outcome 2: Investigate a number of disciplinary perspectives including painting, typography, mathematics, engineering, architecture, product design, and interiors in the studio projects (apply multi-disciplinary perspectives).
Outcome 3: Apply explicit concepts to creative original works to learn the connections between knowledge, research, and design (think critically).
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DEA 1110 - [Making a Difference by Design] (crosslisted) COGST 1111
Fall, summer. Next offered 2018-2019. 3 credits. Student option grading.
Lab fee: $15 (charged to bursar bill). Enrollment limited.
B. Hollis, G. Kar, R. Verma.
Making a Difference by Design is a course about leadership, creative problem solving and social change. Design is not just about products, it is about strategy, communication, empowerment, growth and social change, and has a specific impact on health and hospitality. DEA 1110 was conceived as a companion to DEA 1100, Design Generation(s), although it is not necessary to take both classes. The course is not a class about becoming a designer. It presumes no pre-requisites or artistic abilities. It is an introductory level course for non-designers and designers alike which examines how design affects you personally and globally, and how you can affect design process to innovate in various fields.
Through case studies and familiar examples, DEA 1110 explores successful design thinking in hospitality, health and other fields. The focus is on how leaders use design as a tool for improving the human condition.
Outcome 1: Demonstrates a basic understanding of creative design process - information gathering, idea exploration, iterative feedback loops, prototyping and testing - and an awareness of key factors affecting creativity and teamwork (innovate in research, design or practice).
Outcome 2: Recognize design as both a leadership and strategic business planning tool with both tangible and intangible outcomes and impacts across a variety of stakeholders (apply multi-disciplinary perspective).
Outcome 3: Understand the relationship between health, hospitality and design.
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DEA 1150 - Design Graphics and Visualization
(LAD-HE)
Spring. 4 credits. Letter grades only.
Prerequisite: DEA majors must earn B- or higher in DEA 1150 to enroll in subsequent studios. Minimum cost of materials: $200; technology fee: $10. Enrollment preference given to: DEA undergraduate majors. Other students by permission only.
S. Yoon.
This course immerses students in the act and art of design communication. Students focus on a series of exercises covering both manual and digital visualization techniques to effectively present and communicate ideas to oneself and others. Students will become experienced with Adobe Suite (Photoshop, Illustrator), AutoCAD and SketchUp software applications in addition to manual hand skill development (sketching, drafting, rendering).
Outcome 1: Apply a variety of visualization skills and techniques for effective communication.
Outcome 2: Use sketches and renderings as design and communication tools using both manual and digital media.
Outcome 3: Produce competent presentation graphics across a range of tools.
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DEA 1200 - Art and Science: Sustainability, Multiculturalism and Transdisciplinarity
(D-HE, LAD-HE)
Fall. 3 credits. Letter grades only.
Enrollment preference given to: DEA Majors.
J. Westwater.
The relationship between art and science has been thoroughly discussed by philosophers. This course will review the discussion on this topic and investigate the role of art/science in a variety of professions and its role in creativity. Sustainability, multiculturalism and transdisciplinarity will be explored as manifestations of integrated minds and communities.
Outcome 1: Appreciate interdisciplinary and transdisciplinary approaches. Students will develop an appreciation of the relationships between various disciplines and proactively engage in interdisciplinary and transdisciplinary activities.
Outcome 2: Understand the contribution of art and science to creativity. Students will recognize the role that art and science play in the creative act and develop skills that allow them to use alternative approaches to problem solving.
Outcome 3: Solve problems collaboratively. Students will collaborate in multidisciplinary teams to address issues related to sustainability multiculturalism and globalization.
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DEA 1500 - Introduction to Environmental Psychology (crosslisted) COGST 1500 , PSYCH 1500
(D-HE, LAD-HE, SBA-HE)
Spring, summer. 3 credits. Student option grading.
G. Evans.
Environmental Psychology is an interdisciplinary field concerned with how the physical environment and human behavior interrelate. Most of the course focuses on how residential environments and urban and natural settings affect human health and well-being. Students also examine how human attitudes and behaviors affect environmental quality. Issues of environmental justice and culture are included throughout. Hands-on projects plus exams. Lecture and discussion sections. DEA 1501 - Writing in the major (WIM) option also is available (by instructor permission) for 4 credits.
Outcome 1: Provide overview of knowledge about the environment and human behavior (grounding in field)
Outcome 2: Understand cultural and life course diversity in human-environment interactions (sensitivity to diversity)
Outcome 3: Learn how to analyze problems like an environmental psychologist (develop critical thinking skill)
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DEA 1501 - Introduction to Environmental Psychology - Writing in the Major (crosslisted) COGST 1501 , PSYCH 1501
(D-HE, LAD-HE, SBA-HE)
Spring. 4 credits. Student option grading.
Permission of instructor required. Enrollment limited to: 15 students per section.
G. Evans.
Human-Environment Relations is an interdisciplinary field concerned with how the physical environment and human behavior interrelate. Most of the course focuses on how residential environments and urban and natural settings affect human health and well-being. Students also examine how human attitudes and behaviors affect environmental quality. Issues of environmental justice and culture are included throughout. Hands-on projects plus exams. Lecture and discussion sections. WIM section attend a regular lecture but also meets weekly with a graduate writing tutor. The two principal objectives of WIM section:
1. More in depth discussion and analysis of the materials covered in the course.
2. On going, systematic opportunity to improve your writing skills.
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DEA 2020 - Introduction to Sustainable Design
(LAD-HE) (CU-SBY)
Fall. 3 credits. Student option grading.
Enrollment limited. Enrollment preference given to: DEA undergraduate majors.
D. Ramzy.
What is sustainability and how does it apply to the built environment? Through lecture, case studies and field trips, this seminar will explore this complex concept through the lenses of ecology, materials science, technology, economy, health, culture and community. It will consider all phases of design: from planning through construction and operations, to reuse and disassembly. Appropriate for DEA, Architecture, Landscape Architecture, CRP, Engineering, Real Estate and Hotel School students.
Outcome 1: Identify the basic concepts and tenets of sustainable design and understand overlapping environmental, social and economic issues related to sustainability.
Outcome 2: Communicate the benefits and challenges to designing sustainably which include strategies, practices, and terms used in green building.
Outcome 3: Determine the right questions to ask when assessing designs, materials & processes for sustainable performance.
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DEA 2030 - Digital Communications
(LAD-HE)
Spring, summer. 2 credits. Letter grades only.
Enrollment priority given to: DEA majors.
D. Ramzy.
Visual communication can clarify, distill and translate complex designs, data and ideas. It is persuasive and powerful. The class introduces principles of graphic design and visual communications using a range of representation techniques. It explores ways to communicate ideas digitally through text, and image while considering the meaning of form and its symbolic and cultural dimensions. The focus will be on the applications in Adobe Creative Cloud, including Illustrator, InDesign and Photoshop.
Outcome 1: Learn to use a variety of contemporary representational applications.
Outcome 2: Understand and apply basic graphic design principles.
Outcome 3: Develop formal, material and presentation skills in a manner that appropriately reflect on and communicate intent.
Outcome 4: Understand how to critically evaluate representation techniques appropriate to a given situation.
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DEA 2040 - High Performance Buildings
(CU-SBY)
Spring. 3 credits. Letter grades only.
Y. Hua.
A “high performance building” is one that integrates and optimizes all major building performance attributes, including occupant safety and comfort, energy efficiency, environmental impact, aesthetics, life-cycle performance, and cost-effectiveness. This course introduces all major building systems, and a wide array of design strategies and system approaches for creating, high performance buildings that both satisfy user needs and support sustainability and resilience.
Outcome 1: Develop a comprehensive and integral understanding of building systems, their key elements and mechanisms of function, and corresponding design, and operation strategies.
Outcome 2: Understand the implications of a variety of design and system decisions for the performance of buildings; and analyze the pros and cons of different design strategies and building systems solutions.
Outcome 3: Conduct building performance evaluation and diagnosis, and make planning, design, operation, and renovation recommendations.
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DEA 2201 - Magnifying Small Spaces Studio
(LAD-HE) (CU-SBY)
Fall. 4 credits. Letter grades only.
Prerequisite: DEA 1101 and DEA 1150 (minimum grades of B-); or permission of instructor. Minimum cost of materials: approx. $50 field trip and supplies. Enrollment limited.
K. Gibson.
Much of the worlds’ population lives and works in small spaces. This studio explores behaviors and ways in which design responds to micro environments. Inquiry will experiment with issues of commodity, firmness and delight through the tenants of reduction and scale. In reducing one’s carbon footprint, how small is too small? Prototypes range from tents and pods to nomadic structures.
Outcome 1: Collect, analyze and report complex information and its significance in a clear and concise manner (write, speak, and use visual communication effectively).
Outcome 2: Apply creative problem-solving techniques to develop innovative solutions to wicked design problems (critical thinking; innovative research, design or practice).
Outcome 3: Effectively work in dynamic team situations (work effectively with others).
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DEA 2203 - StudioShift
(LAD-HE) (CU-CEL)
Spring. 4 credits. Letter grades only.
Prerequisite: DEA 1101 and DEA 1150 (minimum grades of B-) or permisssion of instructor. Enrollment limited.
R. Gilmore.
Temporal spaces dominate the interior landscape at this point in history, reflecting the fleeting nature of information in a society consumed with momentary experiences. This studio will both expand and contract notions of spatial/environmental communication through brand-forward environments, exhibit-forward environments, and social advocacy experience.
Outcome 1: Analyze protocols of exhibit design including code requirements, display techniques, graphic design user-interface, lighting, and construction documentation.
Outcome 2: Demonstrate design methodologies for exhibit design by completing three distinct projects.
Outcome 3: Manage the construction and installation of on-campus exhibits / installations.
Outcome 4: Identify and support a non-profit with design solutions which appropriately address their most critical issues.
Outcome 5: Work in teams to create viable options for non-profit collateral.
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DEA 2510 - History of Design Futures
(HA-HE)
Fall. 3 credits. Student option grading.
Enrollment limited.
R. Militello.
This course addresses the history and theory of environmental design with an emphasis on the role of design in cultural and technological change. Readings, lectures, discussion, and analytical exercises isolate key projects, methods, and ideas across time periods from classical to 20th Century, encouraging a speculative approach to historical material.
Outcome 1: Develop an understanding of key projects, methods, movements, and periods in architecture, interior design and furniture, object design, and art, and the relation of these to their cultural and technological contexts
Outcome 2: Develop a speculative and expansive approach to historical material
Outcome 3: Explore the use of both writing and design in formulating and presenting a thesis or position
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DEA 2550 - Design Strategy and Management
(LAD-HE)
Fall. 3 credits. Letter grades only.
Enrollment preference given to: DEA undergraduate majors.
D. Ramzy.
How can design play a role in an organization’s success in the 21st century? This course will introduce students to facility planning and management, as well as present an overview of design thinking as it is applied to the function of organization. We will look at a diverse range of strategic design applications including branding, organizational culture, user experience, scenario planning, workplace strategy, operations and maintenance. A series of guest lectures from leading practitioners will provide a framework for the class.
Outcome 1: Students will develop a clear understanding of the different roles and disciplines involved in design, business, and management
Outcome 2: Recognize the strategic and tactical value of design opportunities in business and develop the ability to apply design skills to solving 21st century organizational challenges
Outcome 3: Identify FPM’s key role in organizational success, demonstrate the phase of FPM and identify the skills needed to deploy them
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DEA 2700 - Healthy Places: Design, Planning and Public Health
(LAD-HE, SBA-HE) (CU-CEL, CU-SBY)
Fall. 3 credits. Letter grades only.
N. Wells.
Drawing from public health, environmental psychology, design, urban planning , architecture and landscape architecture, we examine how the physical environment influences health and health behaviors. We consider various contexts from rooms and buildings to parks and cities. Outcomes include physical and mental health, diet, physical activity and obesity.
Outcome 1: Comprehend the influence of the built and natural environment on human health and health behaviors.
Outcome 2: Think critically and apply theory as well as health impact assessment methods to real world challenges.
Outcome 3: Apply multidisciplinary perspectives including from planning, public health, environmental psychology.
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DEA 2730 - Human Centered Design Methods
(LAD-HE) (CU-UGR)
Fall. 3 credits. Letter grades only.
Enrollment preference given to: DEA undergratuate majors. Other students by permission only.
K. Green.
This course explores the use of design methods to conceptualize problems, generate ideas, and re-evaluate the objects, environments, and interfaces produced by design. Through lectures, discussions, exercises, and critiques, the course addresses the contexts of design while providing a hands-on overview of design methods.
Outcome 1: Apply a range of design methods to a series of problems, both assigned and self-directed, in the area of design.
Outcome 2: Analyze historic and present-day objects, environments, organizations, and techniques, with an aim toward proposing new connections or speculative futures.
Outcome 3: Formulate a position with respect to issues in contemporary design culture, expressed through both writing and design.
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DEA 2750 - Lighting Design: Light InForming Space
(LAD-HE)
Spring. 1 credit. Student option grading.
Prerequisite: DEA 1110 , DEA 1150 . Enrollment preference given to: DEA undergraduate majors. Other students by permission only.
R. Gilmore.
Light brings both necessity and nuance to the built environment, creating functional spaces and conjuring apparitions in perception, scale, time and sensory impulses. Developing a working knowledge of lighting design while seeking light as a transformative element, students will create lighting installations, lighting design documentation and build light fixtures.
Outcome 1: Immersion in luminance as a spatial opportunist.
Outcome 2: Competency in principles of light, color theory, luminaire typologies, lighting calculations, energy efficiency and re-lamping techniques.
Outcome 3: Identify / classify luminaire / lighting types, luminance categories.
Outcome 4: Manipulation and integration of lighting technology and spatial dynamics.
Outcome 5: Model-making and hands-on experience building a light fixture.
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DEA 2900 - Human Factors for Inclusive Design
Fall. 3 credits. Student option grading.
G. Shaw.
This course serves as an introduction to human factors and the basics of inclusive, human-centered design. Individual variability requires different design solutions, and we will evaluate, model, and apply methods to support the needs of a wide variety of individuals. Students will work with researchers and student groups locally, and other universities nationally and internationally for project work.
Outcome 1: Comprehend disciplines and fields: Students will be able to relate cognitive and physical human factors methods to inclusive designs of products and spaces.
Outcome 2: Think critically: Students will be able to identify, evaluate, plan, and execute projects relating to inclusive design and special populations.
Outcome 3: Innovate in research, design, or practice: Students will be able to create new designs utilizing human factors systems-modeling approaches.
Outcome 4: Write, speak, and use visual communication effectively: Students will be able to explain and justify their project outcome through oral presentations, written reports or both to a variety of stakeholders.
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DEA 3030 - Interior Materials and Sustainable Elements
(LAD-HE) (CU-SBY)
Fall. 3 credits. Letter grades only.
Cost of materials: approximately $10. Enrollment limited. Recommended for juniors and seniors.
R. Gilmore.
A sustainable approach to the evaluation and selection of materials and finishes for creating products and places for people has the potential to ensure the future survival of our planet. This course provides an introduction to basic material properties and asks students to morph the materials sensibilities, understand performance testing, building codes, and formulate a life-cycle cost analysis. Emphasis on “green” methodologies and assessment, including LEED building rating systems.
Outcome 1: Understand the limits of a material and its unlimited possibilities (innovate in research, design or practice)
Outcome 2: Critically assess a material’s life cycle, from resource extraction to end-of-life or cradle-to-cradle potential (think critically, direct own learning)
Outcome 3: Create and present visual/verbal research on current sustainable products as a member of a presentation team (work effectively with others, write, speak, and use communications effectively).
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DEA 3050 - Construction Documentation: CAD and BIM
(LAD-HE)
Spring. 3 credits. Student option grading.
Prerequisite: DEA 1150 with B- or higher or permission of instructor. Minimum cost of materials: $50; required field trips: $10. Enrollment limited.
Staff.
A continuous dialogue between the idea for an interior space and the reality of its final built form is contained within construction documents, also known as working drawings and specifications. Students study the history of architectural documentation, the organization of construction drawings, schedules, and specifications, and the detailing of interior elements and construction methods by touring a local millwork shop. Each student completes a comprehensive set of construction documents. Student must be experienced with AutoCad.
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DEA 3055 - Hospitality, Health and Design Industry Immersion Seminar (crosslisted) HADM 3055
(LAD-HE, SBA-HE)
Spring. 1 credit. Student option grading.
Enrollment limited to: juniors and seniors. Co-meets with DEA 6055 /HADM 6055 .
B. Hollis, M. Shepley, R. Verma.
For description, see HADM 3055 .
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DEA 3301 - Design UX with Technology Studio
(LAD-HE)
Fall. 4 credits. Letter grades only.
Prerequisite: completion of DEA DEA 1101 and DEA 1150 with B- or higher or permission of instructor. Minimum cost of materials: $150; shop fee: $10; optional field trip: approx. $10. Enrollment limited.
S. Yoon.
This intermediate-level studio focuses on designing innovative commercial and/or learning/workplace environments. Various types of users experience design and evaluation approaches to design-problem solving will engage students in innovative interior design processes and outcomes. Effective use of technology will be emphasized throughout the course.
Outcome 1: Gather, evaluate, and apply information and research findings to innovatively solve design problems
Outcome 2: Demonstrate creative and critical thinking and originality through presentation in variety of ideas, approaches, and concepts
Outcome 3: Develop a good understanding of team work dynamics: collaboration, consensus building, and leadership; and work effectively with others
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DEA 3302 - Sustainable Consumerism: The New Retail Studio
Spring. 4 credits. Letter grades only.
Prerequisite: DEA Majors only. Students must have 1) completed two studio courses at the 2000 or 3000 level and 2) be experienced with AutoCAD, Illustrator, Photoshop and In-Design software applications. Minimum cost of materials: $50 field trip fee, supplies. Enrollment limited.
K. Gibson.
Studio engages students in problem-solving, digital and design skills to develop an innovative retail interior which responds to immerging consumer and environmental requirements. Cultural, social, economic and physical mediating factors which influence consumer behavior and shopping patterns are explored.
Outcome 1: Demonstrate proficiency with space planning, sketching, diagramming, perspectives, and FF& E.
Outcome 2: Verbally and visually explain design ideas using discipline-specific terminology and methods.
Outcome 3: Collect, analyze and report complex information and its significance in a clear and concise manner.
Outcome 4: Apply knowledge of design principles, constructs, codes and construction methods.
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DEA 3304 - Health and Healing Studio
(LAD-HE) (CU-CEL)
Spring. 4 credits. Letter grades only.
Prerequisite: completion of DEA 1101 and DEA 1150 with B- or higher or permission of instructor. Minimum cost of materials: $50 field trip fee, supplies.
M. Shepley.
Balancing the needs of patients experiencing acute health issues with the systematic needs of a variety of medical professionals requires spatial environments that must nurture the human spirit. Environmental influences can create both efficient and effective healing mechanisms that not only treat the patient, but restore the body’s ability to heal. In this studio, students will utilize spatial constructs that establish code-compliance, critical adjacencies, workflow circulation, and formulate health care facilities that employ evidence-based design principles.
Outcome 1: Extend design responsibility to a variety of end user in health care settings (apply multi-disciplinary perspectives).
Outcome 2: Complete a programmatic analysis and research assessment of specific health care delivery systems and environments (innovate in research, design or practice).
Outcome 3: Design a health care environment which promotes the patient’s ability to heal and the medical staff’s ability to be productive (think critically, write, speak and use visual communications effectively).
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DEA 3500 - The Ambient Environment
(CU-SBY)
Fall. 3 credits. Student option grading.
Recommended prerequisite: DEA 1500 . Co-meets with DEA 6520 .
A. Hedge.
Introduces human-factor considerations in lighting, acoustics, noise control, indoor air quality and ventilation, and the thermal environment. Views the ambient environment as a support system that should promote human efficiency, productivity, health, and safety. Emphasizes the implications for planning, design, and management of settings and facilities. Visit: ergo.human.cornell.edu.
Outcome 1: Explore key concepts in each of the major topic areas covered i.e. indoor climate (thermal conditions, ventilation and air quality), indoor lighting, and indoor acoustics.
Outcome 2: Understand environmental measurement techniques and be familiar with methods.
Outcome 3: Conduct a human factors analysis of an indoor environment.
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DEA 3510 - Ergonomics and Anthropometrics
Fall. 3 credits. Student option grading.
Recommended prerequisite: DEA 1500 . Co-meets with DEA 6510 .
A. Hedge.
Implications of human physical and physiological characteristics and limitations on the design of settings, products, and tasks. An introduction to engineering anthropometry, biomechanics, control/display design, work physiology, and motor performance. Includes practical exercises and field project work. Visit: ergo.human.cornell.edu.
Outcome 1: Define key concepts and be familiar with terminology in human information processing, physiological and person-technology models for ergonomic design.
Outcome 2: Conduct and report on an ergonomic analysis of a product or system and undertake a comparative analysis.
Outcome 3: Apply critical skills and knowledge to improve the ergonomic design of a product or system and report on this.
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DEA 3530 - Planning and Managing the Workplace: Evidence-Based Design and Organizational Ecology
(LAD-HE)
Fall. 3 credits. Letter grades only.
R. Sagha Zadeh.
Discover how to plan, design, and manage high-impact work environments. Partner with practicing design professionals to engage in high-quality design generation and peer-reviewed quality research dissemination on real projects that focus on healthcare facilities and research laboratories and expand to corporate workspaces. Interpret, and apply evidence to improve performance, quality, and efficiency for knowledge workers and organizations.
Outcome 1: Communicate orally and through written reports with FPM representatives and clients and strengthen professional and ethical skills.
Outcome 2: Explore and develop an in-depth understanding of specific health, productivity, and safety issues and ways that the physical environment contributes in high-performance work settings.
Outcome 3: Collect and synthesize information to create a comprehensive visual and written report on a key issue relevant to Evidence-Based Design.
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DEA 3550 - Research Methods in Human-Environment Relations
Spring. 3 credits. Student option grading.
H. Sadatsafavi.
Develops students’ understanding and competence in the use of research and analytical tools to study the relationship between the physical environment and human behavior. Emphasizes evaluation of internal and external validity as well as measurement reliability and validity. Topics include research design, unobtrusive and obtrusive data-collecting tools, the processing of data, and effective communication of empirical research findings. Students will need to see instructor for section assignments.
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DEA 3590 - Problem-Seeking through Programming
(LAD-HE, SBA-HE) (CU-CEL)
Fall. 3 credits. Letter grades only.
Minimum cost of materials: $100. Co-meets with DEA 6500 . Grade only for all DEA majors. Grade option for non-majors.
L. Maxwell.
An architectural program is used to define the design problem, guide the design process and evaluate design solutions. Students will develop skills in preparing a program while keeping in mind the potential audiences. This course emphasizes the role of social science research and environment - behavior interaction in facility planning and in the design process.
Outcome 1: Identify different design programming methods and understanding key issues to consider in selecting and implementing a particular programming approach through lectures, readings, and hands-on exercise (grounding in disciplines and fields).
Outcome 2: Utilize social science research, facility management skills, and design concepts to develop a program of space requirements (multidisciplinary perspectives).
Outcome 3: Develop critical success factors in developing and managing an effective programming process by participating in a major programming project for a real client (critical thinking).
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DEA 3600 - [Design City]
Fall. Next offered 2018-2019. 1 credit. S/U grades only.
Field trip fee covers cost of hotel and chartered bus, but no meals; trip fee billed to student’s bursar account. Estimated course fee: $200. Please note: last day to drop this class will be announced. Bursar bill will be charged after this date. Permission of instructor required. Enrollment limited to: DEA majors and DEA minors. Not open to freshmen for credit, however course may be repeated for credit up to 4 times (during sophomore, junior, senior, or graduate years). Enrollment limited to: 40 students.
D. Ramzy.
Field study of guided tours to architectural and interior design firms, FPM consulting and firms specializing in ergonomics, installations, exhibits, and showrooms in New York City, Toronto, or other major cities. Topics and themes change yearly.
Outcome 1: Hone visual and analytical skills, critical thinking, and reflections on professional practice.
Outcome 2: Participate in interdisciplinary group discussions and develop team presentations.
Outcome 3: Interview and network with DEA alumni to identify discipline-related opportunities.
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DEA 4000 - Directed Readings
(CU-UGR)
Fall or spring. 1-15 credits, variable. Student option grading.
Permission of instructor required.
Staff.
For study that predominantly involves library research and independent reading.
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DEA 4010 - Empirical Research
(CU-UGR)
Fall or spring. 1-15 credits, variable. Student option grading.
Permission of instructor required.
Staff.
For study that predominantly involves data collection and analysis or laboratory or studio projects.
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DEA 4020 - Supervised Fieldwork
(CU-UGR)
Fall or spring. 1-15 credits, variable. Student option grading.
Permission of instructor required.
Staff.
For study that involves both responsible participation in a community setting and reflection on that experience through discussion, reading, and writing. Academic credit is awarded for this integration of theory and practice.
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DEA 4030 - Teaching Apprenticeship
Fall or spring. 1-15 credits, variable. Student option grading.
Permission of instructor required.
Staff.
For study that includes teaching methods in the field and assisting faculty with instruction. Students must have demonstrated a high level of performance in the subject to be taught and in the overall academic program.
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DEA 4040 - Professional Practices and Ethics
Spring. 1.5 credits. Letter grades only.
Enrollment limited to: DEA seniors only. Other students by permission of instructor.
Staff.
Historically, the Professional Practice course introduced organizational and management principles for delivery of interior design and facility management services. In recent years, design practice has changed dramatically, and while the course will cover key elements of business practice, along with project management, delivery and communication, students will also be directed to alternative, often multidisciplinary, ways of working in design with a focus on two critical aspects of professional practice: entrepreneurship and ethics.
Outcome 1: Apply fundamentals of professional practice; ethics, liability, sustainability, profitability.
Outcome 2: Comprehend management of creative process, including business models and strategy.
Outcome 3: Identify/create opportunity for professional practice.
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DEA 4100 - [Diversity and Facility Design]
(LAD-HE)
Spring. Next offered 2018-2019. 3 credits. Letter grades only.
Prerequisite: DEA 1500 , DEA 1110 , or permission of instructor. Some of the exercises will require travel off campus.
L. Maxwell.
The course examines the role of culture, gender, stage in the life cycle, and disability in planning facilities of various types including public spaces, educational and healthcare spaces, residential spaces and the workplace. This course examines the issues of diversity from the perspectives if the implicit and explicit assumptions about the user and how we purposely plan facilities in a diverse society.
Outcome 1: Examine implicit and explicit design assumptions related to potential users of various facility types (critical thinking).
Outcome 2: Examine what design means in a culturally diverse society. Students use human factors, psychological, and design perspectives to examine environments (multidisciplinary perspectives).
Outcome 3: Consider the value of full participation of different types of users in various facilities and settings (ethical principles).
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