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Fall 2018 CSCI 7000 (Graduate Level): Current Topics in Computer Science
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Updated as information becomes available
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Section NumberInstructorTopicShort Description for StudentsOther info for potential students to know
(pre-reqs, expectations, plans for the course, etc.)
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001MorrisonValidation and Uncertainty Quantification in Computational ModelsComputational models are used to describe physical and engineering systems in nearly every aspect of our lives, from the way an airplane wing interacts with turbulent air to how the human heart pumps blood. We use computational models in transportation, combustion, meteorology, genetics, cancer research, and so on. Typically a computational model is the implementation of and solution to an underlying mathematical model; this mathematical model is our best description of the physical reality. However, there are some reasons why the computational model results may not be reliable. Incorrect or over-confident predictions made with an unreliable model can have grave consequences for human health and safety, environmental sustainability, financial stability, etc. Validation and uncertainty quantification are the major processes by which we check that our underlying mathematical model reliably models the system of interest, and that all uncertainties are properly accounted for. Specific topics will include Cox's theorem and the foundations of Bayesian probability, working with incomplete or qualitative information, the principle of maximum entropy, model calibration and selection, data assimilation, validation under uncertainty, and making extrapolative predictions.
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002EisenbergTechnology and the YoungThis class will go beyond the issues of the moment (things like, "which websites are kids looking at?") and explore the larger abiding issues of how young people respond to, appropriate, and create new technologies. The underlying goal will be to use these larger historical themes as ways to guide the design and creation of new technologies for the young.Combined with CSCI 4830-002
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003SchnabelInterdisciplinary Seminar on Computing and SocietyThis seminar will examine, from an interdisciplinary perspective, ways in which the applications of computing technologies are and may be impacting human lives, and some of the cultural and ethical issues associated with these applications. The range of possible topics is broad; areas that may be considered include robotics, neural implants, genomics, autonomous vehicles, super-intelligence, and workforce dislocation. Addressing these societal issues associated with these topics requires expertise from fields including computing, sociology, anthropology, ethics, philosophy, law and public policy, and more, and ideally the class will contain students who bring all of these perspectives.To be combined with an ATLS course number
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004PalmerComputational Lexical SemanticsLexical semantics is becoming an increasingly important part of Natural Language Processing (NLP), as the field is beginning to address semantics at a large scale. This graduate seminar will cover key issues in computational lexical semantics. We will start with an introduction to theoretical models of lexical semantics and events, considering both their adequacy as linguistic models and their place in NLP. We will focus particularly on computational lexical resources such as PropBank, VerbNet and the Generative Lexicon, and examine their strengths and limitations with respect to NLP applications. We will introduce approaches to developing automatic classifiers that are intended to make use of these resources and to offer richer representations of sentences in context. These techniques can be fully supervised (requiring hand-labeled training data), semi-supervised, or unsupervised (learning lexical information from unlabeled text). Combined with LING 7800-004
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005KollaComplexity TheoryComputational complexity is the study of the limits of efficient computation. This graduate course will cover many of the most prominent algorithmic resources (time, space, non-determinism, randomness, interaction, quantum, etc.), and seek to understand why tasks can require large amounts of these resources. Further, these resources will be compared in their computational strength. In many cases (such as the P versus NP problem), answering these questions unconditionally is difficult, so this course will explore the known theory (completeness and reductions, oracle results, polynomial hierarchy assumptions) underlying current understanding. We will often use techniques from linear algebra and probability theory to study the results presented in the class, so knowledge of those topics is required. We will also cover basics of Quantum computation and Quantum complexity.
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007SzafirIntro to Virtual RealityThis course introduces students to the field of Virtual Reality. Topics will include:
• The historical development of Virtual Reality
• Computer graphics and 3D modeling
• Human-Computer Interaction relating to Virtual Reality
• Modern Virtual Reality technologies
• Context and use of Virtual Reality

By the end of the course, students will have gained knowledge and skills to:
• Understand the fundamental concepts relating to Virtual Reality such as presence, immersion, and engagement
• Read deeply, understand, and critique academic research papers relating to Virtual Reality
• Create simple computer generated environments for virtual exploration
• Program interactive elements for virtual experiences
• Work successfully with a group of peers from a variety of disciplines on a team project
• Communicate and present individual and group project work
• Demonstrate competence with several modern Virtual Reality technologies such as Google Cardboard, Google SketchUp, Unity, and the Oculus Rift
Combined with CSCI 4830-007, ATLS 4519-007, 5519-007
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008LayerAlgorithms for Computational Biology and BioinformaticsComputer science is a core component of modern biologic research and is critical to the future of personalized medicine. Genome sequencing produces vast and complex data that are intractable without efficient algorithms. This course covers the core data structures and algorithms which form the basis for research in topics ranging from evolution to the cause and treatment of many diseases, including cancer. Topics include string matching, indexing, compression, and succinct data structures. No prior knowledge of biology, DNA, or genetics is required.
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010Becker/FrongilloStats, Opt, and ML SeminarResearch-level seminar that explores the mathematical foundations of machine learning, in particular how statistics and optimization give rise to well-founded and efficient algorithms.Combined with ATLS 8500-001
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