| | 01 | TBA | | (None) / | Clark | No Final | 0 | 0 | 0 | | |
| 02 | TBA | | (None) / | Feher | Default - none | 0 | 2 | 0 | | |
| 03 | TBA | | TBA | Ching | Default - none | 0 | 0 | 0 | | |
| 04 | TBA | | TBA | Jha | Default - none | 0 | 0 | 0 | | |
| 07 | TBA | | TBA | Lew | Default - none | 0 | 0 | 0 | | |
| 08 | TBA | | TBA | Chakrabartty | Default - none | 0 | 1 | 0 | | |
| 09 | TBA | | TBA | Wang, Dorothy | Default - none | 0 | 0 | 0 | | |
| 11 | TBA | | TBA | Nagulu | Default - none | 0 | 0 | 0 | | |
| 13 | TBA | | TBA | O'Sullivan | Default - none | 0 | 0 | 0 | | |
| 28 | TBA | | TBA | Yang | Default - none | 0 | 0 | 0 | | |
| 29 | TBA | | TBA | Li | Default - none | 0 | 0 | 1 | | |
| 30 | TBA | | TBA | Shen | Default - none | 0 | 0 | 0 | | |
| 32 | TBA | | TBA | Kurenok | Default - none | 0 | 0 | 1 | | |
| 34 | TBA | | TBA | Mell | Default - none | 0 | 0 | 0 | | |
| 36 | TBA | | TBA | Kamilov | Default - none | 0 | 0 | 0 | | |
| 37 | TBA | | TBA | Zeng | Default - none | 0 | 0 | 0 | | |
| 38 | TBA | | TBA | Zhou | Default - none | 0 | 0 | 0 | | |
| 39 | TBA | | TBA | Villa | Default - none | 0 | 0 | 0 | | |
| 40 | TBA | | TBA | Zhou | Default - none | 0 | 0 | 0 | | |
| 41 | TBA | | TBA | Murch | Default - none | 0 | 0 | 0 | | |
| 42 | TBA | | TBA | Sinopoli | Default - none | 0 | 0 | 0 | | |
| 43 | TBA | | TBA | Lu | Default - none | 0 | 0 | 0 | | |
| 44 | TBA | | TBA | Chen | Default - none | 0 | 0 | 0 | | |
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| | 01 | M-W---- | 4:00P-5:20P | TBA | Kurenok | No Final | 50 | 15 | 0 | | |
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| | 01 | -T-R--- | 4:00P-5:20P | Brauer Hall / 012 | Chen | May 7 2025 6:00PM - 8:00PM | 75 | 90 | 4 | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | -T----- | 5:30P-6:30P | Brauer Hall / 012 | Chen | Default - none | 75 | 9 | 0 | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | In this course, students will learn through hands-on experience the application of analytics to support data-driven decisions. Through lectures and the execution of a project (to be defined at the beginning of the semester), students will learn to use descriptive, predictive, and prescriptive analytics. Lectures will focus on presenting analytic topics relevant to the execution of the project, including analytic model development, data quality and data models, review of machine learning algorithms (unsupervised, supervised, and semi-supervised approaches), model validation, insights generation and results communication, and code review and code repository. Students are expected to demonstrate the application of these concepts through the execution of a one-semester project. Students can propose their own projects or choose from a list of projects made available by the lecturer. Projects should reflect real-world problems with a clear value proposition. Progress will be evaluated and graded periodically during the semester, and the course will include a final presentation open to the academic community. Prerequisites: ESE 520 (or Math 493 and 494), CSE 417T, ESE 415, and declaration of the MS in DAS. |
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| Description: | The nanometer length scale holds a unique significance for optical engineering because it is home to the wavelengths of visible and infrared light. The behavior of a light wave is particularly sensitive to structural features formed at or below the scale of its wavelength and, as a consequence, nanophotonics encompasses many new and useful phenomena not found in macroscopic systems. In this course, we will explore the physics of light-matter coupling before using it as a guide to engineer new optical material properties via nanofabrication, with applications in computing, telecommunications, biomedical sensing, solar energy harvesting, robotics and more. Key topics covered in the course include Mie resonant dielectric antennas, plasmonic antennas, negative and zero refractive index metamaterials, chiral metamaterials, metasurface lenses and holograms, nonlinear and time dependent metasurfaces, Bragg mirrors, 3D photonic crystals, photonic crystal slab waveguides and cavities, guided mode resonators, photonic crystal lasers. |
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| | 01 | -T-R--- | 2:30P-3:50P | TBA | Lawrence | May 7 2025 3:30PM - 5:30PM | 30 | 15 | 0 | | |
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| Description: | This course is a graduate level course taught in two parts. Part 1 covers frequency domain analysis of multivariable systems, robustness theory and structured singular value mu analysis, linear quadratic optimal control system design using state and output feedback architectures, H-infinity optimal control, LQG/LTR, and output feedback projective controls. Part 2 covers the design of direct model reference adaptive controllers for uncertain nonlinear systems, Lyapunov stability theory, Barbalat lemma, neural networks, state feedback model reference adaptive control, and adaptive observer-based loop transfer recovery output feedback. Homework and computer design projects use aerospace examples. The adaptive controllers are developed to be an increment added to the robust control baseline architecture (covered in part 1).Prerequisite: ESE 543 Control Systems Design by State Space Methods or ESE 551 Linear Dynamic Systems or equivalent. |
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| | 01 | ----F-- | 1:00P-3:50P | TBA | Wise | Exam Last Day of Class | 30 | 12 | 0 | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | A rigorous introduction to recent trend and developments in systems and controls. Focuses are on the discussion of multidisciplinary applications of complex dynamical systems that motivate emerging topics in dynamics and control, data science, and learning, and on the review of essential tools and state-of-the-art methods for addressing control, computation, and learning for these large-scale systems. Topics to be covered include stochastic control, geometric control, and systems-enabled data science approaches to control and learning of high-dimensional systems. Technical tools to be discussed include basics of stochastic calculus, stochastic differential equations, and stochastic control; basics and applied differential geometry; principles of optimal control and dynamic programming, as well as reinforcement learning. Applications of these classical and advanced methods to complex networks, multi-agent systems, ensemble systems, neural networks, and manifold learning will be discussed. Students will be exposed to the state-of-the-art research in the field, and are expected to apply the learned knowledge to conduct a final project.
Prerequisite: Linear algebra (Math 429) or equivalent, ESE 415 Optimization, ESE 551 Linear Dynamic Systems, and ESE 520 Probability and Stochastic Processes. Students without completing the listed prerequisite coursework may consult with the instructor for permission to enroll in this course. |
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| Description: | The goal of this course is to introduce modern learning and planning methods for safe robot autonomy. The course will consist of two parts. The first part will focus on motion planning and decision-making methods for autonomous robot systems. Such methods will include dynamic programming, randomized methods (e.g., probabilistic roadmaps and RRT), and reinforcement learning. More advanced planning methods that leverage formal methods, automata theory, and estimation theory will be discussed as well. In the second part, students will present research papers related to topics covered in the first part or their extensions (e.g., deep reinforcement learning, multi-agent systems, or sensor-based planning and control).
Prereqs: ESE 520 , ESE 415 or ESE 4031 or ESE 513, and ESE 441 or ESE 543, or permission of instructor
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| | 01 | M-W---- | 11:30A-12:50P | TBA | Kantaros | No Final | 19 | 18 | 0 | | |
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| | 01 | -T-R--- | 2:30P-3:50P | Whitaker / 318 | [TBA] | May 7 2025 3:30PM - 5:30PM | 25 | 25 | 0 | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | This class will develop a fundamental understanding of the physics and mathematical methods that underlie biological imaging and critically examine case studies of seminal biological imaging technology literature. The physics section will examine how electromagnetic and acoustic waves interact with tissues and cells, how waves can be used to image the biological structure and function, image formation methods and diffraction limited imaging. The math section will examine image decomposition using basis functions (e.g. fourier transforms), synthesis of measurement data, image analysis for feature extraction, reduction of multi-dimensional imaging datasets, multivariate regression, and statistical image analysis. Original literature on electron, confocal and two photon microscopy, ultrasound, computed tomography, functional and structural magnetic resonance imaging and other emerging imaging technology will be critiqued. |
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| | 01 | -T-R--- | 8:30A-9:50A | Green Hall / L0120 | Culver, O'Sullivan, Tai, Shimony | May 2 2025 1:00PM - 3:00PM | 50 | 50 | 3 | | |
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| | 01 | -T-R--- | 1:00P-2:20P | TBA | O'Sullivan, Culver | No Final | 0 | 0 | 13 | | |
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| | 01 | TBA | | TBA | Wormleighton | No Final | 0 | 0 | 1 | | |
| 02 | TBA | | TBA | Sorrells | No Final | 0 | 1 | 0 | | |
| 03 | TBA | | TBA | Ching | Default - none | 0 | 0 | 0 | | |
| 04 | TBA | | TBA | Patwari | Default - none | 0 | 0 | 0 | | |
| 07 | TBA | | TBA | Lew | Default - none | 0 | 0 | 0 | | |
| 08 | TBA | | TBA | Chakrabartty | Default - none | 0 | 0 | 1 | | |
| 09 | TBA | | TBA | Wang, Dorothy | Default - none | 0 | 0 | 0 | | |
| 10 | TBA | | TBA | Lawrence | Default - none | 0 | 0 | 0 | | |
| 11 | TBA | | TBA | Feher | Default - none | 0 | 0 | 0 | | |
| 13 | TBA | | TBA | O'Sullivan | Default - none | 0 | 0 | 0 | | |
| 22 | TBA | | TBA | Nagulu | Default - none | 0 | 0 | 0 | | |
| 28 | TBA | | TBA | Yang | Default - none | 0 | 0 | 0 | | |
| 29 | TBA | | TBA | Li | Default - none | 0 | 1 | 1 | | |
| 30 | TBA | | TBA | Shen | Default - none | 0 | 0 | 0 | | |
| 32 | TBA | | TBA | Kurenok | Default - none | 0 | 0 | 0 | | |
| 34 | TBA | | TBA | Mell | Default - none | 0 | 0 | 0 | | |
| 36 | TBA | | TBA | Kamilov | Default - none | 0 | 0 | 2 | | |
| 37 | TBA | | TBA | Zeng | Default - none | 0 | 0 | 0 | | |
| 38 | TBA | | TBA | Zhou | Default - none | 0 | 0 | 0 | | |
| 39 | TBA | | TBA | Villa | Default - none | 0 | 0 | 0 | | |
| 40 | TBA | | TBA | Clark | Default - none | 0 | 0 | 1 | | |
| 41 | TBA | | TBA | Sotiras | Default - none | 0 | 0 | 0 | | |
| 42 | TBA | | TBA | Zhu | Default - none | 0 | 0 | 0 | | |
| 43 | TBA | | TBA | Culver | Default - none | 0 | 0 | 0 | | |
| 44 | TBA | | (None) / | Kantaros | Default - none | 0 | 0 | 0 | | |
| 45 | TBA | | TBA | Chamberlain | Default - none | 0 | 0 | 1 | | |
| 46 | TBA | | TBA | Murch | Default - none | 0 | 0 | 0 | | |
| 47 | TBA | | TBA | Wang | Default - none | 0 | 0 | 1 | | |
| 48 | TBA | | (None) / | Wang | Default - none | 0 | 0 | 0 | | |
| 49 | TBA | | (None) / | Chen | Default - none | 0 | 1 | 0 | | |
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| | 01 | TBA | | (None) / | Lawrence | Default - none | 10 | 1 | 0 | | |
| 02 | TBA | | TBA | Sorrells | Default - none | 9 | 1 | 0 | | |
| 03 | TBA | | TBA | Ching | Default - none | 9 | 0 | 0 | | |
| 04 | TBA | | TBA | Zhou | Default - none | 9 | 1 | 0 | | |
| 06 | TBA | | TBA | Zhang, Silvia | Default - none | 9 | 0 | 0 | | |
| 07 | TBA | | TBA | Lew | Default - none | 9 | 1 | 0 | | |
| 08 | TBA | | TBA | Chakrabartty | Default - none | 9 | 0 | 0 | | |
| 09 | TBA | | TBA | Goodhill | Default - none | 9 | 0 | 0 | | |
| 10 | TBA | | TBA | Min | Default - none | 9 | 0 | 0 | | |
| 11 | TBA | | TBA | Wang, Yong | Default - none | 9 | 1 | 0 | | |
| 13 | TBA | | TBA | O'Sullivan | Default - none | 9 | 0 | 0 | | |
| 17 | TBA | | TBA | Schaettler | Default - none | 9 | 0 | 0 | | |
| 18 | TBA | | TBA | Shrauner | Default - none | 9 | 0 | 0 | | |
| 27 | TBA | | TBA | Nehorai | Default - none | 9 | 0 | 0 | | |
| 28 | TBA | | TBA | Yang | Default - none | 9 | 3 | 0 | | |
| 29 | TBA | | TBA | Li | Default - none | 10 | 1 | 0 | | |
| 30 | TBA | | TBA | Shen | Default - none | 10 | 0 | 0 | | |
| 32 | TBA | | TBA | Kurenok | Default - none | 10 | 0 | 0 | | |
| 34 | TBA | | TBA | Mell | Default - none | 10 | 0 | 0 | | |
| 36 | TBA | | TBA | Kamilov | Default - none | 9 | 0 | 0 | | |
| 37 | TBA | | TBA | Anastasio | Default - none | 9 | 0 | 0 | | |
| 38 | TBA | | TBA | Zeng | Default - none | 9 | 0 | 0 | | |
| 39 | TBA | | TBA | Patwari | Default - none | 9 | 0 | 0 | | |
| 40 | TBA | | TBA | Sotiras | Default - none | 999 | 2 | 0 | | |
| 41 | TBA | | TBA | Jha | Default - none | 999 | 0 | 0 | | |
| 42 | TBA | | TBA | Raman | Default - none | 999 | 0 | 0 | | |
| 43 | TBA | | TBA | Tai | Default - none | 999 | 1 | 0 | | |
| 44 | TBA | | TBA | Zhu | Default - none | 999 | 0 | 0 | | |
| 45 | TBA | | TBA | Wang | Default - none | 999 | 0 | 0 | | |
| 46 | TBA | | TBA | Sinopoli | Default - none | 999 | 0 | 0 | | |
| 47 | TBA | | TBA | Monosov | Default - none | 999 | 1 | 0 | | |
| 48 | TBA | | TBA | Eggebrecht | Default - none | 999 | 1 | 0 | | |
| 49 | TBA | | TBA | Kantaros | Default - none | 999 | 0 | 0 | | |
| 50 | TBA | | TBA | Nagulu | Default - none | 999 | 0 | 0 | | |
| 51 | TBA | | (None) / | Clark | Default - none | 999 | 1 | 0 | | |
| 52 | TBA | | (None) / | An | Default - none | 999 | 0 | 0 | | |
| 53 | TBA | | (None) / | Murch | Default - none | 999 | 0 | 0 | | |
| 54 | TBA | | (None) / | Lu | Default - none | 999 | 0 | 0 | | |
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| | 01 | TBA | | TBA | O'Sullivan | Default - none | 20 | 8 | 0 | | |
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