Building on the foundations set in EQM I, Engineering Quantitative Methods II offers an advanced exploration of interdisciplinary, analytical engineering topics within a project-based framework. EQM II focuses on more complex subjects such as frequency domain analysis of signals, dynamics in both two and three dimensions, introductory concepts in machine learning, embedded systems, microcontrollers, and fundamental controls including transfer functions and proportional-differential control. The course projects are carefully designed to synergize with these topics, providing students with opportunities to apply theoretical knowledge in practical settings. Emphasizing self-directed learning, this course challenges students to develop and refine their skills in a more nuanced and complex engineering environment, bridging theory and practice.

This course introduces students to using an entrepreneurial and design-thinking view to solving real-world challenges including the pathway to commercializing research. It is about changing methods of thinking and equipping graduate students to be able to understand and manage innovation in the corporate world. This course equips participants with the cognitive tools and methodological rigor essential for excelling in the dynamic arena of technology-driven ventures. Through empirical problem-solving, rigorous analysis of innovation commercialization pathways, cultivation of a scientifically grounded entrepreneurial mindset, engagement with domain experts, and dedicated scientific inquiry, students develop competencies deeply rooted in evidence-based decision-making and hypothesis testing.

Through a mentor-led experiential program, this course will enable students to learn-by-doing, leading to the development of a fully functioning MVP (Minimally Viable Product). Students will learn the following key aspects of:

• Product Development: Identifying real problems, empathy with users; interview techniques; distinguishing between a “want” and a “real need.”
• Designing a product that will solve a real problem: Architecture, system design, building submodules, unit testing, system integration, system testing, testing “fit for use,” productize, manufacture.
• Project planning, managing & tracking: Learn about Gantt charts, Scrum, Kanban, Scrumban etc., and select the right tool for the project.
• Roles & responsibilities in a product development organization: Product Owner, Developers, SQA (Testers), Manufacturing, QC, Documentation,
• Customer Support Integrating continuous feedback from different constituencies: prospective users, mentors, teaching team, classmates, etc.
• Iterative development mindset: learn development techniques to “fail fast” and “fail frequently” to deliver fit-for-use products faster.
• In addition, the course will enable students to learn a number of ‘soft’ skills such as leadership, team development, conflict resolution, stakeholder management, and project management.

This course delves into the crucial transition from invention to innovation, examining the journey of ideas from conception to real-world impact. Students will explore historical and contemporary examples of how translation and innovation intertwine, often leading to disruptive changes. The course highlights the challenges businesses face in this translation process and provides frameworks for navigating the path from disruptive ideas to market success. The course is designed to explore the journey from conceptualizing inventions to realizing impactful innovations. Participants will gain a comprehensive understanding of the key stages involved in innovation, covering aspects such as ideation, prototyping, market analysis, and strategic implementation. Through a combination of theoretical insights and practical case studies, this course aims to equip learners with the knowledge and skills necessary to navigate the complex landscape of innovation. Topics include identifying market needs, refining inventions into viable products, assessing feasibility, and developing effective strategies for successful innovation adoption. Invited guest speakers who have experienced this journey firsthand will offer valuable insights.

Focusing on the dynamic nature of entrepreneurial strategy, this course examines strategies for creating and responding to structural changes in the market. It introduces the Grabber-holder framework as a tool for developing successful entrepreneurial strategies and covers various topics, including first-mover advantages, strategies for latecomers in mature markets, and turning challenges into opportunities. It is an immersive course designed to equip aspiring entrepreneurs with the strategic mindset and adaptive skills required to thrive in today's fast-paced and ever-changing business landscape. This course goes beyond traditional approaches to strategy by focusing on agility, innovation, and resilience, essential qualities for entrepreneurial success. The course emphasizes the dynamic nature of entrepreneurship and provides practical insights into crafting and adjusting strategies in response to evolving market conditions, emerging trends, and competitive forces. The course includes case studies, modeling exercises, and a term project to practically apply these concepts. 

Targeted at graduate students from diverse fields, this course provides an opportunity to design and prototype a meaningful physical project. It covers the journey from discovering a product, through redesign, to the creation of a customer-ready prototype using various manufacturing processes. Students will learn about design guidelines, materials choices, and gain hands-on experience in product realization. It is a comprehensive course that explores the synergy between the creative design process and the practical aspects of efficient and effective manufacturing. This course is tailored for individuals aspiring to bridge the gap between design concepts and their tangible realization in the manufacturing realm. Participants will embark on a journey through the entire product lifecycle, from initial design ideation to the intricacies of production and quality control. The curriculum covers fundamental principles of design thinking, CAD (Computer-Aided Design) tools, materials selection, and manufacturing processes. Special emphasis is placed on optimizing design for manufacturability, costeffectiveness, and sustainability. Through hands-on projects, case studies, and collaborative workshops, participants will gain practical insights into the challenges and opportunities inherent in the intersection of design and manufacturing. Topics include prototyping, rapid iteration, supply chain considerations, and the integration of emerging technologies such as additive manufacturing.

This course offers an in-depth exploration of Digital Health and the Biodesign innovation process. Students will engage academic and industrial experts in a dynamic learning environment, tackling realworld digital health challenges. The curriculum emphasizes the application of biodesign principles in identifying needs, ideating solutions, and evaluating them against key criteria. The course is designed for individuals seeking a profound understanding of the intersection between biology, technology, and healthcare. This course uniquely integrates principles of biodesign with cutting-edge digital health technologies, offering a comprehensive exploration of innovative solutions in the rapidly evolving healthcare landscape. Participants will embark on a journey through the fundamentals of biodesign, learning to identify unmet healthcare needs, ideate bio-inspired solutions, and develop prototypes that leverage the latest advancements in digital health. The curriculum encompasses topics such as biosensors, wearable devices, data analytics, and artificial intelligence applied to healthcare. Through hands-on projects, case studies, and collaboration with industry experts, learners will gain practical experience in designing and implementing biotechnological solutions for digital health challenges. The course also addresses ethical considerations, regulatory aspects, and the business implications associated with biodesign in the digital health domain.

AMCS 201 and AMCS 202 may be taken separately or in either order. Part of a fast-paced two-course sequence in graduate applied mathematics for engineers and scientists, with an emphasis on analytical technique. A review of practical aspects of linear operators (superposition, Green's functions and Eigen analysis) in the context of ordinary differential equations, followed by extension to linear partial differential equations (PDEs) of parabolic, hyperbolic and elliptic type through separation of variables and special functions. Integral transforms of Laplace and Fourier type. Self-similarity. Method of characteristics for first-order PDEs. Introduction to perturbation methods for nonlinear PDEs, asymptotic analysis, and singular perturbations. 

AMCS 201 and AMCS 202 may be taken separately or in either order. Part of a fast-paced two-course sequence in graduate applied mathematics for engineers and scientists, with an emphasis on analytical technique. A review of linear spaces (basis, independence, null space and rank, condition number, inner product, norm and Gram-Schmidt orthogonalization) in the context of direct and iterative methods for the solution of linear systems of equations arising in engineering applications. Projections and least squares. Eigen analysis, diagonalization and functions of matrices. Complex analysis, Cauchy-Riemann conditions, Cauchy integral theorem, residue theorem, Taylor and Laurent series, contour integration and conformal mapping

Solution of nonlinear equations. Optimality conditions for smooth optimization problems. Theory and algorithms to solve unconstrained optimization; linear programming; quadratic programming; global optimization; general linearly and non-linearly constrained optimization problems. 

The course introduces mathematical foundations underlying modern algorithms for regression, classification, clustering, and dimension reduction in data-rich settings. These mathematical tools, needed to understand machine learning algorithms, are traditionally taught in disparate courses, making it hard for ML students to efficiently learn them. The course bridges a gap between mathematical and machine learning courses, introducing the mathematical concepts with a minimum of prerequisites and in the context of machine learning and data science applications. The goal is to build intuition into these mathematical concepts and practical experience with applying them. Numerical computations and applications with real data will accompany the theory. This course is meant to complement application oriented machine learning and data science courses. 

This course is for the absolute beginner in computing. It covers the basics of programming in Python including variables, expressions, loops, conditions, lists, strings, functions, and standard and file I/O in a hands-on fashion. It also covers, at an introductory level, some Object-Oriented programming aspects like classes, modules and packages. Programming practice activities is divided between in-class ungraded lab exercises and for-credit homework assignments. Progress will be assessed via timeconstrained programming quizes and final exam. This course is offered for credit only to non-CS students. Computer Science students can register the course but will not earn any credit.

This course contains elements of programming, statistics, electronics, materials and synthetic biology. It describes the fundamental principles and methods of different engineering fields to provide the necessary background for future specialization in the tracks of this program. The course aims to apply engineering principles to understand the physical, chemical and mathematical basis of biological systems. The students will learn the origin of electrical biosignals, the fundamental operation principles of modern electronics (sensing and control instrumentation) used at the interface with biological systems including EEG, ECG, and biochemical sensors. They will learn about the basics of fabrication of devices involving microfluidics and microarray device design principles. The students will be then introduced to the different types of reactor configurations commonly used as bioreactors, the operational parameters related to these reactors, and the optimization of the reactors to maximize cell yield. The course will then introduce the principles of material science interfacing with biology, in order to design artificial implants and matrices for biomedical applications. This will broaden the knowledge of the chemical, physical and biological properties of the materials, with a particular focus on the materials recently used in the biomedical field. In particular, students will develop a critical analysis of biomaterial development and methods of characterization. Furthermore, the course will also introduce cutting-edge techniques associated with 3D bioprinting. Finally, students will be introduced to data analytics and modeling with a particular focus on R and MATLAB through hands-on exercises. Using R, students will learn to plot data distributions, calculate summary statistics, perform dimension reduction analysis (PCA, and other related techniques) and run elementary bioinformatics scripts. In the modeling part, students will work with simple mathematical models for synthetic biology (biological switch and oscillator) and basic predictive models (KNN, decision trees and SVM) using MATLAB.

The course is compulsory for all incoming MS students in Mechanical Enginnering and is designed to provide hands-on experience in performing basic laboratory experiments. A wide range of topics will be covered to reflect research interests of Mechanical Engineering faculty: laboratory safety, mechanical workshop, oscilloscope, Optics and image processing, 3D printing, fluid and solid mechanics, and dynamics. 

This course introduces the tools and models that will allow attendees to develop high confidence in the resulting system's proper operation prior to any operational test. Included are tools for model-based systems engineering, and cyber-physical system verification and validation currently in use by the CPS industry. Numerous examples will be considered, from aerospace, automotive, medical devices etc. The frequent presence of human operators is also acknowledged and discussed in-depth. Various verification and validation formalisms (formal methods) are described and applied to simple examples. 

 Autonomous robots are designed to perform assigned tasks with minimal human supervision. This requires the robot’s capabilities to perceive its surroundings to find its own location and identify obstacles to avoid and destination to reach, and to compute a trajectory and control its motors to maneuver toward its destination along the trajectory. For this reason, robotics scientists and engineers should have keen understanding of operations of sensors and actuators and foundations of control and planning to realize autonomy of robotic systems. This course introduces the basic tenets of robot planning and control and related concepts in localization, sensing, and perception.