{"id":141,"date":"2019-07-05T20:48:49","date_gmt":"2019-07-05T20:48:49","guid":{"rendered":"http:\/\/oasis.eng.buffalo.edu\/?page_id=141"},"modified":"2024-12-28T05:32:44","modified_gmt":"2024-12-27T21:32:44","slug":"teaching","status":"publish","type":"page","link":"https:\/\/oasis.eng.buffalo.edu\/index.php\/teaching\/","title":{"rendered":"Teaching"},"content":{"rendered":"\t\t
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Analytics and Computing for Industrial Engineers (IE 322) - University at Buffalo (SUNY)<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The \u201cbig data revolution\u201d has placed emphasis on computational techniques in Industrial Engineering. Large-scale data collection, processing, visualization and analysis are now commonplace among researchers and practitioners. Now more than ever, there is a need not only to develop new techniques, but also to implement and use them.\u00a0<\/span>The purpose of this course is to provide students with the knowledge and skills necessary to manage, manipulate, analyze, and derive insights from large data sets using IE related computational tools, such as R. Data and problems will be representative of typical problems faced by Industrial Engineers across a wide variety of industries including manufacturing, service, healthcare, and transportation industries.<\/span><\/p>

Department: Industrial and Systems Engineering; Role: Instructor; Fall 2021, Fall 2023<\/strong><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Data-driven Risk and Decision Analysis (IE 600 TUT, IE 670 TUT) - University at Buffalo (SUNY)<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data science is an interdisciplinary approach to collect, pre-process and\u00a0analyze data from various types of systems to help in informed decision-making.\u00a0Data-driven risk analytics is based on principles of data-science that would\u00a0help to\u00a0identify and assess the risks of a system by collecting data on some\u00a0measurable goals \/ KPIs, analyzing historical patterns and gathering insights\u00a0from the past to predict systemic risks in the future. In this course, we will\u00a0discuss various research\u00a0papers that are leveraging the state-of-the-art\u00a0advanced data-driven techniques to analyze risks of various systems,\u00a0contributing to risk-informed decision making. The course includes discussions on concepts and applications of\u00a0Bayesian models, multivariate tree boosting models, neural networks, time\u00a0series analysis, and other ensemble models in the context of risk and decision\u00a0analytics.\u00a0<\/p>

Department: Industrial and Systems Engineering; Role: Instructor; Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Data Analytics & Predictive Modeling (IE 500 \/ 459) - University at Buffalo (SUNY)<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data analytics is the\u00a0use of computational statistics and data mining to draw insights and build\u00a0predictive models based on large data sets. As data becomes more prevalent\u00a0across many different areas of importance in engineering, policy\u00a0analysis, and\u00a0management, analytics is becoming an increasingly important topic. This course\u00a0assumes a working knowledge of regression and statistics and builds from this\u00a0to introduce modern data analytics. The course covers fundamental\u00a0concepts of\u00a0predictive modeling and major classes of methods beyond linear regression, including\u00a0additive models, tree-based models, boosting, bagging, and model averaging. The course focuses on the\u00a0application\u00a0and interpretation of the methods while also providing an understanding of the\u00a0underlying basis and theory behind them. The course is offered to both graduate and undergraduate students.<\/p>

Department: Industrial and Systems Engineering; Role: Instructor; Fall 2019, Fall 2020, Fall 2021, Spring 2023, Fall 2023<\/strong><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Advanced Data Analytics &\u00a0Predictive Modeling (IE 600TUT) - University at Buffalo (SUNY)<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This course uses computational statistics and data mining to draw insights and build predictive models based on large data sets. As data becomes more prevalent across many different areas of importance in engineering, policy analysis, and management, analytics is becoming an increasingly important topic. This course assumes a working knowledge of regression and statistics and builds from this to introduce modern data analytics. The course covers major classes of methods beyond linear regression, including additive models, tree-based models, Bayesian networks, multi-level models, boosting, bagging, neural nets, support vector machines and model averaging. The course focuses on the application and interpretation of the methods while also providing an understanding of the underlying basis and theory behind them. Lab sessions, the midterm, and the term project are primarily data-driven analytics exercises. Opportunities are given to students to work on projects of their own interest, provided they are relevant and aligned with the learning outcomes of the course.<\/p><\/div>

Department: Industrial and Systems Engineering; Role: Instructor; Spring 2019<\/strong><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Graduate Seminar Course (CE 691) - Purdue University<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This course is an orientation to the Construction Engineering and Management Specialty area and provides an overview of the requirements for M.S. and Ph.D. degrees in Civil Engineering with a focus in the Construction Engineering and Management discipline. The course brings together the experience and expertise from eminent faculty members from within the CEM department at Purdue University and from other universities.<\/p>

Department: Civil Engineering; Role: Co-instructor; coordinator & organizer\u00a0(Spring 2018, Fall 2017<\/strong>)<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Life Cycle Engineering and Management of Construction Facilities (CEM 201 \/ CE 222) - Purdue University<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This course introduces concepts relating to the engineering and construction of facilities throughout their life cycle. Topics that are explored include the nature of the construction industry, construction contracts, legal and management organization of construction companies, basics of the design and construction process, as well as an introduction to the role of estimating and project scheduling. Cost, time, safety and quality concepts of construction management relationships are also discussed. The primary objective of this course is to provide students with the basic knowledge and skills to be able to manage civil engineering projects through out their entire life cycle. The concepts developed in the class are applied in a course project that will require students to apply the acquired knowledge.<\/p>

Departments: Civil Engineering and Construction Engineering and Management; Role: Teaching Assistant & Co-instructor (Spring 2016, Fall 2014<\/strong>); Enrollment: 100 students<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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General Chemistry (GEN-CHEM 177) - Iowa State University<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This course introduces concepts of general chemistry to explore chemistry at a greater depth and with more emphasis on concepts, problems, and calculations. Students majoring in physical and biological science majors, chemical engineering majors, and all others are recommended to take this course. Topics that are taught in this course include principles and quantitative relationships, stoichiometry, chemical equilibrium, acid-base chemistry, thermochemistry, rates and mechanism of reactions, changes of state, solution behavior, atomic structure, periodic relationships, chemical bonding.<\/p>

Department: Chemistry; Role: Teaching Assistant (Fall 2010<\/strong>); Enrollment: 120 students<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Analytics and Computing for Industrial Engineers (IE 322) – University at Buffalo (SUNY) The \u201cbig data revolution\u201d has placed emphasis on computational techniques in Industrial Engineering. Large-scale data collection, processing, visualization and analysis are now commonplace among researchers and practitioners. Now more than ever, there is a need not only to develop new techniques, but<\/p>\n

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