Teaching @ UB

Discover Industrial and Systems Engineering (Spring 2016, 2017, 2018, 2019, 2020)

Come and learn about the exciting discipline and profession of industrial engineering (IE). What is IE? What do IEs do? Where are they employed? This course covers the broad application areas and methodologies of the IE discipline. It culminates with the current day practice, careers, employers and the role of IEs in integrated systems design. There will be one lecture period per week.

• My TA (Spring 2018): Sarah Schwartz

Applied Probability & Statistics Inference (Fall 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015; Summer 2010)

Introduces probability and its application to engineering problems. Examines sample space, random variables, expected values, limiting theorems, error analysis, and provides introduction to random processes. Prerequisite for enrollment is MTH 241 College Calculus III or equivalent.

• Students are required to use UBlearns to access the complete course materials
• My TAs (Fall 2015): Fatemeh Aarabi, Cai Gao, and Xiaomei Wang
• Syllabus: PDF (subject to change)

Decision Analysis (Fall 2010, 2013, 2015, 2017, 2019; Winter 2020)

This course provides an overview of modeling techniques and methods used in decision analysis, including multiattribute utility models, decision trees, and Bayesian models. Psychological components of decision making are discussed. Elicitation techniques for model building are emphasized. Practical applications through real-world model building are described and conducted, including business management, supply chain and logistics, transportation, health care, and homeland security. Each student will work on a separate project throughout the semester, including presentations and written reports.

• Students are required to use UBlearns to access the complete course materials
• My TAs (Fall 2019): Puneet Agarwal
• Syllabus: PDF (subject to change)

Applied Stochastic Processes (Spring 2009, 2010, 2011, 2012, 2013, 2014, 2016, 2017, 2018, 2019, 2020)

A continuation of IE 575 (Stochastic Methods). Topics include limit theorems, classical and Bayesian statistical inferences, discrete-time and continuous-time Markov chains, simple Markov queueing networks, and an introduction to dynamic programming.

• Students are required to use UBlearns to access the complete course materials
• My TAs (Spring 2018): Bairong Wang
• Syllabus: PDF (subject to change)

Game Theory (Fall 2009, 2011, 2014, 2016, 2018)

This course targets the graduate students who may have little or no game theory background, but have serious interests in conducting a project/research/thesis related to game theory. This course will start with the fundamentals of individual and group decision making analysis, then introduce topics including mechanism design, the signaling game, the screening game, the repeated game, the differential game, the behavioral game, and the evolutionary game. Finally, this course will introduce some state-of-the-art game-theoretic research on supply chain management, transportation, health care, and homeland security. Each student will work on a separate project throughout the semester, including presentations and written reports.

• Students are required to use UBlearns to access the complete course materials
• Syllabus: PDF (subject to change)