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Teaching

Teaching Statement

Education can be achieved by increasing the student’s motivation with effective teaching. I have always believed that my best teachers were not necessarily the most knowledgeable, but were the ones who took each class as a challenge to deliver a concept and develop a holistic view of what the course is about and how it is employed in the real world. This reflects an important aspect of my teaching philosophy, which is to tailor teaching to individual student differences. I believe that how I lecture and manage class activities should allow all types of students to learn. Students learn best when they are engaged interactively with the class. Interactive student engagement is necessary for conceptual reasoning and quantitative problem-solving. In teaching classes and labs, I aim to bring this forward to help my students achieve higher levels of learning and retention after graduation. My teaching prioritizes active and student-centered learning.

During the past four years, I had the opportunity to experience different teaching roles at Auburn University. My teaching experience began as a Graduate Teaching Assistant (GTA) for Methods Engineering, Linear Programming, Statistics, Deterministic and Stochastic Operations Research classes. My responsibilities included teaching labs and class sessions, along with one-on-one and group interactions with students during office hours, and grading homework, exams, and projects. In teaching Methods Engineering lab, I created a diverse and inclusive environment by giving team-based learning problems to students and challenge them to think critically through solving real-world problems. They correlated what they learn in the classroom with real-life situations and processed the information with these structured exercises. My aim in designing the related lab-work, and reforming teams each week was to foster the development of students’ critical thinking, creativity, problem-solving skills and effective communication. My GTA work was recognized by being selected to receive ‘Dr. Saeed Maghsoodloo Annual Assistantship’ by Industrial and Systems Engineering Council in 2018.

To further develop my teaching skills, I will teach the Big Data I course with full authorities in the Harbert College of Business at AU during the fall 2018. The course includes the concepts about data quality, security, cleaning, preparation, warehousing, processing, and mining. For this course, I have the responsibility of creating a student-centered learning environment for 40 undergraduate students. Building rapport with students, interactions before and after classes, and initiating and maintaining class discussions are key components of my teaching. I have planned several interactive teaching activities for this class such as think-pair-share/alone-together-alone methods, gallery walk/carousel, digital response systems, online discussion boards, and jig-saw group activity to foster the students’ learning.

To measure the understanding of my students, and my own success as an instructor, I use a variety of assessment methods. I believe that a combination of homework assignments, periodic tests and a project would provide a good assessment of the students’ understanding. Using all these methods, in addition to the self-assessment tools such as in-class interactive group activities would provide me with an overall representation of the students’ understanding and consequently, I could evaluate the effectiveness of my teaching methodologies for that particular group of students.

My academic background and teaching experience prepared me to interact with a diverse range of students and teach various courses at both undergraduate and graduate levels in operations research, data analytics and statistics. In teaching an undergraduate course, my aim is to emphasize how these fundamental concepts are related to other concepts that the students have already studied. In teaching a graduate course I would focus more on the process of generating new knowledge through the scientific methods. In the process, I would utilize examples from my research, as well as the research of others, to ensure that the students become exposed to different research strategies so that they can find a strategy that would work for them. This would enable the graduate students to become researchers and confident in their ability to tackle problems and generate knowledge that would be of benefit to the scientific community and/or industry.

 

Teaching Experience

Auburn University, Harbert College of Business                                   Fall 2018
Instructor

 

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  • Stochastic Operations Research, undergraduate level                                                        Spring 2018

- Instructor: Dr. Erin Garcia

- Responsibilities: Grading, tutoring during office hours

 

  • Stochastic Operations Research, graduate level                                                                   Fall 2017

- Instructor: Dr. Alexander Vinel 

- Responsibilities: Grading, tutoring during office hours

 

  • Linear Programming & Network flows, graduate level                                                    Spring 2016, Spring 2017

- Instructor: Dr. Alexander Vinel 

- Responsibilities: Teaching a session, grading, tutoring during office hours

 

  • Stochastic Operations Research, undergraduate level                                                       Fall 2016

- Instructor: Dr. Alexander Vinel

- Responsibilities: Grading, tutoring during office hours

 

  • Deterministic Operations Research, undergraduate level                                                Fall 2015

- - Instructor: Dr. Mark Shall

- Responsibilities: Teaching a session, grading, tutoring during office  hours

 

  • Methods Engineering, undergraduate level                                                                          Spring 2015

- Dr. LuAnn Carpenter

- Responsibilities: Teaching labs, grading, tutoring during office  hours

Auburn University, Industrial & Systems Eng. Dept.                           Spring 2015 to present
Graduate Teaching Assistant

 

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  • Big Data I, undergraduate level                                                                        

- Responsibilities: Teaching the class with 40 students

 

 

 

 

Teaching Linear Programming and Network Flows, Spring 2017

  • Facility layout planning, undergraduate level                                                                        

- Responsibilities: Teaching course numerical examples and problem

   solving sessions

Tabriz University, Tabriz, Iran                                                                        Spring 2010
Teaching Assistant

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Tutoring                                                                                                                         Fall 2009 - Present

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  • Operations Research

  • Matlab

  • Statistics

  • Metaheuristic Algorithms

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Graduate Level Courses

Optimization Related Courses

Data Analytics Related Courses

  • Linear Programming and Network Flows

- Linear programming theory and algorithms, simplex method, optimality conditions, duality, sensitivity analysis, network problems and algorithms, overview of related software such as Gurobi and AMPL

 

  • Stochastic Operations Research

-  Stochastic programming, multi-stage stochastic optimization, risk theory, stochastic inventory control, stochastic queuing theory, Markov chains, Markov decision processes, approximate dynamic programming

 

  • Integer and Nonlinear Programming

- Modeling techniques in integer programming, Branch-and-bound, branch-and-cut, Column generation, Software for integer programming, Convex sets and functions, Convex optimization problems, Interior-point methods, Second-order cone programming, Semi-definite programming

 

  • Systems Optimization

Unconstrained and constrained optimization, Steepest Descent method, Conjugate Gradient method, Newton's method,  optimality conditions, duality, convex and nonconvex optimization, multi objective optimization.

 

  • Multi-criteria Decision Making

multiple and contradicting objectives, decision trees, domination and pareto optimal solutions, weighted average method, Analytical Hierarchy Process (AHP), goal programming, multi-attribute problems, utility function

 

  • Adaptive Optimization

- Local and global optimum, NP-hard problems, Evolutionary Algorithm (EA), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO)

 

  • Dynamic Programming

- Deterministic and stochastic dynamic programming problems in finite and infinite horizons, under the total, discounted and long run average cost criteria

 

  • Queuing Systems

- Continuous Time Markov Chains, Markovian Queueing Systems, Kendall's notation, M/M/1, M/M/m, Analysis of waiting time distribution, Mean waiting time in M/G/1, Open vs. Closed queueing networks, Jackson Networks

 

  • Scheduling and Sequencing

- Single machine, parallel machines, flow shop, flexible flow shop, job shop, open shop scheduling, exact and heuristic algorithms, makespan, tardy jobs, sequence-dependent setup time, no-wait scheduling, batch scheduling

 

  • Facility Layout Planning (Design of Industrial Systems)

-  Facilities design functions, process and schedule design, material handling and storage equipment, facility layout and location, flow systems, space requirements, warehouse operations and order picking

  • Big Data

- Big Data basics and terminology, Data Quality, Data Warehouses, Data Mining, acquisition, extraction, cleansing, transformation, and loading of data, implementing Big Data

 

  • Predictive Modeling

-  Supervised and unsupervised learning, tree-structured models, regression models, logistic regression models, neural networks, nearest neighbor methods, clustering methods, time series

 

  • Applied Data Visualization 

- Introduction to visualization, Github, HTML, CSS, Javascript, D3.js, dc.js, graphic design. graphical integrity, visualization process,  statistical graphs, maps, trees, networks, high-dimensional data. filtering, parallel coordinates

 

  • Data Management

- Data mining process and techniques, machine learning, data warehouse, data preprocessing such as data cleaning and transformation, classification techniques, prediction algorithms, clustering methods

 

  • Applied Engineering Statistics (Statistical Methods)

- The experiment, the design and the analysis, Single-Factor experiments, Factorial Experiments, Fixed, random and mixed models, Nested and nested-factorial experiments, Experiments of two or more factors-restrictions on randomization, Taguchi approach to the design of experiments

Others

  • Fuzzy Logic and Fuzzy Systems

- ​Crisp and fuzzy sets, fuzzy relationships, fuzzification and defuzzification, rules and inference, membership Functions, fuzzy associative memories, classification and clustering, image processing, fuzzy control systems, MATLAB toolbox 

  • Manufacturing and Production Economics

- ​Accounting and financial decisions, cost analysis, business and project valuation, project risk and uncertainty, long-term capacity planning, capital budgeting decisions

  • Manufacturing Floor and Process Control

- ​Warehousing floor control, Material Resource Planning, Computer Numerical Control, Programmable Logic Controllers, 5S visual systems, Industrial Robotics, Control of production mix and leveling, Kanbans and CONWIP

  • Electronics Manufacturing

- ​Electronics packaging and electronics manufacturing technologies including; current and future trends, design and quality, and manufacturing for high volume

  • Professional Development

Professionalism, academic ethics and research skills, funded research process, getting writings published, resumes/CVs, ePortfolio, networking and job search, cover letter, the interview process, professional communications, teamwork/leading teams

During my master and Ph.D. degrees, I registered for various graduate level courses. The breadth of this training will allow me to teach a wide range of courses in my future career. In these classes not only I improved my knowledge about the subject, but also observed the instructive techniques and drawbacks of the teaching methods that the instructors used and think about effective teaching methods based on the subject. Following is the list of graduate level courses that I studied with their brief description.

I categorized the courses into three parts: Optimization related courses, data analytics related courses, and others.