Teaching and Advising
I enjoy teaching as it gives me the opportunity to educate and engage students intellectually. My teaching philosophy is based around the idea of active and experiential learning, where students learn the concepts and techniques by participating and engaging in various activities such as educational games, case studies, discussions, and problem-solving. I believe that such an approach would not only help the intellectually gifted but also the below-average students to learn and succeed. In particular, my goal as a teacher is preparing future professionals and leaders to use a quantitative approach (such as data analytics and simulation/mathematical modeling) for decision-making as well as improving business operations in manufacturing and service industry. Since I alternate teaching in engineering and business schools, I have tailored the learning outcomes for each student group depending on their background, technical expertise, and career paths, while ensuring to follow my teaching philosophy and achieving the goals.
Course Taught
- Department of Marketing, Robert J. Trulaske, Sr. College of Business, University of Missouri
- MRKTNG 4910/7910 - Data Analytics and Machine Learning for Business: This course trains the a business school student to apply the tools and techniques necessary for making data-driven decisions. It covers analytics overview, most commonly used supervised (e.g., decision trees, random forests) and unsupervised machine learning models (k-means clustering, association rule mining), their core principles, and real-life applications. The course provides hands-on training on data preparation and machine learning model development and interpretation. Applications pertaining to multiple industries are covered. Examples of applications include but are not limited to healthcare (disease prediction), online retailers (Amazon’s “Frequently bought together” feature”), video streaming sites (Netflix’s recommendation system), and finance (loan default prediction).
- Semsters Taught: Spring 2018, Spring 2019, Spring 2020, Fall 2020
- Software Used: MS Excel, Weka
- MRKTNG 8810 - Python for Marketing Analytics: This course introduces the science of processing data using expert systems for faster and smarter decision-making, and provides hands-on training of using Python for data cleansing, pre-processing, data visualization, predictive modeling, association analysis, clustering, and text mining.
- Semsters Taught: Spring 2020, Spring 2021
- Software Used: Python, Jupyter Notebook
- ACCTCY 8401/MRKTNG 8001 - Analytics for Supply Chain Management: The course will provide state-of-the-art models, concepts and solution methods important in the design, control, operation and management of supply chains. The emphasis will be on the applications of operation research models to optimize the components of an integrated supply chain. Case studies and management games will be used to illustrate the course material. The course is appropriate for students interested in working in industry in the supply chain area, as well as for those planning to pursue research in this area.
- Semsters Taught: Fall 2020
- Software Used: MS Excel
- MRKTNG 4910/7910 - Data Analytics and Machine Learning for Business: This course trains the a business school student to apply the tools and techniques necessary for making data-driven decisions. It covers analytics overview, most commonly used supervised (e.g., decision trees, random forests) and unsupervised machine learning models (k-means clustering, association rule mining), their core principles, and real-life applications. The course provides hands-on training on data preparation and machine learning model development and interpretation. Applications pertaining to multiple industries are covered. Examples of applications include but are not limited to healthcare (disease prediction), online retailers (Amazon’s “Frequently bought together” feature”), video streaming sites (Netflix’s recommendation system), and finance (loan default prediction).
- Department of Industrial and Manufacturing Systems Engineering, University of Missouri
- IMSE 4410/7410 - Data Engineering and Predictive Modeling: This course covers the theoretical and applied aspects of descriptive and predictive analytics. Topics coverted include data extraction, data cleaning, data wrangling/feature engineering, visulaization, machine learning models for classification and prediction, unsupervised learning, apriori algorithm. Hands-on tutorial on open source data analytics software is provided for small instances of real-life applications.
- Semsters Taught: Fall 2018, Fall 2019, Fall 2020
- Software Used: MS Excel, R
- IMSE 4370/7370 - Service Systems Engineering and Management: Service systems contribute to more than 75% of US GDP and provide close to 80% employment. Unlike goods, services are perishable, intangible, and require a high level of customer interaction. As a result, the traditional operations management methods must be adapted for the unique characteristics of service systems. Further, the demand for engineers and managers with emphasis on service systems is far greater than the supply. This course prepares the students for a career in service systems and will discuss quantitative models, concepts and solution methods important in the design, control, operation and management of service systems. Applications covered include aircraft routing strategy, patient scheduling, designing distribution network of a supermarket chain, reservation policies for hotels, and vendor selection. Besides, a hands-on tutorial will be provided to solve the optimization models for these applications.
- Semsters Taught: Fall 2017, Spring 2020
- Software Used: MS Excel, Python
- IMSE 4280/7280 - Systems Simulation: Many business systems (e.g., manufacturing facility, call centers, hospitals, retailers, transportation and logistics) experience complex problems at the strategic, tactical and operational levels. The complexity of these systems and the uncertain nature of the environment often make simulation the only feasible analytic tool to model and study the design and operations of these systems. This course enables the students to design, develop and evaluate a simulation model for quantitative decision-making. This course covers the important topics, concepts, and tools in discrete-event simulation. It covers all the steps involved in conducting a simulation experiment/project, namely, data collection, random number generation, input modeling, discrete-event modeling and experimentation, statistical testing, alternative comparison methodologies, and output analysis. The course adopts real-life applications to illustrate the concepts and provides hands-on training.
- Semsters Taught: Fall 2018, Fall 2019
- Software Used: MS Excel, Simio, Minitab, IBM SPSS
- IMSE 4410/7410 - Data Engineering and Predictive Modeling: This course covers the theoretical and applied aspects of descriptive and predictive analytics. Topics coverted include data extraction, data cleaning, data wrangling/feature engineering, visulaization, machine learning models for classification and prediction, unsupervised learning, apriori algorithm. Hands-on tutorial on open source data analytics software is provided for small instances of real-life applications.
Student Advising
As the major academic advisor, I have recruited and graduated 3 Ph.D. students and 2 M.S. students since joining MU in 2017. Currently, I am advising 8 Ph.D. students and 3 M.S. students. In addition, I am currently co-advising 2 graduate students at foreign universities - 1 Ph.D. student from Eskisehir Technical University, Turkey (currently at MU as a Fullbright Scholar in 4th year of his Ph.D. program) and 1 MS student from IIT Madras, India. Besides, I have mentored over 25 undergraduate students and provided them with research experience by involving them in sponsored research projects (e.g., NSF REU, NSF IRES, and industry-sponsored research). The students I have supervised and mentored have joined top-tier companies in the US, such as Boeing, Honeywell, Rockwell Automation, Lockheed Martin, Edward Elmhurst Health, and Lochmueller Group.
Current Students
- Pyam Oveys, Ph.D.
- Paul Antonacci, Ph.D.
- Negar Jahanbakhsh Javid, Ph.D.
- Mahima Naznin, Ph.D.
- Arash Alizadeh, Ph.D.
- Arghadeep Mitra, Ph.D.
- Celiker Busra, Ph.D.
- Fatemeh Pourdehghan, Ph.D.
- Sai Kiran Singraj, MS
- Ray Wood, MS
- Parth Jahagirdar, MS
Graduated Advisees
- Shitao Yu, Ph.D., Summer 2023
- Mohamed Salama, Ph.D., Spring 2022
- Haya Salah, Ph.D., Spring 2022
- Dustin Smith, MS, Spring 2019
- Alexander Jackson, MS, Spring 2019
Supervisor/Mentor (students who are not my advisees but were supported on my grants)
- Nima Golghamat Raad, PhD ISE (funded on AKDOT project as GRA, 2023-24)
- Hemanth Sai Yeddulapalli, MS EECS (funded on AKDOT project as GRA, 2024)
- Alicia Esquivel Morel, PhD EECS (funded on NSF PFI project as GRA, 2024)
Mentor/Advisor for Undergraduate Students
Thomas Willerth (IE honors), Stephen Swingle (IE honors), Ben McIntire (2023 Summer NSF REU Student), Abdullahi Ayantayo (2023 Summer NSF REU Student), Dylan Nojadera (2023 Summer NSF REU Student), Brianna Abam (2023 Summer NSF REU Student), Lilyan Groat (2023 Summer NSF REU Student), Brian Yang (2023 Summer NSF REU Student), Emma Lewis (2023 Summer NSF REU Student), Daniel Grenier (2023 Summer NSF REU Student), Carson Swain (2023 Summer NSF REU Student), Aditya Khowal (2023 Summer NSF REU Student), Zachary Bazile (2023 Summer NSF REU Student), Charan Govarthanaraj (2023 Summer NSF IRES Student), Vishnu Arun (2023 Summer NSF IRES Student), Prashish Lamsal (2023 Summer NSF IRES Student), Nikhil Vyas (2023 Summer NSF IRES Student), Sienna Schreiber (IE, 2023), Erik Starrenburg (IE, 2023), Mikey Joyce (EECS, 2022), Emily Pagel (IE, 2020), Trenton Grimshaw (IE, 2020), Grace Floyd (IE, 2020), Jacob Beeth (IE, 2020), John Tocco (IE, 2019), Alex Stone (IE, 2019), Dustin Smith (IE, 2018), Joshua Zack (IE, 2018), Abdulah Sibalo (Economics, 2018)