Teaching and Advising
I enjoy teaching as it gives me the opportunity to educate and engage students intellectually. My teaching philosophy is based on the idea of active and problem-based learning, where students learn relevant theories, techniques, critical thinking, and problem-solving skills by engaging in various activities beyond traditional lectures, such as hands-on tutorials, educational games, case studies, and research projects. I believe that such an approach would not only help intellectually gifted students but all learners to succeed. My goal as a teacher is to engender next-generation professionals, researchers and leaders with the skills to effectively tackle and manage complex analytical problems. I understand there are diverse types of learners, and I strive to employ numerous teaching methods and structured learning strategies to fit their unique needs. Specifically, I believe curiosity is the key to learning and retaining acquired knowledge, and thus, I aspire to create excitement and trigger intellectual curiosity among students. In addition, I aim to bridge the research-teaching gap by systematically integrating current research methods in all my courses. I am a strong proponent of applying theories and methods into practice and, therefore, embrace technology in the classroom, especially hands-on tutorials of in-demand tools for solving realistic problems. I also nurture effective teamwork and peer learning since it is a critical component for success in today’s fast-paced and interconnected world. I strongly believe the above-mentioned practices would foster students’ skills in systems thinking, problem-solving, technology adoption, communication, and collaborative working. Consequently, I aim to empower students to become lifelong learners and be well-prepared to enter/re-enter the workforce and make an impact.
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
- Soham Basu, MS and Ph.D.
- Pyam Oveys, Ph.D.
- Paul Antonacci, Ph.D.
- Negar Jahanbakhsh Javid, Ph.D.
- Mahima Naznin, Ph.D.
- Arash Alizadeh, Ph.D.
- Arghadeep Mitra, MS and Ph.D.
- Celiker Busra, Ph.D.
- Fatemeh Pourdehghan, Ph.D.
- Ali Fallah, MS
- Matt Floyd, MS
Graduated Advisees
- Fatemeh Pourdehghan, MS, Spring 2025
- Mahima Naznin, MS, Spring 2025
- Arash Alizadeh, MS, Fall 2024
- Parth Jahagirdar, MS, Summer 2024
- Sai Kiran Singraj, MS, Summer 2024
- Ray Wood, MS, Spring 2024
- Shitao Yu, Ph.D., Summer 2023
- Mohamed Salama, Ph.D., Spring 2022
- Haya Salah, Ph.D., Spring 2022
- Dustin Smith, M. S., Spring 2019
- Alexander Jackson, M.S., Spring 2019
Supervisor/Mentor (students who are not my advisees but were supported on my grants)
- Garrett Robison, PhD ISE (funded on project with US Army as GRA, 2023-25)
- Michael Geisecke, PhD ISE (funded on project with US Army as GRA, 2023-25)
- Nima Golghamat Raad, PhD ISE (funded on AKDOT project as GRA, 2023-24)
- Chandra Bhamidipati, MS EECS (funded on NSF PFI project as GRA, 2024)
- Zack Murray, BS EECS (funded on NSF REU Supplement, 2024)
- 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
MU Students: Luke Richey (In Progress), Thomas Willerth (IE honors/In Progress), Stephen Swingle (IE honors/In Progress), Matt Floyd (IE, 2024), Marc Rosner (Marketing, 2024), 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)
NSF REU 2024: Neel Shah, Matt Lamborne, Joseph Couzens, John Maxwell, Ayhum Asfour, Emily Pham, Jakob Fick, Johann Zhang, Vishnu Arun
NSF REU 2023: Ben McIntire, Abdullahi Ayantayo, Dylan Nojadera, Brianna Abam, Lilyan Groat, Brian Yang, Emma Lewis, Daniel Grenier, Carson Swain, Aditya Khowal, Zachary Bazile
NSF IRES 2024: Ben McIntire, Siddarth Tummala, Ryan Brophy, Abhinav Sairam, Snowbird Rubio, Faisal Shaikh, Rishi Jain NSF IRES 2023: Charan Govarthanaraj, Vishnu Arun, Prashish Lamsal, Nikhil Vyas