Teaching Experience
2020-2021 Teaching Excellence Award, MBA program, The Wharton School, UPenn
2019-2020 Teaching Excellence Award, The Wharton School, UPenn
Columbia Business School B5101 - Business Analytics (Executive MBA)
In this course, you will learn to identify, evaluate, and capture business analytic opportunities that create value. Toward this end, you will learn basic analytic methods and analyze case studies on organizations that successfully deployed these techniques. In the first part of the course, we focus on how to use data to develop insights and predictive capabilities using machine learning, data mining and forecasting techniques. In the second part, we focus on the use of optimization to support decision-making in the presence of a large number of alternatives and business constraints. Finally, throughout the course, we explore the challenges that can arise in implementing analytical approaches within an organization.
Boston University BA305 - Business Decision Making with Data
This is an advanced analytics course on data-driven decision-making in business environments. Business analytics professionals need to be able to i) uncover patterns in the data (descriptive analytics); ii) use the data to make predictions about future outcomes (predictive analytics); and iii) leverage this data to make optimal business decisions (prescriptive analytics). This course takes a holistic approach to analytics, covering all three descriptive, predictive, and prescriptive pillars. We explore advanced business analytics topics, including data reduction, classification, decision analysis, and optimization. We link data models to strategy relying on statistical programming in Python and introduce novel techniques used in practice. Case studies and projects apply topics to practical business problems.
The Wharton School OIDD612 - Business Analytics
“Managing the Productive Core: Business Analytics” is a course on business analytics tools and their application to management problems. Its main topics are optimization, decision making under uncertainty, and simulation. The emphasis is on business analytics tools that are widely used in diverse industries and functional areas, including operations, finance, accounting, and marketing.
The Wharton School OIDD353/653 - Mathematical Modeling and Applications in Finance
Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods, such as basic statistics and the methodologies (mathematical programming and simulation) taught in OIDD 612 Business Analytics or OIDD 321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.