I serve as a lecturer and researcher at the Department of Economics, Faculty of Economics and Business, University of Indonesia (FEB UI). My teaching focus centers on bridging advanced economic theory, regional development paradigms, and computational data science into policy-relevant knowledge. By anchoring quantitative applications within structural economics, I equip both undergraduate and graduate students with the empirical toolkits required to analyze real-world policy shifts and navigate the shifting institutional landscape of Southeast Asia.
Introduction to Data Science (Undergraduate)
This course introduces undergraduate students to data science pipelines within economics using the CRISP-DM framework. Moving from inference to prediction, it covers foundational computational thinking, environment setup in Google Colab, data cleaning, and exploratory data analysis (EDA). Students gain practical skills in implementing basic supervised and unsupervised learning methods (such as linear regression, logistic classification, and k-means clustering) using R and Python to interpret analytical outputs in an economic and policy context.
Southeast Asia Economic Studies (Undergraduate)
This course applies international trade, macroeconomic, and microeconomic theories to the complex architecture of cross-border economic cooperation across the ASEAN region. It examines the real-world linkages between regional integration agreements and domestic performance, tracing currents from global liberalization and sub-regional growth triangles to bilateral CEPAs and mega-regional blocks like RCEP. The curriculum emphasizes a multi-disciplinary approach to analyzing trade barriers, the digital economy, and energy grid alignment.
Quantitative Methods and Analysis (Graduate)
This graduate-level course provides advanced training in econometrics, regression diagnostics, and causal inference using Stata. The curriculum focuses on manipulating national macro and micro datasets (including household and regional surveys like Susenas, Sakernas, IFLS, and Podes) to isolate the precise impacts of fiscal and socioeconomic variables for empirical research and policy evaluation.
Data Science for Economic Analysis (Graduate)
This course bridges classical econometrics with modern big data engineering and machine learning tools using Python. Graduate students learn to deploy supervised and unsupervised algorithms, classification techniques, time-series forecasting, and basic neural networks to process large-scale, unstructured datasets and translate dense computational models into strategic public policy narratives.
Cost-Benefit Analysis (Graduate)
This course builds advanced capacity in financial and economic project appraisals, equipping graduate students with the quantitative simulation frameworks needed to value social benefits, environmental externalities, and long-term returns on investment (ROI) for public interventions. The curriculum covers asset valuation in primary and secondary markets, social discount rates, shadow pricing, and willingness-to-pay (WTP) surveys.
Public Infrastructure Management (Graduate)
This course examines the institutional, financial, and economic frameworks governing large-scale public investments. The curriculum guides graduate candidates through the complexities of public infrastructure development, focusing on procurement decision-making, public-private partnership (PPP) models, Special Purpose Vehicle (SPV) contract networks, risk management, and the integration of sustainable green financing and ESG frameworks.