Nakul Padalkar

Associate Professor of the Practice, Administrative Sciences

Nakul Padalkar is an associate professor of the practice at BU’s Metropolitan College. With a PhD in management information systems from Texas Tech University and a decade of industry and academic experience, Dr. Padalkar is an expert in the intersection of technology, economics, and society. Prior to joining Metropolitan College, Padalkar was an assistant professor of data science and analytics at Georgetown University, where his teaching focused on education for technologies such as blockchain, computer vision, machine learning, and artificial intelligence, as well as the issues surrounding them. His ongoing research on blockchain evaluates its application for traceability and transparency of battery supply chains, specifically for illicit financial flows. His research on machine learning extends to the ethical applications of the large language models for deep fake detections for the institutional press, causal inference of the deep vision models, and the reproducibility and replicability (ethical AI) of the machine learning models. Through his work, he seeks to apply disruptive technologies to solve industry, societal, and governance problems.

Research Interests

Courses

Scholarly Works

Publications
Padalkar, N. R. “Unveiling the digital shadows: Exploring the role of technology in illicit financial flows.” In 11th IMF Statistical Forum: Measuring money in the digital age vol. 11, International Monetary Fund (2023).

Desai, A., Abdelhamid, M., and Padalkar, N. R. “What is reproducibility in artificial intelligence and machine learning research?” (2024). arXiv preprint arXiv:2407.10239

Padalkar, N. R., and Yashwinder Ghura, T. S. “DEFIYIELD: Exploitation of open blockchain platforms.” In Americas’ Conference on Information Systems (2023): 1–8.

Padalkar, N. R., and De Lima, V. “Recommendation systems with causal inference-based regularization.” In Americas’ Conference on Information Systems (2023): 1–5.

Padalkar, N. R., and Jin, Y. “Microgrid cooperatives in emerging countries—Impact of decentralization in rural India.” In Americas’ Conference on Information Systems (2021): 1–17.

Padalkar, N. R. “’Stonks to the moon’: Evidence from Reddit posts and corresponding market manipulation.” In Americas’ Conference on Information Systems 2021): 1–5.

Padalkar, N. R., Sheikh-Zadeh, A., and Song, J. “Business value of smart contract: Case of inventory information discrepancies.” In Americas’ Conference on Information Systems (2020): 1–10.

Padalkar, N. R., Sheikh-Zadeh, A., and Song, J. “Impact of blockchain on information discrepancies in inventory operations.” In INFORMS 2020 Virtual Conference (2020).

Padalkar, N. R., Sheikh-Zadeh, A., and Song, J. “Value creation in smart contract enabled supply chain.” In BIG XII+ MIS Symposium (2019): 1–10.

Padalkar, N. R., and Blatchley, C. “Comparison of color matching functions and cone fundamentals.” In Nebraska Academy of Sciences (2010).

Padalkar, N. R., and Rodriguez, J. “Printing process optimization and control using Taguchi’s robust design.” In Technical Association of Graphic Arts Proceedings (2008).

Padalkar, N. R., Oswal, R., and Virdhe, A. “Process variability optimization and control.” In IARIGAI, International Research Conference (2007).

Padalkar, N. R., Oswal, R., and Virdhe, A. “Process variability optimization and control: Application of Taguchi’s robust design to printing processes.” In Institute of Engineers Proceedings (2006).

Working Papers
Nakul Padalkar, Samuel Pastoriza, and Monroe Farris. “Neural Network based recommendations: A Design Science Approach.” In In Preparation (2022): 1–8.

Nakul Padalkar, Daniel Cisek, and Jacob McIntosh. “Human Bias Spillover in Computer Vision.” In In Preparation (2022): 1–11.

Nakul R. Padalkar. “Evaluation of Blockchain Technology for Cycle Counting in supply chains with Information Discrepancies.” In Working Paper (2020): 1–45.

Faculty Q&A

New Associate Professor and Disruptive Technologies Expert Finds Ways to Blend Machine Learning, Blockchain Tech with Conservation Efforts

Welcome to MET, Dr. Padalkar! Which program at MET will you be lecturing in? What is your area of expertise?
I am housed in Metropolitan College’s Applied Business Analytics program under the Department of Administrative Sciences.

My work involves studying disruptive technologies and their social implications. Some of my current research includes blockchain and distributed ledger technologies, which entials exploring traceability and geo-indication, addressing technology and illicit financial flows, and evaluating distributed energy markets in emerging countries.

I also study machine learning and computer vision, with particular focus on social and environmental applications, such as wildlife conservation, effectively detecting large-scale fake images generated by the language-vision models, and investigating computational biases and causality in deep learning models.

My recent publications include work on blockchain and illicit financial flows published at the International Monetary Fund’s 11th Statistical Conference and a study on the ‘Reproducibility of Artificial Intelligence and Machine Learning Research’.

How does the subject you work in apply in practice? What is its application?
Disruptive technologies have the potential to be harmful to social and economic well-being. My research focuses on the smaller aspects of these technologies to evaluate possible pathways to curb the ill effects of their large-scale use. For example, illicit financial flows are detrimental to a developing country’s economic well-being, and technologies like blockchain can help create effective tracking mechanisms. My current research in illicit financial flows explores the reverse logistics (recycling of used batteries) of lead acid batteries in Asia and the utilization of distributed ledger technologies.

What courses are you teaching at MET?
I teach Business Analytics Foundations (MET AD 571), Web Analytics for Business (MET AD 688), and Quantitative and Qualitative Decision Making (MET AD 715).

How does your past experience help to inform your teaching?
My extensive research and collaboration with industrial partners in blockchain, machine learning, and computer vision provide me with real-world insights that I bring into the classroom. These experiences enable me to teach students the theoretical aspects and the practical applications of disruptive technologies. I have consistently used my experience in currency printing to discuss the mechanisms of cryptocurrencies and their relevance to financial systems.

Please highlight a project within one of your courses that most interests your students.
One of the most engaging projects is the application of computer vision in wildlife conservation. Students work on real-world data, such as analyzing camera trap images to detect and classify species, which directly applies machine learning concepts to solve environmental challenges.

What “real-life” exercises do you bring to class?
I incorporate real-life exercises that involve analyzing real datasets, developing machine learning models, and applying blockchain technology to solve practical problems like product traceability or wildlife conservation. These exercises help students understand the tangible impact of their work.

Is there anything else you would like to add?
I spend my free time hiking around nearby national forests and capturing images. Coincidentally, I have made a habit of using those pictures in my computer vision class to teach students about image processing and applications of computer vision.

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