Research

AI adoption. Firm value.
Financial markets. Labor markets.

My research examines the economics of AI adoption: when firms deploy it, how it creates value, and how its diffusion reshapes corporate decisions, financial markets, and internal labor markets.

Job market paper

Working paper

When Firms Adopt AI Matters: Diffusion and the Value of AI Deployment

This paper measures enterprise AI deployment from corporate disclosures and studies how the value of adoption depends on a firm’s position in the technology-diffusion process. It examines the role of industry diffusion in valuation, complementary investment, and financing decisions.

Accepted for presentation: Ph.D. Student Poster Session, 2027 American Finance Association Annual Meeting (Washington, DC · January 2–5, 2027).

Paper on SSRN

Work in progress

With Burton Hollifield

When Does the Market Value Corporate AI Capability? Evidence from Stock Returns

This project studies when corporate AI capability is reflected in stock returns and how that valuation changes across firms and over time.

Work in progress

With Liyan Shi
and Ali Shourideh

Flattening the Ladder: Artificial Intelligence, Hierarchy Compression, and the Internal Redistribution of Wages

This project studies AI adoption, organizational hierarchy, and the distribution of wages within firms.

Working paper

Revise & resubmit
American Economic Journal: Macroeconomics

Determinate Indeterminacy: Keynesian Search in Stochastic Overlapping Generations Models

This paper studies how the endogenous wealth distribution disciplines beliefs in stochastic overlapping-generations models with Keynesian search. It develops a neural-network solution for a calibrated 58-period lifecycle economy.

Published research

Machine learning &
scientific applications

Google Scholar

My earlier work develops machine-learning methods for physical prediction and scientific computing.

2024
Machine learning prediction of mechanical properties in metal additive manufacturing
Parand Akbari, M. Zamani, and A. Mostafaei
Additive Manufacturing 91, 104320. · Article ↗
2024
Machine learning predictions of spatter behavior in LPBF additive manufacturing
Parand Akbari, M. Zamani, and A. Mostafaei
Materialia 38, 102268. · Article ↗
2023
Surrogate modeling of melt pool temperature field using deep learning
A. Hemmasian, F. Ogoke, Parand Akbari, J. Malen, J. Beuth, and A. B. Farimani
Additive Manufacturing Letters 5, 100123. · Article ↗
2022
MeltpoolNet: Melt pool characteristic prediction in metal additive manufacturing using machine learning
Parand Akbari, F. Ogoke, N.-Y. Kao, K. Meidani, C.-Y. Yeh, W. Lee, and A. Barati Farimani
Additive Manufacturing 55, 102817. · Article ↗

AI adoption. Firm value.Financial markets. Labor markets.

When Firms Adopt AI Matters: Diffusion and the Value of AI Deployment

When Does the Market Value Corporate AI Capability? Evidence from Stock Returns

Flattening the Ladder: Artificial Intelligence, Hierarchy Compression, and the Internal Redistribution of Wages

Determinate Indeterminacy: Keynesian Search in Stochastic Overlapping Generations Models

Machine learning &scientific applications

Machine learning prediction of mechanical properties in metal additive manufacturing

Machine learning predictions of spatter behavior in LPBF additive manufacturing

Surrogate modeling of melt pool temperature field using deep learning

MeltpoolNet: Melt pool characteristic prediction in metal additive manufacturing using machine learning

AI adoption. Firm value.
Financial markets. Labor markets.

Machine learning &
scientific applications