Farhad Pourkamali Anaraki

Assistant Professor of Data Science

Mathematical and Statistical Sciences, University of Colorado Denver

Director of Mathematics, Information & Data Science (MINDS) Lab

Email: farhad.pourkamali@ucdenver.edu

Farhad Pourkamali is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver (CU Denver). Previously, he was an Assistant Professor of Computer Science at the University of Massachusetts Lowell (2018-2022) and received his PhD in Electrical Engineering from the University of Colorado Boulder in 2017. His research endeavors seek to answer the following question across scientific and engineering disciplines: how to assure data quality and adequacy under resource constraints when quantifying confidence in a model’s prediction (e.g., capturing the behavior and failure of materials under extreme conditions)? Answering this question requires strong bonds between foundational and use-inspired research. Recognizing data science needs in use-inspired research provides a starting point for working on foundational aspects to develop new learning algorithms that yield practical value. At the same time, applying the developed methods to solve complex problems offers valuable feedback for future improvements. Therefore, interdisciplinary research has been the centerpiece of his research activities. He received the Best Paper Award at the IEEE International Conference on Machine Learning and Applications (ICMLA) for his interdisciplinary work on developing machine learning methods to predict damage to buildings caused by earthquakes. Dr. Pourkamali has been awarded $1M+ in combined funding from several agencies, including ARL, AFRL, NASA, and NSF.

Research Areas

  • Scientific Machine Learning: development and use of artificial intelligence and machine learning in the context of computational decision support for complex systems (e.g., materials science, structural engineering, disaster risk assessment, smart manufacturing)

  • Exploring the interplay between data collection, predictive modeling, model assessment, and trustworthiness in limited data scenarios

  • Development of rigorous and scalable machine learning algorithms that can adapt to large-scale settings and changing environments (continual/progressive learning)

  • Development of adaptive algorithms for analyzing disparate information sources

Research Grants


Muti-Scale Models Based on Machine Learning and a Fiber Network Model

Air Force Research Laboratory's Information Directorate (AFRL/RI)

Visiting Faculty Research Program (VFRP)


GOALI: Experimental and Computational Approaches to Tailor Properties of Additively Manufactured Semi-Crystalline Polymers (data science supplement)


Data Science Approaches to Advance High Solids Loading Additive Manufacturing


MATH 6388: Statistical and Machine Learning

MATH 4/5388: Machine Learning Methods