Biography
Farhad Pourkamali is currently a tenure-track Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver (CU Denver). He earned his Ph.D. in Electrical Engineering from the University of Colorado Boulder (CU Boulder), where he subsequently completed a one-year postdoctoral fellowship in the Department of Applied Mathematics. Prior to his current role, he served as an Assistant Professor in the Computer Science Department at the University of Massachusetts Lowell (UMass Lowell) from 2018 to 2022. In 2021, he received the Visiting Faculty Research Program (VFRP) award from the Air Force Research Laboratory Information Directorate (AFRL/RI). Since the fall of 2022, he has been a part of CU Denver, where he leads the Mathematics, Information, and Data Science (MINDS) Lab. His main emphasis is on advancing machine learning techniques to reduce the necessity for human input, enhance their computational and statistical efficiency, and create accessible cyberinfrastructure resources. He is committed to addressing practical problems in various domains, including materials science and natural hazards analysis, and is actively seeking opportunities for collaboration.
Research Interests
The objective of my research is to advance the foundations and computational methods for building intelligent systems that can operate with minimal human supervision while understanding and quantifying their own uncertainty. By improving computational and data efficiency, developing novel approaches for calibrating and adapting pre-trained models, and applying these methods across domains from computer vision to scientific simulations, my work aims to create AI systems that are both high-performing and trustworthy. These AI models have been deployed in domains ranging from multiscale composite material modeling and additive manufacturing to remote sensing for environmental and geospatial applications.
Probabilistic Neural Networks
These models are designed to provide direct estimates about the variability in neural network outputs. For example, in regression settings, the model produces a parameterized predictive distribution, which can be used for uncertainty quantification. This is important because it enables more informed decision-making by explicitly modeling predictive confidence.
Model Calibration
I work on adapting calibration methods, such as temperature scaling and conformal prediction, to pre-trained vision and language models affected by domain shifts. Well-calibrated models produce reliable uncertainty estimates, which are critical for safe and trustworthy AI in high-stakes applications.
Multimodal Representation and Fusion
In many applications, data comes from multiple modalities such as text, images, and sensor measurements. Merging these in a probabilistic framework allows for consistent uncertainty estimates across modalities, leading to more robust and coherent predictions.
Model Reproducibility and Explainability
Training neural networks is inherently stochastic, leading to variability in results. Quantifying this training-induced variability is critical for scientific rigor. In addition, explainability methods help interpret model predictions, ensuring transparency and accountability.
Selected Publications
The complete collection of my publications can be found on Google Scholar.
Evaluation of Classification Models in Limited Data Scenarios with Application to Additive Manufacturing
Framework for unbiased test loss estimates with fewer labels in sparse-data regimes, reducing variance via adaptive sampling distribution.
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
Two-stage inverse design framework combining candidate narrowing with conformal inference filtering, validated on benchmarks and micromechanics case study.
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Grants
High-Throughput Design and Analysis of Novel Ceramics
Funding Agency: Army Research Lab (ARL)
Data Science Approaches to Advance High Solids Loading Additive Manufacturing
Funding Agency: Army Research Lab (ARL)
Multi-Scale Models based on Machine Learning and a Fiber Network Model
Funding Agency: National Aeronautics and Space Administration (NASA)
Learning from Imbalanced Data with Confidence and Minimal Supervision
Funding Agency: Air Force Research Laboratory Information Directorate (AFRL/RI)
Teaching
MATH 6388: Statistical and Machine Learning
Offered: Fall 2022, Fall 2023, Fall 2024
MATH 4/5388: Machine Learning Methods
Offered: Spring 2023 and Spring 2025
MATH 1376: Programming for Data Science
Offered: Spring 2024
MATH 5718: Applied Linear Algebra
Offered: Fall 2025
MATH 3382: Statistical Theory
Offered: Spring 2025
Next Course
Coming soon...
Lab Members
Matthew Knodell
Applied Math Ph.D. Student (2024–present)
Courtney Franzen
Applied Math Ph.D. Student (2024–present)
Rachel Drummond
Applied Math Ph.D. Student (2023–present)
Colin Furey
Applied Math Ph.D. Student (2023–present)