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 develop learning algorithms that work for dynamic and multi-modal data with minimal human intervention.

• Dynamic Data: Addressing dynamic data is crucial as the world is full of evolving, non-static information. Algorithms capable of adapting to changes over time without manual updates are essential for real-time applications, such as advanced manufacturing, weather forecasting, and precision medicine.
• Multi-Modal Data: Multi-modal data refers to information that comes in various forms, such as text, images, audio, and video. Learning algorithms that can effectively integrate and interpret this diverse data can lead to more robust and versatile applications. This is particularly relevant in fields like autonomous driving, where the system must understand visual, auditory, and sensor data, or in healthcare, where patient data might include clinical notes, radiology images, and lab results.
• Minimal Human Intervention: The emphasis on reducing human intervention is crucial for scalability and accessibility. Algorithms that require minimal fine-tuning and human guidance can be more easily deployed across different domains and by users with varying levels of expertise. This is particularly important for applications in resource-limited settings or where rapid deployment is necessary.
• Interdisciplinary Collaboration: I always look for interdisciplinary collaborations, as developing algorithms for dynamic and multi-modal data often requires expertise from computer science, statistics, cognitive science, engineering, and domain-specific knowledge.

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
• This paper presents a novel framework that enables the generation of unbiased estimates for test loss using fewer labeled samples, effectively evaluating the predictive performance of classification models in data-limited applications. The framework's key innovation lies in developing an adaptive sampling distribution that iteratively identifies influential testing samples based on interactions between learner and evaluator agents. Notably, the adaptive distribution dynamically adjusts the evaluator agent's supervisory role by prioritizing inputs with discrepancies between the agents and considering the evaluator's uncertainty. Comprehensive experimental analyses on synthetic data and two sparse data sets from material extrusion additive manufacturing problems validate the framework's superiority over uniform and fixed sampling distributions. First, the proposed framework provides unbiased estimates of the test loss across various data sets, sampling ratios, and evaluator models. Second, the introduced adaptive sampling distribution significantly reduces the standard deviation of the test loss estimator compared to uniform sampling, achieving a 50% reduction for a 10% sampling ratio in the filament selection benchmark. Third, the framework demonstrates its efficacy in model selection to determine the optimal number of hidden units with a reduced number of test samples. Overall, this work offers a promising framework for evaluating classification models in applications where acquiring labeled data is time-consuming and resource-intensive, including materials science and engineering.
• Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
• This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage of the proposed framework, a machine learning model termed the "learner" identifies a limited set of candidates within the input design space whose predicted outputs closely align with desired outcomes. Subsequently, in the second stage, a separate surrogate model, functioning as an "evaluator," is employed to assess the reduced candidate space generated in the first stage. This evaluation process eliminates inaccurate and uncertain solutions, guided by a user-defined coverage level. The framework's distinctive contribution is the integration of conformal inference, providing a versatile and efficient approach that can be widely applicable. To demonstrate the effectiveness of the proposed framework compared to conventional single-stage inverse problems, we conduct several benchmark tests and investigate an engineering application focused on the micromechanical modeling of fiber-reinforced composites. The results affirm the superiority of our proposed framework, as it consistently produces more reliable solutions. Therefore, the introduced framework offers a unique perspective on fostering interactions between machine learning-based surrogate models in real-world applications.

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, Fall 2022 and Fall 2023
  • https://github.com/farhad-pourkamali/MATH6388
• MATH 4/5388 Machine Learning Methods, Spring 2023
  • https://github.com/farhad-pourkamali/Machine-Learning-Methods
• MATH 1376: Programming for Data Science, Spring 2024
  • https://github.com/farhad-pourkamali/ProgrammingForDataScience

Lab Members

• Matthew Knodell (Applied Math PhD Student, 2024-present)
• Courtney Franzen (Applied Math PhD Student, 2024-present)
• Rachel Drummond (Applied Math PhD Student, 2023-present)
• Colin Furey (Applied Math PhD Student, 2023-present)