Compressive K-SVD. - Farhad Pourkamali-Anaraki; Shannon M. Hughes (ICASSP, 2013)

Kernel compressive sensing. - Farhad Pourkamali-Anaraki; Shannon M. Hughes (ICIP, 2013)

Efficient recovery of principal components from compressive measurements with application to Gaussian mixture model estimation. - Farhad Pourkamali-Anaraki; Shannon M. Hughes (ICASSP, 2014)

Memory and Computation Efficient PCA via Very Sparse Random Projections. - Farhad Pourkamali-Anaraki; Shannon M. Hughes (ICML, 2014)

Efficient Dictionary Learning via Very Sparse Random Projections. - Farhad Pourkamali-Anaraki; Stephen Becker; Shannon M. Hughes (CoRR, 2015)

A randomized approach to efficient kernel clustering. - Farhad Pourkamali-Anaraki; Stephen Becker (GlobalSIP, 2016)

Randomized Clustered Nystrom for Large-Scale Kernel Machines. - Farhad Pourkamali-Anaraki; Stephen Becker (CoRR, 2016)

Estimation of the sample covariance matrix from compressive measurements. - Farhad Pourkamali-Anaraki (IET Signal Process., 2016)

Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means. - Farhad Pourkamali-Anaraki; Stephen Becker (IEEE Trans. Inf. Theory, 2017)

Randomized Clustered Nystrom for Large-Scale Kernel Machines. - Farhad Pourkamali-Anaraki; Stephen Becker; Michael B. Wakin (AAAI, 2018)

Efficient Solvers for Sparse Subspace Clustering. - Farhad Pourkamali-Anaraki; Stephen Becker (CoRR, 2018)

Large-Scale Sparse Subspace Clustering Using Landmarks. - Farhad Pourkamali-Anaraki (MLSP, 2019)

The Effectiveness of Variational Autoencoders for Active Learning. - Farhad Pourkamali-Anaraki; Michael B. Wakin (CoRR, 2019)

Improved fixed-rank Nyström approximation via QR decomposition: Practical and theoretical aspects. - Farhad Pourkamali-Anaraki; Stephen Becker (Neurocomputing, 2019)

Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction. - Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili; Lydia Morawiec (ICMLA, 2020)

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. - Sina Sharif Mansouri; Farhad Pourkamali-Anaraki; Miguel Castano; Ali-Akbar Agha-Mohammadi; Joel Burdick; George Nikolakopoulos (MED, 2020)

Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects. - Farhad Pourkamali-Anaraki (CoRR, 2020)

Uncertainty Quantification of Structural Systems with Subset of Data. - Mohammad Amin Hariri-Ardebili; Farhad Pourkamali-Anaraki; Siamak Sattar (CoRR, 2020)

A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty. - Sina Sharif Mansouri; Christoforos Kanellakis; Björn Lindqvist; Farhad Pourkamali-Anaraki; Ali-akbar Agha-mohammadi; Joel Burdick; George Nikolakopoulos (IEEE Robotics Autom. Lett., 2020)

Efficient Solvers for Sparse Subspace Clustering. - Farhad Pourkamali-Anaraki; James Folberth; Stephen Becker (Signal Process., 2020)

An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering. - Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (ICMLA, 2021)

Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation. - Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (IEEE Access, 2021)

Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties. - Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (IEEE Access, 2021)

Adaptive Data Compression for Classification Problems. - Farhad Pourkamali-Anaraki; Walter D. Bennette (IEEE Access, 2021)

Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. - Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili; Stephen Becker; Maziar Raissi (Big Data, 2021)

D-CBRS: Accounting for Intra-Class Diversity in Continual Learning. - Yasin Findik; Farhad Pourkamali-Anaraki (ICIP, 2022)

Structural uncertainty quantification with partial information. - Mohammad Amin Hariri-Ardebili; Farhad Pourkamali-Anaraki (Expert Syst. Appl., 2022)

Evaluating Regression Models with Partial Data: A Sampling Approach. - Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (CoDIT, 2023)

Advancing Precision Medicine: An Evaluative Study of Feature Selection Methods. - Carol M. Kiekhaefer; Farhad Pourkamali-Anaraki (ICMLA, 2023)

Evaluation of classification models in limited data scenarios with application to additive manufacturing. - Farhad Pourkamali-Anaraki; Tahamina Nasrin; Robert E. Jensen; Amy M. Peterson; Christopher J. Hansen (Eng. Appl. Artif. Intell., 2023)

Adaptive Conformal Prediction Intervals Using Data-Dependent Weights With Application to Seismic Response Prediction. - Parisa Hajibabaee; Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili (IEEE Access, 2024)

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning. - Farhad Pourkamali-Anaraki; Jamal F. Husseini; Scott E. Stapleton (IEEE Access, 2024)

Special Issue: Big Scientific Data and Machine Learning in Science and Engineering. - Farhad Pourkamali-Anaraki (Big Data, 2024)

Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons. - Farhad Pourkamali-Anaraki (CoRR, 2024)

Two-stage surrogate modeling for data-driven design optimization with application to composite microstructure generation. - Farhad Pourkamali-Anaraki; Jamal F. Husseini; Evan J. Pineda; Brett A. Bednarcyk; Scott E. Stapleton (Eng. Appl. Artif. Intell., 2024)

Adaptive activation functions for predictive modeling with sparse experimental data. - Farhad Pourkamali-Anaraki; Tahamina Nasrin; Robert E. Jensen; Amy M. Peterson; Christopher J. Hansen (Neural Comput. Appl., 2024)

Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions. - Farhad Pourkamali-Anaraki (CoRR, 2025)

Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction. - Farhad Pourkamali-Anaraki (CoRR, 2025)