Farhad Pourkamali Anaraki, PhD
Assistant Professor of Computer Science
University of Massachusetts Lowell
Email: farhad_pourkamali [at] uml [dot] edu
Office: Dandeneau Hall, 1 University Ave, Lowell, MA
Farhad Pourkamali is an assistant professor in the Department of Computer Science at the University of Massachusetts (UMass) Lowell since fall 2018. He is the director of the Computational and Statistical Data Science (CSDS) Lab. Before joining UMass Lowell, Prof. Pourkamali spent one year as a postdoctoral research associate in the Department of Applied Mathematics at the University of Colorado Boulder, and he received his PhD in Electrical Engineering from the same institution.
Prof. Pourkamali's research areas are machine learning, computational mathematics, optimization, and information extraction from various types of data sets. He focuses on the computational and statistical aspects of machine learning as well as a wide range of application domains. Notably, he works on developing efficient and principled techniques to analyze complex high-dimensional data in unsupervised scenarios (clustering), and when labeled data is scarce (active learning). Moreover, Prof. Pourkamali conducts interdisciplinary research in various areas, such as structural engineering and materials science (additive manufacturing).
- Scalable machine learning and optimization methods (e.g., scalable clustering and data summarization)
- Statistical aspects of machine learning algorithms (e.g., proving guarantees on performance)
- Generative modeling and neural networks (e.g., variational autoencoders and feature extraction)
- Graph partitioning and approximation algorithms (e.g., spectral clustering and related methods)
- Randomized numerical linear algebra (e.g., randomized SVD and PCA)
- Learning from imbalanced data (e.g., improving robustness and performance of existing methods)
- Applications of machine learning in structural engineering, materials science, and other domains
Professional Activities and Service
- Panelist (2x), National Science Foundation (NSF) Proposal Review
- Reviewer: ICML, AISTATS, IEEE Transactions on Signal Processing, etc.
- Graduate Committee Member, Computer Science Department, UMass Lowell
- Faculty Senate Member, UMass Lowell
- Faculty Senate Graduate Policy and Affairs Committee (GPAC) Member, UMass Lowell
- Invited talk: Computational and Applied Math Seminar, Department of Mathematics, Tufts University, March 9, 2020.
- Prof. Pourkamali is organizing a seminar on Machine Learning in fall 2019, every Wednesday 4:30 to 5:30 PM in DAN 321. This seminar is an excellent opportunity for interested students and faculty members at UMass Lowell. For more information, you can see this page.
- Prof. Pourkamali will serve as a session chair at the IEEE Machine Learning for Signal Processing Workshop (MLSP). The session is Poster Session 2, Signal Detection, Pattern Recognition, Semi/Un-supervised Learning, at 16:00-18:30pm on Monday, October 14, 2019.
- Prof. Pourkamali is a program committee member for the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS).
- New paper accepted to Neurocomputing: "Improved fixed-rank Nystrom approximation via QR decomposition: Practical and theoretical aspects." The paper is available online here, July 2019.
- New paper accepted to IEEE Machine Learning for Signal Processing (MLSP): "Large-scale sparse subspace clustering using landmarks." The paper is available on arxiv here, August 2019.
- New grant with Min Hyung Cho: "A Next Generation Electromagnetic Solver for Multilayered Media and its Applications in Photonic Passive Cooling Devices." This proposal is funded by UMass Lowell Kennedy College of Sciences Seed Grant.
- Received travel award to attend the National Science Foundation (NSF) Grants Conference, 2019.