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IBA faculty co-authors research paper on emerging Big Data challenges.

Dr. Tahir Syed, Assistant Professor – Department of Computer Science, has co-authored a paper titled, "Potential Deep Learning Solutions to Persistent and Emerging Big Data Challenges—A Practitioners' Cookbook". The research paper has been published in the high-impact HEC-recognized ACM Computing Surveys journal, with a W-ranking.

Dr. Tahir has worked extensively with fundamental Machine Learning problems such as class imbalance and distribution drift, and specializes in designing new losses for optimizing neural networks for specific needs. He was nominated to design Pakistan's first graduate Data Science curriculum and in 2020, when the IBA Karachi began the said program, he joined the IBA Karachi family.

Abstract:

The phenomenon of Big Data continues to present moving targets for the scientific and technological state-of-the-art. This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the auto-encoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.

For more information: https://dl.acm.org/doi/abs/10.1145/3427476