Mohammad Arshad is a journalist and writer for Marktechpost Media Inc. His work focuses on artificial intelligence and machine learning research, with a particular interest in optimizing deep neural networks and exploring innovative approaches in the field. His articles have been featured in Frontiers and Cureus Journal of Medical Science.
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Mohammad Arshad's coverage primarily focuses on specific AI research papers, models, and frameworks with a strong emphasis on industry-specific content. Given this focus, he is likely to be most receptive to pitches that offer in-depth insights or commentary related to cutting-edge advancements in artificial intelligence and machine learning.
Pitches could include interviews or contributions from researchers involved in the development of new AI models or frameworks, especially those affiliated with prestigious institutions mentioned in his articles. Additionally, experts who can provide critical analysis of the methodologies and potential impact of these newly introduced AI concepts may find success reaching out to Mohammad.
As there is no specified geographic focus for Mohammad's coverage, pitches should highlight global perspectives and developments within the field of artificial intelligence rather than focusing on any particular region.
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By: Mohammad Arshad
DataSP: A Differentiable All-to-All Shortest Path Machine Learning Algorithm to Facilitate Learning Latent Costs from Trajectories
By: Mohammad Arshad
Generative AI models, particularly Large Language Models (LLMs), have seen a surge in adoption across various industries, transforming the software development landscape.
By: Mohammad Arshad
FairProof: An AI System that Uses Zero-Knowledge Proofs to Publicly Verify the Fairness of a Model while Maintaining Confidentiality
By: Mohammad Arshad
Hierarchical Graph Masked AutoEncoders (Hi-GMAE): A Novel Multi-Scale GMAE Framework Designed to Handle the Hierarchical Structures within Graph
By: Mohammad Arshad
Microsoft AI for Good Introduces Pytorch-Wildlife: An Open-Source Deep Learning Platform Built on PyTorch
By: Mohammad Arshad
This AI Paper from Siemens Explores the Integration of the Graph Modality in LLM for General Graph Instruction Following Tasks
By: Mohammad Arshad
This AI Paper from Georgia Institute of Technology Introduces LARS-VSA (Learning with Abstract RuleS): A Vector Symbolic Architecture For Learning with Abstract Rules
By: Mohammad Arshad
This AI Paper Proposes Approximation Decision Boundary ADBA: An AI Approach for Black-Box Adversarial Attacks
By: Mohammad Arshad
Abstract: This article develops multiple novel climate risk measures (or variables) based on the television news coverage by Bloomberg, CNBC, and
By: Mohammad Arshad, Tyler E. Rice-Canetto, Michael Schiraldi, Javed Siddiqi, Louis Reier
Microvascular decompression (MVD) is a neurosurgical operation used to treat trigeminal neuralgia (TN). The surgery is performed through a retrosigmoid approach, where a Teflon pledget is placed in between the offending vessel (most commonly the superior cerebellar artery) and trigeminal nerve. The surgery is performed within the superior aspect of the cerebellopontine angle (CPA) through a small working corridor that is triangulated by the petrous bone and tentorium. Patient positioning is the foundation to accessing this corridor and performing a successful operation. This article provides a detailed guide on the positioning steps of the park bench position typically employed at Arrowhead Regional Medical Center (ARMC) in Colton, CA, USA. We then review pertinent surgical anatomy in a MVD for trigeminal neuralgia and describe the “Arrowhead" technique to perform this operation.
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