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Manish Bhattarai
Affiliation
Theoretical Division, Los Alamos National Laboratory, Los Alamos, USA
Topic
Non-negative Matrix Factorization,Positive Matrix,Tensor Decomposition,Language Model,Automatic Determination,Family Classification,Hyperparameters,Information Retrieval,Large Datasets,Low-rank Approximation,Machine Learning,Model Performance,Neural Network,Non-negative Factorization,Question Answering,Scientific Literature,Similarity Score,Subject Matter Experts,Tensor Factorization,Topic Modeling,Transformer,Accuracy Of Model,Adoption Of Machine Learning,Adversarial Attacks,Adversarial Defense,Adversarial Examples,Adversarial Perturbations,Adversarial Robustness,Anomaly Detection,Automatic Model,Batch Size,Benchmark,Benign Samples,Bias Term,CIFAR-100 Dataset,Capability Of Methods,Citation Network,Class Imbalance,Class Imbalance Problem,Clear Statement,Client Participation,Cloud Data,Collaborative Filtering,Column Vector,Communication Rounds,Compression Ratio,Computational Overhead,Computer Vision,Consecutive Epochs,Contralateral,
Biography
Manish Bhattarai received the M.S. and Ph.D. degrees from the Department of Electrical and Computer Engineering, The University of New Mexico. He is currently a Postdoctoral Research Associate with the Theoretical Division, Los Alamos National Laboratory (LANL), Los Alamos, NM, USA. At LANL, he is part of the Tensor Factorizations Group, which specializes on large scale data factorization and improving the laboratory’s high-performance processing and computing abilities. He has extensively worked on developing HPC empowered ML algorithms for mining big data, such as distributed matrix and tensor factorization. His current research interests include: machine learning, computer vision, deep learning, tensor factorizations, and high performance computing.