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Mahabubul Alam
Also published under:
Affiliation
The Pennsylvania State University, University Park, USA
Topic
Quantum Circuit,Quantum Computing,Quantum Gates,Deep Neural Network,Quantum State,Qubit State,Neural Network,Quantum Advantage,Quantum Error Correction,Decoherence,Error Rate,Machine Learning,Types Of Attacks,Actual Hardware,Allocation Decisions,Classical Computer,Cloud Computing,Convolutional Neural Network,Divide-and-conquer Approach,Hybrid Classical-quantum,MNIST Dataset,Multilayer Perceptron,Noise Sources,Output Feature,Quantization Noise,Quantum Algorithms,Quantum Machine,Quantum Neural Network,Quantum Simulation,Security Issues,Short Lifetime,Single Qubit,Threat Model,Trainable Parameters,2D Plane,3rd Party,AdaGrad,Adaptive Challenges,Architecture Properties,Attack Scenarios,Authentication,Autonomous Vehicles,Average Loss,Boolean Logic,Charge Trapping,Circuit Parameters,Classical Counterpart,Classical Features,Classical Optimization,Classification Algorithms,
Biography
Mahabubul Alam (Student Member, IEEE) received the B.Sc. degree in electrical and electronic engineering from the Bangladesh University of Engineering and Technology (BUET) in 2015. He is currently pursuing the Ph.D. degree in electrical engineering with Pennsylvania State University. Before joining the graduate school, he worked as an ASIC Physical Design Engineer with PrimeSilicon Technologies for over a year. He spent the summer of 2018 and 2020 as an Intern at Qualcomm and Intel, respectively. His current research interests include quantum computing, machine learning, design automation, and hardware security. He received the Best Paper Award (BPA) at ASEE (2020), BPA nomination at ISQED (2020), and secured second place at the Student Research Competition at ICCAD (SRC@ICCAD-2020).