Md. Ashaduzzaman Niloy

Education (Newest First)

MS in EEE at Bangladesh University of Engineering and Technology (BUET)

Prospective date of completion: April, 2025

Grade: 3.58 out of 4.00 (Thesis in progress)

Thesis Concentration: Photonics, All Optical Communication, Quantum Computation, Machine Learning, Deep Learning.

Specialization Courses:

EEE 6011: Semiconductor Detector for Image Sensors (Obtained Grade: B+, 70-80%):

Introduction to Image Sensors; High energy photon detection, Advantages of semiconductor detectors; Imaging Systems: Xerographic mode detection and Flat panel detection; Mode of Conversion: Direct and Indirect approach, Imaging performance: Sensitivity, Resolution, Quantum efficiency, Noise and Lag; Photoconductor properties for high energy photon detection; Dielectric relaxation, Carrier Schubwegs, Shockley-Ramo Theorem; Photon interaction mechanisms in Photoconductor; Ionization Energy; Potential photoconductors; Atomic Structure of a-Se: Density of states, Optical Properties of a-Se; Charge trapping and absorption limited sensitivity; cascaded system model, noises in digital imaging sensors, signal and noise propagations, Detective Quantum Efficiency; Modulation Transfer Function; Dark current and its reduction mechanisms in semiconductor detectors, Recombination and ghosting in semiconductor detectors; Practical a-Se multilayer detector; avalanche detector; Sensing and storage elements, Clinical applications of complete systems.

EEE 6204: Optical Fiber Communications (Obtained Grade: A+, 90-100%):

Introduction to optical communication, Different types of fibers, specialty fibers Wave Equation and Coupling Modes in optical waveguide and NLSE Fiber loss, Chromatic dispersion, Birefringence and PMD Chromatic dispersion compensation, Higher order dispersion Fiber nonlinearities: SPM, XPM, FWM Optical transmitters and receivers, Optical Amplifiers, Optical Filters Advanced Optical Modulation and Detection Schemes, Receiver noise analysis, BER calculation, Sensitivity calculation, Sensitivity degradation Introduction to Soliton transmission Optical networks, PON, SONET/SDH, OFDM, OTDM and WDM transmission systems.

EEE 6608: Machine Learning and Pattern Recognition (Obtained Grade: A+, 90-100%):

Introduction to algorithms and principles involved in machine learning. Linear regression, logistic regression. Discriminative learning. Fundamentals of representing uncertainty, learning from data, supervised learning. Support vector machines and kernel trick. Model selection and feature selection. Combining features, classifiers, and boosting. Ensemble methods. Clustering and unsupervised learning. Expectation maximization regularization. Hidden Markov models. Learning from Bayesian networks. Probabilistic inference. Collaborative filtering. Reinforcement learning. Neural networks representation and learning. Deep neural network and manifold learning. Design and analysis of machine perception systems. Design and implementation of a technical project applied to real-world problems of images, text, and robotics.

EEE 6407: Carbon Nanotechnology (Obtained Grade: A+, 90-100%):

Nanomaterials and nanostructures: graphene, carbon nanotubes, fullerenes, molecules and organic nanostructures. Synthesis methods of nanostructures: electric arc, pulsed laser deposition, chemical vapor deposition (CVD); thermal CVD, catalytic CVD, micro wave CVD (MWCVD), plasma enhanced CVD (PECVD), spray pyrolysis. Physical and opto-electronic properties; characterization techniques. Applications: carbon nanotube and graphene based devices, bio-sensors, bio-inspired nanostructures, molecular motors, fuel cells and solar cells.

EEE 6509: Solar Cells (Obtained Grade: B+, 70-80%):

Introduction to basic solar cell operation; Thermodynamic limits and carrier statistics; Diode photo-response; Homojunction and Heterojunction solar cells; Light management in solar cells: Light trapping, Texturing, Anti-reflection coating; Silicon-based Monocrystalline, polycrystalline and Amorphous solar cells; Thin film and next generation solar cells: Multi-junction, Intermediate band and Hot-carrier solar cell, Quantum dot and nanowire solar cells, CIGS, CZTS, Perovskite and Organic Solar cells; Solar cell efficiency and economic considerations of solar cell design.

EEE 6512: Nanoscale Device Modeling and Simulation Techniques (Obtained Grade: A, 80-90%):

Concepts of quantum mechanics and Operators Solution techniques of Schrodinger equation: Analytical models, Perturbation theory, Finite difference method, Finite element method, using basis functions for solving Schrodinger equation. Change of basis function, tight binding model for finding band structure of materials. Non-equilibrium Green’s Function (NEGF) formalism: correlation and scattering functions, self energy and green’s function, transmittance, current flow. Modeling of semi-infinite leads in NEFG formalism; Electron-photon, electron-phonon and electron-electron interaction in NEGF formalism. Solution and use of Poisson’s equation for device simulation. The many-body problem: Multi-electron wavefunction, First-principles (ab-initio) simulation, Hartree-Fock (HF) approach, Density functional theory (DFT) Nanoscale simulation tools: Schrodinger-Poisson self-consistent simulation, HF and DFT simulations. Applications: Field Effect Transistor (FET) Simulation, Carrier transport in 1D, 2D and 3D.