Research
Machine Learning Applications in Wireless Communication Networks
Goal: The project aims to detect user anomalies and inference patterns in wireless networks, with the goal of improving communication systems' data rates.
Senior design project: Machine Learning Applications in Wireless Communication Networks: Interference Detection and Authentication Aspects.
Machine Learning Applications in Magnetic Domain Pattern Images
To extract parameters from and/or generate new images of magnetization patterns using micromagnetic simulation data, the images of simulated magnetization patterns will be input into Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for training.
Benefit: This is an opportunity to create domains with out-of-range parameters (which you can’t execute in micromagnetic simulation).
Decoding Motor imagery-based EEG Signals using Machine Learning
Brain-computer interfaces represent a life-changing possibility for disabled patients to regain control of their lives, for example through highly functional prosthetics or movement devices. However, everyday movements, like walking, and the increased brain activity it causes pose significant difficulty for BCI decoders.
This paper proposes a method to clean EEG data and create a decoder resistant to walking-induced artifacts to classify left and right-hand motor imagery. We first perform Artifact Subspace Reconstruction to remove major artifacts. Bandpass in the alpha and mu bands to find physiologically relevant frequencies, Common Average Reference Filter to improve spatial resolution, extract trials and samples, find the mean percent difference in power spectrum density between resting and motor imagery to identify event-related desynchronization best, perform fisher score feature selection to reduce dimensionality, and finally train and cross-validate our model offline and test online with evidence accumulation.
The paper is attached under publications.
Some of the code for this work is available at https://github.com/zkhodzhaev/ML_brain_computer_interface
EEG data before and after ASR processing. The upper subplot shows the raw signal, and the lower illustrates the filtered signal without the spike.
The topological plot of the grand averaged mu power from the last 0.5s of the task trials, with and without spatial filtering.
Feature Extraction/ Selection
Fischer scores measuring statistical significance for the first experimental run 1 to 4 in session one for subject 41. The y-axis shows Fischer score values ranging from 8 to 30 Hz with ∆2. The x-axis shows the number of channels from one to fifty-five. The color represents the discriminant power of the Fischer score, with higher scores indicating features that better differentiate the conditions/classes:
Feature extraction for testing the ML model
Rethinking Fair and Stable Representation Learning
Machine Learning, particularly in the context of Graph Neural Networks (GNNs), is being deployed across various applications, including safety-critical domains.
While the accuracy of these systems remains a critical consideration, it is equally important to examine them through the lenses of fairness and stability. Prior research has introduced a unified framework to learn stable and fair node representations.
In this work, we revisit and refine this framework, presenting a simplified version demonstrating superior performance in accuracy and fairness/stability metrics.
Furthermore, we introduce a set of straightforward baseline methods that prove effective and competitively perform, often surpassing the performance of previous state-of-the-art (SOTA) approaches.
The paper is attached under publications.
The code for our work is available at https://tinyurl.com/AMLFinalCode
and specifically to my contribution (MLP): https://github.com/zkhodzhaev/representation_learning
Tunable Spike-Timing-Dependent Plasticity (STDP) in Magnetic Skyrmion Manipulation Chambers
Unconventional Computing
Skyrmion stability and movement under different temperatures, current densities, and physical constraints.
Skyrmion: Material Growth
Novel Materials: Dynamics of Hopfions
Study of resonant spin dynamics of topological spin textures.
Using micromagnetic simulations, e.g., MuMax3 and OOMMF to find resonant spin dynamics of a three-dimensional topological spin texture hopfion in different chiral magnets and identifying the ground state spin configuration of hopfions, effects of anisotropies, geometric confinements, and demagnetizing fields. Calculate the resonance frequencies and spin-wave modes of spin precession dynamics under multiple magnetic fields.
Novel Materials: Antiferromagnetic Material Switching
NiO Antiferromagnetic Switching:
Heterostructure growth using a sputtering system
Hall bar measurement device fabrication using photolithography
A temperature effect on the antiferromagnetic domain switching
Laser-induced dynamics
THz resonance dynamics
You cracked it!
Electric Field Effects on the Chocolate Flow
The electric field was applied along the direction of the flow of chocolate, and the effects of the electric field on temperature, gradient, and pressure were investigated.
PID was used to control and set temperature. Solid-state relays heated the system, and nitrogen gas pressed it. Cu plates created an electric field.
The graph of chocolate weight versus time was graphed in real time.
