• M. Ali, A. N. Haque, N. Sadik, T. Ahmed, and M. Z. Baten, "Predicting Strongly Localized Resonant Modes of Light in Disordered Arrays of Dielectric Scatterers: a Machine Learning Approach", Optics Express (Scopus Indexed Q1, Impact Factor: 3.833)

• M. Ali, N. Sadik, A. N. Haque, and M. Z. Baten, "Nature and Tunability of Light Localization in Disordered Arrays of Coalesced Nanowires", Optics Express (Scopus Indexed Q1, Impact Factor: 3.833) [Under Review]

• N. Rashid, M. A. Faisal Hossain, M. Ali, M. I. Sukanya, T. Mahmud, and S. A. Fattah, "AutoCovNet: Unsupervised Feature Learning Using Autoencoder and Feature Merging for Detection of COVID-19 from Chest X-ray Images", Journal of Biocybernetics and Biomedical Engineering (Scopus Indexed Q1, Impact Factor: 5.687), Elsevier

• M. Ali, N. Sadik, and M. Z. Baten, "Strong Localization of Light in Disordered Arrays of Coalesced and Inhomogeneous Self-organized Nanowires", International Conference on Materials for Advanced Technologies (ICMAT 2023). 

• M. Ali, M. A. Faisal Hossain, M. I. Sukanya, "Real-time Density-Based Dynamic Traffic Light Controller Using FPGA", IEEE REGION 10 CONFERENCE (WIECON-ECE 2021)  

• N. Rashid, M. A. Faisal Hossain, M. Ali, M. I. Sukanya, T. Mahmud and S. A. Fattah, "Transfer Learning Based Method for COVID-19 Detection From Chest X-ray Images," 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, Japan, 2020, pp. 585-590, doi: 10.1109/TENCON50793.2020.9293850.

Applying Machine Learning in Nanophotonics

Supervisor: Dr. Md Zunaid Baten

Research topic: Predicting Strongly Localized Resonant Modes of Light in Disordered Arrays of Dielectric Scatterers: a Machine Learning Approach

Research Group: Solid-state Electronics and Photonics (SSEP) research group 

Research Tools: MEEP, Tensorflow, Origin

• Generated a dataset of 8400 Self-organized GaN nanowire arrays of different diameters & fill factors to apply machine learning techniques. 

• Explored different machine learning models for determining the characteristics of resonant modes in disordered arrays of dielectric scatterers.

• Proposed an autoencoder-based model to predict the localized wavelength of the resonant modes, which is significantly faster than FDTD analysis.

• Recently published a journal article in Optics Express based on the findings of this work. 

Some figures from my recent work can be found below.

Undergraduate Thesis

Thesis Supervisor: Dr. Md Zunaid Baten

Research topic: Finite Difference Time Domain-based Analysis of Anderson Localization in Non-ideal Disordered Systems

Research Group: Solid-state Electronics and Photonics (SSEP) research group 

Research Tools: MEEP, MPB, Lumerical, Origin

• My undergraduate thesis mainly focuses on disordered photonics. I mainly work on the effect of Anderson localization of light in MBE-grown GaN-nanowire arrays on Silicon substrate. The study explores the effects of variation in the areal density & diameter of the nanowires on the quality factor & resonant wavelength of the localized field. The study also analyzes the mean distance between multiple scatterers in both coalesced & non-coalesced nanowire structures. 

• To work on this study, sufficient knowledge of Finite-Difference Time-Domain analysis tools such as MEEP, MPB, Lumerical, etc are required. A basic understanding of light localization, Ioffe-Regel criterion, EM fields, quality factor, scattering events, Fabry-Perot resonators, etc is also needed.

• It is a great experience to work under the supervision of Dr. Md Zunaid Baten, who is a cordial mentor & an expert In this field of study. Moreover, our SSEP research group also consists of some amicable alumni & seniors who always provide a helping hand whenever needed. Click here to know more about our research group.

• Currently, our thesis group is working on preparing a manuscript on this field of study.

Some figures from my recent work can be found below.

Deep Learning & Biocybernetics Related Experience

• Worked on different transfer learning-based methods for COVID-19 detection from chest x-ray images with a group of four under the supervision of Prof. Shaikh Anowarul Fattah  & Tanvir Mahmud. The group also focused on autoencoder-based unsupervised feature learning & feature merging for detection of COVID-19 from chest x-ray images. The proposed method offers very satisfactory performances compared to the state-of-the-art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on the 3-class, and 99:39% on the 2-class classification. Our group published one conference & one journal article from this study.

VLSI Design & Verification Related Experience

• Worked on real-time density-based analysis of a dynamic traffic light controller (TLC) system using FPGA with a group of three under the supervision of Mr. Rajat Chakraborty. In this study, real-time traffic density is detected using an array of IR sensors placed on each road of a four-way junction. Continuous monitoring of these sensors provides real-time vehicle density to ensure proper signal switching. Features e.g, minimum smart delay feature, making way for emergency vehicles, traffic violation detector, and priority sequencing have been incorporated to reflect real-life scenarios. Real-time implementation of the model has also been verified using a microcontroller. Our group published one conference article from this study.