Fazle Rabbi Dayeen
Applied Physicist | Semiconductor Materials | Thin-Film Characterization & Metrology
Chicago, IL | fdayeen@hawk.illinoistech.edu | github.com/frdayeen | linkedin.com/in/frdayeen
SUMMARY
Ph.D. physicist specializing in thin-film and interfacial materials characterization, semiconductor-relevant metrology, and scientific data analysis. Hands-on research experience at Argonne National Laboratory using synchrotron X-ray techniques, electron microscopy, vacuum-based instrumentation, cleanroom practices, and high-throughput experimental workflows. Skilled in Python, MATLAB, process optimization, experimental troubleshooting, and structure-property analysis for advanced materials and semiconductor applications.
EDUCATION
Illinois Institute of Technology
Chicago, IL
Ph.D. in Physics
PROFESSIONAL EXPERIENCE
Illinois Institute of Technology
Chicago, IL
Research Assistant | Materials & Soft-Matter Physics
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- Designed and executed in-situ thin-film and interfacial materials characterization experiments to investigate nanoscale structure, phase behavior, and structure-property relationships.
- Worked in cleanroom and controlled laboratory environments for sample preparation, handling, contamination awareness, and materials characterization.
- Worked with SEM, TEM, XPS, vacuum-based scientific instrumentation, and synchrotron X-ray characterization workflows.
- Developed Python-based automated workflows to process large diffraction datasets and extract lattice parameters, coherence lengths, peak positions, and structural trends.
- Improved data-processing and experimental efficiency by approximately 40% through workflow automation and standardized analysis procedures.
- Refined experimental protocols to improve reproducibility, measurement reliability, and process consistency.
Argonne National Laboratory - Advanced Photon Source
Lemont, IL
Visiting Doctoral Researcher | Synchrotron Materials Characterization
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- Conducted synchrotron X-ray diffraction, scattering, and reflectivity measurements using GIXD, GIXOS, XRD, and XRR to characterize nanoscale structure in thin-film and interfacial material systems.
- Prepared and characterized controlled sample sets under defined temperature, pressure, and environmental conditions during multi-day beamline campaigns.
- Worked with vacuum-based instrumentation and experimental hardware while following established procedures for sample handling, measurement reliability, and contamination awareness.
- Collaborated with beamline scientists to troubleshoot alignment, calibration, instrumentation, and data-quality issues in real time.
- Integrated structural measurements with material-performance metrics to establish structure-property relationships relevant to stability, reliability, and material optimization.
- Produced publication-quality datasets, figures, structural analyses, and technical documentation for multidisciplinary research teams.
Illinois Institute of Technology
Chicago, IL
Teaching Assistant
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- Mentored undergraduate students in computational physics and scientific data analysis using Python, MATLAB, and C++.
- Developed hands-on projects connecting experimental data, computational modeling, and physics concepts.
RELEVANT PROJECTS & RESEARCH
Thin-Film & Interfacial Materials Characterization
- Characterized nanoscale structure, phase behavior, molecular organization, lattice order, and structural disorder using GIXD, GIXOS, XRD, and XRR.
- Extracted lattice parameters, domain coherence lengths, peak positions, and other structural metrics using Gaussian and Lorentzian peak-fitting methods.
- Developed Python-based data-processing workflows for analyzing large experimental datasets and comparing material behavior across different compositions and experimental conditions.
- Investigated structure-property relationships and threshold-like structural changes relevant to materials stability, reliability, and performance.
Computational Materials Science - DFT+U Simulation of LaNiO3
- Used VASP and Density Functional Theory (DFT+U) to investigate the lattice structure and electronic properties of LaNiO3.
- Studied relationships between atomic structure, electronic behavior, conductivity, and material properties.
- Applied Python-based computational workflows for simulation post-processing, visualization, and comparative analysis.
- Applied computational materials methods relevant to oxide electronics, electronic materials, and semiconductor research.
Scientific Data Mining & Machine Learning
- Developed a Python-based computational framework to analyze more than 35,000 scientific publications.
- Applied Natural Language Processing and Latent Dirichlet Allocation (LDA) to identify emerging research topics and analyze changes in scientific research trends over time.
- Developed the Literature Topic Co-occurrence and Frequency (LiTCoF) workflow for large-scale scientific literature analysis.
- Research resulted in a peer-reviewed publication in the Journal of Industrial Ecology.
Statistical & Computational Materials Modeling
- Developed C++ simulations to investigate structural heterogeneity and statistical behavior in weighted planar stochastic lattice systems.
- Applied statistical mechanics, multifractal analysis, dynamic scaling, regression, and data-collapse methods.
- Research resulted in a peer-reviewed publication in Chaos, Solitons & Fractals.
TECHNICAL SKILLS
- Materials Characterization & Microscopy: SEM, TEM, XPS, XRD, XRR, GIXD, GIXOS, thin-film and interfacial characterization, in-situ measurements, structural analysis.
- Semiconductor & Process: Thin-film metrology, cleanroom practices, vacuum-based instrumentation, process characterization, experimental troubleshooting, contamination awareness, process optimization, reliability, and structure-property analysis.
- Scientific Computing & Programming: Python, MATLAB, C++, NumPy, Pandas, SciPy, Matplotlib, scikit-learn, statistical analysis, model fitting, automated data processing.
- Computational Materials Science: VASP, DFT+U, electronic-structure calculations, lattice modeling, computational materials analysis, scientific visualization.
- Experimental Methods: Vacuum systems, sample preparation, calibration verification, experimental protocol development, measurement troubleshooting, reproducibility, data-integrity verification.
- Tools & Workflow: Jupyter, Git, Unix/Linux, technical documentation, experimental protocols, scientific reporting, multidisciplinary collaboration.