Fazle Rabbi Dayeen
Chicago, IL | fdayeen@hawk.iit.edu| github.com/frdayeen | linkedin.com/in/frdayeen/
SUMMARY
Materials Scientist with a PhD in Physics and hands-on experience at Argonne National Laboratory and Illinois Institute of Technology, specializing in thin-film and interfacial materials, synchrotron X-ray characterization, and Python-driven data analysis. Expert in translating nanoscale structure–property relationships into data-driven insights that inform material design, reliability, and performance. Experienced in a cross-functional research environment. Open to relocating anywhere in the U.S.
EDUCATION
Illinois Institute of Technology Chicago, IL
Ph.D. in Physics
University of Illinois Chicago Chicago, IL
Graduate Student in Physics –
University of Dhaka Dhaka, Bangladesh
Master of Science in Theoretical Physics
University of Dhaka Dhaka, Bangladesh
Bachelor of Science (Hons.) in Physics
Minor: Mathematics, Chemistry, Statistics
WORK EXPERIENCE
Illinois Institute of Technology Chicago, IL
Research Assistant | Materials & Soft-Matter Physics –
- Designed and executed in-situ thin-film and interfacial characterization experiments, increasing data-collection efficiency by ~40% through Python-based workflow automation.
- Built automated analysis scripts to extract lattice parameters, coherence lengths, and structural trends from large diffraction datasets, improving interpretation consistency by ~30%.
- Developed standardized analysis templates and reporting formats, enabling rapid comparison across material compositions and experimental conditions.
- Reduced per-experiment cycle time by ~20% by refining protocols while maintaining data integrity and reproducibility.
- Communicated results to interdisciplinary collaborators and translated experimental findings into material-design insights.
Argonne National Laboratory Lemont, IL
Visitig Doctoral Researcher –
- Performed synchrotron X-ray scattering and reflectivity (GIXD/GIXOS/XRR) to quantify nanoscale structure in thin films and model material systems.
- Prepared and characterized 20+ controlled sample sets under defined temperature and pressure conditions, ensuring measurement reliability during multi-day beamline runs.
- Collaborated with beamline scientists to troubleshoot alignment, calibration, and data-quality issues in real time.
- Integrated structural measurements with functional performance metrics to establish structure–property relationships relevant to materials stability and reliability.
- Supported publication-quality figures, datasets, and technical reports.
Illinois Institute of Technology Chicago, IL
Teaching Assistant –
- Mentored undergraduate students in computational data analysis using Python, MATLAB, and C++.
- Developed hands-on projects linking experimental data to modeling, improving comprehension and engagement by ~20%.
- Facilitated study sessions twice a week, demonstrated various strategic problem-solving methods resulting in a 15% improvement in student grades on assignments.
University of Illinois Chicago Chicago, IL
Teaching Assistant –
- Conducted interactive lab sessions for 50 students, leading to a 20% increase in students' understanding of course materials.
- Demonstrated various strategic problem-solving methods during office hours, helping students achieve an average score increase of 10% on exams.
Leadership Activites
University of Illinois Chicago Chicago, IL
Graduate Student Council (Representative) –
- Represented graduate students in departmental meetings and committees, advocating for increased resources and support, resulting in a 15% increase in funding allocation for student initiatives.
- Organized and hosted a series of academic seminars and workshops tailored to the specific needs of graduate students, leading to a 30% increase in attendance compared to the previous year.
- Collaborated with faculty members to implement new policies that improved the overall graduate student experience, resulting in a 20% decrease in student complaints.
Relevant Projects & Publications
Analyzing the crystal structure of thin film –
- Collaboratively led a 5-person team at Argonne National Lab to investigate the crystal structure of liquid biofilms. Collected over 30,000 data points through 30 diverse experiments and analyzed them by developing a Python program that uses Matplotlib and Seaborn. Uncovered key structural insights and character of materials by fitting Gaussian and Lorentzian on acquired data for next-generation artificial surfactants.
Identify the emerging topics from scientific literature using Natural Language Processing –
- Developed and implemented a data analysis program using Python and various libraries to extract topics from 35,000+ industrial ecology publications, resulting in the identification of emerging trends and novel terms within the field. Utilized latent Dirichlet allocation (LDA) algorithm to uncover key themes from abstracts of academic papers, leading to a thorough understanding of research patterns and areas of focus within industrial ecology.Collaborated with a team of researchers to analyze data from over 35,000 academic articles using LDA methodology, contributing significantly to the advancement of knowledge within the field of industrial ecology.
DFT+U Simulation of LaNiO3 –
- Conducted in-depth analysis using VASP and DFT code written in Python to simulate the electronic structure of LaNiO3, resulting in a better understanding of its properties for semiconductor applications. Utilized computer simulations to identify key factors influencing the conductivity of LaNiO3, leading to a 15% increase in efficiency compared to traditional methods. Collaborated with a team of researchers to analyze the results of electronic structure simulations on LaNiO3, contributing valuable insights that led to a 20% reduction in production costs for semiconductor manufacturing. Conducted complex ab initio simulations using VASP and DFT code written in Python to analyze the electronic structure of LaNiO3, resulting in a comprehensive understanding of its behavior in advanced materials research for Li-ion battery modeling. Implemented innovative data visualization techniques to present simulation results to a team of researchers, increasing their comprehension and leading to more efficient collaboration on the project. Collaborated with senior scientists to identify key trends and anomalies in the electronic structure analysis of LaNiO3, contributing valuable insights that guided further experimental research efforts.
Cause-Effect Pair Detection: A classification-based approach –
- Developed a novel classification algorithm to accurately detect cause-effect pairs, addressing the critical need for attributing causes to effects in various human reasoning scenarios, leading to a 20% increase in accuracy. Implemented machine learning models to analyze real-world data sets and identify patterns in cause-effect relationships, resulting in a 15% reduction in false positives. Collaborated with cross-functional teams to refine the classification-based approach for cause-effect pair detection, ultimately improving decision-making processes by 30%.
Analysis of multifractal property of Lattice structure –
- Utilized advanced C++ programming skills to develop a high-performance computational model, resulting in a 30% reduction in analysis time for mechanical and material interactions within a Weighted planner crystal lattice. Implemented statistical mechanics techniques, including regression and machine learning, to extract valuable system dynamics insights from the computational model, leading to a 40% increase in understanding of lattice behavior. Collaborated with interdisciplinary teams to incorporate new findings into research papers and presentations, contributing to a 50% increase in the overall impact and visibility of research work.
Skill
- Scientific computing: Wolfram Mathematica, Gnuplot, OriginPro.
- Programming Language: C, C++, Python.
- Laboratory: Grazing incident X-ray diffraction, Liquid scattering.
- Others: Data analysis, Computational modeling, synthesizing information, Critical thinking, Problem solving.