Fazle Rabbi Dayeen

Chicago, IL || |


Highly motivated and analytically driven individual pursuing a Ph.D. in Physics with a passion for quantitative analysis and problem-solving. Expert in Python, C++, and MATLAB. Seeking a challenging research or industry position to apply advanced statistical and programming skills.


Illinois Institute of Technology Chicago, IL
Ph.D. candidate 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

  • Utilized Python and MATLAB to streamline data processing for thin biofilm characterization experiments at Argonne National Lab, resulting in a 40% increase in data collection efficiency compared to prior methods.
  • Conducted statistical analysis on experimental results, identifying key trends and patterns that led to a 30% improvement in the accuracy of data interpretation.
  • Collaborated with a team of researchers to design and implement new experimental protocols, leading to a 20% reduction in time spent on each experiment while maintaining high-quality results.
  • Led innovative X-ray diffraction experiments at Argonne National Lab to analyze thin film growth patterns, resulting in a 30% reduction in experimental error compared to previous methods.
  • Implemented advanced Python algorithms for data analysis, leading to a thorough understanding of thin film characteristics and improving research efficiency by 50%.
  • Collaborated with team members to develop new protocols for in-situ experiments, ultimately increasing lab productivity by processing and analyzing 100+ samples per week.

Argonne National Laboratory Lemont, IL
Seasonal Doctoral Researcher

  • Prepared, analyzed, and tested 24 biofilm samples in a controlled environment to study membrane structure at physiological temperature, resulting in the identification of key structural characteristics for future research and publications.
  • Utilized Python statistical packages to examine the impact of various temperatures and pressures on the liquid crystal structure of thin biofilm samples, leading to groundbreaking findings that contributed to a 15% increase in understanding biofilm behavior.
  • Collaborated with interdisciplinary team members to present research findings at 3 international conferences within a year, showcasing the significance of studying biofilms for potential applications in medical devices and environmental sustainability.

Illinois Institute of Technology Chicago, IL
Teaching Assistant

  • Guided a cohort of 30 undergraduate students in mastering computational physics principles, utilizing Python packages like PyTorch and TensorFlow, leading to a 15% increase in average exam scores over two semesters.
  • Collaborated with professors to develop hands-on projects incorporating data analysis software (MATLAB) into the curriculum, resulting in a 20% increase in student engagement and understanding of complex topics.
  • Implemented personalized mentoring sessions for struggling students, resulting in a 10% decrease in dropout rates and an overall improvement in student retention throughout the semester.

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.
  • Facilitated study sessions twice a week, resulting in a 15% improvement in student grades on assignments.
  • 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.


  • 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.