Shah Dad Hasil | Computational Methods | Research Excellence Award

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Mr. Shah Dad Hasil | Computational Methods | Research Excellence Award

University of Electronic Science and Technology of China | China

Mr. Shah Dad Hasil is an emerging researcher in computer science currently pursuing graduate studies at the University of Electronic Science and Technology of China. His academic work lies at the intersection of artificial intelligence, bioinformatics, and computational drug discovery, where he focuses on designing intelligent computational tools to accelerate biomedical research. His work integrates machine learning algorithms, molecular docking techniques, and molecular dynamics simulations to evaluate molecular interactions and predict the biological activity of potential therapeutic compounds. A major focus of his current research is the development of AI-based predictive models targeting Trypanosoma cruzi, the parasite responsible for Chagas disease, aiming to support the discovery of new antiviral and antiparasitic drug candidates. Hasil has also demonstrated interdisciplinary research interests, contributing to studies in renewable energy technologies and computational cryptography. His research outputs have appeared in several peer-reviewed publications, including articles published in the Journal of Marine Science and Engineering. In addition to his research contributions, he has strong programming expertise in Python and C++ and practical experience working with deep learning frameworks such as TensorFlow and PyTorch. His long-term academic goal is to pursue a Ph.D. and advance AI-driven methodologies that address critical challenges in drug discovery and biomedical science.

 

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Featured Publications

Johannes Krotz | Computational Methods | Best Researcher Award

Published on by Global Energy Awards

Dr. Johannes Krotz | Computational Methods | Best Researcher Award

Postdoctoral Fellow at Notre Dame, United States

👨‍🎓 Profiles

🌟Summary

👨‍🎓 PhD candidate in Mathematics with a minor in Computer Science, specializing in probabilistic and data-driven methods for numerical PDEs and hybrid Monte Carlo methods for complex systems simulations. Experienced in statistical modeling, computational physics, and advanced simulations with a strong background in teaching and academic leadership. Currently working as a Postdoctoral Researcher at the University of Notre Dame.

🎓 Education

🎓 PhD in Mathematics (Minor in CS)
University of Tennessee Knoxville, 2021–2024

  • Dissertation on Probabilistic & Data-Driven Methods in Numerical PDEs
  • GPA: 4.0

📊 M.Sc. in Statistics
University of Tennessee, 2022–2024

  • GPA: 3.9

📚 M.Sc. in Mathematics
Oregon State University, 2019–2021

  • GPA: 4.0

⚛️ M.Sc. in Physics
University of Konstanz, 2015–2019

  • GPA: 4.0 (Honors)

🔢 B.Sc. in Mathematics & Physics
University of Konstanz, 2012–2018

  • GPA: 3.5 (Mathematics), 3.3 (Physics)

💼 Professional Experience

🔬 Postdoctoral Researcher
University of Notre Dame, 2024–Present

  • Research on hybrid Monte Carlo & deterministic kinetic transport algorithms for exascale simulations in neutron transport.

🧑‍💻 Graduate Research Assistant (GRA)
University of Tennessee/ORNL, 2023–2024

  • Advancing dynamic likelihood filters for stochastic advection-diffusion equations in collaboration with ORNL and UTK.

💼 Research Intern
Oak Ridge National Lab (ORNL), 2021–2022

  • Developed hybrid algorithms for simulating complex particle systems in 2D & 3D.

🌍 Research Intern
Los Alamos National Lab (LANL), 2020

  • Focus on high-fidelity discrete fracture networks and Poisson-disk sampling algorithms for triangulations.

🔬 Research Interests

  • 🧠 Computational Mathematics: Hybrid Monte Carlo methods, kinetic transport equations, and numerical simulations for complex physical systems.
  • 🔍 Stochastic Processes: Advanced data-driven filtering techniques and applications in fluid dynamics, advection-diffusion, and PDEs.
  • 💻 Statistical Modeling: Development of methods for high-dimensional data and stochastic modeling.
  • 🌐 Interdisciplinary Work: Collaborating across fields of mathematics, physics, and engineering to tackle real-world computational challenges.

🏆 Awards

  • 1st & 3rd place at the UTK SIAM Research Showcase (2023, 2024)
  • Randall E. Cline Award (2022) for research excellence

🖥 Technical Skills

  • Python, C++, R, Matlab, LATEX, and more
  • Basic Fortran, AWK

🔗 Professional Memberships

  • SIAM, AWM, AAAS, UCW

 Publications

A Hybrid Monte Carlo, Discontinuous Galerkin Method for Linear Kinetic Transport Equations

  • Authors: Johannes Krotz, Cory D. Hauck, Ryan G. McClarren
  • Journal: Journal of Computational Physics, Vol. 514
  • Year: 2024
Variable Resolution Poisson-Disk Sampling for Meshing Discrete Fracture Networks
  • Authors: Johannes Krotz, Matthew R. Sweeney, Jeffrey D. Hyman, Juan M. Restrepo, Carl W. Gable
  • Journal: Journal of Computational and Applied Mathematics, Vol. 407
  • Year: 2022
Dynamic Likelihood Filters for Advection Diffusion Equations
  • Authors: Johannes Krotz, Jorge M. Ramires, Juan M. Restrepo
  • Journal: The Monthly Weather Review
  • Year: Under review
Minimizing Effects of the Kalman Gain on Posterior Covariance Eigenvalues, the Characteristic Polynomial and Symmetric Polynomials of Eigenvalues
  • Authors: Johannes Krotz
  • Journal: Arxiv (preprint)
  • Year: 2024