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Prof. Aldo Humberto Romero
Department of Physics and Astronomy
Eberly College of Arts and Sciences
WVU Home

Welcome to the web page of Prof. Aldo Romero's group. 

Current Position: Eberly Family Distinguished Professor, Physics and Astronomy Department, West Virginia University
Contact: Office: 304-2936317 | Cell: 304-4357440 | Email: alromero at mail.wvu.edu

Dr. Aldo Humberto Romero leads a dynamic research group at West Virginia University, specializing in computational materials science. With a strong emphasis on high-performance computing and artificial intelligence, the group is at the forefront of exploring the properties and potentials of various materials, ranging from nanostructures and disordered materials to crystalline materials in one, two, and three dimensions. Utilizing state-of-the-art ab initio and many body theory methods, Romero's team is dedicated to predicting, analyzing, and profoundly understanding materials at the atomic and molecular levels. This interdisciplinary group, composed of highly motivated graduate students and postdoctoral researchers, collaborates extensively with leading scientists nationally and internationally. Their research has far-reaching applications in energy storage, electronics, and spintronics, making significant contributions to academic and industrial research.

Expertise

  • High-Performance Computing: Proficiency in C, C++, Python, and Fortran, with a focus on computational efficiency in parallel environments (CPU and GPU).
  • Data Science: Skilled in Python libraries like Pandas, Matplotlib, SciPy, and NumPy, as well as statistical computing in R and Julia. He is also expert in many of the different existing packages used for neural network training such as PyTorch, Tensorflow, Keras, 
  • Software Development: Creator of several GitHub-hosted packages, including ABINIT (density functional theory and many body theory), PyChemia (high-throughput), PyProcar (band and fermi energy electronic structure analysis), MechElastic (elastic properties), TDEP (anharmonic thermal properties), and DMFTwDFT (electronic structure from dynamical mean field theory).
  • Multidisciplinary research: collaborations with other departments, mostly on artificial intelligence applications, such as Forensics Science, Biology, Data Science, and Genetics.