Sudheer Ganisetti
Materials Science Researcher & Data Scientist
About me
My name is Sudheer. I am a computational materials science researcher and certified data scientist. I received my bachelor’s degree in science with majors in mathematics and physics from Andhra University, India. Obtaining 25th rank in the all-India level entrance exam conducted for registering in the master’s program allowed me to move to Pondicherry University. During the course work, I was so fascinated to see the capacity of computers unraveling the hidden beauty of physics. With that motivation, I have done my thesis in computational materials science. I obtained my master’s degree in physics with a specialization in condensed matter physics from Pondicherry University. However, I felt that the time frame during the thesis did not allow me to explore more of the topic. The desire made me move to Ruhr University Bochum for my second master’s program in materials science and simulations. At Bochum, I had the opportunity to work on problems that originated at different length scales (from atomistic to continuum scale) in various materials. With a solid foundation in the basics of materials science, I have registered for a doctoral degree at the University of Erlangen, and I am currently pursuing my PhD. As part of my doctoral thesis, I have been working on glass simulations. I have been a co-author for a scientific software called IMD to perform molecular dynamics simulations. Also, I have developed several codes in python, C, C++, BASH, AWK, etc., to compute various structural, dynamical, transport, and mechanical properties of glasses by analyzing the datasets obtained with quantum and classical molecular dynamics simulations. Currently, I am working on state-of-the-art machine learning algorithms to compute parameters for the classical force fields by fitting the data obtained with the ab-into calculations.
Download Resume
- Develop end-to-end Pipelines for Data Science Projects
- Provide data-driven Solutions for Business Enhancement
- Predict Material Properties using Machine Learning Algorithms
- Data Analysis & Visualization
- Problem Solving
- Method Development
- Python Programming
- Scientific Software Development
Skills
Programming Skills
Python
90%
C
90%
SQL
80%
C++
40%
Fortran
50%
R
50%
BASH Scripting
90%
AWK Scripting
90%
MPI Programming
40%
Python Modules
NumPy
90%
Matplotlib
90%
Pandas
90%
Scikit-learn
80%
SciPy
60%
Django
40%
Scientific Softwares
IMD
90%
LAMMPS
90%
GULP
60%
DL_POLY
60%
LibAtoms
50%
VASP
60%
QuantumEspresso
60%
Potfit
75%
ThermoCalc
40%
OpenPhase
70%
ABAQUS
40%
Operating Systems
Linux Desktop
90%
Linux Server
90%
Windows Desktop
90%
Graphical Editors
Inkscape
90%
GIMP
80%
CorelDraw
80%
Plotting Tools
gnuplot
90%
matplotlib
90%
xmgrace
70%
Version Controls
GIT
90%
SVN
90%
Text Processors
LaTeX
90%
Microsoft Office
90%
Linguistic Skills
English
80%
German
35%
Telugu
90%
Education
- Thesis: Atomistic Simulations of Silica Glass: Topological Anisotropy & Mechanical Properties
- Advisor: Prof. Erik BITZEK
Experience
Responsibilities include:
- Generation of DFT data to prepare machine learning force-fields for MD simulations
- Implementation of machine learning algorithms for fitting the force-fields
- Validattion of the machine learning force-fields by computing various properties for oxide glasses
- Computation of NMR parameters for oxide glasses
- Generation of MD force-fields for Pd-nano particles using classical fitting methods
Responsibilities include:
- Prepared glass samples using MD and DFT simulation methods
- Studied the role of CaF on structure, biocompatibility, and anti-bacterial properties of bio-glasses
- Computed structural, dynamical, transport, and mechanical properties for various crystalline and amorphous materials
- Written more than 100 tools in python, C, bash, and awk for computing and analyzing several glass properties (few of them can be found in github)
- Generated works for several projects
- Implementing machine learning algorithms to fit the force-fields for amorphous materials
Responsibilities include:
- Atomistic Simulations were performed to study topological anisotropy of glasses under the framework of ‘Topological Engineering of Ultra-strong Glasses’ sponsered by the German Science Foundation (DFG)
- As a co-author of software package IMD, a polarizeable potential model was implemented using c programming language for performing moelcular dynamics simulations
- Written scripts in python, C, bash and awk for computing and analysing several glass properties
- Served as an adminstrator for a high-performance workstations dedicated for visualization purposes at chair of general materials properties, University of Erlangen
- Prepared and presented scientific reports and talks in seminars and international conferences
Responsibilities include:
- Analyzed the effect of silicon on the diffusion path of carbon in steel using Ab-initio Density Functional Theory (DFT) simulations
- Optimized the shape of a bridge on the banks of a river to withstand high mechanical loads by solving the moving boundary problem using Phase Field simulations, subroutines were writen in c programming language
Responsibilities include:
- Taught physics to the 1st, 2nd and 3rd year students of Bachelore of Science
- Supervised the laboratory experiments and prepared study materials for the students
Projects
Machine Learning
Identification of heart disease - Supervised Learning
Multiscale Modelling
Multiscale Modelling of the influence of Oxygen …
Phase Field
Building of Bridges using Phase Field Method
DFT
Diffusion of Carbon in High Silicon Steel
Monte Carlo Simulations
Simulated Annealing Of A Finite System
