Data Science Analytics
Resume:
Gemechis Degaga, Ph.D.
Contact details
********@*****.***
https://github.com/GsGithub17
*** ********* ***** **, *********,
OH 43062
Profile Summary
Dynamic and innovative
Computational Scientist with
extensive expertise and a
profound background in Data
Science, Data Analytics, and
AI/ML technologies.
Demonstrated success in working
with Big Data and Generative AI
by employing high-performance
computing techniques across
cloud platforms such as AWS and
GCP to enhance research
scalability and efficiency. Skilled
in the application of advanced
mathematical models alongside
physics and chemical theories to
unravel complex material,
chemical, and biological systems.
Data Science and AI/ML Skills
Python • Shell Scripting • R • C++ •
FORTRAN • GitLab • BitBucket •
Pandas • NumPy • VS Code •
Jupyter Notebook • SQL • NoSQL •
VAEX • BigQuery • PySpark •
Docker • Kubernetes • Seaborn •
Plotly • Matplotlib • JAX •
TensorFlow • Keras • PyTorch •
GNN • GAN • RNN • NLP • LLMs •
LangChain • RAG • Streamlit •
QLoRA • RDKit • BIOCONDA
Chemical, Materials, and
Biosystems Modeling Skills
Schrödinger, OpenEye (Orion),
AutoDock-VINA, Molecular
Dynamics, GROMACS, AMBER,
CHARMM, LAMMPS, NAMD, VMD,
XCrySDen, JMOL, MOLDRAW,
PyMol, PMV, QUANTUM ESPRESSO,
VASP, CRYSTAL, GAUSSIAN
Operating Systems
LINUX, Macintosh, and Windows
Experience
Principal Data and AI/ML Scientist at Corellia AI 2023.02–Present.
Spearheaded the development and optimization of multiple AI/ML and computational workflows, enhancing scalability and computational efficiency for high-throughput tasks that screen and analyze billions of data points powered by parallel distributed computing framework on Google Cloud Platform (GCP).
Senior Data Scientist at Champions Oncology Inc. 2021.02–2023.01
Developed data processing and engineering workflow for an internal data- base containing over 20 Billion compounds for virtual screening campaign for structure based drug discovery programs.
Data Science and ML Research Scientist as a Postdoctoral Research Associate at Oak Ridge National Laboratory. 2019.06–2021.01
Preformed chemical and biological data mining, parsing, and engineering with high-level wrapper python coding and developed vaccine designing machine learning method based on Deep Generative Adversarial Networks to design novel peptides with better affinity to key receptors in adaptive immunity.
Education
Ph.D. Chemistry Michigan Technological University, USA. 2013–2018
Thesis title: Computational Quantum Chemical Investigation of Physico- chemical, Spectroscopic Properties, and Diffiusion Mechanisms of Hydro- carbon Molecules in Metal-Organic Frameworks (MOFs) for Energy Efficient Separation Technology.
M.Sc. Physics University of Trieste, ITALY. 2010–2013
Thesis title: Hubbard Enhanced DFT Computation of Electronic Structure and Structural Stability of Copper Doped Poly- morphs of Titania. M.Sc. Materials Science Addis Ababa University, Ethiopia. 2008–2010
Thesis title: Model Computation of Water Clustering effect in the Negative Ion Proton Transfer Reactions, NI-PTRs.
B.Sc. Physics Haramaya University, Ethiopia. 2002–2006
Project Title: The investigation of Spin and Charge Density Waves of Cr single crystal at near absolute zero temperature with the use of FTIR Select Publications
1. Vardhan Satalkar, G.D. Degaga, Wei Li, Yui Tik Pang, Andrew C. McShan, James C. Gumbart, Julie C. Mitchell, Matthew P. Torres, Generative β-Hairpin Design Using a Residue-Based Physicochem- ical Property Landscape, Biophysical Journal, 2024. 2. G.D. Degaga, M. Trought; E. J. Crumlin, S. Nemsak, M. Seel, K.A. Perrine, R. Pandey, Investigation of N2 adsorption on Fe3O4(001) using ambient pressure X-ray photoelectron spectroscopy and density functional theory. J. Chem. Phys, 152, 054717, 2020. Awards and Supports
• First Place Poster Presentation, MTU, GSG Graduate Research Col- loquium (GRC), Spring 2018.
• Outstanding Graduate Student Research Award, Spring 2017, Robert and Kathleen Lane (Scholarship), Chemistry Department, Michigan Technologically University.
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