Mohammad Madani
Manchester,USA
Summary
AI professional with proven track record in developing cutting-edge artificial intelligence systems. Adept at designing and implementing machine learning models that solve complex problems. Focused on team collaboration and delivering reliable solutions that adapt to changing requirements. Expertise in neural networks and data analysis.
Overview
6
6
years of professional experience
Work History
AI Engineer Consultant (Life Science)
EPAM Systems, Inc (Gilead Science)
11.2024 - Current
- Developed in house pre trained protein language models for accurate prediction of antibody-antigen interaction sites (59% accuracy increase), accelerating drug discovery
- Fine-tuned dual language model for binding affinity and paratope predictions (improved 11% MAE compared to SOTA) using PyTorch-based deep learning models, enhancing functional assays
- Developed a cloud-based antibody language model (package) as a replacement for third-party commercial platform, saving $400,000 a year in licensing fees
- Increased productivity (24% improvement) in antibody target selection through ML-driven analyses, reducing experimental trials
- Created clear documentation and tutorials for interdisciplinary teams, effectively communicating complex AI methodologies (Python, PyTorch, AWS)
Research Assistant
University of Connecticut
08.2019 - 12.2024
- Conducted advanced research in deep learning, molecular dynamics, and geometric deep learning, resulting in 12 first-author publications in top-tier ML journals
- Implemented ML-based predictive tools for accurate characterization of proteins and small molecules, significantly advancing drug discovery
- Developed equivariant generative diffusion models for accurate 3D prediction of protein-ligand complexes and antibody structures
- Created a physics-informed, pocket-aware diffusion model for targeted small-molecule generation, enhancing binding affinity predictions
- Engineered and deployed a pre-trained large language model for generating novel antibody sequences, accelerating therapeutic antibody design
Machine Learning Intern
Merck
06.2024 - 09.2024
- Developed an in-silico protein design platform deployed on cloud (AWS, RDKit, Python), eliminating third-party vendor reliance and saving $244K annually
- Established a company-wide database (SQL, RDKit) to centralize protein design data, improving data retrieval efficiency by 23%
- Fine-tuned generative AI models (RFdiffusion, ProteinMPNN) for antibody design, achieving 16% improvement in novel antibody identification accuracy
- Enhanced protein stability prediction accuracy (11% improvement) through fine-tuning ESM-based models for mutation analysis
- Performed computational mutagenesis on Human LOX, accurately identifying pathogenic hotspot regions, significantly aiding variant risk assessment
Machine Learning Intern
Vertex Pharmaceutical Inc
05.2023 - 10.2023
- Developed a deep neural network-based model for predicting solid-state NMR chemical shifts of small drug molecules, achieving 97.4% accuracy and reducing production costs by $1M/year
- Engineered an automated chemical-shift prediction software deployed on Vertex’s HPC, streamlining research workflows and significantly enhancing computational efficiency
- Built a generative diffusion model that attained an 88% experimental matching rate, accelerating validation cycles for small-molecule drug candidates
- Designed precise machine learning algorithms (GNN, Transformers) capable of accurately predicting chemical shifts across 13 atom types, significantly improving predictive reliability
- Integrated expertise in deep learning, protein folding, NLP, and large language models to advance computational drug discovery and structural biology initiatives
Machine Learning for Material Research Intern
Samsung Semiconductor Inc
05.2022 - 05.2023
- Developed and deployed a cloud-based TransformerGraph model internally at Samsung to predict inorganic material properties, saving $300K/year in trial-and-error experimentation
- Filed a patent for the discovery of two novel inorganic battery materials identified through advanced deep-learning methods (TransformerGraph)
- Created a generative diffusion model to design 3D structures of stable semiconductor materials, significantly accelerating the discovery of candidate materials
- Constructed an advanced predictive model that outperformed existing methodologies in accurately forecasting 9 key properties of inorganic materials
- Published research findings in Nature Computational Materials, demonstrating excellence and innovation in computational materials science
Education
Ph.D. - Mechanical Engineering
University of Connecticut
Storrs, CT12.2024
Master of Science - Computer Science and Engineering
University of Connecticut
Storrs, CT12.2023
Master of Science - Mechanical Engineering
Sharif University of Technology
01.2019
Bachelor of Science - Mechanical Engineering
University of Tehran
09.2016
Skills
- Protein design
- Drug discovery
- Computational Biology
- Machine Learning Algorithms
- Geometric Deep Learning
- Model Development & Evaluation
- Large language model
- Diffusion models
- Python
- C
- PyTorch
- Tensorflow
- Keras
- AWS
- Azure
- Molecular Dynamics
- Density Functional
- Deep learning models for protein
- ESM
- RFdiffusion
- AlphaFold
- ProteinMPNN
- Rosetta
Google Scholar
https://scholar.google.com/citations?user=1omxYXcAAAAJ&hl=en
Timeline
AI Engineer Consultant (Life Science)
EPAM Systems, Inc (Gilead Science)
11.2024 - Current
Machine Learning Intern
Merck
06.2024 - 09.2024
Machine Learning Intern
Vertex Pharmaceutical Inc
05.2023 - 10.2023
Machine Learning for Material Research Intern
Samsung Semiconductor Inc
05.2022 - 05.2023
Research Assistant
University of Connecticut
08.2019 - 12.2024
Master of Science - Computer Science and Engineering
University of Connecticut
Master of Science - Mechanical Engineering
Sharif University of Technology
Bachelor of Science - Mechanical Engineering
University of Tehran
Ph.D. - Mechanical Engineering
University of Connecticut
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