I am a Chemical Engineering PhD candidate at LSU in Romagnoli Group utilizing the benefits of physics & machine learning to enhance the design of electrochemical systems.

     "Live as if you were to die tomorrow. Learn as if you were to live forever" 
                         - Mahatma Gandhi

• 🔭 Seeking to build data-driven models for real-world applications
• 🌱 Developing Deep Learning and physics-based models for materials (small molecules like solvent, ions; big molecules like surfactant, polymers) and processes (like electrodialysis, electrodeionization, capacitive deionization).

• Chemical Modeling with Physics and Data-driven method
• Chemometrics Data Engineering & Science
• Computational Molecular design
• Material & Process Optimization
• Molecular Simulation of Materials
• Machine Learned Force Field (MLFF) development

• Languages: Python, MATLAB
• Machine learning: Scikit-Learn, TensorFlow, Keras, PyTorch, MLflow, Docker, Streamlit, PySpark
• Chemical Eng. & Chemistry: Aspen Plus, GROMACS, LAMMPS, Gaussian, Rdkit, Deep Graph Library (dgl).
• Platforms: Linux, Git
• Soft Skills: Research, Leadership, Event Management
• Proficiency in the use of Microsoft Office Power Point, Word, Excel, and JMP
• Synthesis & characterization: Nanocrystals synthesis, catalyst synthesis, X-ray diffraction (XRD), Diffuse Reflectance IR Fourier Transform Spectroscopy (DRIFTS), UV etching and Design of Experiment.

• Bridging Physics and Data-Driven: Developed numerical model and data-driven model to perform optimization studies for two common electrochemical systems (electrodialysis and electrodeionization). Under review
• Physicis Informed Machine learning: Developing PINN and Neural ODE to resolve limitations of physics ODEs in capturing selective ion separation in electrodialysis. In preparation
• Transfer Learning for missing data: assess the possibility of resolving missing data with transfer learning. Code
• Feature Embedding: Combined information from experiment, molecular structure and molecular simulation with machine learning to enhance predictive modeling of membrane properties. Code
• Generative Molecular Design: Combined generative artificial intelligence (AI), predictive modeling, reinforcement learning and molecular dynamics to create molecules with desired properties. Code
• Facial Recognition: Collaborated on the development of software utilizing Mediapipe + Blender framework to track facial structure and emotion classification via a trained CNN-based classifier. Code
• Failure detection in pumps: Participated in BPX hackathon and developed a LSTM-based data-driven model to estimate ESP run life. Ranked 3rd out of 30 submissions and received the Implementation award for code reproducibility. Code
• Active Learning modeling: Developed codes to train active learning models based on different query strategies. Presently testing the methods on problems such as protein adsorption, structure-property modelling, & electrochemical separation performance. Code
• Transformer: Trained transformer to encode sequence and classify with PyTorch, & HuggingFace. Code 1 & Code 2
• KNN guided molecular design: Developing a molecular design optimization framework integrating k-Nearest Neighbour and Genetic Algorithms. Code
• Piano Music Generation: Trained two deep learning LSTM models as 1) critic of good or bad music and 2) composer to generate new music. Tools: Python, PyTorch, Scikit-Learn.
• Facial Recognition: Collaborated on the development of software utilizing Mediapipe + Blender framework to track facial structure and emotion classification via a trained CNN-based classifier. Code
• Tox24 challenge: Predict the in vitro activity of compounds from chemical structure. Code

• Website: teslim404.com
• Email: [email protected]
• LinkedIn: Teslim Olayiwola
• Twitter: teslim404
• Google Scholar: Teslim Olayiwola