Hello DeepLearning.AI community!
I’m excited to be part of this vibrant group of AI enthusiasts and professionals. Here’s a brief introduction:
About Me
Industrial Thermal Power Engineer with extensive experience in power plant design, operation, and optimization.
MBA graduate, bringing a strategic and business-oriented perspective to engineering challenges.
PhD in Thermal Engineering, specializing in advanced heat transfer modeling and simulation techniques.
Personal Interests
Arts: I have a strong appreciation for the arts, which fuels my creative approach to problem-solving.
Sports: I enjoy a diverse range of sports, embracing the variability and excitement they offer.
Goals in the DeepLearning.AI Community
My primary objective is to collaborate on projects that leverage machine learning for physical simulations, aiming to democratize engineering simulations. By integrating ML with thermal and fluid dynamics, we can make high-fidelity simulations more accessible and cost-effective.
Next Steps in My Journey
To align with my goals and transition into a physics-aware AI role focused on sustainability engineering, I plan to:
- Tailor My Résumé: Highlight thermal and CFD projects, and showcase a GitHub portfolio featuring deep learning experiments, such as Physics-Informed Neural Networks (PINNs) and surrogate model demonstrations.
- Apply to Relevant Roles: Target positions at companies like PhysicsX, Rescale, Monumo, and COMSOL, which are actively seeking talent in this domain.
- Network Strategically: Connect with technical recruiters and professionals at Seeq, Capalo AI, and Vsim on LinkedIn to express my interest and share my latest projects.
- Leverage Upcoming Projects: Utilize my forthcoming Coursera capstone project, potentially centered on a physics-informed neural network, as a discussion point in interviews and networking conversations.
Educational Pathways I’m Exploring
To further strengthen my expertise in computational engineering and machine learning, I’m considering the following programs:
Purdue University’s Online Computational Engineering Concentration: This 100% online program offers a numerically rigorous curriculum that explicitly integrates machine learning into engineering contexts, closely mirroring a Computational Science and Engineering (CSE) with ML degree.
Illinois Institute of Technology’s Master of Data Science (MDS): With performance-based admission and a pay-as-you-go model, this program provides strong foundations in machine learning. To tailor it towards physical simulations, I plan to augment it with additional projects focusing on numerical methods.
MIT xPRO’s Machine Learning, Modeling, and Simulation Program: This program offers a hands-on approach to understanding computational tools used in engineering problem-solving, connecting science and engineering skills to machine learning and data science principles.
Let’s Connect!
I’m eager to collaborate and learn from fellow community members. If you’re working on ML applications in physical simulations or have insights to share, please reach out!
Looking forward to engaging discussions and innovative collaborations!