Rishi Ranade

- Collaborate with cross-functional engineering and research teams to develop physics-AI capabilities, scalable model architectures, and efficient end-to-end pipelines for accelerating surrogate modeling in CAE and EDA domains. - Design and implement reference CAE workflows leveraging NVIDIA technologies—including PhysicsNeMo, Omniverse, Warp, and NIMs—to enable high-fidelity, GPU-accelerated multiphysics simulations. - Partner with ISVs and LHAs to embed physics-AI capabilities and GPU-accelerated frameworks into commercial software products and design and development processes. - Contribute to the advancement of the field through peer-reviewed publications, patents, and technical thought leadership; present innovative work at top-tier conferences and industry platforms.

- Research and develop techniques to integrate AI into CAE and scientific simulation workflows by developing AI surrogate models and pipelines. - Accelerate the adoption of Physics based machine learning in NVIDIA Enterprise technologies. - Publish and present innovative AI enhanced engineering and scientific simulation techniques in relevant ML conferences and journals. - Collaborate with technical marketing team, AI teams, partners, and business leaders defining and creating a wide range of deliverables and assets to highlight how NVIDIA Enterprise products and AI/Deep Learning improve and accelerate the way people ideate, create and design.

Managing R&D team and projects focussed on productization of AI/ML research applied to engineering simulation across various business units. Project portfolio spans accelerating PDE solvers, improving workflows with LLMs, geometry representation and meshing improvement, ROM etc. Responsibilities include: Hands on contribution to research and software development, strategizing team vision, planning projects and research directions, collaborating with internal cross functional teams, leading technical engagements with broader research and academic communities, publishing scholarly work and patenting novel technologies, making hiring decisions, mentoring R&D teams

Worked as part of a central team in the CTO office to develop and research novel methodologies impacting engineering simulation across different BUs

- Development of machine learning-based models for turbulent combustion closure using experimental and cheap numerical data. - Chemical mechanism reduction and chemistry acceleration using machine learning techniques.

- TA for MAE 306 - Heat Transfer and Fluid Mechanics Laboratory. Managed 12 sections with each having a class size of about 20 students. - Responsibilities include teaching material related to the experiment, setting up experiments, assisting students and grading weekly experiment reports.

- Explore ANN techniques for flamelet based turbulent combustion closure. - Validation of dynamic polyhedral mesh adaption of a five stage ignition burner.

Worked in the Innovation department on a project related to the mathematical modeling of circulating fluidized bed combustor (CFBC).

- Provide engineering solutions to problems related to Air, lube and fuel filtration. - Design filter assemblies and components using PRO E Modeling. - Develop analytical tools and calculators using MS Excel and MATLAB - Prepare documentation including DFMEA, DFM, P diagram, Interface Matrix etc. related to design projects

- Conducted a Fractional Factorial Design of Experiments of the Electrostatic Spray Painting Process to improve the productivity and reduces costs. - Conducted Taguchi Design of Experiments on the CO2 Rotary Welding Process to improve the quality of welding and understand important process parameters. - Modeled the convection curing oven using ANSYS Fluent 12 to study pressure, velocity and temperature profile of air and suggested improvements. - Trained in Engine and Filtration technologies, Theory of Constraints, Statistical Process Control, Design of Experiments, FMEA etc.

Worked on the analysis and optimization of stabilizing disc used in boiler burners to improve flame stability using CFD.

Development of Machine Learning based methods for modeling turbulent flames and complex fuel chemistry