About Me

I am a Machine Learning Scientist and Computational Physicist with over a decade of experience decoding complex dynamical systems. My work operates at the cutting edge of Generative AI for Science, moving beyond static structure prediction to model the breathing, moving reality of biological machinery.

Unusually for a researcher, I have maintained a parallel career as a fractional Head of AI through Sengar Consulting BV. This dual track, publishing at NeurIPS while simultaneously shipping production grade models for Fintech and Biotech clients, has sharpened my ability to translate abstract mathematics into high-ROI business applications.

My scientific journey has spanned Europe’s leading institutions, including Imperial College London, EPFL, TU Eindhoven, TU Delft and collaborations with Univesity of Cambridge/MIT/AMOLF/University of Surrey. I have mentored over 10 researchers and delivered cross-border consulting projects for clients based in US, UK, EU, Middle-East and East Asia.

Find Me Online

You can find more about my work or connect with me on the following platforms:

Awards & Fellowships

  • Marie Skłodowska‑Curie Postdoctoral Fellowship – Certificate of Excellence (with Prof. Max Welling): proposed a project on generative modeling of RNA dynamics using graph neural networks and latent diffusion. The proposal received a certificate of excellence.
  • SNSF Postdoc.Mobility Fellowship (Score: 8.31/9): proposed a generative model to detect the effect of small‑molecule ligands on G‑protein coupled receptors (GPCRs) using GNNs and latent diffusion in a low‑dimensional space.
  • IIT‑JEE 2011: achieved a rank within the 99.9th percentile, earning admission to IIT Delhi.

Grants & Funding Experience

I have extensive experience in writing grant proposals and securing funding. I have written or co‑written successful grant applications for EU agencies, NIH, SNSF, and UKRI, spanning topics from machine learning to chemical engineering.

Technical Arsenal

  • Generative AI & DL: Diffusion Models (Latent/Score-based), Flow Matching, Geometric Deep Learning (PyG), Transformers, Autoencoders.
  • Scientific Computing: Molecular Dynamics (All-atom/Coarse-grained), Monte Carlo Methods (kMC, VMMC), Statistical Mechanics, Fluid Dynamics.
  • ML Ops & Infrastructure: PyTorch (DDP/FSDP), Docker, Kubernetes, Run:AI, Slurm, High-Performance Computing (HPC) Clusters.
  • Languages: Python (Expert), C/C++ (Simulation Kernels), Mathematica.

Feel free to reach out via email at adityasengariitd@gmail.com.