My work focuses on integrating ML with structural biology, biophysics, and experimental feedback to design peptides and proteins with therapeutic potential. I have experience across the full in silico workflow: structure modelling (AlphaFold, Boltz, Rosetta), docking and molecular dynamics, property prediction (binding affinity, solubility, permeability, serum stability, PK), and closed-loop ML–wet lab cycles. This combination has enabled the discovery of peptides now progressing through clinical development.

As a founder and Chief R&D Officer, I’ve built and led interdisciplinary teams across machine learning, computational biology, software engineering, and wet lab science. I focus on creating clear strategy, enabling deep collaboration, and delivering meaningful scientific and business outcomes. I’ve hired and mentored ML teams, secured partnerships with global pharma companies, and driven long-term R&D roadmaps from concept to clinical impact.

I build advanced machine learning systems that power real-world drug discovery. My experience spans generative models (diffusion, autoregressive, and geometric deep learning), multi-objective optimization, active learning, and uncertainty-aware prediction. So far, I have led the development of 60+ custom ML models across protein design, property prediction, and experimental decision-making. I specialize in translating cutting-edge research into robust, production-ready tools used by interdisciplinary scientific teams.

I have extensive experience architecting and scaling scientific computing systems on the cloud and on-prem. This includes running petascale simulations, deploying multi-GPU training pipelines, orchestrating large-scale Kubernetes workloads, and optimizing inference with ONNX/TensorRT. I’ve designed and implemented infrastructure that processes millions of protein sequences, integrates tightly with ML pipelines, and supports data-driven discovery at industrial scale.

I thrive at the interface of research and engineering, driving high-risk, high-impact innovation. I develop next-generation tools that accelerate molecular discovery, including closed-loop ML workflows relying on cloud labs for automated design–make–test-analyze cycles. My team builds peptide-specific ML models that expand the accessible design space to 3500+ non-natural amino acids and enable high-throughput optimization. I have also explored VR-based 3D protein structure analysis for more intuitive interaction with complex molecular geometries. My approach blends rapid prototyping with rigorous scientific validation to push the boundaries of computational drug discovery.