Expertise in AI/ML emerges from early career numerical methods implementation (ML) for computer simulations of quantum mechanical spin states, quantum statistics approaches in NMR and Vibrational spectroscopy, molecular modeling, electronic structure modeling, molecular mechanics and dynamics.
Clinical Data Platform and Analytics:
Biostatistics & Clinical Informatics: Statistical programming (R, SAS, Python), trial design, data quality assurance (DQA), validation.
Data Engineering (Clinical Focus): ETL/ELT pipeline development, data warehousing (e.g., Snowflake, cloud-native DBs), FHIR/CDISC data standards expertise.
Advanced Analytics & AI/ML: Predictive modeling for patient response, biomarker identification, algorithm development for novel data interpretation.
Technology Architecture & Cloud Enablement
Enterprise Architecture (R&D Domain): Designing scalable, resilient, and interoperable architectures (API-first approach) that connect preclinical, clinical, and manufacturing data. Cloud Engineering (GxP Focus): Expertise in AWS/Azure/GCP services, specifically regarding deployment, security, and compliance in a regulated life sciences environment. DevOps/MLOps (Continuous Delivery): Automating infrastructure and application deployment (IaC), and standardizing the deployment of validated AI analytical models into production.
Collaboration and Knowledge Management:
Scientific Data Curation: Expertise in standardizing terminology, metadata tagging, and ontology management to make experimental and clinical data universally searchable.
Knowledge Graphs & Semantic Search: Developing and maintaining a knowledge graph connecting molecular targets, trial outcomes, patient cohorts, and patent literature to inform IP strategy.
Secure Collaboration Platforms: Implementing tools (e.g., SharePoint, Teams, ELNs) with strong governance to ensure that all invention-related discussions and documents are captured and secured for later patent filing.
AI/ML-IP Process
AI/ML-IP Knowledge Graph: This is the foundation for IP optimization. It ensures that the knowledge gained from ML models (the why behind a molecular design) is semantically linked to the raw experimental data (spectroscopy, preclinical assays) and to existing patent landscapes. This link demonstrates non-obviousness and enablement—key requirements for successful patent filing.
Lab-to-Clinic Provenance
In the highly regulated and IP-sensitive biotech space, the digital chain of custody for every data point is crucial. Using technologies like Blockchain/DLT provides an undeniable, tamper-proof record of when an experiment was conducted, when a design was finalized, and when a clinical outcome was observed. This provides date of invention for IP defense..
Translational Platform
The R&D pipeline is tightly coupled to the IP portfolio. Questions such as: “If we succeed in Phase II, which of our 12 pending patent claims are immediately strengthened?” or “Is our lead candidate’s IP defensible against Competitor X’s new filing?” This capability is designed to use data to inform these multi-billion-dollar strategic decisions.
Knowledge acquisition from ML based drug design, spectroscopy, preclinical, clinical sciences through corporate leadership