Sakshi S.

• Spearheading clinical insights strategy through collaborations with global regulatory agencies, pharma companies , patient groups and academia to accelerate digital health tool development and optimize drug development.
• Led development of analytics integrating clinical and digital data to forecast disease progression and support regulatory-grade evidence generation across neurological programs.
• Operationalized regulatory frameworks and automated analytics, cutting analysis timelines from months to days—enabling faster evidence generation, improving cross-team productivity, and scaling standardized processes across therapeutic areas.
• Led the development of a decentralized trial data collection framework to reduce data missingness and strengthen data integrity, establishing the foundation for scalable, remote-ready clinical evidence generation.
• Serving as a thought leader through participation in leading consortia and workgroups (e.g., DiMe, ASCPT) and speaking engagements at major industry forums.

• Led cross-indication landscape assessments of digital health technologies across 5 diseases, identifying validation priorities and informing regulatory strategies that guide DHT adoption and evidence development.
• Built, mentored and scaled a high-performing clinical data analytics team; drove consensus-based execution with disease consortia to develop COAs, biomarkers, and fit-for-purpose tools.
• Directed reusable analytics frameworks, including AI/ML for sensor-derived data in neurology and rare disease contexts, enabling scalable, evidence-aligned insights while considering privacy and integrity.
• Led development of algorithm transparency and metadata-driven decision frameworks that transformed process insights into strategies aligned with regulatory-grade evidence generation and scientific validity standards.
• Ensured privacy compliance;optimized datasets and analytical workflows for efficiency and reproducibility.
• Drove strategic fundraising and grants development for complex initiatives; secured cross-consortia support and scalable budgets.

• Designed and implemented robust data pipelines for ingestion, cleaning, quality assessment, and analytics of continuous glucose monitoring (CGM) data, significantly improving efficiency and accuracy in data workflows.
• Developed evaluation frameworks to explore CGM’s potential in clinical trials and drug development, laying the groundwork for future regulatory-grade digital endpoint development.
• Supervised and mentored doctoral interns in digital health analytics and laying the foundation for algorithmic literacy and, activity identification and verification frameworks.
• Strengthened scientific communication and visibility by publishing peer-reviewed articles, presenting at conferences, and translating technical findings into actionable insights for cross-functional and regulatory audiences.

• Project: Data driven approach using machine learning for precise analyte concentration estimation using SERS
• Nano-Bio Group and NanoBioElectronics Lab
• A data driven approach was taken to extract key features in SERS measurements that provide strong correlation with the concentrations of analyte
• Machine learning algorithms were then used to quantify the analyte concentration using a well-studied molecule

• Taught a section for Digital Electronics lab in Spring Semester
• Taught a section for Electronic Devices lab in Fall semester

• Project: Dual-Modality Multi-Site Sensing
• Nano-Bio Group and NanoBioElectronics Lab
• A novel approach was developed to achieve higher precision in analyte quantification via surface enhanced Raman spectroscopy (SERS).
• Signal modulation was accomplished by inducing diversity on sensing substrates through varying the concentration of nanoparticles on the SERS substrate
• SERS and electrochemistry were used to develop a sensing modality in which SERS quantification is obtained bypassing the need to calculate surface area.
• Project: Detection of lead contaminants in water
• NanoBioElectronics Lab
• Worked towards the selection and optimization of electrochemical techniques for the detection of lead in sediments employing graphene-oxide-based sensing platforms.

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