The Problem

Assay Performance and Velocity Limits in His-Tagged Screening

Current antibody discovery workflows that use HIS-tagged proteins suffer from baseline drift and low sensitivity, obscuring weak-affinity binders and compromising data reproducibility. High-throughput screening is further bottlenecked by slow assay cycle times, forcing teams to choose between data quality and development velocity when validating leads.

My Contribution

Driving the Biosensor Lifecycle from Bench to Market

Owned the end-to-end development, scientific validation, and cross-functional launch of the His XT biosensor, bridging advanced surface chemistry with commercial product strategy.

• Translating Assay Science into Product Requirements: Converted persistent user pain points in His-tagged antibody screening — such as baseline drift and low sensitivity for weak-affinity interactions — into clear technical specifications.
• Leading Scientific Validation: Directed the core development of the HIS XT biosensor, establishing rigorous protocols to test binding kinetics and baseline stability.
• Cross-Functional Execution: Collaborated across R&D, QA, and commercial teams to navigate technical trade-offs and drive the product from bench to commercial readiness.
• Market Positioning: Partnered with go-to-market teams to position the biosensor for high-throughput binder discovery, ensuring technical capabilities aligned with customer workflow needs.

The Outcome

Clear product-market fit from day one

The HIS XT biosensor launched commercially and was adopted in antibody discovery workflows across biotech labs worldwide. Achieved measurable performance gains in signal resolution, drift reduction, and throughput. Strong customer feedback validated real-world usability and a first quarter that exceeded sales forecast demonstrated clear product-market fit.

Skills Demonstrated

The Problem

Balancing Target Confidence with Pipeline Velocity

In patient-derived cancer immunotherapy, promising antibody candidates often stall at target validation due to limited initial data and high biological uncertainty. The main challenge was to set strict data-confidence thresholds to quickly identify promising targets and discard weaker ones — improving pipeline speed and avoiding costly failures.

My Contribution

Driving Validation Frameworks & Pipeline Execution

I owned the end-to-end target validation lifecycle, treating candidate advancement as a strategic decision-making problem rather than an endless scientific inquiry.

• Experimental Design & Assay Strategy: Directed end-to-end validation, designing multi-method experimental frameworks to confirm antibody-target interactions. Meticulously selected platforms (e.g., overexpression, ELISA, Western blot) to balance speed, sensitivity, and biological relevance.
• Strategic Trade-Offs & Alignment: Balanced pipeline velocity with biological risk by prioritizing decision-critical data over exhaustive testing, driving cross-functional alignment on clear Go/No-Go validation criteria.
• Data-Driven Decision-Making: Triangulated evidence across complementary systems, focusing on trend consistency rather than a single "perfect" dataset. Effectively communicated confidence levels to stakeholders, accelerating viable targets and off-ramping weak candidates.

The Outcome

Accelerated Pipeline Velocity & Resource Efficiency

Optimized pipeline velocity by implementing a trend-driven decision framework that increased early-stage target confidence. This streamlined selection process systematically off-ramped high-risk candidates, eliminating unnecessary testing and maximizing downstream resource efficiency. Improved collaboration between discovery and development teams by implementing clear, structured communication of target confidence levels.

Skills Demonstrated

The Problem

Stability in a Constantly Changing System

Taste receptor cells regenerate every 5–20 days, yet connections to the nervous system remain stable — a biological paradox. We investigated whether Neuropilin-1 (Nrp1) guided this rewiring process, with no clear evidence or well-defined path forward.

My Contribution

Iterative Framework Development Under Ambiguity

Treated this as a structured discovery problem — forming hypotheses, designing experiments, and iterating rapidly when approaches failed.

• Experimental Design: Transgenic mouse models to trace Nrp1-expressing neuron connections across taste modalities.
• Recognizing Failure Early: Initial genetic labeling produced noise — stepped back and rethought the strategy.
• Iterative Pivot: Shifted to viral labeling; refined recovery times, injection techniques, and conditions until usable.

The Outcome

Progress Over Perfection

Established a functional visualization system for studying neural innervation — a 0 → 1 achievement. Data revealed Nrp1 promotes broad cross-modality connectivity, upending the initial hypothesis and directly reshaping downstream research strategy.

What This Taught Me

The Science-to-Product Mindset

Forming hypotheses mirrors defining problems. Designing experiments mirrors testing solutions. Iterating on methods mirrors improving products. Unexpected results mirror strategic pivots. Progress depends on adapting quickly and moving forward when the path isn't clear.