I design statistical and ML-driven measurement systems for human-centered decision contexts where validity and interpretability are critical. My background in psychometrics and experimental design informs how models are constructed, evaluated, and deployed in environments involving behavioral data. I build and analyze structured instruments such as surveys and questionnaires, ensuring constructs are measurable, reliable, and aligned with intended outcomes. Where appropriate, I integrate inferential modeling and predictive techniques to link behavioral signals with downstream business or operational metrics. Experimental frameworks are structured to control bias, isolate causal drivers, and validate system performance prior to broad release. The result is AI systems grounded in measurement theory rather than purely correlational optimization.
Representative Work
- Designed and analyzed survey-based behavioral measurement frameworks for enterprise analytics platforms, aligning psychometric constructs with deployable ML features.
- Applied experimental design principles to evaluate model-driven insights in decision-making contexts involving human interpretation.
- Integrated structured behavioral indicators into predictive and diagnostic analytics systems supporting enterprise stakeholders.
Core Technologies
Psychometric modeling; survey and instrument design; construct validity assessment; experimental design frameworks; inferential statistics; regression and classification modeling; bias evaluation; causal reasoning patterns; human-centered metric definition; statistical validation techniques.
I build systems that extract structured behavioral signals from both unstructured communication data and physiological measurement streams. My work spans NLP-driven analysis of text and transcripts as well as signal processing across modalities such as EEG, fMRI, MRI, MEG, vocal analysis, eye tracking, motion capture, and galvanic skin response. I transform qualitative and high-dimensional behavioral data into quantifiable indicators aligned with decision-making and modeling objectives. Approaches include psycholinguistic feature engineering, sentiment and intent modeling, semantic similarity scoring, and structured signal feature extraction from time-series data. Pipelines are designed to preserve interpretability while supporting downstream predictive, diagnostic, and experimental workflows. Where high-stakes interpretation is involved, validation layers ensure extracted signals reflect meaningful constructs rather than spurious correlations.
Representative Work
- Built transcript and communication analysis systems extracting psycholinguistic and engagement indicators for use in analytics and predictive modeling pipelines.
- Conducted multivariate analysis of neuroimaging and physiological datasets, integrating structured behavioral features into experimental and applied ML workflows.
- Developed feature extraction frameworks enabling large-scale analysis of behavioral and biometric signals for research and enterprise contexts.
Core Technologies
Psycholinguistic feature engineering; sentiment and intent modeling; semantic similarity and embedding pipelines; time-series signal processing; neuroimaging analysis (EEG, fMRI, MRI, MEG); vocal signal analysis; eye tracking and motion capture processing; galvanic skin response analysis; structured feature extraction workflows; multimodal behavioral data integration; interpretability-focused modeling approaches.
I design evaluation frameworks that account for how model outputs are interpreted, trusted, and acted upon by human stakeholders. Beyond statistical performance metrics, I incorporate interpretability analysis, threshold alignment, and user-context validation to ensure models behave consistently within real decision environments. Evaluation strategies include quantitative benchmarking, structured qualitative review, and scenario-based testing to surface edge cases and unintended behaviors. Where AI systems influence sensitive or high-impact decisions, I implement human-in-the-loop mechanisms and escalation pathways to manage uncertainty. I also assess bias, construct validity, and behavioral alignment to reduce the risk of misapplied or overconfident outputs. The objective is to ensure AI systems remain technically sound while meaningfully aligned with human cognition and operational use.
Representative Work
- Designed validation protocols for analytics and LLM-driven insight systems, aligning model thresholds with stakeholder risk tolerance prior to production release.
- Implemented human-in-the-loop review workflows for interpretability and escalation in high-stakes inference contexts.
- Conducted bias and construct-alignment analyses for behavioral and language-driven models used in enterprise decision support.
Core Technologies
Model evaluation frameworks; interpretability analysis; threshold calibration; human-in-the-loop integration; bias detection and mitigation; construct validity assessment; scenario-based testing; risk-aligned metric design; uncertainty handling strategies; qualitative-quantitative evaluation synthesis.