Ruhil D.

I am a Materials Science Ph.D. and AI/ML consultant specializing in machine learning, industrial AI, materials informatics, chemistry-driven process optimization, and decision-support systems. I help technical teams turn messy experimental, operator, sensor, process, and production data into predictive models, dashboards, and AI workflows that support faster decisions, lower chemical usage, better process control, and measurable operational savings. I have worked directly with CTOs, process engineers, plant operators, project managers, and technical leadership to translate operational problems into deployable AI solutions. My focus is accelerating decision making. What I deliver: • Real-time prediction models for industrial and chemistry-driven processes • Decision-support AI tools for operators, engineers, and managers • Machine learning models for process optimization, quality control, and performance prediction • Transformation of operator logs and process records into clean, model-ready datasets • Exploratory data analysis to identify process drivers, anomalies, and failure patterns • Anomaly detection for sensor, production, experimental, and operator-recorded data • Time-series modeling for process monitoring, forecasting, and live decision support • Tree-based regression and classification models for interpretable industrial prediction • Hierarchical classification workflows for complex scientific and materials datasets • Deployment-ready dashboards and applications for technical users Relevant industrial AI experience: • Built predictive models for real-time process variables, including treatment performance, effluent quality, chemical response, and operational stability. • Developed decision-support workflows that helped operators evaluate process conditions and accelerate treatment decisions. • Converted inconsistent operator logs and process records into structured datasets for machine learning, dashboarding, and deployment. • Created models to support chemical dosing optimization, reducing unnecessary chemical use while maintaining target process outcomes. • Built classification and prediction workflows for complex materials, chemistry, and industrial datasets. • Developed exploratory analytics and anomaly-detection workflows to identify abnormal process behavior and data-quality issues. • Designed model-development pipelines covering data cleaning, feature engineering, model training, evaluation, interpretation, and deployment. • Delivered working applications and dashboards for stakeholders including engineers, managers, operators, and executive leadership. Core expertise: • Industrial AI and machine learning model development • Materials informatics and chemistry-driven process modeling • Exploratory data analysis and process-data diagnostics • Anomaly detection and outlier analysis • Time-series modeling and real-time prediction • Regression, classification, and hierarchical classification • Tree-based modeling, feature importance, and model interpretation • Decision-support dashboard and application development • Scientific data cleaning, visualization, and reporting • End-to-end project execution from raw data to deployed tool Selected project areas: Industrial Process Prediction and Decision-Support AI Developed industrial AI work includes developing machine learning models and operator-facing decision-support tools for real-time process prediction, chemical dosing support, treatment performance monitoring, and process optimization. I converted operator-recorded and real-time process data into structured datasets, predictive models, and user-facing applications that helped operators and engineers make faster treatment, dosing, and process-control decisions. These tools reduced decision time, supported live process monitoring, and identified potential operational savings of $100K–$200K per year through improved chemical use and process control. Materials Informatics and Scientific ML Built predictive models for materials-property estimation, chemistry-performance relationships, and scientific feature engineering. Work includes modeling alloy properties, chemistry-driven performance, and complex experimental datasets. Anomaly Detection and Process Diagnostics Created workflows to identify abnormal sensor behavior, inconsistent operator records, outlier samples, and unusual process conditions. These analyses helped improve data quality and model reliability before deployment. Why Work With Me? I combine deep scientific training with practical AI/ML development. My strength is translating complex technical data into models, dashboards, and decision-support tools that operators, engineers, and managers can use. I can help you move from raw, messy data to a working AI solution that supports better decisions, faster analysis, and measurable operational value.