Dr. Margi - Applied ML & GenAI

DR. MARGI ENGINEER

LinkedIn — Email — +1-980-***-**** — Website — Google Scholar

ACHIEVEMENTS

• Delivered AI, Generative AI, Agentic AI, and RAG-based applications leveraging LLMs, LangChain, Python, SQL, and cloud technologies (AWS, Azure, GCP).

• Developed machine learning, predictive modeling, and advanced analytics solutions using statistical methods, feature engineering, and explainability techniques to drive data-driven decision-making.

• Built scalable ETL pipelines, automated analytics workflows, and production-ready data solutions using Databricks, PyS- park, SQL, and modern MLOps best practices.

EXPERIENCE

Data Scientist / Applied ML Researcher — EQUI-Tech Lab, Clemson University Aug 2022 – Present

• Applied ML Evaluation of GenAI Systems 2024–2026 Led end-to-end analysis of customized Generative AI chatbot systems using quantitative evaluation metrics. Compared system outputs against baseline models across accuracy, reliability, hallucination rates, readability, and bias-related signals. Conducted statistical analysis to identify performance tradeoffs and failure modes, and translated findings into data-driven recommendations that informed system design and deployment decisions.

• Behavioral Data Analysis Using Webcam Eye Tracking 2023–2025 Designed and analyzed a multimodal dataset (n=57) combining eye-tracking signals, facial expression data, task perfor- mance metrics, and survey measures. Quantified attention distribution, fixation patterns, cognitive load, and task efficiency, and applied statistical comparisons across user groups. Connected behavioral patterns to usability outcomes, supporting evidence-based recommendations.

• Privacy & Security Behavior Analytics 2023–2025

Analyzed behavioral logs and user interaction data to identify key decision-making patterns, informing design recommenda- tions and improving understanding of privacy-related user behaviors.

• Large-Scale Text & Sentiment Analysis 2022–2023

Conducted NLP-based analysis of 385 text sources (medical articles, websites, blogs) to evaluate readability, sentiment, and linguistic complexity. Applied text preprocessing, feature extraction, and statistical analysis to identify information quality gaps, finding that 72% of sources exceeded recommended readability thresholds. Synthesized insights into data-backed content standards and evaluation metrics.

• Accessibility Analytics in Mission-Critical Systems 2023 Evaluated real-world transportation systems using task metrics, observational data, and usability measures to identify navigation and interaction breakdowns for users with physical disabilities. Delivered quantitative and qualitative findings that informed workflow optimization and accessibility improvements.

• Exploratory Behavioral Research Under Uncertainty 2021 Conducted exploratory data collection and thematic analysis to model how users adopt and adapt digital technologies dur- ing high-stress conditions. Developed a conceptual framework supported by empirical observations to explain technology- mediated coping behaviors.

Teaching Instructor — Computer Science, Clemson University Aug 2024 – Dec 2024

• Designed and taught an undergraduate Python programming and data analysis course, covering core programming con- cepts, data manipulation, and problem-solving. Guided students through hands-on assignments involving real-world datasets.

Mentor — Undergraduate and Graduate Research Training Aug 2023 – Present Mentored 5+ researchers in data collection, preprocessing, statistical analysis, and ethical data practices. Guided dataset management and synthesis workflows for behavioral and eye-tracking datasets. Teaching Assistant & Researcher — Computer Science & HCI, Clemson University Aug 2019 – Dec 2022

• Supported instruction in Distributed Computing, Applied Data Science, Databases, Systems, and HCI, teaching 500+ students.

• Awarded Outstanding Graduate Teaching Assistant (2021) for instructional excellence.

• Conducted applied research in systems, security, and privacy, including: Hardware & Security Lab (2022): GPU vulnerability analysis and mitigation strategies. PERSIST Lab (2021–2022): Performance evaluation of lightweight cryptographic algorithms on batteryless IoT platforms with NIST.

HATLab (2019–2020): Development of Android privacy tools to detect data leaks and enable user-controlled security settings.

GENAI & APPLIED ML PROJECTS

Diabetes Digital Twin and Behavioral Decision Intelligence System July 2026 Developed a multimodal machine learning platform that forecasts glucose trajectories, estimates self-management cognitive burden, performs counterfactual health simulations, and generates adaptive recommendations using behavioral analytics, time-series modeling, and explainable AI.

Global News Intelligence Engine May 2026

Built an automated global news-analytics platform with scalable NLP pipelines for event–country attribution, topic discovery

(BERTopic), and sentiment/complexity scoring. Generated intelligence summaries on emerging themes, locations, sentiment, and cross-country media attention.

Medical Misinformation Detection System Sep 2025

Built an end-to-end NLP pipeline to classify and verify health-related claims using structured and unstructured data. De- signed data ingestion workflows with automated crawling of trusted medical sources (PubMed, WHO) and implemented LLM-based classification to label claims as Supported, Contradicted, or Inconclusive. Evaluated model behavior using error analysis and qualitative validation, emphasizing explainability through evidence-based citations and plain-language rationales. AI-Powered HR Policy Analytics Assistant (Nestl e) Aug 2025 Developed an internal decision-support system leveraging document parsing, vector embeddings, and retrieval-based NLP models to enable efficient policy search and analytics. Designed evaluation metrics around retrieval accuracy and task com- pletion, resulting in a 65% reduction in manual policy lookup time. Deployed a Python-based pipeline with a lightweight user interface for enterprise use.

NewsGenie: Real-Time Information Analytics System Jun 2025 Built a data-driven information filtering and categorization system integrating NLP models, vector similarity search, and external news APIs. Applied text preprocessing, classification, and sentiment analysis to organize real-time news streams and reduce information overload. Evaluated system performance using relevance and consistency metrics. InsightForge: Analytics Assistant for Business Data May 2025 – Sep 2025 Designed and implemented an analytics assistant to support exploratory data analysis and insight generation on struc- tured datasets. Applied statistical analysis, feature aggregation, and visualization pipelines using Python (Pandas, Mat- plotlib/Seaborn). Enabled non-technical users to generate actionable insights and recommendations approximately 3x faster. GazeAeye: Multimodal Behavioral Analytics System Mar 2025 Developed a domain-specific analytics assistant integrating eye-tracking datasets with NLP-based retrieval. Analyzed mul- timodal behavioral data to support research and product workflows, emphasizing transparent data sourcing, citation, and reproducibility.

DATA SCIENCE & ML SKILLS

Programming & Data: Python, R, SQL, MySQL, Pandas, NumPy, SciPy, Excel, Jupyter Notebooks Generative AI & Agentic Systems Agentic AI workflows, AI agents, Multi-agent orchestration, Tool-calling architectures, RAG systems, LangChain Prompt engineering, LLM evaluation Machine Learning & AI: Supervised and unsupervised learning, regression, classification, clustering, ensemble methods, feature engineering, dimensionality reduction, recommendation systems, anomaly detection, predictive modeling, model evaluation, explainable AI (XAI)

Deep Learning & NLP: NLP, sentiment analysis, topic modeling, text classification, named entity recognition (NER), transformer models, vector embeddings, retrieval systems, LLM evaluation, prompt engineering, Hugging Face Transformers, BERTopic

Statistics & Experimentation: Hypothesis testing, A/B testing, experimental design, statistical modeling, causal inference, confidence intervals, regression analysis, survey analysis, exploratory data analysis (EDA), Bayesian thinking, multivariate testing

Data Engineering & Pipelines: ETL/ELT pipelines, data ingestion, preprocessing, feature extraction, workflow automa- tion, web scraping, data cleaning, data validation, reproducible analytics workflows, pipeline orchestration Big Data & Cloud: Spark, Databricks, Hadoop (basic), AWS, Azure, Google Cloud Platform (GCP), PySpark, distributed computing, scalable analytics

MLOps & Deployment: Model deployment, model monitoring, experiment tracking, version control, Docker, API integra- tion, CI/CD fundamentals, Git, reproducible machine learning workflows Visualization & Business Intelligence: Matplotlib, Seaborn, Plotly, Tableau, Power BI, Shiny, dashboards, KPI develop- ment, executive reporting, data storytelling

Research & Analytics: Quantitative analysis, qualitative analysis, mixed-methods research, behavioral analytics, survey research, human-centered data science, predictive insights, decision support Tools & Platforms: Git, GitHub, LangChain, Hugging Face, Gradio, SPSS, Tableau, Power BI, Jupyter, VS Code EDUCATION

Ph.D. Computer Science, Clemson University May 2026 MS Computer Science, Clemson University December 2021 ME Computer Engineering, Gujarat Technological University May 2018 BE Information and Technology, Gujarat Technological University May 2016 CERTIFICATIONS & RECOGNITION

• Introduction to Statistics — Stanford Online 2024

• Applied Generative AI Specialization — Purdue University 2025

• Outstanding Graduate Teaching Assistant — Clemson University 2021

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