AI, Behavioral Analytics & Digital Health Researcher

MARGI ENGINEER

LinkedIn — *******@*******.*** — +1-980-***-**** — margiengineer.com — Google Scholar SUMMARY

Research Scientist with 7+ years of experience designing and conducting interdisciplinary research at the intersection of AI, human behavior, digital health, and human-computer interaction. Ph.D. in Computer Science with expertise in machine learning, multimodal behavioral analysis, NLP, experimental design, and mixed-methods research. Experienced in building novel computational systems, developing research methodologies, analyzing complex behavioral and textual datasets, and translating findings into peer-reviewed publications, AI systems, and evidence-based recommendations. Passionate about advancing scientific understanding through rigorous experimentation, human-centered AI, and data-driven discovery. EXPERIENCE

Doctoral Researcher — AI, Human Behavior & Digital Health — Clemson University Aug 2022 – Present

• Applied ML Evaluation of GenAI Systems 2024–2026 Developed and evaluated methodologies for assessing 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 analyses to identify performance tradeoffs, failure modes, and design implications, contributing to research on trustworthy and responsible AI.

• 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 performance metrics, and survey measures. Quantified attention distribution, fixation patterns, cognitive load, and task efficiency, and conducted statistical comparisons across participant groups. Investigated relationships between behavioral signals and usability outcomes to advance understanding of human-information interaction.

• Privacy & Security Behavior Analytics 2023–2025

Investigated how users interact with privacy and security features in smartphone ecosystems using behavioral logs, observational data, and survey responses. Applied exploratory and mixed-methods analyses to identify decision- making strategies, risk-related behaviors, and technology adoption patterns, contributing to research on privacy and user behavior.

• Large-Scale Text & Sentiment Analysis 2022–2023

Conducted computational analysis of 385 text sources (medical articles, websites, and blogs) to investigate readability, sentiment, linguistic complexity, and information accessibility. Applied NLP methods, feature extraction, and statistical analyses to identify information-quality gaps, finding that 72% of sources exceeded recommended readability thresholds. Developed evidence-based evaluation criteria for digital health communication.

• Accessibility Analytics in Mission-Critical Systems 2023 Investigated accessibility challenges in transportation systems using task metrics, observational data, and usabil- ity measures. Identified navigation and interaction barriers for users with physical disabilities and generated empirical evidence to support accessibility-focused design improvements.

• Exploratory Behavioral Research Under Uncertainty 2021 Conducted exploratory data collection and thematic analysis to examine how individuals adopt and adapt digital technolo- gies during periods of uncertainty and stress. Developed a conceptual framework grounded in empirical observations to explain technology-mediated coping behaviors and adaptation strategies. Teaching Instructor — Computer Science, Clemson University Aug 2024 – Dec 2024

• Designed and taught an undergraduate Python programming and data analysis course, covering core programming concepts, 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, teach- ing 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.

MACHINE LEARNING & GENAI 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 dis- covery (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. Designed 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 multimodal behavioral data to support research and product workflows, emphasizing transparent data sourcing, citation, and reproducibility.

RESEARCH METHODS, MACHINE LEARNING & TECHNICAL SKILLS Programming & Scientific Computing: Python, R, SQL, MySQL, Pandas, NumPy, SciPy, Jupyter Notebooks, Excel, computational modeling, data preprocessing, reproducible scientific workflows Machine Learning & Computational Modeling: Supervised and unsupervised learning, regression, classification, cluster- ing, ensemble methods, feature engineering, dimensionality reduction, recommendation systems, anomaly detection, predictive modeling, behavioral modeling, explainable AI (XAI), model evaluation and validation Deep Learning, NLP & Generative AI: Natural Language Processing (NLP), sentiment analysis, topic modeling, text classification, named entity recognition (NER), transformer architectures, vector embeddings, retrieval systems, Retrieval- Augmented Generation (RAG), large language model (LLM) evaluation, prompt engineering, Hugging Face Transformers, LangChain, BERTopic

Research Design & Statistical Methods: Experimental design, hypothesis testing, causal inference, statistical modeling, confidence intervals, regression analysis, Bayesian analysis, multivariate testing, survey research, exploratory and confirmatory data analysis, quantitative and qualitative methodologies Behavioral & Human-Centered Research: Mixed-methods research, human-subjects research, behavioral analytics, eye- tracking analysis, affective computing, human-AI interaction, cognitive load assessment, usability evaluation, survey design, interview studies, observational research

Multimodal Data Analysis: Eye-tracking, facial expression analysis, behavioral log analysis, survey data, interview data, text analytics, multimodal feature extraction and integration Data Engineering & Research Pipelines: Data collection, data ingestion, ETL/ELT pipelines, workflow automation, web scraping, feature extraction, data cleaning, data validation, reproducible analytics workflows, pipeline orchestration, research data management

Big Data & Research Infrastructure: Spark, Databricks, Hadoop (basic), AWS, Azure, Google Cloud Platform (GCP), distributed computing, scalable analytics, cloud-based research environments Scientific Software & Reproducibility: Git, GitHub, Docker, API integration, experiment tracking, version control, CI/CD fundamentals, reproducible machine learning workflows, research documentation Visualization & Scientific Communication: Matplotlib, Seaborn, Plotly, Tableau, Power BI, Shiny, scientific visualiza- tion, dashboards, data storytelling, technical reporting, academic writing and presentation Tools & Platforms: Git, GitHub, Hugging Face, LangChain, 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 PUBLICATIONS

Margi Engineer, Durwa Chavan and Dr. Emma Dixon ”We don’t fit into Algorithms of AI”: Co-Customizing LLM-Based Chatbot Applications With People Living With MCI and Dementia”, ASSETS 2027, Under Review Margi Engineer, Durwa Chavan, Arwen Declan, Davaid Neyens, Andrew Duchowski and Dr. Emma Dixon ”Investigating How Older Adults With And Without Dementia Engage With Digital Dementia Information Using Webcam-based Eye Tracking”, 2026, Taylor & Francis International Journal of Human Computer Interaction (IJHCI), Under Revision Margi Engineer, Stephen Becker and Dr. Emma Dixon ”Smartphone Engagement and Nomophobia in Cognitively Diverse Older Adults”, Elsevier- Computers in Human Behavior, 2026 Sushant Kot, Margi and Emma Dixon ”Opportunities for the Design of Person-centered Generative AI Tools for Use by People Living with Dementia”, 2024 ACM Transaction on Computer-Human Interaction (TOCHI), 2025 Elizabeth, Sushant, Margi, Stephen and Emma, ”Training Adults with Mild to Moderate Dementia in ChatGPT: Exploring Best Practices” 2024 SIGAI-IUI, Accepted- Poster

Engineer, Margi, Sushant Kot, and Emma Dixon. ”Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis.” JMIR Formative Research 7 (2023): e48143.

Engineer, Margi, et al. ”Insight into the importance of fog computing in Internet of Medical Things (IoMT).” 2019 Interna- tional Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC). IEEE, 2019. Engineer, Margi, and Ankit Shah. ”Performance analysis of lightweight cryptographic algorithms simulated on arduino UNO and MATLAB using the voice recognition application.” 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). IEEE, 2018.

Shah, Ankit, and Margi Engineer. ”A survey of lightweight cryptographic algorithms for iot-based applications.” Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS-2018. Springer Singapore, 2019. Engineer, Margi. “Synopsis of IOT: Internet of Things.” International Journal for Scientific Research and Development, 1 Oct. 2017, ijsrd.com/Article.php?manuscript=IJSRDV5I70463. PAPER REVIEWER

IUI Program Committee Member by invitation Finland, 2027 MobileHCI UK, 2026

Creative & Cognition Germany, 2026

CUI Germany, 2026

CHI - SIGCHI Barcelona, 2026

IUI - ACM SIGCHI Cyptuss, 2026

INTERACT - SIGCHI Minas Gerais, Brazil, 2025

CHI Late breaking work- SIGCHI Yokohama, Japan 2025 IUI- SIGCHI Cagliari,Italy 2025

UIST- SIGCHI California,USA 2023

CERTIFICATIONS & RECOGNITION

• Introduction to Statistics — Stanford Online 2024

• Applied Generative AI Specialization — Purdue University 2025

• Outstanding Graduate Teaching Assistant — Clemson University 2021

• Google UX Design Professional Certificate 2024

• Usable Security 2024

• Career Essentials in Generative AI - Microsoft 2024

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