Career & Research Impact

Presentations & Conferences

(Highlight talks, conference papers, invited presentations)

  • Presented AI & Prompt Engineering for Business Strategy (Johnson & Johnson, 2025)

    • Led a high-level AI training session for a department, helping business teams understand AI’s role in decision-making and how to craft effective prompts for AI models.

    • Strengthened AI adoption at an enterprise level by teaching teams how to communicate business needs in AI-friendly language.

  • Presented at AAAI 2025 KGML Bridge – Geoinformatics-Guided ML for Power Plant Classification (Philadelphia, 2024, with Dr. Hao Liu & Dr. Aparna Varde)

  • Presented at IEEE Sensors Conference 2024 (Japan) – Power Plant Detection Using GIS, CNN & ViTs (Presented by Dr. Aparna Varde)

  • Presented at New Jersey Big Data Alliance Symposium, 2024 – ML-GIS Research on Solar Panel Detection & Energy Estimation

  • Presented at CESAC Symposium 2023 – Presented Findings on Virtual Reality in STEM Education


AI & Machine Learning Research

(research, methodologies, impact)

🔹 Graph Neural Networks & AI for Drug Discovery (Data Science Lab - AMIA 2025 Submission)

  • Used biomedical knowledge graphs & Graph Neural Networks (GNNs) to improve drug-drug interaction prediction.

  • Integrated biomedical ontologies & structured pharma datasets to enhance AI-driven clinical research.

🔹 Geoinformatics-Guided Machine Learning for Power Plant Classification. (Data Science Lab - AAAI Conference 2025, (paper))

  • Developed a GIS-CNN-ViT deep learning framework to satellite image classification accuracy and interpretability.

  • Integrated spatially aware feature representations to enhance generalization in ML models.

  • Defined a novel mathematical framework for integrating domain knowledge into ML pipelines, optimizing model explainability.

🔹 Power Plant Detection for Energy Estimation using CNNs and Vision Transformers. (Data Science Lab - IEEE Sensors 2024, (paper))

  • Implemented multi-scale computer vision architectures for automated object detection and classification in complex environments.

  • Designed pipeline that fuses CNNs for spatial feature extraction and ViTs for long-range dependencies.

🔹 Geospatial AI & Machine Learning for Energy & Environment (Clean Energy & Sustainability Analytics Center, Montclair State University, 2024)

  • Solar Panel Detection & Energy Estimation in Satellite Imagery (NJ Big Data Alliance Symposium, 2024)

🔹 Machine Learning & GIS for Environmental Policy (EPA & CESAC Research, 2023)

  • Built ML & GIS models to analyze Brownfield funding allocation & clustering patterns.

  • Applied Random Forest (44% variance explained), Moran’s I, and Spatial Error Models (SEM) to study geographic disparities in environmental funding.

🔹 Virtual Reality & AI for STEM Education (Montclair State University – Virtual Reality for Education Lab, 2023)

  • Contributed to “The Human Brain Time”, a fully immersive VR application for neuroanatomy education, now deployed on Meta Quest App Lab, expanding access to interactive STEM learning.
    Career Experience - Applied Research & Data Science

Career Experience - Applied Research & Data Science

(Industry work, and real-world applications of AI)

Johnson & Johnson – Data Science Specialist Co-op (RDQ Department, 2024-Present)

🔹 Innovation Design Team

  • Designed an AI risk & compliance framework based on ISO/IEC 23894:2023, analyzing failures in AI-driven healthcare systems (IBM Watson, NaviHealth).

  • Conducted industry research with 50 biopharma professionals, identifying data drift (78%) and regulatory alignment (85%) as major challenges in AI adoption

  • Developed a centralized vendor & audit intelligence platform for 100+ vendors, integrating Hugo, AWS S3, and Drupal to automate real-time audit tracking & regulatory compliance reporting.

  • Reduced manual compliance efforts by 60% and improved traceability for top-site pharmaceutical drugs.

🔹 Data Science Team

  • Developed NLP-driven entity harmonization & knowledge graphs, improving pharma research data processing.

  • Worked on topic modeling & trend analytics, processing millions of records from scientific literature, vendor reports, and internal R&D data.

  • Improved document retrieval efficiency by 40%, enhancing AI-driven insights in pharmaceutical R&D.

Data Science Lab (2023-Present)

  • Led AI research projects and team-based competitions, mentoring graduate students in NLP, Knowledge Graphs, and AI model development.

  • Conducted weekly research discussions & technical deep dives, guiding teams on ML model optimization, interpretability, and knowledge extraction.

  • Assisted students in publishing research papers, preparing conference submissions, and presenting findings at technical symposiums.

Hu-Au Virtual Reality (VR) Lab (2023-2024)

  • Guided students in VR development & AI integration, helping them understand 3D modeling, interactive simulation design, and AI-driven learning environments.

  • Led technical training sessions on Unity, C#, and Python for VR applications, bridging AI & virtual learning technology.

  • Collaborated with faculty to expand research initiatives, ensuring projects align with educational technology advancements and funding opportunities.

Clean Energy Sustainability and Analytics Center (2023-2024)

  • Mentored undergraduate students & new research assistants, guiding them through GIS data preprocessing, ML model development, and environmental policy applications.

  • Led team discussions & technical workshops on spatial machine learning, geostatistical modeling, and energy forecasting techniques.

  • Assisted in writing & reviewing research proposals, ensuring projects aligned with policy impact and scientific rigor.


Awards & Achievements

(Competitive recognition, honor societies)

  • RAISE 2025 Finalist – "Our Future With AI: Utopian or Dystopian?" (AI ethics & policy challenge.)

  • Alpha Epsilon Lambda Honor Society (Top 10% of Graduate Students at Montclair State University.)