Peer-Reviewed Research
We’re proud to highlight our founder’s contributions to AI research, healthcare innovation, and applied machine learning. Each paper is peer-reviewed and indexed in leading scientific and medical libraries. These publications reflect the expertise and values that guide AIVahan today.
Methodology for Safe and Secure AI in Diabetes Management
Journal of Diabetes Science and Technology, 2025
Indexed in: NIH National Library of Medicine (NLM), PubMed Central, MedlinePlus, Ovid, Atypon, and other scientific platforms.
Focus: Framework for safety, security, and governance in AI-enabled clinical systems
Relevance: Establishes a structured approach for evaluating and deploying AI in high-risk, regulated healthcare settings.
A novel feedback system for pedagogy refinement in large lecture classrooms
ICCE 2018 – 26th International Conference on Computers in Education
EID: 2-s2.0-85060062895
Focus: Data-driven feedback systems for large-scale classroom environments
Relevance: Demonstrates early contributions at the intersection of analytics, human-technology interaction, and learning systems.
Predictive Analytics of Sensor Data Based on Supervised Machine Learning Algorithms
Published in: 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS)
Publisher: IEEE
Focus: Applying ML techniques to time-series sensor data
Relevance: Early applied data science work that informs today’s approach to enterprise-grade predictive modelling.
Big Data Trends and Analytics: A Survey
International Journal of Computer Applications, 2017
Focus: Survey of emerging big-data architectures and analytics practices
Relevance: Establishes foundational understanding of large-scale data processing which is core to modern AI systems.
An Efficient Feature Selection Technique using Genetic Algorithm for Activity Recognition of Elder People
International Journal of Computer Applications, 2017
Focus: Computational feature-selection techniques for activity-recognition systems
Relevance: Early exploration of optimization and ML methods that later feed into responsible AI modelling practices.
Featured Media & Press
The following section share media features and published work that highlight our founder’s contributions to AI research, responsible innovation, and community impact.
Could your tech devices have emotional intelligence, too?
Featured in Science Blog, October 14, 2020
Gupta’s long-term goal is to build empathetic robots and machines that attune to your mood to provide people 24-hour therapy, and make mental health care more affordable, specifically for suicide prevention.
I didn’t want to be a cog in a machine – I wanted to grow with a company while helping it grow.”
— Shreya Gupta
Published in Built In LA, Jun. 30, 2021
Building Bots with Heart
Caitlin Dawson | Featured in USC News, July 27, 2020
New computer science alumna Shreya Gupta hopes to bring humanity to deep learning by developing empathetic robots that can attune to your emotions.
Patents and Intellectual Properties
This section highlights legally recognized innovations authored by Shreya Gupta, reflecting her contributions to applied AI and technology development.
Interactive Communication System with Natural Language Adaptive Components
Patent Jurisdictions: US, WO
Publication No.: US 2022/0075960 A1
Published: March 10, 2022
Focus: Human-adaptive natural language interactions
Relevance: Demonstrates an early commitment to building systems that adjust to user context – a foundation of today’s human-centric AI design.
** Research publications listed above were authored prior to the establishment of AIVahan International and reflect Shreya Gupta’s individual academic and professional work.