Special Session on AI-Driven Computational and Mathematical Modelling

ICCMM 2026 Special Session

Session Overview

The Special Session on AI-Driven Computational and Mathematical Modelling brings together emerging approaches in artificial intelligence, computational modelling, mathematical modelling, simulation, optimization, and decision support systems.

The session covers applications across climate and environmental systems, smart agriculture, smart cities, intelligent transportation, energy systems, finance and economics, industrial systems, smart manufacturing, resource optimization, circular economy systems, image processing, computer vision, and pattern recognition.

This Special Session aims to provide a platform for discussing how AI-driven modelling can support analysis, prediction, optimization, and evidence-based decision-making across scientific, technological, environmental, economic, and industrial domains.

AI-driven computational and mathematical modelling with smart cities, climate systems, energy, agriculture, transportation and industrial technologies

Special Session Scope

Focus Areas

The Special Session on AI-Driven Computational and Mathematical Modelling brings together the following focus areas across AI-enabled scientific, technological, environmental, economic, and industrial applications.

AI-Based Simulation and Optimization

AI-based simulation, optimization, and decision support systems.

Climate and Environmental Systems

AI-driven climate modelling and environmental systems.

Smart Agriculture and Food Security

Smart agriculture and food security using AI.

Smart Cities and Transportation

AI for smart cities, intelligent transportation, and urban planning.

Energy Systems and Smart Grids

Energy demand forecasting, smart grids, and renewable energy optimization.

Finance and Economics

AI applications in finance and economics.

Industrial Systems and Manufacturing

AI applications in industrial systems and smart manufacturing.

Resource Optimization and Circular Economy

AI for resource optimization, waste reduction, and circular economy systems.

Image Processing and Pattern Recognition

AI-based image processing, computer vision, and pattern recognition.

ICCMM 2026 Special Session

Invited Talk Schedule

Invited presentation under the Special Session on AI-Driven Computational and Mathematical Modelling.

13 November 2026

9:00 AM | Sri Lankan Time (UTC+05:30)

Human-Environment as a Whole in the Era of AI and Open Science

This talk focuses on human-environmental interaction and presents research outcomes from more than ten multidisciplinary projects led by Dr. Wang. The presentation discusses crowdsourced human observation data, GeoAI techniques, Earth observation data, and open science workflows for understanding complex human-environment interactions.

Read Speaker Bio Hide Speaker Bio View the speaker’s academic profile, professional background, and external profiles

Speaker Profile

Speaker Bio

University Profile Google Scholar LinkedIn Harvard CGA ORCID

Dr. Siqin (Sisi) Wang is an Associate Professor (Teaching) of Spatial Sciences with the Spatial Sciences Institute at the University of Southern California, United States.

Her professional roles include serving as an Associated Chair for the Spatial Data Lab affiliated with Harvard University. She is also a CGA Associate and Visiting Scholar at the Center for Geographic Analysis, Harvard University. Her research interests include GIScience, spatiotemporal big data analytics, computational social science, digital health geography, human-centered GeoAI, human mobility and migration, smart cities, and human-climate interactions.

Her research explores the use of spatial data, GeoAI, crowdsourced observations, Earth observation data, and open science approaches to understand complex human-environment interactions. She was recognized as one of the Geospatial World 50 Rising Stars for her contributions to geospatial research and innovation.

Further Information Will Be Published Soon

Details about the Special Session on AI-Driven Computational and Mathematical Modelling will be updated on this page.