Faculty-advised research — team forming
→ Core contributors included as co-authors
Optimization AI
Systems analysis of carbon cost variability across supply chain configurations using multi-agent optimization with statistical validation. Examines how configuration choices routing, sourcing, mode selection produce differential carbon cost outcomes across industry verticals. Contributing to Prof. Kathleen Park's Humanitarian Logistics Innovation book.
Supply chain carbon costs are not fixed they vary as a function of configuration choices: routing decisions, sourcing geography, transportation mode, load consolidation, and temporal scheduling. This variability is systematically underweighted in supply chain optimization because carbon costs are difficult to model, vary across jurisdictions and accounting standards, and are rarely integrated into real-time decision support. This research examines how multi-agent optimization with statistical validation can quantify carbon cost variability across supply chain configurations.
How does carbon cost vary as a function of supply chain configuration choices, and can multi-agent optimization with statistical validation enable configuration-aware carbon cost optimization across humanitarian and commercial logistics contexts?
Multi-agent optimization across supply chain configuration space; carbon cost modeling across routing, sourcing, mode, and temporal variables; statistical validation of variability findings across industry verticals; ablation study isolating contribution of each configuration dimension to carbon cost outcome; comparison against static carbon cost assumption baseline; application to humanitarian logistics scenarios drawn from case studies in Prof. Park's book.
Open roles
Research Assistant
OpenSupport data collection, carbon cost modeling, and statistical analysis. Run experiments across supply chain configuration space. Support literature review and academic writing.
Skills: Python, Statistics, Supply Chain, Research Methods, Academic Writing
Apply →ML Engineer
OpenImplement multi-agent optimization system across configuration space. Build carbon cost modeling layer. Design validation experiments and run ablation studies.
Skills: Python, ML, Optimization, Statistical Modeling, Simulation
Apply →Team
Lead Researcher
FilledRashanjot Kaur
Designed multi-agent optimization architecture, carbon cost modeling approach, statistical validation framework, and research methodology.
Skills: Multi-Agent Systems, ML, Statistical Analysis, Optimization, Research Design
Co-author / Faculty Advisor
FilledProf. Kathleen Park
Domain expertise in humanitarian logistics. Book author for "Humanitarian Logistics Innovation." Guiding humanitarian application context and academic positioning.
Skills: Humanitarian Logistics, Supply Chain Management, Operations Research, Academic Publishing
