A collection of things I've built, problems I've explored, and challenges I've competed in.
Industry ProjectsJul'25 - Jun'26
H-adGen: Framework for Hyperlocalized Personalization of Video Advertisements
A multimodal AI framework that localizes pre-recorded video advertisements by replacing spoken brand and location-specific entities while preserving the original speaker’s voice and visual identity. Integrates speech recognition, zero-shot voice cloning, lip synchronization, facial restoration, and multilingual content moderation to generate regionally tailored advertisements in Hindi, Marathi, Tamil, and Telugu.
Developed a data-driven supply chain planning methodology for a multi-stage network connecting plants, mother hubs, Clearing and Forwarding Agents (CFAs), and customers. Combined inventory planning with Mixed-Integer Linear Programming (MILP) to optimize production and distribution decisions, evaluate capacity constraints, and analyze cost and demand-fulfillment trade-offs, earning National Runner-Up recognition in the case competition.
Developed a data-driven supply chain planning methodology for a multi-stage network connecting plants, mother hubs, Clearing and Forwarding Agents (CFAs), and customers. Combined inventory planning with Mixed-Integer Linear Programming (MILP) to optimize production and distribution decisions, evaluate capacity constraints, and analyze cost and demand-fulfillment trade-offs.
Key Highlights
Supply Chain Network Modeling: Structured production, inventory, and distribution decisions across plants, mother hubs, CFAs, and customer demand.
Inventory Planning: Developed an inventory norm methodology using average demand, forecast variability, lead times, safety stock, reorder points, and days of cover.
MILP Formulation: Formulated a mathematical optimization model to minimize modeled production, transportation, inventory, and unmet-demand penalty costs while satisfying operational constraints.
Production & Capacity Planning: Incorporated fixed 25 kL production batch sizes and plant- and category-level capacity constraints into the planning model.
Scenario Analysis: Evaluated a 70% Overall Equipment Effectiveness (OEE) scenario to assess its impact on effective production capacity, modeled costs, and unmet demand.
Bottleneck Analysis: Interpreted optimization outputs to identify structural capacity bottlenecks and evaluate production planning and resource allocation decisions.
Optimization Tool Development
Excel-Driven Workflow: Implemented spreadsheet data import to populate the optimization model.
Configurable Constraint Engine: Enabled users to add custom constraints and modify or remove default constraints for flexible scenario evaluation.
Optimization Backend: Developed the backend using Python and FastAPI, with PuLP for MILP formulation and the CBC solver for optimization.