Ali Rasekh

Designed and developed AI-driven mobility and tourism solutions using large-scale mobility datasets. Built agentic AI systems for personalized route planning and dialog-based recommendations, including data-driven pipelines and explainable recommendation methods.

Conducting PhD research focused on multimodal learning and generative AI. Working on Video-LLMs and foundation models, including temporal understanding and explainable multimodal systems.

Conducted research and published in top-tier AI venues. Developed novel diffusion-model methods for human pose prediction.

Architected and deployed production AI services for personalization and product discovery serving 40M+ monthly active users. Led work on recommendation engines, anomaly detection for fraud/bot filtering, and graph-based knowledge graph models using Neo4j, alongside scalable real-time data pipelines.

Developed home price prediction models using large-scale Canadian real-estate datasets. Engineered and optimized regression models with XGBoost and ensemble learning, including feature engineering and selection.

Developed and maintained enterprise-level financial and HR software solutions. Worked with C# and .NET to deliver and support application functionality.

Worked as a research assistant at NUS. Responsibilities and specific technical contributions were not provided.

PhD in Artificial Intelligence focused on multimodal learning, generative AI, video LLMs, and foundation models. Published in NeurIPS 2025, ICLR 2025, BMVC 2025, and ICRA 2023.

Worked as an AI researcher developing diffusion-model methods for human pose prediction. Published a paper in ICRA, a top-tier robotics conference.

Bachelor of Science in Computer Engineering.

Master of Artificial Intelligence with an MST thesis on interpretable pixel-wise segmentation of lung nodules in CT images.

Served as a research assistant in 2019.