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Ali Taherdoustmohammadi

Foto di Ali Taherdoustmohammadi

Dottorando in Ingegneria Informatica E Dei Sistemi , 41o ciclo (2025-2028)
Dipartimento di Automatica e Informatica (DAUIN)

Profilo

Dottorato di ricerca

Argomento di ricerca

LLM-Based Data Modelling, Co-Simulation and Control for Buildings and Districts

Tutori

Presentazione della ricerca

Poster

Keywords

Controls and system engineering
Data science, Computer vision and AI
Parallel and distributed systems, Quantum computing
Software engineering and Mobile computing

Biografia

Ali Taherdoust is a Ph.D. Candidate in Computer and Control Engineering at Politecnico di Torino (41st Cycle), operating within the DAUIN department and the Energy Center. With a strong focus on the digital transition of the built environment, his research explores LLM-powered WebGIS systems, spatial data engineering, and the development of High-Resolution Energy Community Digital Twins.
Ali brings a uniquely multidisciplinary perspective to his doctoral research. His foundational background spans Civil Engineering and Geography (GIS & Remote Sensing), as well as a Master’s degree in Architecture, where his thesis focused on the parametric design of responsive facades. Bridging the gap between the physical and digital worlds, he recently solidified his transition into computer science by graduating with top honors (110/110) with an M.Sc. in Digital Skills for Sustainable Societal Transitions from Politecnico di Torino.
Currently, Ali’s work centers on the complex intersection of operational technology, edge computing, and semantic urban models (BIM-to-CIM integration) for Smart Buildings and Energy Communities. He is highly focused on modern software architecture and tempospatial data engineering, leveraging a robust technical stack to build "Cognitive" digital twin agents. His backend and database engineering toolkit includes Python, Django, FastAPI, PostGIS, 3DCityDB, and time-series databases (TimescaleDB, InfluxDB). For data visualization and user interfaces, he utilizes JavaScript and React to build interactive, state-managed spatial dashboards.
A core component of Ali's research is integrating advanced AI directly into spatial and temporal systems. He utilizes LangChain, Transformers, and Parameter-Efficient Fine-Tuning (PEFT) for open-source models to develop NLP-to-SQL interfaces, allowing users to interact with complex spatiotemporal datasets using natural language. Alongside machine learning frameworks like Scikit-Learn and TensorFlow for time-series forecasting, Ali aims to develop full-stack, AI-driven applications and IoT edge gateways that support active control, privacy-preserving data flows, and intelligent decision-making in responsive urban grids.

Ricerca

Gruppi di ricerca