I'm a husband and father, a researcher at heart, and a computer engineer by profession. I've always been fascinated by technology, and I have a particular love for robotics — hence, Turgibot.
I hold a Master's degree in Computer Science from the Open University of Israel and am currently pursuing a PhD focused on Artificial Intelligence and Robotics. My research interests center around intelligent agents: how they understand their environment, reason about goals and plans, make decisions, and collaborate with humans and other agents.
My path combines academic research with hands-on engineering. Over the years, I've worked as a software engineer and technical lead, conducted and published peer-reviewed research, taught computer science and robotics, and built plenty of systems, prototypes, and experiments along the way.
Turgibot is my personal space on the web — part portfolio, part research notebook, and part knowledge base.
It's where I document what I've learned, built, researched, taught, and achieved. You'll find finished projects and publications alongside technical notes, experiments, ideas, and subjects I'm still exploring.
I don't want the things I learn to disappear into old folders, repositories, papers, or forgotten notes. I want to build a growing, searchable record of my work and knowledge — and, whenever possible, make it useful to others too.
Some things here are finished. Others are still evolving.
That's the point. Turgibot is a record of the journey, not just the results.
Guy Tordjman and Nadav Voloch
DSTRA-GNN predicts ETA with a vehicle-centered dynamic graph and explicit route awareness, cutting error on SUMO and Porto taxi data versus static-graph baselines.
Guy Tordjman and Nadav Voloch
Traffic-DSTG-Gen and TrafficLab generate route-aware dynamic graphs from SUMO and evaluate dynamic GNN ETA models in a live testing environment.
Guy Tordjman and Nadav Voloch
A toolchain that turns SUMO simulations and real trajectories into the same route-aware dynamic graph representation, plus a web tester for ETA models.
Nadav Voloch, Neev Penkar, and Guy Tordjman
A smart real-time estimation method that uses predicted future congestion in simulation to improve ETA over static Dijkstra routing.
A slide-deck workbook I wrote for learning core PDDL — domain vs problem, actions, logic, fluents, and time — with a try-it loop on every lesson.
A hosted reading workbook for Meneguzzi and Fraga Pereira's IJCAI-21 survey, with the paper kept next to the companion so we can discuss goal recognition as planning together.
A SUMO GUI that builds route-aware graph datasets from simulated traffic and real trajectories — the dataset-generation side of the NiDS 2026 paper, with two public Kaggle releases.