Amartaivan Sanjjamts

🎓 Education

• The University of Osaka — Ph.D. (Information Science and Technology)
  Apr 2023 – March 2026
  Skills: Agent-Based Modeling · Data-Driven Modeling · Applied Machine Learning · Evaluation of Complex Systems
• The University of Osaka — Master’s in Information Science and Technology
  2016 – 2018
  Skills: Mathematical Optimization & Modeling · Data Visualization · Equation-Based Modeling · 2D Packing Problems
• National University of Mongolia — Bachelor of Computer Science (Management of Information Systems)
  

💼 Working Experience

• NTT Center for Computing Technologes, NTT R&D
  2026.4 – Now · Tokyo, Japan
  
• ICT Group Mongolia — Head of Research and Development
  2021 – 2023 · Ulaanbaatar, Mongolia
  Projects: Research on Integrating Digital Solutions · Advancing Smart City Innovations
• Tavan Bogd Group — Head of IT Department
  2020 – 2021 · Ulaanbaatar, Mongolia
  Projects: IT Strategic Planning & Policy Design · Enterprise IT Solutions · ERP and Internal Platform Development
• mtes Neural Networks — Research and Development Project Manager
  2018 – 2020 · Tokyo, Japan
  Projects: Private Cloud Platform for Structural Health Monitoring · Time-Series Data Analysis & Modeling · Ready-to-Use Templates Development
• Unitel Group
  • Senior Billing Engineer
    2012 – 2015 · Ulaanbaatar, Mongolia
  • IT System & Billing Engineer
    2008 – 2012 · Ulaanbaatar, Mongolia
  Projects: Gateway Systems Implementation · Over-the-Air (OTA) System Integration · Convergence Billing System Deployment · Mobile Billing Integration · Business Support Platform Design & Maintenance

🚀 Recent Research Projects & Prototypes

• Data-Driven Framework for Agent-Based Pedestrian Modeling and Simulation
• Real-Time Flock Detection in Pedestrian Trajectories
• Space Utilization and Emergent Behaviors in Group vs. Single Pedestrians
• Real-Time 3D Reconstruction of Pedestrians from Trajectory Data

• 🧠 Interactive Showcases of Agent-Based Modeling and Simulation (ABMS)
  Prototype visualization series demonstrating fundamental ABM principles.
• 🧩 Modeling Complex Systems via ABMS
  Conceptual exploration of emergent phenomena across domains.

📄 Publications & Research Outputs

• “Preliminary Study on Space Utilization and Emergent Behaviors of Group vs. Single Pedestrians in Real-World Trajectories”, arXiv preprints, arXiv:2508.18939v1, 2025 [PDF]
• “Efficient Calibration of Heterogeneous Agent-Based Pedestrian Model Using PSO With Deep Learning Surrogates”, IEEE Access, 2025 [PDF]
• “Impact of Scheduling Methods on Agent-Based Evacuation Dynamics”, International Symposium on Scheduling, Tokyo, Japan, 2025. [Abstract]
• “Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models”, arXiv preprints, arXiv:2502.15252, 2025. [PDF]
• “Data-Driven Agent-Based Pedestrian Modeling and Simulation in Indoor Environments”, Winter Simulation Conference, Florida, USA, 2024. [PDF Abstract]