Monthir Ali

VR/AI Research Engineer

University of Utah • Part time

Jan 2019 - Dec 2024 • 5 yrs 11 mos

• Developed and deployed ML models for VR attention prediction and behavioral analysis using PyTorch and TensorFlow, processing 100+ hours of multimodal VR sensor data (gaze, IMU, spatial) with 78% prediction accuracy • Built end-to-end data pipelines for VR spatial memory prediction, implementing automated collection, preprocessing (Pandas/NumPy), feature engineering, and model evaluation workflows for immersive environments • Created real-time AI-driven VR systems in Unity (C#) integrating eye-tracking and procedural scene generation, optimizing performance under varying GPU loads while maintaining <100ms inference latency • Engineered multimodal deep learning architectures (LSTM + CNN) for temporal sequence modeling in VR, improving user attention guidance efficiency by 30% in controlled studies (N=100+) • Designed parallel simulation frameworks to evaluate VR ML model performance against live behavioral data, creating benchmarking tools for continuous validation of AI systems in immersive environments • Developed computer vision algorithms using OpenCV for real-time gaze-triggered spatial interaction analysis in VR distributed processing pipelines • Published peer-reviewed research in IEEE/ACM conferences on AI-driven VR attention modeling and human-centered ML systems

Software Engineer

BananIT • Full time

Feb 2015 - Feb 2017 • 2 yrs

• Developed multimodal sensor fusion pipelines combining gaze-tracking and IMU data to detect attention patterns under cognitive load in VR systems, implementing real-time predictive models for fatigue detection • Built data visualization tools and attention-aware prompt systems using Python and OpenCV for adaptive interface prototyping • Created lightweight data synchronization pipelines for timed trials, processing high-frequency sensor data streams