Bishoy M. Galoaa

I'm an upcoming PhD student in the Electrical and Computer Engineering department at Northeastern University's College of Engineering. I work under the supervision of Prof. Sarah Ostadabbas in the Augmented Cognition Lab (ACLab). My research is driven by a passion for building intelligent systems that transcend human limitations—blending work in computer vision, medical AI, and human-machine synergy.

• Multi-Object Tracking: Developing transformer-enhanced algorithms for tracking subjects in complex environments with occlusions and varying lighting conditions.
• Human-Machine Interaction: Exploring motion analysis and anomaly detection for assistive technology, focusing on exoskeleton control and rehabilitation.
• Medical AI: Creating personalized prognostic models for oncology that enhance clinical decision-making through interpretable machine learning.

• More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions (ICML 2025)
• Dragontrack: Transformer-enhanced graphical multi-person tracking (WACV 2025)
• Classification of Infant Sleep–Wake States (WACVW 2025)
• Multiple toddler tracking in indoor videos (WACVW 2024)
• Real-Time Uncertainty Detection for Safe, Adaptive Exoskeleton Control (ICRA Workshops 2025)
• Personalized predictive model for Salivary Gland Cancer (COSM 2024)
• AI Model for Optimizing Treatment of Salivary Gland Malignancies (AAO-HNSF 2024)
• Machine Learning–Assisted Decision Making in Orthopaedic Oncology
• Survival Prediction in Undifferentiated Pleomorphic Sarcoma (CTOS 2024)

This section highlights a creative experiment where I aimed to build one novel idea per day over a month.

• Inattention NotaBene – A novel regularization method that strategically "forgets" less important features through a stacked dropout mechanism, offering an alternative to traditional attention mechanisms.
• ROCKET – An innovative path planning system that identifies collision paths first to find optimal trajectories in complex environments using inverse collision sampling.
• Secretary Template Matching – An online template matching algorithm inspired by the Secretary Problem, dynamically adjusting thresholds based on observed data patterns for improved real-time decision-making.
• 2F1B – A novel optimization technique introducing controlled oscillation in neural network training by alternating two forward steps with one backward step, enhancing the optimization trajectory.
• Knock-Knock – An optimization algorithm inspired by bat echolocation, emitting "echo signals" to navigate complex loss landscapes effectively.

Originally shared as a public challenge on LinkedIn.

• Best Poster Presentation Award – COSM-AHNSF (2025)
• COE Outstanding Graduate Student Award – Northeastern University (2025)
• COE Outstanding Graduate Student Award – Northeastern University (2024)
• Best of Scientific Orals – AAO-HNSF Annual Meeting & OTO EXPO (2024)