Jumabek Alikhanov

Jumabek
Alikhanov

PhD in AI | ML Researcher | CEO
Bridging Computer Vision, Cybersecurity, and Positive Computing through innovative AI research

About Me

I am an AI researcher and entrepreneur with a unique interdisciplinary background spanning computer vision, cybersecurity, and data science for mental health. I completed my PhD in February 2025, conducting research across multiple labs in collaboration with professors from the USA, Europe, and South Korea.

As CEO and Founder of HumbleBee.AI, I lead AI research and product development, delivering cutting-edge solutions to clients globally. My work focuses on developing efficient, practical AI systems that bridge the gap between academic research and real-world applications.

Education

PhD (February 2025)

Combined studies in computer networks, cybersecurity, positive computing, data science for mental health, and computer vision

Master's Degree

AI / Deep Learning / Computer Vision, Inha University

Bachelor's Degree

Computer Software Engineering, Tashkent University of Information Technology

Key Skills & Expertise

Research Contributions

My research bridges multiple domains, creating novel methodologies that address real-world challenges in AI and machine learning.

LITE & PRIME

Developed innovative paradigms for multi-object tracking in video analytics, integrating efficient ReID features and optimizing the speed-accuracy trade-off in tracking systems. LITE represents a paradigm shift in how tracking systems handle identity features.

Context-Filtered Features (CFF)

Created methodologies for detecting opportune moments in sensor data for mental health interventions. This work in positive computing enables better smartphone-user receptivity prediction and stress detection from mobile sensor data.

Impact of Sampling in ML

Investigated how sampling strategies affect machine learning performance in cybersecurity applications, particularly in network intrusion detection systems. This work combines statistics, computer networks, and machine learning to improve security systems.

Selected Publications

LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration
With Dilshod Obidov, Hakil Kim | Available on CatalyzeX
Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction
IEEE Internet of Things Journal, 2024
A Reproducible Stress Prediction Pipeline with Mobile Sensor Data
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2024
Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods
IEEE Access, 2023
Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches
IEEE Access, 2022

Professional Profiles

HumbleBee.AI

CEO & Founder

DBLP

Academic Publications

Upwork

Top-Rated Plus Freelancer

Bold.pro

$100K+ in Global AI Projects

CatalyzeX

Research Papers

GitHub

Code & Projects

Let's Connect

I'm always interested in collaborating on innovative AI projects, discussing research opportunities, or exploring consulting engagements.