profile photo
name:Mikhail Kennerley
role:Lead Research Engineer, Vision AI
phd:ECE · NUS · advised by Robby T. Tan
focus:domain adaptation, semi-supervised learning, object detection
// about

My name is Mikhail Kennerley, and I am a Lead Research Engineer at Home Team Science & Technology Agency (HTX), where I work on computer vision and AI systems. I obtained my PhD in Electrical and Computer Engineering from the National University of Singapore, where I was advised by Prof. Robby Tan and Prof. Bharadwaj Veeravalli. During my doctoral studies, I was also affiliated with Institute for Infocomm Research (I2R), A*STAR, where I collaborated with Dr. Wang Jian-Gang.

Alongside my research and engineering work, I am actively involved in community initiatives through Mendaki Club, where I lead career-focused workshops aimed at helping youths in the Malay/Muslim community explore educational pathways and professional opportunities. I have also contributed to youth development initiatives through programmes organised by the National Youth Council (NYC).

Outside of work, I spend a fair amount of time painting Warhammer 40,000 miniatures, mostly Black Templars (which I find unexpectedly therapeutic) usually accompanied by a good cup of filter coffee.

// news
// research

My research focuses on learning robust visual models under imperfect supervision, particularly for object detection and semantic segmentation. I study domain adaptation, semi-supervised learning, and dataset unification under distribution shift.

// speaking
topic role host date
Youth Conversations on Online Harms Panel Moderator & MC National Youth Council & Ministry of Law, Singapore 2025
Domain Adaptation and Dataset Unification for Object Detection Research Speaker Cambridge Image Analysis Group, University of Cambridge, UK 2025
Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection Research Speaker A*STAR Institute for Infocomm Research 2024
Domain Adaptation in Day-to-Night Data Shift Research Speaker A*STAR Institute for Infocomm Research 2023
// teaching