About me

I am the Director of AI Development at OraQ AI, where I lead the strategic direction and development of AI-powered diagnostic, prognostic, and treatment recommendation systems for dentistry. With a PhD in Computer Science and Artificial Intelligence from the University of Sussex and over a decade of experience, I specialize in applying AI, Machine Learning, and Neurotechnology to solve complex real-world problems.

Beyond my industry role, I am an Adjunct Professor at the Canadian Centre for Behavioral Neuroscience of the University of Lethbridge, where I explore the intersection of AI and Neuroscience. My research aims to leverage computational neuroscience insights to advance AI while using AI to better understand brain function.

Passionate about mentorship and diversity in STEM, I actively support underrepresented communities through hackathons, mentorship programs, and community-building initiatives.

Academic Projects

Automated homecage behaviour monitoring using AI

Documenting a mouse’s “real world” behavior in the “small world” of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. homecage automated montioring

Experience dependent changes in brain activity at multiple scales

Changes in neuronal activity can be used by the brain to store information about the outside world. In this project we study experience dependent changes in the spatiotemporal dynamics of stimulus evoked cortical responses using optical wide-field imaging and single-unit recordings.

In this work we study how patterns of evoked activity are modified by experience at meso and microcircuit scales using voltage and extracellular glutamate transient recordings over widespread regions of mice dorsal neocortex or single-unit activity recordings with multi-shank silicon probes in rat cortex.

Study of brain activity at multiple scales

The brain is formed of complex networks that communicate at different spatiotemporal scales. In order to study such communication processes, one has to look into brain activity at multiple scales simultaenously.

In this paper we recorded brain activity over large cortical areas using VSD wide-field imaging and single-unit activity at multiple brain areas simultaneously.

Reverberation of evoked cortical activity during anaesthesia

Memory and learning are some of the fundamental cognitive processes in animals. Despite continuous research progress in this area, our understanding of memory formation and consolidation processes is far from complete. The study of the neuronal activity implicated in such processes, in many cases, involves long recordings during sleep and awake periods, which in turn, can be complicated because of technological or experimental problems. In this project we show that the hallmarks of memory formation and consolidation can be studied during anaesthesia and that these processes depend on cortical state.

Simulated spatial navigation

Using sensory immediate (egocentric) information from its surroundings, an agent can make predictions about its location in space (allocentric). These predictions can improve the performance of a simulated agent that learns to navigate in a virtual world when similar sensory information is found in the future.


egocentric-allocentric learning systems egocentric-allocentric learning systems

This project uses reinforcement learning to solve a spatial navigation task in a simulated world where an agent exploits sensory-based predictions about its spatial location. A similar strategy can be used not only in spatial tasks, but also in situations where local information can be used to predict consequenses in a more global domain. [paper]

Automated homecage behaviour monitoring

I am interested in developing tools to monitor behaviour and brain activity in the homecage in an automated manner. With the increase in computational power and reduction in costs, microcomputers such as the Raspberry Pi open a great opportunity to design high-throughput systems to study rodent behavior. homecage automated montioring

We are currently working to develop such systems and combine them with state-of-the-art machine learning algorithms to learn more about the brain.[paper]

Publications