Research

I’m interetested in understanding the brian and apply learned mechanisms to better people’s lives. I’m also passionated about programming and engineering and believe the synergy between neuroscience and engineering can create powerful brain-machine interfaces that revolutionize how people live, think, and interact with the societies.

In the initial phase of my PhD, I delved into the neural underpinnings of memory forgetting. Through literature review and behavioral assays, I confirmed that single-trial aversive conditioning in Drosophila results in short-term memory. Intriguingly, we found that this ‘forgotten’ information is not irretrievable; it can be resurrected through context-dependent cues. To dissect the neural circuits responsible for this memory recovery, I exploited the genetic tools available in Drosophila, manipulating specific neurons using optogenetics and thermogenetics. My research identified a pair of dopamine neurons—one in each hemisphere—as key players in the memory recovery process. Additionally, we discovered that altering the contextual reminder could lead to the implantation of false memories, mediated by a distinct dopamine pathway. To analyze the data, I developed Python-based pipelines for both behavioral and imaging studies.

During my Master time, I independently built a behavioral setup for operant learning in larval zebrafish, which includes behavioral tracking, visual patterns presentation, and stimuli delivery (e.g., electric shocks). All these are automatically controlled by my software BLITZ. In the course of this project, I also mentored three undergraduate students.

In another collaborative project, I measured distraction in human decision-making. I automated the experimental procedures with MATLAB and modeled population responses with a psychometric function.

In my early scientific career, I seized the opportunity to learn from top-tier neuroscientists. At Engert Lab, Harvard University, I learned how to design and conduct behavioral experiments and utilized my expertise in MATLAB to help with the data analysis. In Gilles Laurent’s Laboratory at Max Planck Institute for Brain Research, I utilized my computational skillset in building a predictive model for local field potentials in reptilian cortex.

I enjoy new challenges and adapt quickly to new environments as demonstrated in the above experiences. With my passion for engineering, I also built a multi-sensory robot in Cajal Experimental Neuroscience Bootcamp. To follow the advances in AI and neurotechnology, I attended the NIN Neurotechnology Summer School in Amsterdam and equipped myself with AI skills such as Machine Learning with Tensorflow on Google Cloud.