Work with thought leaders and academic experts in vision

Dr. Haikun Huang holds a Ph.D. in Computer Science from UMass Boston and is a postdoctoral research fellow at George Mason University. His graduation thesis was titled AI-driven Computational Design Tools For Synthesizing Human-centric Design and won the Umass Boston year's graduate program award. With a strong background in AR/VR/MR, computational design, graphics, HCI, and vision, he is passionate about applying artificial intelligence techniques to create innovative 3D content creation tools and virtual experiences.   Dr. Huang has published his research in prestigious conferences such as IEEE VR and ACM CHI, and his work has been recognized with a Best Paper Honorable Mention Award at CHI 2019. He is also an active reviewer for IEEE VR and CHI, contributing to advancing these fields. From 2017 to 2023, he has successfully published 21 papers, which have been cited more than 470 times. In the first half of 2023 alone, it was cited more than 180 times. At the same time, his h-index is 12, and i10-index is 15.   In addition to his academic achievements, he has years of industry experience, particularly in the game development sector. He has also been a columnist for popular game development forums in China, where he shared his expertise and insights with fellow developers.   He has also served as a teaching assistant for various computer science courses, including Computer Games Programming, Computer Vision, Programming in C, and (Computer Architecture and Organization at UMB. These experiences have allowed him to hone his teaching skills and effectively communicate complex concepts to students.   As a co-founder and CTO of Great Victory Legends, he gained valuable experience leading technical teams and developing cutting-edge solutions. He is confident in bringing this expertise into the classroom and providing students with a comprehensive understanding of the subject matter.   He also runs his studio as a freelance and sells the tools on Unity Asset Store. The tools he develops are all about practical tools to improve development efficiency. UPython 3 Pro is his masterpiece. It provides real-time communication between Unity and Python. It was used in the research projects he was involved in. AR/VR researchers deeply love it.

My core interests lie in understanding how children and adolescents perceive and interpret social signals and how emerging functional specificity of the developing brain supports this process. My approach primarily involves using the face processing system as a model domain. Faces are dynamic stimuli from which we extract many different kinds of information (e.g., gender, age, emotional state, mate potential, social status, trustworthiness, intentions, “person knowledge”). All of these processes must be executed accurately and rapidly for many faces over the course of a single day, making face processing among the most taxing perceptual challenges confronted by people in their day-to-day life. Given that faces are also the pre-eminent social signal, studying developmental changes in the behavioral and brain basis of face processing in typically developing individuals and in those affected by social-emotional disorders may index a core set of developmental changes within the broader social information processing system. I employ converging methodologies, including functional (fMRI) and structural magnetic resonance, and diffusion tensor imaging (DTI) along with detailed behavioral paradigms in both typically developing populations and those with developmental disorders to examine development from early childhood to adulthood.

Resourceful and highly enthusiastic research scientist with a diverse cultural and multidisciplinary background. A valuable team player having contributed to scientific research on fundamental and translational projects in the UK, Italy and the USA. I’m also an effective communicator who enjoys collaborating with, training and learning from colleagues. I have focused my career around four heavily interconnected fields: Visual Neuroscience, Neuro-electronic interfacing, retinal rescue and microscopy. Visual Neuroscience: Focused on elucidated the intricacies of the visual system, I have investigated retinal and cortical circuits using a variety of approaches, including in vivo electrophysiology, ex vivo optophysiology/Multi-electrode arrays, and immunohistochemistry. My contributions focused mostly on the influence of melanopsin signaling in retinal coding. Neuro-electronic interfacing: Dedicated to the seamless integration of neural tissue (neurons, glia, vascular system) with computational equipment (electrodes, nano-particles, optogenetics, chemogenetics), I have investigated the biophysical, molecular and ultra-structural properties of this interface in both fundamental and translational projects. Retinal rescue: Dedicated to the study and treatment of retinal dystrophies, I have worked on an array of degeneration models (Diabetic Retinopathy, P23H, CRX, RD1, RS1, RD10, RCS), and investigated cutting edge treatment strategies (optogenetics, nano-particles, bio-electronic prostheses, low molecular weight compounds, biologics). Microscopy: For the past 15 years, I have been at the forefront of developing cutting-edge structural and neurophysiological imaging techniques. These innovative assays have been successfully applied across a diverse spectrum of scales: from investigating intricate biological interactions at the nanoscopic level using electron microscopy to mapping the complex neuro-glio-vascular interactions of hundreds of retinal cells through multiphoton imaging.