Autonomous Experimentation Development for Polymer Nanocomposite Research
This project integrates autonomous experimentation powered by AI to study process-structure-property relationships in polyelectrolyte-based polymer nanocomposites.
Scanzello Lab for Translational Osteoarthritis Research
Our laboratory focuses on understanding the stimuli and clinical consequences of immune system activation and inflammation in osteoarthritis (OA) and related joint injuries, with the goal of developing novel therapeutics to treat this debilitating joint disease. OA is the most common musculoskeletal disease and a leading cause of chronic joint pain and musculoskeletal disability worldwide.
Immunoengineering for Cardiovascular Disease
Cardiovascular disease is the leading cause of death worldwide. There is an urgent clinical need for treatments distinct from current options. To tackle this problem, we leverage the immune system, protein engineering, and pharmacokinetic modeling.
Precise Genome Editing and Engineering for Human Health
We welcome highly motivated undergraduate researchers to work together!
Studying cell migration in confined microenvironments
When cells grow or move within dense microenvironments their nuclei are subjected to different degrees of deformation. We investigate the mechanisms underlying migratory strategies, cell function and cell fate in scenarios where cells experience mechanical stress.
Basic Epilepsy Research: Exploring the Interface Between Neuroscience and Engineering
Join us to study how epilepsy develops using in vivo and in vitro models, along with engineering approaches.
AI-enhanced wearable biosensor system for objective and proactive mental healthcare
Current psychiatric practices for diagnosing mental illness based on consultation and questionnaires are intermittent and subjective in nature. Here, we propose to develop an AI-enhanced wearable system for real-time and quantitative assessment of mental health. Utilizing aptamer-based non-invasive biosensors integrated with wireless flexible electronic patches, our technology can provide continuous monitoring of biomarkers relevant to psychological states. Through advanced algorithms based on artificial neural networks, our system will allow superior sensitivity and specificity compared to traditional methods. Our goal is to achieve a clinically-deployable prototype with robust biomarker detection and algorithmic prediction capabilities, paving the way towards proactive mental healthcare.