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1ical diagnosis has always been very observational, decisions were based off of what could be seen. There have been many successful attempts to add more quantitative metrics in medicine, but still diagnoses tend to be \'one-size-fits-all\'. The next big leap in the field comes in the form of personalized medicine, where each patient has their own customized treatment that\'s shaped through empirical research. We are developing a platform where surgeons can upload ultrasounds of an artery, which is then turned into a 3D mesh and is run through our ML model. This model, trained on geometric and physics-based data from ultrasounds, allows us to quickly and accurately predict critical flow parameters for surgeries like coronary interventions. The goal is to minimize the cost to make these parameters available to surgeons from any clinic, making diagnoses more informed.","leads":[{"name":"Rishabh Sharma","linkedin":"https://www.linkedin.com/in/rishabh2003sharma/"},{"name":"Ahash Ganeshamoorthy","linkedin":"https://www.linkedin.com/in/ahash-ganeshamoorthy/"},{"name":"Jatin Mehta","linkedin":"https://www.linkedin.com/in/jatin-r-mehta/"}],"members":[],"partnership":"UW Fluid Flow Physics Group","technologies":[],"themes":[],"result":"In progress","resultUrl":"https://radielhealth.com/"},{"id":"legacy-reinforcement-learning-chess-engine","started":"","term":"Past project","year":0,"title":"Reinforcement Learning Chess Engine","summary":"We are developing an artificial intelligence chess engine based on the work of Deep Mind on their chess engine\u2501Alpha-Zero. We are also iteratively testing and modifying the model to improve performance on hardware with much more limited processing power than what was available to Deep Mind when creating Alpha-Zero.","leads":[{"name":"Amya Singhal"},{"name":"Thomas Fortin"}],"members":[],"technologies":[],"themes":[],"result":"Completed"},{"id":"legacy-reinforcement-learning-for-doom-1993","started":"","term":"Past project","year":0,"title":"Reinforcement Learning for Doom (1993)","summary":"The project aims to build an RL agent that plays Doom (1993), improving upon the work from Playing FPS Games with Deep Reinforcement Learning (Lample & Chaplot 2017). They are currently implementing a DTQN model to train an agent in Vizdoom.","leads":[{"name":"Karman Singh"},{"name":"Krish Sethi"}],"members":[],"technologies":[],"themes":[],"result":"Completed"},{"id":"legacy-see-dr-mobile-diabetic-retinopathy-screening","started":"","term":"Current project","year":0,"title":"See-DR: Mobile Diabetic Retinopathy Screening","summary":"Diabetic retinopathy is a leading cause of preventable blindness, yet many at-risk patients lack access to regular eye screenings due to the cost and scarcity of specialized equipment. See-DR addresses this healthcare gap by transforming any smartphone into a portable screening device that can detect early signs of vision-threatening eye disease. By making screenings accessible in community clinics, pharmacies, and underserved areas, this tool has the potential to catch diabetic retinopathy before it causes irreversible damage, saving sight and improving quality of life for millions of people with diabetes worldwide.","leads":[{"name":"Jessica Yuan","linkedin":"https://www.linkedin.com/in/jessica-yuan1/"},{"name":"Hari Chandrasekhar","linkedin":"https://www.linkedin.com/in/hari-chandrasekhar/"}],"members":[],"technologies":[],"themes":[],"result":"In progress","resultUrl":"https://github.com/jessicayuan1/see-dr"},{"id":"legacy-solar-photovoltaic-output-prediction","started":"","term":"Past project","year":0,"title":"Solar Photovoltaic Output Prediction","summary":"We set out to develop deep learning models that predict solar photovoltaic output. These forecasts on solar energy production can drive better energy decisions, massively reducing carbon emmissions produced by power grids.","leads":[{"name":"Areel Khan"},{"name":"Carter Demars"}],"members":[],"partnership":"Open Climate Fix","technologies":[],"themes":[],"result":"Completed"},{"id":"legacy-stable-diffused-adversarial-attacks","started":"","term":"Past project","year":0,"title":"Stable Diffused Adversarial Attacks","summary":"We are exploring pre-trained computer vision models (i.e. MobileNet_v1) with the goal to exploit vulnerabilities through various adversarial attacks. Our work aims to develop an architecture that automates adversarial attack image generation in model misclassification.","leads":[{"name":"Andy Wu"},{"name":"Dhrumil Patel"},{"name":"Rayaq Siddiqui"}],"members":[],"technologies":[],"themes":[],"result":"Completed"},{"id":"legacy-winddm-conditional-diffusion-models-for-super-resolution-of-wind-data","
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