UT’s Texas Robotics and Machine Learning Lab collaborated with UT’s Amazon Science Hub to host the AI and Robotics Research Symposium from March 4-6.
The three-day symposium included talks from experts, academics and leaders in the field. April Cafaro, senior events coordinator for Texas Robotics, said the event’s purposes included networking opportunities and showcasing faculty and student research in robotics, including multipurpose and medical robotics.
Chaoyi Li presented the Booster T1, the latest version of humanoid robots developed by the Beijing-based tech company Booster Robotics. He demonstrated the robot’s capabilities by playing a video showcasing its lightweight, tiny stature of about 4 feet and 66 pounds and its durability, with it remaining unharmed after a blow by a man wielding a sledgehammer to a cinder block placed on its abdomen. The Booster T1 is also advanced in mobility, replicating human activities like push-ups, soccer and kung fu.
In the panel “AI in Healthcare,” UT professors and researchers Jon Tamir, Ann Majewicz Fey, Alex Huth and Vagheesh Narasimhan discussed how AI and robotics could shape the future healthcare landscape.
Narasimhan, an expert in AI and genomics, discussed how AI can assist doctors by improving risk prediction models. A risk prediction model is a statistical model that uses data to calculate a patient’s likelihood of having a medical issue such as cardiovascular disease. He discussed integrating genomic information to produce a more accurate predictor.
“What we’re trying to do here is not to replace the doctor, but to replace and refine that model that they’re using for risk prediction,” Narasimhan said.
Computer science sophomore Sohan Subudhi attended the event to learn more about AI in healthcare and said it was interesting to hear professors’ thoughts on these fields.
Likewise, computer science sophomore Kunal Tiwari said the symposium was useful for his professional interests.
“I want to learn more about AI and start doing this stuff,” Tiwari said. “Hearing about the different techniques that they’re using at the forefront and how that’s helping make their models more accurate or more applicable are interesting takeaways that I can use later on, on my own.”
