update about

Author: codez266

_pages/about.md

@@ -32,7 +32,7 @@ latest_posts:
 
 Thank you dear visitor for stopping by! I am a final year PhD candidate at University of Michigan, Ann Arbor. My research focus and interests are at the intersection of Machine Learning and Human-Computer Interaction (HCI).
 
-I develop adaptive AI systems that <span style="color:deeppink;">enable people reason under risk and uncertainty in complex decision-making scenarios</span> by modeling their underlying thought processes—not just their observable behaviors. For example, in education, inferring students' conceptual gaps requires reconstructing their mental models from their learning trajectories, not just identifying surface-level mistakes. I borrow from <span style="color: deeppink">cognitive science and probabilistic machine learning</span> to design AI with experts' mental model to improve Human-AI interaction. By modeling people's latent cognitive states, my methods <span style="color: deeppink">improve reasoning of AI systems beyond observed behaviors</span>, improving overall learning efficiency and accuracy. I bring in strong computational and model building skills from my prior industry experience to build systems for Human-AI interaction and my training in HCI allows me to conduct real world study for improving Human-AI interaction. For example., I recently built a bayesian network from a massive dataset of 3M records to model personal information and using it to study personalization - privacy trade-off. The following three broad directions describe my research focus and future vision.
+I develop adaptive AI systems that <span style="color:deeppink;">enable people reason under risk and uncertainty in complex decision-making scenarios</span> by modeling their underlying thought processes—not just their observable behaviors. For example, in education, inferring students' conceptual gaps requires reconstructing their mental models from their learning trajectories, not just identifying surface-level mistakes. I borrow from <span style="color: deeppink">cognitive science and probabilistic machine learning</span> to design AI with experts' mental model to improve Human-AI interaction. By modeling people's latent cognitive states, my methods <span style="color: deeppink">improve reasoning of AI systems beyond observed behaviors</span>, improving overall learning efficiency and accuracy. I bring in strong computational and model building skills from my prior industry experience to build systems for Human-AI interaction and my training in HCI allows me to conduct large scale evaluations in people's work context for improving Human-AI interaction. For example., I recently built a bayesian network from a massive dataset of 3M records to model personal information and using it to study personalization - privacy trade-off. The following three broad directions describe my research focus and future vision.
 
 1. <b>Desiging computational models that can understand and improve expert decision-making</b> <span style="color:deeppink;">(AI to critique not obey)</span>: Furthering the design of computational models that can <span style="color:deeppink;">understand and reason about experts' decision processes, and how they reason about and balance principles in their decisions</span>. For example, understanding how instructors balance providing the answer versus guiding students in tutoring scenarios.