update about

Author: codez266

_pages/about.md

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 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 to reason under risk and uncertainty in complex decision-making scenarios</span> by modeling their underlying thought processes and 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. I also have strong Reinforcement Learning (RL) foundations that I have applied to model human behavior, which positions me well to explore <b>RL-based fine-tuning of LLMs</b>. For instance, I developed a <a href="{{ site.baseurl }}/projects/project_modeling_indoor_behaviors" target="_blank">deep RL system <i class="fa-solid fa-link"></i></a> from scratch to simulate indoor human behavior and COVID-19 transmission dynamics (code available on request), demonstrating how RL can capture and reason about complex behavioral patterns. The following three broad directions describe my research focus and future vision.
+I develop adaptive AI systems that <span style="color:deeppink;">enable people to reason under risk and uncertainty in complex decision-making scenarios</span> by modeling their underlying decision processes and 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 these systems. For example, I recently built a bayesian network from a massive dataset of 3M Census records to model personal preferences and used it to study the design of personalization agents that respect users' privacy. I also have strong Reinforcement Learning (RL) foundations that I have applied to model human behavior, which positions me well to explore <b>RL-based fine-tuning of LLMs</b>. For instance, I developed a <a href="{{ site.baseurl }}/projects/project_modeling_indoor_behaviors" target="_blank">deep RL system <i class="fa-solid fa-link"></i></a> from scratch to simulate indoor human behavior and COVID-19 transmission dynamics, demonstrating how RL can capture and reason about complex behavioral patterns. 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.
 
 2. <b>Designing Human-AI interaction to support and continuously improve through interaction with experts:</b> Designing user interfaces and interactions that naturally support and augment experts' work practices, and enable AI to <span style="color:deeppink;">unaimbiguously understand user goals, and mental processes during interactions</span> to improve AI systems during use.
 
-3. <b>Advancing the responsible use of AI in helping people become experts:</b> Designing methods to support equitable education and advancing the responsible use of AI in education by  <span style="color:deeppink;">studying decision process of educators and designing technologies that leverage established pedagogical principles</span> to augment educators's expertise.
+3. <b>Advancing the responsible use of AI in helping people become experts:</b> Designing methods to advance the responsible use of AI help people become experts by <span style="color:deeppink;">studying decision process of educators and designing technologies that leverage established pedagogical principles</span> to augment educators's expertise.
 
-I have strong connections with the Wikipedia open-source community. I have contributed to the codebase that powers the mobile version of Wikipedia, and I had also administered their Google Summer of Code internship program in 2016,17. For my services I was nominated by Wikimedia Foundation to attend Google summer of code mentor summit in 2017, and invited to present my research at the monthly Wikimedia research showcase in 2021. Currently, I contribute to the Wikipedia community in research, such as serving on the program committee of Wiki Workshop, Wikipedia's annual research workshop.
+I have strong connections with the Wikipedia open-source community. In 2014-18, I extensively contributed to the codebase that powers the mobile version of Wikipedia and was a prominent code reviewer for mobile Wikipedia. I had also administered Wikimedia Foundation's Google Summer of Code internship program in 2016,17. For my services I was nominated by the Wikimedia Foundation to attend the Google summer of code mentor summit in 2017, and invited to present my research at the monthly Wikimedia research showcase in 2021. Currently, I contribute to the Wikipedia research community, such as serving on the program committee of Wiki Workshop, Wikipedia's annual research workshop.