I am fascinated by representational accounts of cognition. I examine whether representations are similar across natural and artificial minds, how we can build representational models of cognition, and how these ideas can inform human–AI systems. I am also interested in characterizing how attentional processes adaptively select information to produce decision representations, and in how uncertainty is represented. Beyond individual decision-making, I am interested in understanding differences across individuals and systems, and in how those differences might be leveraged to build systems of collective intelligence. To answer these questions, I use experimental and computational methods.

Currently, I am a postdoctoral scholar working primarily with Brandon Turner at Ohio State University. I finished my dual PhD in Cognitive Science and Psychology, advised by Jennifer Trueblood at Indiana University, my Masters in Psychological Sciences from Vanderbilt University and my Masters in Mathematical Sciences at the University of Hyderabad.

Some questions I am currently working on are:

1. What is the geometry of mental representations in natural and artificial minds?

   I find that despite having different evolutionary histories and architectures, natural and artificial minds can have similar representations, even in specialized medical domains. This has important applications. For instance, we developed cognitive models and human-AI systems.

2. What is attention?

   I think of attention as the selection of information (or computations) for a decision representation. This raises related questions of how selected information impacts decision making, and what information is selected in the first place. I am currently developing models linking attention and decision making in category learning and multiattribute choice by varying presentation format and salience. I am also working with several experts in the field on a synthesis of attention in decision-making.

3. How do we produce collective intelligence?

   We find that if you put enough novices together, they can classify white blood cell images better than experts. When crowdsourcing judgments from an app, the best approaches model individual decision idiosyncrasies and task features. I am currently exploring how one might intelligently combine two minds by modeling their individual decision behavior.

4. How do we scale experimentation and modeling?

   We conducted an exploratory registered report, recruiting more than 2000 individuals and assigning them to one of 144 conditions to conduct a large experiment in multi-attribute choice. In another project, we used a switchboard analysis to test hundreds of wisdom of the crowd models. I am currently working on formalizing the switchboard approach.

I always have time to chat about life, the universe, and everything. Shoot me an email!

See Publications, Projects, Computational Methods, Background, Curriculum Vitae (CV) Google Scholar

News

• October 2026: I am visiting Princeton and presenting work on a Representational Framework for Natural and Artificial Minds in Decision Making
• September 2026: I am presenting my work on Converging Representations in Humans and Machines in Medical Domains at the Fall Retreat at OSU.
• July 2026: I organized a symposium on Computational Theories of Attention in Decision Making at the Society for Mathematical Psychology. We got to hear from Gordon Logan, Brad Love, Ian Krajbich, Will Hayes and Jordan Deakin. I presented work on a comparative cognition project, comparing attention across humans, pigeons and rats.
• July 2026: I presented work on Switchboard Modeling in the fun Annual Summer Interdisciplinary Conference at Vancouver, organized by Richard Shiffrin.
• June 2026: I attended the Neuro Monster Conference (International Conference on the Mathematics of Neuroscience and AI) at Rome.
• May 2026: I attended the Sloan-Nomis Workshop on Cognitive Foundations of Economics organized at Columbia University
• May 2026: I gave a talk at Audio Information Research Lab about how AI can be used in computational modeling in decision making. Broadly, AI can be (i) black predictive boxes, (ii) instrumental components, (iii) alternate information processing systems.
• April 2026: I won the internal Decision Science Collaborative Grant for my project on the representations of white blood cells.
• April 2026: I was interviewed by the Smooth Brain Society about wisdom of the crowds, representations and depression on social media link
• March 2026: I presented my work at the Center for Conflict and Cooperation at NYU
• November 2025: I presented our work on the Switchboard Analyses at Mathematical Psychology at Psychonomics
• August 2025: I started my postdoc with Brandon Turner
• May 2025: I finished my PhD!
• April 2025: I won the Outstanding Researcher Award at Indiana University
• April 2025: Indiana University published a short piece on our PNAS Nexus article link
• March 2025: Our paper on Depression on Social Media got published at PNAS Nexus. open access link
• Jan 2025: Our paper on the impact of presentation effects on the attraction effect got Published at Judgment and Decision Making open access link
• ...
• August 2022: I moved to Indiana University along with Jennifer Trueblood
• August 2019: I started my PhD at Vanderbilt University