About Me

Hello there! I am a research assistant at the CoNSens lab under Matthias Niemeier, where I perform comparative research into artificial and biological neural networks to advance the understanding of visual information processing in the brain. Specifically, I investigate the optimization principles that give rise to the emergence of the dual-stream topology in the visual cortex. I recently completed my BSc. (Hons) in computer science, cognitive science and mathematics. Previously, I worked at Ashton Anderson's CSSLab, where I researched the effect of LLM use on human creativity. Our findings revealed that while LLMs improve immediate performance, they introduce a risk of cognitive homogenization and reduced independent exploration in subsequent tasks. Outside of academics, my main focus is music. I love to make music on my laptop and play guitar with friends. I also frequently go down random hobby rabbit holes. Recently, I whittled a chess set , and biked to montreal from toronto !

Affiliations: CSSLab, CoNSens

Publications

• Reza T., Jordan E., Luo S., Tang J., Patel K., Niemeier M. (2025). From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks Submitted to Nature Neuroscience
  
  Abstract

  The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing question is whether this division reflects intrinsic architectural priors or emerges from task demands. We trained a single convolutional network to perform classification and grasp prediction of 3D objects, without imposing modular structure. Dual-stream topology - functionally distinct visuomotor and perceptual pathways - emerged spontaneously with rich cross-communication. Shapley value analyses revealed that action- and perception-selective features developed progressively across depth, reflecting task-driven hierarchical specialization. Time-resolved EEG showed that model activity mapped onto dissociable temporal components in human cortex: ventral-aligned signals emerged early and late, where dorsal- and ventral-aligned responses coincided in the intervening interval. These results demonstrate that task optimization alone can explain core features of dorsal-ventral organization, and that distinct temporal roles for perception and action arise naturally atop a shared feedforward scaffold, without requiring architectural hard-coding or recurrence.

  [Preprint] [Github]


• Kumar H., Vincentius J., Jordan E., Anderson A. (2025). Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking CHI Conference on Human Factors in Computing Systems 2025
  
  Abstract

  Large language models are transforming the creative process by offering unprecedented capabilities to algorithmically generate ideas. While these tools can enhance human creativity when people co-create with them, it’s unclear how this will impact unassisted human creativity. We conducted two large pre-registered parallel experiments involving 1,100 participants attempting tasks targeting the two core components of creativity, divergent and convergent thinking. We compare the effects of two forms of large language model (LLM) assistance—a standard LLM providing direct answers and a coach-like LLM offering guidance—with a control group receiving no AI assistance, and focus particularly on how all groups perform in a final, unassisted stage. Our findings reveal that while LLM assistance can provide short-term boosts in creativity during assisted tasks, it may inadvertently hinder independent creative performance when users work without assistance, raising concerns about the long-term impact on human creativity and cognition.

  [Paper]