Ajay Vikram P

Ajay Vikram P

I am a master's student in Computer Science at Duke University, specializing in AI/ML and advised by Dr. Bhuwan Dhingra.

Previously, I obtained a B.Tech in Computer Science from NIT Karnataka with Dr. Jeny Rajan. I also spent time as a research intern at IISc's NeuRonICS Lab.

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Research

My research interests are in embodied intelligence, with the goal of advancing robots to understand and interact with the physical world. My work in multimodal perception, state estimation, efficient inference, VLMs, robot autonomy and computational neuroscience has shaped my interests in:

  • Multimodal World Modeling: How can world models learn grounded physical dynamics from exteroceptive observations (vision, audio, touch), proprioception, and language?
  • Hierarchical Planning and Control: How can these grounded models couple hierarchical reasoning and planning with closed-loop control for reliable action, failure anticipation, and recovery on long-horizon tasks?

Updates

Nov 2026 Won 3rd place & Most Inclusive Design Awards at the Neurotech BCI Hackathon.
Sept 2026 Presented a poster at IROS 2026.
Sept 2026 Presented 2 posters at the Duke Neurology Research Symposium.
May 2026 Participated in Duke Data+ over the summer.
May 2026 Our abstract on resting-state brain networks was accepted to EMBC 2026.
Apr 2026 NeuromorphicCore.ai featured our ICASSP 2026 work.
Jan 2026 Our neuromorphic star-tracking work was accepted to ICASSP 2026.
Aug 2025 Joined Duke University as a Master's student in Computer Science.
Mar 2025 Presented our neuromorphic tracking work through a poster and live demo at IISc Open Day.
Jan 2025 Helped organize and participated in the Bangalore Neuromorphic Engineering Workshop at IISc.
Oct 2024 Our team placed 5th in the IEEE BioCAS 2024 Grand Challenge on Neural Decoding.

Publications

Characterization of Resting-State fMRI in Gray and White Matter: Comparison of Signal Measures and Network Patterns

Under review
Ajay Vikram P, Bharat Karri, Arnav Gupta, Tina Yuan, Leonard E. White, Andrew M. Michael
3D U-Net pipeline for correcting head motion in fMRI volumes

A Novel Deep Learning Approach to Functional MRI Head Motion Correction without Motion Regressors

Under review
Ajay Vikram P, Bharat Karri, Andrew M. Michael

Experience

Service

  • Graduate Teaching Assistant, Probabilistic Machine Learning, Duke University Jan – Apr 2026
  • Organizer & Participant, Bangalore Neuromorphic Engineering Workshop, IISc Jan 2025
  • Placement Coordinator, Career Development Center, NITK Aug 2023 – Apr 2024
  • Volunteer Mentor, Leadership Development, Team Everest NGO Feb – Mar 2024

Blogs

Multi-Task LLM Post-Training

A study of how supervised fine-tuning and reinforcement learning with verifiable rewards affect instruction following, mathematical reasoning, and code generation.

SkillBlenderLC

An LLM-guided humanoid learning framework that identifies missing skills, generates reward functions, and trains new behaviors with reinforcement learning.

Ternarizing CNNs

A radar-based activity-recognition CNN compressed with ternary weights, quantized activations, and integer-only inference for embedded deployment.