PhD candidate in Electrical & Computer Engineering at Purdue University, advised by Prof. Joseph Makin. I received my B.S. in Electrical Engineering from UET Lahore, Pakistan, and worked for approximately six years as an Instrumentation & Control Engineer before beginning my PhD.

My PhD research focuses on applying deep learning to inverse problems and computational neuroscience. In inverse problems, I developed the Restart Posterior Sampling (RePS) framework, a diffusion-based posterior sampling algorithm for image restoration. In neuroscience, I investigate the use of pretrained automatic speech recognition (ASR) models as encoding models of neuronal responses in the auditory cortex, as well as generative diffusion priors for characterizing the tuning preferences of individual neurons.

More broadly, I am interested in diffusion and flow models, inverse problems, and their applications to computational imaging and neuroscience.

📢 Recent Updates

• 🎉 ECCV 2026 Acceptance:
  Solving Diffusion Inverse Problems with Restart Posterior Sampling has been accepted to ECCV 2026.

• 📰 PLOS Computational Biology (2025):
  Deep Neural Networks Explain Spiking Activity in Auditory Cortex

• 🎓 Teaching Assistant — Generative Modeling (Graduate Course) (Fall 2025)

🎤 Conferences

• COSYNE 2023 — Montreal, Canada
  Poster: Understanding Auditory Cortex with Deep Neural Networks

👥 Mentorship

• Ying Shen — M.S. Student (2025)
  Research topic: Modeling the auditory cortex using automatic speech recognition (ASR) models with generalized linear (Poisson) models.

• Brian Yuan — M.S. Student (2024)
  Research topic: Unsupervised pretraining for speech decoding using self-supervised speech representations.

🌱 Outside Research

• Reading: Investing, economics, business
• Sports: Cricket, badminton