Akash Pandey

Akash Pandey

PhD Candidate, Northwestern University

Akash Pandey

I am currently a PhD candidate in Mechanical Engineering at Northwestern University, where I focus on explainable deep learning models for scientific discovery. I am being co-advised by Dr. Sinan Keten and Dr. Wei Chen. Prior to this, I completed a Master’s by research in Applied Mechanics at Indian Institute of Technology (IIT) Madras, specializing in the solid mechanics. I hold a Bachelor of Engineering in Automobile Engineering from Madras Institute of Technology, Anna University. My industry experience includes two internships at Capital One, where I contributed to recommendation system development, and a full-time position at Rolls Royce, where I applied numerical optimization techniques for aero-engine structural analysis.

I develop interpretable AI models for computational biology, time-series analysis, and recommendation systems. I am skilled in Transformer architectures, attribution methods, and protein sequence modeling. My work spans academic research published in Nature Communications Materials, ACS JCIM, ICLR’25 MLGenX workshop and Cell Press Patterns alongside industry experience at Capital One (recommendation systems) and Rolls Royce (aero-engine structural analysis). I also enjoy developing deep learning models for other sequential data such as audio, biosignals, and EMG. My work involving these modalities have been published in ACL’25, ICASSP’23, ACM Multimedia’23, and ACM IASA’22.

You can reach me at akash.pandey@northwestern.edu
Link to my CV

Recent Highlights

  • [March,2025] - Our paper titled “COLOR: A compositional linear operation-based representation of protein sequences for identification of monomer contributions to properties” is accepted at MLGenX workshop in ICLR 2025.
  • [Feb,2025] - Submited our paper on surface-EMG based silent-speech recognition using LLMs to ACL.
  • [Jan,2025] - Our novel eXplainable AI (XAI) method, named COLOR, developed to estimate contribution of each monomer in the primary sequence is now online on arXiv.
  • [June-August,2024] - Completed 10 weeks Data Science PhD internship at Capital One, San Jose.

Services

  • Guiding Julia Levenshteyn, a Senior in Lake Forest College, to develop metrics to quantify explainability in computational biology.
  • Guided Xiaoyuan Zhang, a Master student in EECS department of Northwestern University, to develop a Llama (2 and 3) based deep-learning model to predict text from silent EMG signals on a closed vocabulary.
  • Guided Yueyuan Sui, a Master student in EECS department of Northwestern University, for ACM MM 2023 challenge for human emotion prediction task.
  • Reviewer at Nature Computational Materials and IEEE/ACM Transactions on Computational Biology and Bioinformatics.