Akash Pandey

Akash Pandey

PhD Candidate, Northwestern University

Akash Pandey

I am a Ph.D. candidate in the Department of Mechanical Engineering at Northwestern University. I am being co-advised by Dr. Sinan Keten and Dr. Wei Chen.

Currently, I am working to develop an interpretable deep-learning (DL) model for proteins to estimate the importance of different positions in a sequence of amino acids. In the past, I have developed DL models to study protein’s dynamic as well as mechanical properties.

While working on DL models for proteins, I have developed a special interest in sequence-based DL models. I really enjoy architecting and training DL models from scratch. Due to this interest, I have worked on projects/challenges involving biosignals and audio signals too. In June 2023, I and my other teammate secured the third position in one of the ICASSP’23 challenges and presented that work in the conference. One of my other papers on emotion share prediction using large language model embeddings has been published in ACM MM’23 as one of the first authors.

During June-August 2025, I worked as a Data Scientist PhD Intern at Capital One. During the internship, I worked on Explainable AI methods for transformer-based models to estimate feature importance as well as the importance of different time points in a sequential data. Prior to joining Northwestern, I worked as a Lead and Advanced Engineer at Infosys and Rolls-Royce respectively. In both companies, I was responsible for the stress analysis and fatigue life assessment of Titanium and Nickel alloy-based rotating discs in Rolls-Royce Trent-XWB engines. I have a Masters by Research in Applied Mechanics from Indian Institute of Technology, Madras during which I worked on characterizing the fatigue properties of smart piezoelectric composite material using experimental as well as Finite Element Analysis techniques. 

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

Recent Highlights

  • [June-August,2024] - Completed 10 weeks Data Science PhD internship at Capital One, San Jose.
  • [May, 2024] - Successfully completed my PhD thesis proposal.
  • [May, 2024] - Paper titled “Sequence-based data-constrained deep learning framework to predict spider dragline mechanical properties” published in Nature Communication Materials.
  • [Sept, 2023] - Recipient of Predictive Science and Engineering Design (PSED) fellowship.

Services

  • Guiding Bradley Altman, a Master student in Department of Material Science and Engineering at Northwestern University, for the work on uncertainty quantification for sequential data.
  • 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.