Akash Pandey

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

Currently, I am using Deep Learning (DL) models to study protein’s dynamic as well as mechanical properties. My current research project is to predict the mechanical properties of spider silk and use DL models to lay some design rules for making superior protein-based materials.

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. Recently, one of my papers on emotion share prediction using large language model embeddings has been accepted in ACM MM’23 as one of the first authors.

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

  • [Sept, 2023] - Recipient of Predictive Science and Engineering Design (PSED) fellowship.
  • [July, 2023] - Paper titled Effect of attention and self-supervised speech embeddings on non-semantic speech tasks accepted in ACM MM 2O23.
  • [June, 2023] - Paper titled B-factor prediction in proteins using a sequence-based deep learning model accepted in Cell Patterns.
  • [June, 2023] - Presented paper titled Person identification with wearable sensing using missing feature encoding and multi-stage modality fusion in Signal processing grand challenges track at ICASSP’23.
  • [February, 2023] - Secured third place in e-Prevention: Person Identification and Relapse Detection from Continuous Recordings of Biosignals in ICASSP’23. Inivited to talk about the methodology in the same conference.


  • Currently guiding 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