Ashutosh Singh

About Me

I am a Ph.D. student in the Cognitive Systems Lab at Northeastern University, where I am advised by Prof. Deniz Erdogmus. My research lies at the intersection of signal processing, control, and machine learning. I am fascinated by the ease with which humans process information and make decisions, and I aim to replicate this efficiency in machine learning models.

My work focuses on developing algorithms to recognize patterns in time series data, particularly in bio-signals, to understand complex system behaviors. I have a keen interest in studying human behavior through these patterns. Currently, I am engaged in key projects that involve designing machine learning models to discover emerging patterns in bio-signals, aiding our understanding of human behavior.

I collaborate closely with Prof. Lisa Feldman Barrett to analyze fMRI datasets, uncovering patterns that explain human behavior and emotions. Additionally, I work with Prof. Mathew Goodwin on developing predictive models for psychological states based on bio-signals collected from wearable devices.

I hold a Master of Science (M.Sc.) in Electrical and Computer Engineering (ECE) from Northeastern University (2021) and a Bachelor in Technology (B.Tech.) in Mechatronics with a minor in Robotics from Manipal Institute of Technology (2019). Beyond my primary research, I am passionate about robotics and autonomous vehicles.

In my leisure time, I enjoy engaging in conversations about geopolitics, the stock market, and chess. I also love reading manga and watching anime.

Affiliation

• PEN- (Psychologists, Engineers, Neuroscientists) Cluster at Northeastern — a nexus of experimental and modeling driven studies of brain and peripheral nervous system and their interactions with the body. At Northeastern University I mainly focus on machine learning and cognitive science.

• SPIRAL- Signal Processing Imaging Reasining and Learning group at Northeastern University.

Research Interests

• Machine Learning: Domain Adaptation, Unsupervised Learning, Subspace Clustering, Multimodal analysis
• Signal Processing: Physics inspired models, Signal Unmixing
• Psychology: Mutli Voxel Pattern Analysis, Multi-Modal Analysis

News

• [May 2024] Started my internship at Mitsubishi Electrical Research Laboratories (MERL)
• [Apr. 2024] Our paper on epileptogenesis and multi modal fusion got accepted at to CMIG.
• [Apr. 2024] My collaborator presented poster of our work in SANS 2024.
• [Mar.2024] Got awarded PhD network travel grant for poster presentation at ICLR 2024. See you all at Vienna!!
• [Jan. 2024] Our paper on Learning semilinear neural operators got accepted in ICLR 2024.
• [Dec. 2023] Our paper on Wearable Biosensing to Predict Imminent Aggressive Behavior got accepted in journal of JAMA Network Open.
• [Mar. 2023] Our paper about invariant subspace clustering got accepted in ICASSP 2023.
• [May. 2022] Our abstract poster on emotional correlate got accepted in SAS2022.
• [Nov. 2021] Submitted our article on data visualisation tool in F1000Research.
• [Oct. 2021] Our paper on multi voxel pattern analysis got accepted in EMBC 2021.
• [May. 2021] Graduated from MS in ECE and started Ph.D. in CE with Prof. Deniz Erdogmus.
• [Apr. 2021] Succesfully defended my Master’s thesis ““Variation Is the Norm: Brain State Dynamics Evoked by Emotional Video Clips”
• [Aug. 2020] Interned at Ford Motor Company Greenfield labs.

Publications

Services

Conference Reviewers

• IEEE International Conference on Acoustics, Speech and Signal Processing 2023
• IEEE International Workshop on Machine Learning for Signal Processing 2023
• IEEE International Conference on Acoustics, Speech and Signal Processing 2024

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