Lalit Mohan

PhD Candidate in Cognitive Science | Amateur Musician

Music Cognition Group, Cognitive Science Lab • International Institute of Information Technology, Hyderabad

lalit.mo@research.iiit.ac.in

Education

2023 – Present

PhD in Cognitive Science

International Institute of Information Technology (IIIT), Hyderabad — CGPA: 9.17

2020

B.Tech in Information Technology

Maharaja Surajmal Institute of Technology (MSIT), GGSIPU, New Delhi — CGPA: 8 / Aggregate: 74.04%

2014

Senior Secondary (CBSE)

St. Mary's Inter College, Etawah — Aggregate: 89.40% (PCMB)

2012

High School (CBSE)

St. Mary's Inter College, Etawah — CGPA: 9.6

Current & Previous Positions

Aug 2023 – Present

PhD Student & Research Assistant

Cognitive Science Lab, IIIT Hyderabad

  • Investigating emotion dynamics of music listening and its relationship with individual differences in music engagement and psychological indicators.
  • Teaching Assistant for Music, Mind and Technology, Spring 2025, IIIT Hyderabad.
Feb 2021 – Aug 2023

Computer Vision Practice

NeurIot Labs, Noida

  • Orchestrated edge-to-server video analytics pipelines using NVIDIA DeepStream and YOLO models.
Jul 2020 – Feb 2021

Data Scientist

Algoscale

  • Developed ad-copy generators using OpenAI GPT-2/GPT-3 and interpretable ML (Shapley values).
Oct 2019 – Apr 2020

ML Engineer

BeU Salons, New Delhi

  • Developed facial feature processing and AR for smart mirrors using OpenCV in C++.
Jun 2017 – Jun 2021

Lead Designer (Volunteer)

YWP, New Delhi

  • Led a team of seven designers for mental health accessibility and advocacy initiatives.

Ongoing Doctoral Research

Music is a widely used resource for emotion regulation. We use music to relax, to energize, to focus, to remember, to process emotions, and to dance. Yet, certain kinds of music engagement are also deemed maladaptive due to its associations with poor well-being outcomes and risks of depression. While there is a large body of work on how musical emotions are induced and what functions motivate its use, the field is still lacking research on how music-driven emotion regulation interacts dynamically with a listener's ongoing emotional state. Understanding this coupling is crucial to uncover varying effects and uses of music listening particularly when (and for whom) it ceases to be a beneficial medium and might become maladaptive.

To this end, my doctoral research involves modelling both the listener and their music use. Recent work has uncovered novel insights into psychological predispositions associated with healthy and unhealthy music use via network analyses and structural equation modelling. Current work models year-long music listening histories, obtained from over 300 participants, as a complex dynamical system to understand everyday emotion dynamics of streaming. The thesis will culminate in a longitudinal study coupling individuals' ambulatory signals with the emotion dynamics of music engagement, with implications for psychology-aware recommendation systems and goal-oriented media personalization.

Publications & Projects

  • SC, A., Mohan, L., & Alluri, V. (2025). Role of Sensory Processing Sensitivity in Driving Maladaptive Music Use. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 47).
  • Mohan, L., & Alluri, V. (2025). Sensitivity to External Demands Drives Maladaptive Music Use: A Network Analysis Study. Runner-up, Best Paper Award at the 18th International Conference on Music Perception and Cognition.
  • Mohan, L., & Alluri, V. (2025). To Listen or Not To Listen: Maladaptive Music Engagement and Emotional Well-Being. Poster at R&D Showcase 2025, IIIT Hyderabad.
  • Mohan, L., & Alluri, V. (2024). Tap to Your Own Rhythm: Examining Rhythmic Regularity in Speech-to-Song Illusory Transformation. Poster & Spoken presentation at Systematic Musicology 2024.
  • Arora, J., Tushir, M., Sharma, K., Mohan, L., Singh, A., Alharbi, A., & Alosaimi, W. (2022). MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data. Computers, Materials, & Continua, 73(3), 4801.
  • VisionBee: Multi-object tracking and ROI analytics framework for real-time video processing. (neuriot.com/visionbee.html)
  • Digital Sign Language Interpreter (DSLI): Real-time interpreter for Indian Sign Language (ISL) using Computer Vision and LSTMs.

Certifications

2023
Winter School on Speech and Audio Processing — IIT Kanpur
2023
Philosophy and the Sciences: Introduction to the Philosophy of Cognitive Sciences — University of Edinburgh, Coursera — Grade: 90%
2022
Big Data, Artificial Intelligence, and Ethics — University of California, Davis, Coursera — Grade: 90.08%
2022
Computational Social Science Methods — University of California, Davis, Coursera — Grade: 95.13%
2018
ML using Python — OSAHUB

Awards, Honors & Other Activities

2025
Runner-up, Best Paper Award — 18th International Conference on Music Perception and Cognition (ICMPC)
2019
Designer, Avensis — MSIT
2018
Event Coordinator, Spartacus-CSGO
2019
Quarterfinalist, India Innovation Challenge Design Contest (2018–2019) — Start-up competition by Texas Instruments & DST, for the “DSLI” project
2017
First Prize, Sorpriza — Tech Melange
2014
Elected School Captain — St. Mary's Inter College, Etawah

Technical Skills

Programming: DSA, OOP, Functional & Vibe Coding in Python, C++, R, MATLAB

Libraries / Tools: Scikit-Learn, Pandas, OpenCV, PsychoPy, Librosa, MIR-Toolbox

Specialized Tech: llama.cpp, NVIDIA DeepStream, EEG & Audio Analysis

Music & Design: FL Studio, Audacity, Adobe Creative Suite & Presentations