Research

At some point, when I have more free time, I would like to outline my research interests in more detail. Till then, however, the summary below will have to suffice.

Ongoing Projects and Research Interests

• Machine learning interatomic potentials (MLIPs), particularly for biological systems and problems
• Causal inference and information transfer in molecular systems
• Development of unsupervised machine learning methods for biomolecular systems, in the context of both classical MD and excited-state NAMD simulations
• Enhanced sampling methods and approaches
• Crystal polymorphism and nucleation
• Allostery and information transfer in proteins
• Molecular mechanisms underlying neurodegenerative diseases
• Origin of homochirality in proteins and sugars
• Biomolecular condensates: mechanisms of formation, nucleation, roles in neurodegenerative diseases, roles of nucleic acids, and causal inference methods for their analysis
• Intrinsically disordered proteins (IDPs): function, conformational space, and roles in condensates
• Nonadiabatic molecular dynamics (NAMD) for studying the excited-state behaviour of biomolecules
• Photochemistry of amino acids and proteins, particularly the mechanisms underlying non-aromatic fluorescence (NAF)

Previous Projects

• First-Passage Time analysis to understand the diffusive behaviour of water in heterogeneous biological systems (Master’s thesis)
• Solving Nonlinear Langevin Equations using techniques inspired by perturbation theory (Relevant Paper)
• Developing computational methods to predict aggregate structures of organic dyes
• Studying chaotic systems (Rayleigh oscillator) in the context of biophysics
• Graph theory inspired methods to study/predict chemical properties