I'm a biotechnology undergraduate with a minor in AI/ML, working at the boundary where computational methods meet biological systems. My work centers on building predictive and mechanistic models of complex biological phenomena — not through curve-fitting, but through formulating the underlying dynamical structure.
My primary technical interest is in Neural Ordinary Differential Equations and state-space modeling — frameworks that treat biological processes as continuous-time dynamical systems rather than static input-output mappings. This allows models to capture memory, feedback, and temporal evolution in a physically interpretable way.
I work on systems-level biological simulation: metabolic networks, cognitive state dynamics, and pathway-level modeling. The goal is always the same — reduce complex multi-component biology to a set of coupled equations whose behavior can be analyzed, perturbed, and predicted.
On the ML side, I focus on architectures and methods where the inductive bias is grounded in domain knowledge: physics-informed networks, graph models of biological interaction networks, and hybrid mechanistic-statistical approaches.
Built a Neural ODE–based system to model the continuous evolution of cognitive states (attention, fatigue, stress) from EEG signals. Uses DEAP dataset features to learn dynamical behavior over time.
Approach: Learns continuous-time dynamics using dS/dt = f(S, U, θ) with torchdiffeq (dopri5 solver) instead of discrete prediction.
Built an interpretable dynamical systems model using coupled differential equations (RK4) to simulate interacting cognitive states over continuous time.
Approach: Uses differential equation-based modeling with RK4 integration — learns rate of change instead of direct outputs.
Built a systems-level biochemical simulation integrating glycolysis, the pentose phosphate pathway, and the TCA cycle into a unified dynamical model. Tracks metabolite flux, energy balance, and cofactor dynamics using coupled differential equations.
Approach: Coupled ODE-based system with conserved ATP/NAD pools and validated through pytest-based stoichiometric testing.
Developed a system to estimate AQI at arbitrary geographic locations using spatial interpolation across sparse sensor networks with real-time data handling.
Approach: Uses inverse-distance weighting and spatial interpolation to predict AQI beyond available sensor data.
Open to research collaborations, GSoC discussions, and roles at the intersection of computational biology and machine learning.