I am nineteen years old and a second year Automation and Robotics undergraduate at KLE Technological University in Karnataka, India.
My primary interests are Mathematics, Computer Science, Machine Learning, and Scientific Computing, bridging theoretical foundations with high performance computational systems.
I am expanding my work beyond classical robotics toward numerical analysis, statistical learning theory, and autonomous computational architectures.
Venture aprilai.in, APRIL finance, developing quantitative trading architectures and autonomous intelligent systems.
Core Profile Applied Mathematics, Theoretical Computer Science, Machine Learning, and Open Source Research.
Technical Skills Python, C++, R, and MATLAB, covering Numerical Linear Algebra, High Performance Computing, Statistical Learning, and Quantitative Modeling.
Blogs
Essays on mathematics, statistical learning, scientific computing, and autonomous architectures. Open Blogs
Projects and Timeline
2026 Regime Detection in Financial Time Series via Statistical Learning. Formulating statistical learning frameworks to identify structural breaks in volatility, cross asset correlation, and nonstationary market regimes, validated through rigorous out of sample evaluation.
2026 APRIL and APRIL Vector. Engineering an accelerated vector search engine and semantic retrieval pipeline investigating sub byte quantization across INT8 and 4 bit representations, similarity geometries, and compact embedding topologies.
2026 Open Source Contributions to sktime. Authored algorithmic enhancements to the sktime library, implementing Wavelet decompositions, Hilbert transforms, and advanced signal transformations for time series analysis.
2026 Eigenvector Methods for Systemic Risk Detection. Developing spectral graph theoretical models of financial networks, using dominant eigenvalue dynamics and centrality measures to quantify systemic fragility.
2025 Artificial Intelligence and Robotics Integration Framework. Architecting formal abstraction layers between foundation models and physical robotics in C++ and Python, with emphasis on deterministic safety invariants and real time execution guarantees.
2025 Singular Value Decomposition and Mathematical Computing. Implementing numerical linear algebra algorithms from fundamental principles, optimizing dense matrix factorizations for constrained computational environments.
2024 Optimization Under Uncertainty for Data Driven Systems. Formulating stochastic optimization strategies for decision making subject to noisy inputs, employing probabilistic modeling and Monte Carlo simulations to evaluate algorithmic robustness.
2023 Structure Discovery in Complex Networks Using Machine Learning. Designing unsupervised algorithms for community detection and latent topological recovery in complex graphs, benchmarked against classical spectral methods.
2022 to Present APRIL One Billion Parameter Foundation Model. A bespoke foundation model engineered from first principles in C++ and Python, focusing on Low Rank Adaptation, parameter efficient fine tuning, and ultra low latency inference pipelines.
2022 Numerical Reliability of Machine Learning Under Finite Precision Computation. Investigating rounding error propagation and convergence degradation across mixed precision floating point arithmetic in iterative optimization routines.
2021 Robust Representation Learning for High Dimensional Scientific Data. Developing representation learning architectures to evaluate how alternative mathematical manifolds improve geometric stability and generalization on scientific datasets.
Other Areas Explored Spectral graph theory, eigenvalues and eigenvectors, topological data analysis, neural network quantization, vector search, scientific computing, and mathematical optimization.
aprilai.in · github.com/ved197338 · vedanthsvaidya@gmail.com · Blogs
