I’m 19 years old and a 2nd-year Automation & Robotics undergraduate at KLE Technological University, Karnataka, India.
My main interests are Mathematics, Computer Science, Data Science, Machine Learning, AI, mathematical computing, and finance/technology.
I’m gradually building my profile beyond traditional robotics toward mathematics, computing, AI, and data.
Venture: aprilai.in — APRIL finance, an automated quantitative trading and AI systems venture.
Overall Profile: Mathematics + Computer Science + AI/Data Science + Research + Open Source.
Technical Skills: Python, C++, R, MATLAB, Linear Algebra, Data Science, Machine Learning, AI, Numerical / Scientific Computing.
Projects & Timeline:
2026 — Regime Detection in Financial Time Series Using Statistical Learning: Develop a system for detecting changes in volatility, correlation, and statistical behavior in financial time series, with rigorous out-of-sample evaluation.
2026 — APRIL / APRIL-VECTOR: An AI/data-oriented project focused on vector databases, vector quantization, semantic search, efficient vector representations, and quantization techniques such as INT8 and 4-bit approaches.
2026 — Open Source (sktime): Contributed to sktime, an open-source Python library for time-series analysis. Contribution involved Wavelet, Hilbert, and other signal transformations.
2026 — Eigenvector-Based Systemic Risk Detection: A research and project direction combining linear algebra, spectral graph theory, financial networks, and systemic-risk analysis.
2025 — AI + Robotics Integration Framework: A project direction exploring how AI can provide a higher-level interface for robotics systems, with a focus on Python, C++, safety validation, testing, reliability, and production-oriented design.
2025 — SVD / Mathematical Computing: Projects centered on Singular Value Decomposition, numerical linear algebra, matrix computation, mathematical libraries, and efficient algorithms.
2024 — Optimization Under Uncertainty for Data-Driven Decision Systems: Develop and compare optimization strategies for decision-making under uncertain or noisy inputs, using probabilistic modeling and simulation to evaluate robustness.
2023 — Structure Discovery in Complex Networks Using Machine Learning: Develop an algorithm for identifying communities, influential nodes, and structural patterns in large networks, then compare classical approaches with ML-based methods.
2022–Present — APRIL (1B AI Model): A custom 1-billion parameter AI foundation model engineered from scratch using C++ and Python. Ongoing research exploring LoRA (Low-Rank Adaptation), parameter-efficient fine-tuning, and ultra-low latency inference pipelines for high-performance deployment.
2022 — Numerical Reliability of Machine Learning Under Finite-Precision Computation: Implement selected ML algorithms using different numerical precisions and study how rounding and approximation errors affect accuracy, convergence, memory usage, and computational performance.
2021 — Robust Representation Learning for High-Dimensional Scientific Data: Build a representation-learning system and investigate whether different mathematical representations improve stability, dimensionality reduction, and generalization on scientific datasets.
Other areas explored: Spectral graph theory, eigenvalues, eigenvectors, graph structures, AI quantization, vector search, recommendation systems, scientific computing, financial AI, and mathematical algorithms.
Venture: aprilai.in — GitHub: github.com/ved197338 — Contact: vedanthsvaidya@gmail.com
