Hi, I’m Aditya.
I am an AI engineer and research-oriented undergraduate focused on computer vision, diffusion models, and large language model systems, with an emphasis on reproducible experimentation, production-grade AI pipelines.
My work spans applied computer vision, LLM-based systems, and open-source contributions, including work on Intel OpenVINO. I am particularly interested in the intersection of model architecture, training workflows, and system-level design, and how these choices affect real-world deployment and research validity.
This website serves as a record of my projects, research directions, technical writing, and academic progress.
About Me
I am an undergraduate engineerinng student majoring in Artificial Intelligence and Data Science, with a strong interest in research-oriented machine learning systems and applied LLM engineering.
My interests lie in understanding how modern language models behave under practical constraints, particularly in settings involving parameter-efficient adaptation, model compression, and retrieval-augmented reasoning. I am especially drawn to research that emphasizes clear problem formulation, controlled experimentation, and reproducibility, rather than isolated benchmark gains.
In parallel, I have built production-grade AI systems, including scalable inference services, modular ML pipelines, and deployment-ready backends, following close to production level practices. These systems-level experiences strongly inform how I approach research questions in practice.
I have contributed to open-source optimization efforts within Intel’s OpenVINO ecosystem, and I currently lead Advait, a 300+ member AI community, where I coordinate teams, research- and project-focused initiatives and events.
My long-term goal is to work as a research engineer, bridging the gap between modern machine learning research and reliable, high-impact real-world systems.
Aditya Pratap Singh
Current focus
NLP & LLM systems (RAG, reasoning, system design)
Model architectures, compression, and efficiency
Industrial-grade ML/DL practices and reproducible
experimentation
Machines Converge.
But Humans?
They transcend.
– Aditya
Below are selected systems and research-oriented projects that reflect my current technical focus.
Selected Projects
Tesseract v1 — Text-to-3D Mesh Generation Engine
August,2025
- Built a diffusion-based system for text-to-3D mesh generation with a reproducible, production-oriented inference pipeline.
- Focused on system design: stateless execution, device-aware fallback, modular components, and config-driven experimentation.
Reddit-Persona — LLM-based User Persona Generation
July,2025
- Developed a production-grade LLM system to analyze Reddit user activity and generate structured, UX-oriented personas.
- Implemented chunked inference, modular configuration, and dual interfaces (CLI and Streamlit) to balance cost, scalability, and usability.
MÍMIR — Research-Oriented LLM System (Early Stage)
2025 – Present
- Designing a research-oriented LLM system to study retrieval-augmented reasoning and parameter-efficient adaptation under realistic system constraints.
- Emphasizes reproducible evaluation and system-level trade-offs relevant to long-running ML services, rather than application-level demos.
Ongoing Work
●LLM Compression & Interpretability(paper in preparation)
Manuscript in preparation
Studying how parameter-efficient fine-tuning and compression techniques (LoRA, quantization, pruning) alter internal representations and attention dynamics in transformer models.
●MÍMIR — Cognitive DevOPS Diagnostic LLM System(Early Stage)
Active development
A research-oriented, production-grade LLM system for DevOps and SRE incident reasoning, emphasizing retrieval grounding, structured reasoning, and reproducible evaluation.
Leadership & Community
President,
Advait
September,2025 - Present
Member Count : 300 +
I lead Advait, a 300+ member student-led AI community focused on research-oriented machine learning, systems engineering, and applied AI development.
My role spans both technical leadership and organizational execution, including:
- Designing and driving research, engineering, and project-based initiatives across LLMs, computer vision, and ML systems.
- Organizing technical talks, workshops, and internal study groups, ranging from neural networks fundamentals to production-grade ML practices.
- Mentoring teams on end-to-end system building, emphasizing reproducibility, modular design, and real-world constraints.
- Coordinating cross-functional operations: event management, speaker outreach, sponsorship communication, public relations, and community growth.
- Overseeing technical direction, team structure, and execution quality, while managing social media presence and external communications.
Advait serves as a platform for translating academic curiosity into disciplined engineering practice, and for cultivating a culture centered on rigor, collaboration, and long-term skill development rather than short-term hype.