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Aditya More

Work & Projects

Projects

A collection of my work in machine learning, security research, and educational tools.

12 / 12 projects
01in progress2026
www.adityamore.dev
Illustrated interpretation of BB8 Co-Pilot x Tech Portfolio
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BB8 Co-Pilot x Tech Portfolio

A live, source-grounded portfolio assistant that retrieves verified evidence before answering visitors.

  • Designed section-aware chunking and retrieval over a verified knowledge corpus
  • Implemented OpenAI embeddings with Amazon S3 Vectors semantic search
LLMRAGOpenAI API+6
02in progress2026
neurallog.adityamore.dev
Illustrated interpretation of Neural Log — Personal Activity & Progress System
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Neural Log — Personal Activity & Progress System

A live multi-workspace tracker with an auditable XP ledger, analytics, AI weekly reviews, and long-term progress tools.

  • Created eight evidence-based attributes backed by an auditable XP transaction ledger
  • Built training, nutrition, learning, habits, goals, streaks, records, and calendar analytics
PythonFlaskReact+5
03completed2024
movies.adityamore.dev
Illustrated interpretation of Movie Recommendation Engine
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Movie Recommendation Engine

A live hybrid recommender spanning reproducible ML experiments, a Flask API, a React client, and AWS deployment.

  • Processed 26M+ ratings across 270K users and 45K movies
  • Achieved RMSE 0.7600 with FunkSVD, about 21% better than the baseline
Pythonscikit-learnPyTorch+7
04in progress2026
chat.adityamore.dev
Illustrated interpretation of BB-8: Transformer Language Model
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BB-8: Transformer Language Model

An 11-experiment LLM engineering study from a 112K-parameter character model through Qwen LoRA and grounded retrieval.

  • Implemented embeddings, causal multi-head attention, Pre-LN decoder blocks, and language-model heads from first principles
  • Ran 11 documented experiments from 112K parameters to Qwen2.5-0.5B LoRA fine-tuning
PythonPyTorchTransformers+5
05in progress2026
game.adityamore.dev
Illustrated interpretation of FIFA World Cup 2026 Prediction Platform
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FIFA World Cup 2026 Prediction Platform

A live regional prediction platform with player accounts, public leaderboards, tournament views, and a private command center.

  • Designed two regional scoring systems and role-scoped access from one SQLite-backed deployment
  • Implemented prediction locking, score forecasts, public picks, profiles, leaderboards, and tournament progression
JavaScriptNode.jsSQLite+4
06in progress2026
game.adityamore.dev
Illustrated interpretation of FIFA 2026 AI Prediction Agents
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FIFA 2026 AI Prediction Agents

Scheduled LLM agents authenticate as players, reason over eligible fixtures, and submit structured predictions to the live game.

  • Implemented API-based login, fixture retrieval, prediction submission, logout, and transient-lock retries
  • Enforced structured output, valid teams and match IDs, lock windows, and conservative score forecasts
JavaScriptNode.jsOpenAI API+3
07completed2025
Research / implementation
Illustrated interpretation of Skynet — AQI Prediction System
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Skynet — AQI Prediction System

An AQI forecasting prototype combining satellite observations, ground measurements and weather context.

  • Integrated NASA TEMPO, OpenAQ, weather, and traffic APIs
  • Designed ingestion, preprocessing, and feature-engineering pipelines
Pythonscikit-learnpandas+3
08completed2025
Research / implementation
Illustrated interpretation of LLM Jailbreak Defense Evaluation
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LLM Jailbreak Defense Evaluation

A black-box evaluation framework for jailbreak attempts, refusal behavior, latency, token use, and model failure modes.

  • Built a 13-category dataset from public jailbreak sources and custom augmentations
  • Automated prompt execution, result logging, query-success evaluation, and category-level analysis
PythonLLMAI Security+3
09completed2024
Research / implementation
Illustrated interpretation of Deep Learning Coursework — CPSC 8430
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Deep Learning Coursework — CPSC 8430

A four-repository implementation series covering video captioning, spoken question answering, and generative adversarial networks.

  • Built an encoder-decoder video-captioning model with attention and BLEU evaluation
  • Fine-tuned BERT-Base on Spoken-SQuAD with document stride, mixed precision, gradient checkpointing, and accumulation
PythonPyTorchDeep Learning+5
10completed2021
Research / implementation
Illustrated interpretation of Automatic License Plate Recognition (ALPR)
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Automatic License Plate Recognition (ALPR)

Computer vision system for vehicle license plate detection and text extraction.

  • Built plate detection using OpenCV contour analysis
  • Integrated Tesseract OCR
OpenCVTesseractPython+1
11completed2021
Research / implementation
Illustrated interpretation of COVID-19 Safeguard System
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COVID-19 Safeguard System

Real-time monitoring system using computer vision for safety compliance.

  • Built face-mask detection using TensorFlow
  • Implemented distancing violation detection
Computer VisionTensorFlowOpenCV
12completed2020
Research / implementation
Illustrated interpretation of Alien Invasion
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Alien Invasion

2D arcade-style game built while learning Python fundamentals.

  • Classic space shooter mechanics
  • Player controls and enemy waves
PythonPygame

Research & Experimentation

Experimentation & Case Studies

Hands-on experiments, security research, and applied ML case studies — work that lives at the boundary of exploration and engineering.

01
AI Security Research · Clemson University2024

Adversarial Attacks – Experimentation & Case Study

  • Studied data poisoning, adversarial patches, and evasion attacks on computer vision models.
  • Conducted experiments on monocular depth estimation (MDE) systems in autonomous vehicles.
  • Analyzed impact on scene understanding, perception, and depth estimation pipelines.
  • Evaluated attack transferability across models and threat settings.
Computer VisionAdversarial MLDeep LearningPythonAI Security
02
CPSC 8570 Security Research · Clemson UniversitySpring 2025

LLM Jailbreak Defense Evaluation

  • Replicated and extended the MASTERKEY jailbreak framework for black-box model evaluation.
  • Built a 13-category prompt dataset from public sources and custom augmentations.
  • Automated query-success, refusal, exception, timeout, latency, and token-use analysis.
  • Benchmarked multiple commercial LLMs without requiring internal model access.
LLMPythonEvaluationAI SecurityAdversarial AI
View on GitHub