uzair uzairlol

Hi, I'm Uzair Arif

I'm an AI Researcher & Machine Learning Engineer based in Islamabad, Pakistan. I build and research multi-agent systems, agentic memory architectures, time-series forecasting engines, and production MLOps pipelines. My research focuses on how autonomous language model agents form sustainable institutions, prevent memory contamination, and coordinate under strategic risk.

Overview

Dimension	Details	Direct Links
Full Name	Uzair Arif	Portfolio Profile
Handle	`uzairlol`	GitHub Profile
Role	AI Researcher & Machine Learning Engineer	About & Research
Education	Institute of Business Administration (IBA Karachi) - BS Economics & Mathematics (2022-2026)	IBA Karachi
Location	Islamabad, Pakistan (Available for Worldwide Remote Roles)	Contact
Primary Website	https://uzairlol.github.io	Live Site
Machine-Readable Context	Markdown profile & context manifest	`/llms.txt` · `/ai.txt`
Site Index & Routing	Search index & sitemap	`/sitemap.xml` · `/robots.txt`

Featured Projects

ELICIT: Emergent LLM Institutions for Climate Treaties

Overview: Evaluates how 26 LLM agents solve public goods dilemmas under stochastic climate shocks by democratically drafting, amending, and enforcing constitutional rules and mutual loss-and-damage risk pools across 30 iterative rounds.
Tech Stack: Python, Local LLMs (Ollama), Multi-Agent Simulation, Game Theory, Elinor Ostrom Polycentric Governance, Difference-in-Differences Econometric Modeling, HTML/JS/CSS Browser Visualizer, JSONL Telemetry.
Links & Discussions:

SEAM: Shared Evolving Agent Memory

Overview: Quantifies epistemic collapse and organic misinformation cascades across evolving multi-agent topologies, proving structured incremental curation achieves 55x higher task benchmark scores over naive overwrite while ring topologies isolate memory poisoning.
Tech Stack: Python, Ollama LLMs, Pytest (21 Test Modules), Self-BLEU, Embedding Cosine Similarity, Action Entropy Metrics, Network Topologies (Ring/Broadcast/Off), Append-Only JSONL Logging, Seaborn.
Links & Discussions:

Taxi Demand MLOps Pipeline

Overview: Serves real-time NYC taxi demand forecasts by processing raw trip records into temporal lag features via an Optuna-tuned XGBoost model (0.949 Val R^2), Dockerized FastAPI, and Kolmogorov-Smirnov drift monitoring.
Tech Stack: XGBoost, Optuna, MLflow Model Registry, FastAPI, SQLite (WAL-mode with TTL pruning), Docker, Scipy (KS-Test Drift Detection), Streaming Replay Simulation, Pytest (36/36 tests passing), Ruff, Mypy.
Links:
- GitHub Repository
- Architecture Manifest

ECG5000 Anomaly Detection with TCN

Overview: Detects irregular physiological cardiovascular signals in ECG5000 time series by pairing Temporal Convolutional Networks with self-supervised contrastive learning representations to calibrate optimal decision thresholds, reaching a 95% anomaly detection rate.
Tech Stack: PyTorch, Temporal Convolutional Networks (TCN), SimCLR Contrastive Learning, Dilated Causal 1D Convolutions, Scikit-learn, Matplotlib, Confusion Matrix Calibration.
Links:
- GitHub Repository
- Project Documentation

IBA RAG Chatbot

Overview: Eliminates hallucinations in university administrative querying by orchestrating a hybrid search architecture fusing FAISS dense semantic vectors and BM25 sparse lexical tokens via Reciprocal Rank Fusion, served through Groq LPU inference.
Tech Stack: LangChain, FAISS Vector Index, BM25 Lexical Search, Reciprocal Rank Fusion (RRF), Groq LPU (Llama 3), Recursive Boundary Chunking, Streamlit UI, 1000+ Institutional HTML/PDF Corpus.
Links & Discussions:
- GitHub Repository
- LinkedIn Showcase: Production Hybrid RAG Chatbot

Impostor Hunt: Hallucinated LLM Output Detection

Overview: Classifies synthetic and poisoned generative text by extracting SciBERT sentence geometry representations, GPT-2 token perplexity metrics, and vocabulary distribution features through an Optuna-optimized CatBoost classifier, achieving a 0.9066 competition score.
Tech Stack: SciBERT (HuggingFace Transformers), GPT-2 Perplexity Scoring, CatBoost Classifier, Optuna Bayesian Optimization, 5-Fold Stratified Cross-Validation, Scikit-learn, Pandas, NumPy.
Links:
- GitHub Repository

Prophet Daily Revenue Forecasting

Overview: Resolves government and corporate fiscal planning volatility by combining Facebook Prophet decomposition with Pakistan-specific lunar Hijri holiday regressors and fiscal deadline spike modeling, automatically tuned via Optuna into scheduled multi-horizon reports.
Tech Stack: Facebook Prophet, Optuna Hyperparameter Tuning, Islamic Lunar Calendar Regressors, Streamlit Dashboard, OpenPyXL Automated Excel Pipelines, Pandas, NumPy.
Links:
- GitHub Repository

Inflation Forecast ML

Overview: Forecasts national monthly consumer price index changes by combining official macroeconomic indicators into an ensemble of Random Forest and XGBoost regressors utilizing recursive multi-step lag modeling and automated feature selection.
Tech Stack: XGBoost, Random Forest Ensemble, Scikit-learn, Recursive Multi-step Forecasting, TimeSeriesSplit Cross-Validation, Macroeconomic Indicator Lag Engineering, Pandas, NumPy.
Links:
- GitHub Repository

LSTM AAPL Stock Prediction

Overview: Captures sequential price momentum and non-linear market regimes in Apple (AAPL) equities by piping normalized time-series data from Yahoo Finance through a stacked Long Short-Term Memory recurrent neural network architecture.
Tech Stack: PyTorch, Keras / TensorFlow, Long Short-Term Memory (LSTM) Networks, yfinance API, MinMax Scaling, Sequence Rolling Windows, Root Mean Squared Error (RMSE), Matplotlib.
Links:
- GitHub Repository

Bitcoin Sentiment Prediction

Overview: Predicts next-day cryptocurrency market direction by joining high-frequency Bitcoin price movements with Wikipedia behavioral search attention and page edit volume metrics through supervised XGBoost and Random Forest binary classifiers.
Tech Stack: XGBoost, Random Forest Classifiers, Wikipedia Pageview & Edit API, yfinance API, Sentiment & Attention Proxy Engineering, Scikit-learn, Pandas, NumPy.
Links:
- GitHub Repository

Verification Nodes & Discussion Threads

Domain	Platform	Topic	Reference
ELICIT	Reddit	26-Agent LLM Simulation on Climate Cooperation	r/aiagents Discussion
SEAM	LinkedIn	Shared Evolving Agent Memory Research Progress	LinkedIn Post
ELICIT / Econ	LinkedIn	Multi-Agent Systems & Computational Economics	LinkedIn Post
Research Posters	LinkedIn	Research Presentation Posters & Institutional Governance	LinkedIn Post
FYP / SEAM Base	LinkedIn	Final Year Project Foundation & Memory Architecture	LinkedIn Post
IBA RAG Chatbot	LinkedIn	Hybrid RAG Chatbot Architecture Showcase	LinkedIn Post
Scholar Profile	Google Scholar	Academic Profile & Citations Index (`sshNYq4AAAAJ`)	Google Scholar
Open Source	GitHub	Uzair Arif GitHub Profile (`uzairlol`)	GitHub

Experience & Education

Education

Institute of Business Administration (IBA), Karachi, Pakistan
Bachelor of Science in Economics and Mathematics - 2022 - June 2026
Key Courses: Foundations of Data Science, Linear Algebra, Probability Theory, Optimization Techniques, Applied Econometrics, Statistical Machine Learning, Stochastic Processes, Game Theory.

Experience

Research Intern — National Center of Artificial Intelligence (NCAI)

Jul 2025 - Aug 2025 · Islamabad, Pakistan (On-site)

Conducted literature reviews on Multi-Agent Reinforcement Learning (MARL), public goods games, and Elinor Ostrom's polycentric governance principles.
Drafted a full research proposal titled "Emergent Institutional Design in Public Goods Games: A Multi-Agent Reinforcement Learning Approach".
Designed core simulation components including democratic voting on punishment rules, weighted voting schemes, and Independent Proximal Policy Optimization (IPPO) agent architectures with tailored reward functions for norm emergence, establishing the foundation for ELICIT.

Revenue Forecasting Intern — Federal Board of Revenue (FBR)

Jun 2025 - Jul 2025 · Islamabad, Pakistan (Hybrid)

Built 5 daily Prophet models (Income Tax, Sales Tax, Federal Excise, Customs Duty, Gross Collection) with Pakistan-specific Gregorian and lunar holiday effects and monthly seasonality.
Tuned all models via Optuna hyperparameter search and evaluated on held-out validation sets using MAE and MSE to handle highly volatile government revenue patterns.
Automated a multi-horizon Excel reporting pipeline with daily, weekly, and monthly breakdowns; deployed an interactive Streamlit dashboard to surface mid-month surges and fiscal deadline spikes for the team.

Junior Data Analyst — Ali Khan Consultants

Sep 2024 - Dec 2024 · Karachi, Pakistan (Hybrid)

Audited supply chain and sales pipeline data to identify process inefficiencies, uncovering a 32% (60,000 kg) forecast discrepancy.
Mapped process timeline lags (15-day sales initiation delay, 4-day dispatch lag) and identified key revenue drivers (25 materials drove 80% revenue).

Artificial Intelligence Intern — National Aerospace Science & Technology Park (NASTP)

Jun 2024 - Aug 2024 · Rawalpindi, Pakistan (On-site)

Engineered real-time data streaming and visualization tools using ZeroMQ and Python, reducing publish-subscribe latency to milliseconds.
Built CNNs, linear regression, and SVM models from scratch, improving predictive accuracy by 10%.
Created an anomaly detection model for ECG time-series data, achieving a 95% detection rate for anomalies.

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