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A-SHOJAEI/README.md

Alireza Shojaei

Curious mind building production-grade AI/ML systems. Focused on novel model architectures, uncertainty quantification, and efficient training strategies.

Selected Projects

LLM Inference & Efficient Fine-Tuning

NLP & Question Answering

Information Retrieval

Multimodal & Generative AI

Computer Vision

Molecular ML & Graph Networks

Genomics & Bioinformatics

Tabular ML & Financial

Time Series & Forecasting

Audio & Speech

Systems & RL

Tech Stack

Frameworks: PyTorch, HuggingFace Transformers, PyTorch Geometric, DGL, scikit-learn, XGBoost, LightGBM, FAISS, N-BEATS, PatchTST, TFT

Techniques: LoRA/PEFT, GNNs, contrastive learning, curriculum learning, uncertainty quantification, multi-task learning, DPO alignment, early-exit inference, spectral graph networks, GRPO reinforcement learning, knowledge distillation, Matryoshka representations, 3D Gaussian Splatting, rectified flow matching, Mamba SSMs, RF-DETR, speaker diarization, RNN-T

Infrastructure: MLflow, Docker, NVIDIA RTX 4090, NVIDIA RTX 3090 (dual GPU), AMD Threadripper 3960X

Popular repositories Loading

  1. pet-breed-uncertainty-aware-classifier pet-breed-uncertainty-aware-classifier Public

    Uncertainty-aware pet breed classifier using EfficientNet-B0 with Monte Carlo Dropout and deep ensembles on Oxford-IIIT Pet Dataset (37 breeds). 87.5% top-1 accuracy with calibrated confidence esti…

    Python

  2. adaptive-traffic-anomaly-forecasting-with-uncertainty-quantification adaptive-traffic-anomaly-forecasting-with-uncertainty-quantification Public

    GCN+GRU traffic forecasting with conformal prediction for uncertainty quantification. R²=0.942 on synthetic PEMS-BAY-style network with graph-based anomaly propagation.

    Python

  3. adaptive-traffic-signal-control-via-hierarchical-multi-agent-rl adaptive-traffic-signal-control-via-hierarchical-multi-agent-rl Public

    Hierarchical multi-agent RL for traffic signal control with 3-phase training (pretrain/coordinate/finetune). PPO-based agents with communication networks achieving 90.9% waiting time reduction.

    Python

  4. temporal-credit-degradation-detector temporal-credit-degradation-detector Public

    Stability-weighted LightGBM+CatBoost ensemble for credit risk prediction with calibration monitoring. AUC-ROC 0.8396 with 88.8% precision on degradation detection.

    Python

  5. adaptive-model-serving-optimizer adaptive-model-serving-optimizer Public

    UCB bandit-based model serving optimizer with automatic latency/accuracy/cost tradeoff. 86.7% P99 latency reduction with zero accuracy degradation in production simulation.

    Python

  6. temporal-distribution-shift-detector-with-adaptive-ensemble-reweighting temporal-distribution-shift-detector-with-adaptive-ensemble-reweighting Public

    Bayesian online ensemble reweighting with Thompson sampling for distribution shift detection. XGBoost/LightGBM/CatBoost ensemble achieving 78.7% accuracy on Covertype with drift adaptation.

    Python