# Technical Skills and Expertise

## Deep Learning and Machine Learning

### Core Frameworks
- **PyTorch**: Advanced proficiency in model development, custom layers, and distributed training
- **TensorFlow**: Experience with TensorFlow 2.x, Keras, and TensorFlow Serving
- **Hugging Face Transformers**: Fine-tuning, model deployment, and custom tokenizers
- **scikit-learn**: Classical ML algorithms, preprocessing, and model evaluation

### Specialized Techniques
- **Transfer Learning**: Pre-trained model adaptation, domain adaptation
- **Attention Mechanisms**: Self-attention, cross-attention, multi-head attention
- **Adversarial Training**: GANs, adversarial autoencoders, robust training
- **Multi-task Learning**: Joint optimization, task balancing, shared representations
- **Meta-Learning**: Few-shot learning, model-agnostic meta-learning

## Large Language Models and NLP

### LLM Technologies
- **Parameter-Efficient Fine-tuning**: LoRA, QLoRA, AdaLoRA, Prefix tuning
- **Quantization**: GPTQ, GGUF, 8-bit and 4-bit quantization
- **Model Optimization**: Pruning, distillation, efficient architectures
- **Prompt Engineering**: Chain-of-thought, few-shot prompting, instruction tuning

### NLP Applications
- **Text Generation**: Controlled generation, style transfer, summarization
- **Information Extraction**: Named entity recognition, relation extraction
- **Question Answering**: Reading comprehension, open-domain QA
- **Sentiment Analysis**: Aspect-based sentiment, emotion detection

## Computer Vision and Medical Imaging

### Vision Architectures
- **Convolutional Networks**: ResNet, DenseNet, EfficientNet, Vision Transformers
- **Object Detection**: YOLO, R-CNN family, DETR
- **Segmentation**: U-Net, Mask R-CNN, Segment Anything Model (SAM)
- **Medical Imaging**: Specialized architectures for histopathology, radiology

### Image Processing
- **Preprocessing**: Normalization, augmentation, color space conversion
- **Feature Extraction**: SIFT, HOG, deep features
- **Registration**: Image alignment, geometric transformations
- **Quality Assessment**: Blur detection, artifact identification

## Multimodal AI and Fusion

### Multimodal Architectures
- **Vision-Language Models**: CLIP, BLIP, LLaVA, DALL-E
- **Fusion Strategies**: Early fusion, late fusion, attention-based fusion
- **Cross-modal Retrieval**: Image-text matching, semantic search
- **Multimodal Generation**: Text-to-image, image captioning

### Data Integration
- **Heterogeneous Data**: Combining images, text, tabular data
- **Temporal Fusion**: Time-series integration, sequential modeling
- **Graph Neural Networks**: Relational data modeling, knowledge graphs

## Retrieval-Augmented Generation (RAG)

### Vector Databases
- **FAISS**: Efficient similarity search, index optimization
- **ChromaDB**: Document storage and retrieval
- **Weaviate**: Vector search with filtering
- **Milvus**: Scalable vector database management

### Retrieval Techniques
- **Dense Retrieval**: Bi-encoder architectures, contrastive learning
- **Sparse Retrieval**: BM25, TF-IDF, keyword matching
- **Hybrid Search**: Combining dense and sparse methods
- **Re-ranking**: Cross-encoder models, relevance scoring

### RAG Optimization
- **Chunk Strategies**: Document segmentation, overlap handling
- **Embedding Models**: Sentence transformers, domain-specific embeddings
- **Query Enhancement**: Query expansion, reformulation
- **Context Management**: Relevance filtering, context compression

## Bioinformatics and Computational Biology

### Genomics
- **Sequence Analysis**: Alignment algorithms, variant calling
- **Gene Expression**: RNA-seq analysis, differential expression
- **Pathway Analysis**: Enrichment analysis, network biology
- **Population Genetics**: GWAS, linkage analysis

### Proteomics
- **Protein Structure**: Structure prediction, folding analysis
- **Mass Spectrometry**: Data processing, protein identification
- **Protein-Protein Interactions**: Network analysis, functional prediction

### Systems Biology
- **Network Analysis**: Graph theory, centrality measures
- **Mathematical Modeling**: Differential equations, stochastic models
- **Multi-omics Integration**: Data fusion, pathway reconstruction

## Cloud Computing and MLOps

### Cloud Platforms
- **AWS**: EC2, S3, SageMaker, Lambda, ECS
- **Google Cloud**: Compute Engine, Cloud Storage, Vertex AI
- **Azure**: Virtual Machines, Blob Storage, Machine Learning Studio

### MLOps Tools
- **Model Versioning**: MLflow, DVC, Weights & Biases
- **Containerization**: Docker, Kubernetes, container orchestration
- **CI/CD**: GitHub Actions, Jenkins, automated testing
- **Monitoring**: Model drift detection, performance monitoring

### Distributed Computing
- **Parallel Processing**: Multi-GPU training, data parallelism
- **Cluster Computing**: Spark, Dask, distributed training
- **Resource Management**: SLURM, job scheduling, resource optimization

## Programming and Software Development

### Programming Languages
- **Python**: Advanced proficiency, scientific computing, web development
- **R**: Statistical analysis, bioinformatics packages, visualization
- **SQL**: Database design, query optimization, data warehousing
- **JavaScript/TypeScript**: Web development, Node.js, React
- **Bash/Shell**: System administration, automation scripts

### Development Tools
- **Version Control**: Git, GitHub, collaborative development
- **IDEs**: VS Code, PyCharm, Jupyter notebooks
- **Documentation**: Sphinx, MkDocs, technical writing
- **Testing**: Unit testing, integration testing, test-driven development

## Research and Academic Skills

### Research Methodology
- **Experimental Design**: Hypothesis testing, statistical power analysis
- **Literature Review**: Systematic reviews, meta-analysis
- **Peer Review**: Journal reviewing, conference reviewing
- **Grant Writing**: Research proposals, funding applications

### Communication
- **Technical Writing**: Research papers, documentation, tutorials
- **Presentations**: Conference talks, poster presentations
- **Teaching**: Course development, student mentoring
- **Collaboration**: Interdisciplinary research, team leadership