knowledge_base/skills_expertise.md

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