3. Data, Science & AI

Automates prompt repetition to boost LLM accuracy across lightweight models, achieving significant performance gains in 67% of benchmarks.

Transfers AI session context between models using Japanese semantic compression and coherence validation for seamless session continuity.

Optimizes distributed machine learning training performance using RDMA networking and batch processing techniques.

Processes and cleans text data using IFD methodology and KenLM filtering to remove duplicates and improve quality for NLP applications.

Provides quality metrics for Direct Preference Optimization (DPO) and Reinforcement Learning with Value Regression (RLVR) in language model training.

Provides strategic guidance for integrating WebMCP to enable AI accessibility, LLM-powered UI tools, and browser automation in web applications.

Guidelines for optimizing Azure Cosmos DB performance, including partitioning strategies, query design, and SDK best practices.

Optimizes simulation parameters using DOE, sensitivity analysis, Bayesian optimization, and surrogate modeling for calibration and uncertainty quantification.

Processes simulation output data through extraction, analysis, and visualization to generate statistical summaries, derived quantities, and automated reports.

Validates scientific simulations across execution phases, ensuring numerical stability, physical conservation, and convergence through automated checks.

Orchestrates multi-simulation campaigns for parameter studies, batch job management, and result aggregation in scientific and engineering workflows.

Analyzes computational simulations to identify bottlenecks, optimize performance, and estimate resource requirements through timing, scaling, and memory profiling.

Selects and configures nonlinear solvers for root-finding and optimization, including Newton methods, BFGS, and convergence diagnostics.

Provides Python utilities for extracting structured entities from text using Cohere v2's JSON Schema mode, enabling reliable data pipelines and NER systems.

Enables Python-based multi-model database operations in SurrealDB 2.3.x, including graph relationships, vector search, and RAG application development.

Executes SQL queries directly against Doris database via specified host and port for data retrieval or manipulation.

Provides API access to retrieve Doris Frontend node addresses and raw cluster data for cluster management and monitoring.

A tool for testing AI agents, running simulations, and generating test data for agent evaluation.

AI-powered tool for designing PROTAC molecules, optimizing ternary complex formation, linkers, and ADMET properties to enable targeted protein degradation therapies.

Orchestrates multi-agent AI teams for autonomous hypothesis generation, experimental design, and validation in biomedical research.

Analyzes medication lists to identify potential drug-drug interactions (DDIs) using bioinformatics databases and computational rules.

AI-powered agent for discovering molecular glues to degrade undruggable proteins via E3 ligase interface modulation, accelerating targeted protein degradation therapies.

AI-powered TMB analysis for immunotherapy response prediction, cross-platform harmonization, and biomarker integration in oncology.

AI-driven integration of cellular imaging, laser microdissection, and mass spectrometry for spatially resolved single-cell proteomics.