Research Publications

Our Research Portfolio

Explore our latest breakthroughs in AI research and their real-world applications

Research Project

Domain-Adaptive Language Models for Healthcare

In collaboration with: Leading Medical Research Institute

papers

3 publications

accuracy

94%

deployment

15 hospitals

Overview

Developing specialized language models that understand complex medical terminology, clinical workflows, and patient data while maintaining privacy and compliance with healthcare regulations.

Methodology

Fine-tuned GPT-based models on de-identified medical records and clinical literature. Implemented differential privacy techniques and federated learning to train on sensitive data without centralization. Created custom evaluation benchmarks for medical reasoning and diagnosis support.

Key Outcomes

Published 3 peer-reviewed papers in top-tier conferences (NeurIPS, ICML)

94% accuracy on medical diagnosis assistance tasks

Models deployed in 15 hospital systems

HIPAA-compliant architecture successfully audited

Technologies & Methods

Transformer ModelsFederated LearningDifferential PrivacyBioBERTClinical NLP

Publications

  • •MedLM: Privacy-Preserving Medical Language Models (NeurIPS 2023)
  • •Federated Fine-Tuning for Clinical AI (ICML 2023)
  • •Evaluation Frameworks for Medical AI Systems (JMLR 2024)
R&D Project

Causal AI for Financial Market Prediction

In collaboration with: Global Investment Firm

improvement

+40% accuracy

patents

2 filed

impact

$50M+ value

Overview

Building causal inference models that go beyond correlation to understand true cause-and-effect relationships in financial markets, enabling more robust predictions and risk assessment.

Methodology

Developed novel causal discovery algorithms using structural causal models (SCMs) and do-calculus. Integrated multiple data sources including market data, news sentiment, and macroeconomic indicators. Created counterfactual reasoning systems to simulate "what-if" scenarios.

Key Outcomes

40% improvement in prediction accuracy vs traditional methods

2 patents filed for causal discovery algorithms

$50M+ in improved trading decisions

Framework adopted by 3 major financial institutions

Technologies & Methods

Causal InferenceGraph Neural NetworksStructural Causal ModelsBayesian NetworksTime Series Analysis

Publications

  • •Causal Discovery in Financial Markets (AISTATS 2023)
  • •Counterfactual Reasoning for Risk Assessment (KDD 2023)
Innovation Project

GraphRAG: Next-Generation Knowledge Systems

In collaboration with: Fortune 500 Technology Company

hallucination

-65%

stars

5K+ GitHub

adoption

20+ enterprises

Overview

Pioneering research in combining knowledge graphs with retrieval-augmented generation to create AI systems that can reason over complex, interconnected information with improved accuracy and explainability.

Methodology

Designed hybrid architecture combining graph neural networks (GNNs) with transformer-based retrieval. Implemented multi-hop reasoning over knowledge graphs with attention mechanisms. Created automatic knowledge graph construction from unstructured text using entity extraction and relation classification.

Key Outcomes

65% reduction in hallucination rates compared to standard RAG

Open-sourced framework with 5,000+ GitHub stars

Adopted by 20+ enterprise customers

Keynote presentation at leading AI conferences

Technologies & Methods

Knowledge GraphsGraph Neural NetworksRAGEntity ExtractionSemantic SearchNeo4j

Publications

  • •GraphRAG: Graph-Enhanced Retrieval for LLMs (ACL 2024)
  • •Multi-Hop Reasoning in Knowledge-Grounded Systems (EMNLP 2023)
Applied Research

Efficient RL for Robotics and Autonomous Systems

In collaboration with: Robotics Research Consortium

efficiency

10x better

transfer

95% success

deployment

5 warehouses

Overview

Advancing reinforcement learning algorithms for real-world robotic applications, focusing on sample efficiency, sim-to-real transfer, and multi-agent coordination.

Methodology

Developed model-based RL algorithms that learn world models for planning. Implemented curriculum learning and domain randomization for robust sim-to-real transfer. Created hierarchical multi-agent RL systems for coordinated task execution in warehouse automation.

Key Outcomes

10x improvement in sample efficiency vs baseline methods

95% success rate in sim-to-real transfer tasks

Deployed in 5 autonomous warehouse systems

4 papers accepted at robotics conferences

Technologies & Methods

Reinforcement LearningModel-Based RLPPOSACMulti-Agent SystemsSimulation

Publications

  • •Sample-Efficient Model-Based RL for Robotics (CoRL 2023)
  • •Hierarchical Multi-Agent Coordination (ICRA 2024)
  • •Robust Sim-to-Real Transfer via Domain Randomization (RSS 2023)