Attila Márton Putnoki (PhD Candidate; cognitive information systems, decision support, HCI)Arafat Md Easin (PhD Candidate; LLM integration, intelligent automation, generative models)Georgina Asuah (PhD Candidate; SAP machine learning, custom API integration, analytics)Imre Munkácsi (PhD Candidate; ERP implementation, Industry 4.0, clean-core systems)Attila Selmeci (PhD Candidate; GUI development, version control, SOA in ERP)Dominik Banka (PhD Student; explainable AI, low-code enterprise applications)Kawkab Bouressace (PhD Student; IoT, real-time data quality monitoring)Bochra Jendoubi (PhD Student)Ons Saadallah (PhD Student)+ 11 MSc students of the Data Science master's degree specializationProject experiences (EU / international):
- DATA-EDIH (European Digital Innovation Hub) – Supporting digital transformation of enterprises through AI, data analytics, and advanced technologies
- SAP Manufacturing Execution & Industry 4.0, 2022
- Agricultural SAP implementation, 2023
- 1st SAP UA Community Conference: Central and Eastern Europe, Budapest, 2024 – Organized and hosted by the research group at ELTE Faculty of Informatics
Research Interests:
The BPIE Research Group advances digital transformation by extending traditional ERP strategies with customer-oriented change management and a strong emphasis on sustainability and innovation. Core activities combine generative models, large language models (LLMs), advanced analytics, and natural language understanding to resolve complex ERP challenges. Enterprise architecture best practices such as datatype-driven data management and service-oriented architectures (SOA) ensure interoperability, while AI-generated proxy services and in-memory databases deliver maintainable user interfaces and optimized performance.
Web enablement and SOA design operate across multiple layers. At the composite layer, dynamic development techniques create new applications. At the workflow layer, process engine orchestration (BPM) and edge computing provide real-time data processing. These layers support AI-driven orchestration and adaptive service execution that align with intelligent enterprise principles spanning cloud strategy, DevOps, and data privacy and security, while enabling AI and BI functions across the enterprise.
Furthermore, the group focuses on agentic Artificial Intelligence and adaptive AutoML frameworks. This includes leveraging LLM reasoning for hyperparameter optimization and drift-aware representation learning in time-series forecasting, ensuring that models remain robust and explainable across diverse domains such as finance and industrial analytics. Cognitive Information Systems and human–computer interaction research underpin adaptive decision support environments that cultivate transparency, personalization, and trust through dynamic infocommunication loops.
We welcome postdoctoral candidates interested in joining our multidisciplinary team in any of the following research areas: Generative Models and LLM Integration, Advanced Analytics and Predictive Modeling, Natural Language Understanding, Sustainability and Innovation in ERP, In-Memory Database Architectures, Enterprise Architecture Best Practices, Big Data, Machine Learning and IoT in Enterprise Contexts, Smart Automation and Intelligent Orchestration, Cloud-Native and DevOps-Driven ERP Deployment, Cognitive Information Systems and HCI, Explainable AI and Low-code Technologies in ERP Systems, and End-to-End Digital Transformation
List of the Research Interests:
- Generative Models and LLM Integration
- Advanced Analytics & Predictive Modeling
- Natural Language Understanding
- Sustainability & Innovation in ERP
- In-Memory Database Architectures
- Enterprise Architecture Best Practices
- Commercial & Open-Source Technology Stacks
- Big Data, Machine Learning, and IoT in Enterprise Contexts
- Smart Automation & Intelligent Orchestration
- Cloud-Native and DevOps-Driven ERP Deployment
- Cognitive Information Systems & HCI
- AI- and BI-Powered Enterprise Functions
- End-to-End Digital Transformation
- Explainable AI and Low-code technologies in ERP systems
Key Research Focus Areas:
- Agentic AI & LLM Reasoning: Researching agent-based Artificial Intelligence and the use of Large Language Models to provide reasoning-driven guidance in complex decision-making processes.
- Adaptive AutoML & Time-Series Forecasting: Developing interpretable hyperparameter optimization frameworks and drift-aware feature engineering to enhance forecasting robustness in dynamic environments (e.g., energy, finance)
