Combinatorial Optimization

Efficient Solutions for Complex Decision Spaces

Focuses on solving problems where the goal is to find the best combination from a discrete set of possibilities (e.g., scheduling, routing, resource allocation). Often requires specialized algorithms due to exponential complexity.

Solve large problems in few seconds

Evolutionary Optimization

Nature-Inspired Algorithms for Search and Optimization

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Uses biologically inspired techniques such as genetic algorithms, swarm intelligence, and differential evolution to explore large, nonlinear, and multimodal search spaces.

Solve NP-hard problems in few minutes

Deep Reinforcement Learning

Learning Policies Through Interaction

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Combines reinforcement learning principles with deep neural networks to train agents that make sequential decisions, widely applied in robotics, autonomous systems, and game playing.

Train AI agents to solve hard tasks efficiently

Multiobjective Optimization

Balancing Competing Goals in Complex Systems

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Deals with optimization problems involving multiple conflicting objectives, producing a set of Pareto-optimal solutions instead of a single answer, supporting trade-off analysis.

Optimize across 100+ conflicting objectives simultaneously

Active Learning

Minimizing Labels, Maximizing Insights

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A machine learning paradigm where the model selectively queries the most informative data points to be labeled, reducing annotation costs while improving learning efficiency.

Cut labeling costs by up to 80%

Large Temporal Graphs

Modeling Dynamics in Evolving Networks

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Studies graphs that evolve over time (e.g., social networks,, financial transactions), focusing on scalable algorithms for pattern detection, forecasting, and anomaly detection.

Track and analyze over 1B+ evolving connections in real time.

XAI - Explainability

Opening the Black Box of AI Systems

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Research into making AI models interpretable and transparent, enabling users to understand decisions, build trust, and ensure fairness, accountability, and compliance with regulations.

Provide clear explanations for 100% of AI decisions

Behavioral Clustering in High Dimensions

Discovering Patterns in Complex Behavioral Data

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Applies clustering techniques to high-dimensional data (e.g., user behavior, biological signals, or financial activities) to reveal hidden structures, groups, or anomalies, often requiring dimensionality reduction.

Discover hidden patterns across 10K+ dimensions of behavioral data

Our Applied AI Research Area

From theory to impact, our research turns complex challenges into measurable results.

FinTech

CashVision® for ATMs: Cash prediction, planning and optimization

FinTech

Context’AML® The Most Accurate AI-based Behavioral Monitoring Solution for AML/CFT

RegTech

RegVision®: Revolutionizing Regulatory Compliance with AI-Driven Precision

FinTech

Sanctions Radar: Ensuring Absolute Compliance with Context’AML®

RegTech

Context’AML®: Revolutionizing Anti-Money Laundering with AI-Driven Compliance

FinTech

Context’Fraud®: Stop internal fraud before it stops you

FinTech

Context’Watch®: Real-time intelligence on the entities that matter most

FinTech

CashVision® for ATMs: Cash prediction, planning and optimization

FinTech

Context’AML® The Most Accurate AI-based Behavioral Monitoring Solution for AML/CFT

FinTech

Sanctions Radar: Ensuring Absolute Compliance with Context’AML®

FinTech

Context’Fraud®: Stop internal fraud before it stops you

FinTech

Context’Watch®: Real-time intelligence on the entities that matter most

RegTech

RegVision®: Revolutionizing Regulatory Compliance with AI-Driven Precision

RegTech

Efficient Solutions for Complex Decision Spaces

Focuses on solving problems where the goal is to find the best combination from a discrete set of possibilities (e.g., scheduling, routing, resource allocation). Often requires specialized algorithms due to exponential complexity.

Nature-Inspired Algorithms for Search and Optimization

Uses biologically inspired techniques such as genetic algorithms, swarm intelligence, and differential evolution to explore large, nonlinear, and multimodal search spaces.

Learning Policies Through Interaction

Combines reinforcement learning principles with deep neural networks to train agents that make sequential decisions, widely applied in robotics, autonomous systems, and game playing.

Balancing Competing Goals in Complex Systems

Deals with optimization problems involving multiple conflicting objectives, producing a set of Pareto-optimal solutions instead of a single answer, supporting trade-off analysis.

Minimizing Labels, Maximizing Insights

A machine learning paradigm where the model selectively queries the most informative data points to be labeled, reducing annotation costs while improving learning efficiency.

Modeling Dynamics in Evolving Networks

Studies graphs that evolve over time (e.g., social networks,, financial transactions), focusing on scalable algorithms for pattern detection, forecasting, and anomaly detection.

Opening the Black Box of AI Systems

Research into making AI models interpretable and transparent, enabling users to understand decisions, build trust, and ensure fairness, accountability, and compliance with regulations.

Discovering Patterns in Complex Behavioral Data

Applies clustering techniques to high-dimensional data (e.g., user behavior, biological signals, or financial activities) to reveal hidden structures, groups, or anomalies, often requiring dimensionality reduction.

From theory to impact, our research turns complex challenges into measurable results.

AI Research Focus 2025–2030

AI market expected to grow nearly 4× by 2030

Nearly 1/4 of global AI investment is flowing into generative AI research

66% of organizations adopting AI agents report increased productivity

Estimated Applied AI Research share

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