Work With the World's Best Minds

Zeblok Ingenuity

Benefit from our academic/industrial/government relationships



Underlying Zeblok's Intelligence Marketplace is a partnership program with algorithm originators that provides access to leading edge AI algorithms that are ready to be incorporated into AI models powering your business processes. Zeblok has a strategic relationship with the Center for Visual and Decision Informatics (CVDI), which is a National Science Foundation (NSF) Industry University Cooperative Research Center (IUCRC), that works in partnership with government, industry, and academia to develop the next-generation visual and decision support tools and techniques that enable decision-makers to significantly improve the way they organize and interpret their organizations' information.

As an industry/university cooperative research center, the industrial and academic partners focus on use-inspired research projects that explore innovations with the goal of transforming data into insights.   Researchers work in multiple areas of data science study that are then applied across various domain areas, such as government, healthcare, sustainability, transportation, commerce, and finance.

Through the Zeblok Ingenuity partner program, you get direct access to the world's best minds developing AI algorithms. Zeblok has an exclusive arrangement with Akai Kearu, for its Explainable-AI algorithm. We have non-exclusive licensing arrangement for other algorithms developed by affiliate universities under the CVDI program viz. Drexel University, University of Louisiana,  Tampere University, University of Virginia, Stony Brook University and UNC Charlotte.

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We are focusing on the following areas to write proposals and engage with the industry to do real life projects

Visualization and Data Analytics

Data analytics is the method of examining data sets (structured or unstructured) in order to gain useful insights in order to draw conclusions. Data Visualization represents data in a visual context by making explicit the trends and patterns inherent in the data.


Typical areas of interest would be:

  • Visual Analytics and Interactive Visualization of complex datasets

  • Predictive Analytics and Deep Pattern Analysis

  • Edge Analytics Providing the ability to process imagery and video data at the edge

  • Massive Data Management: Data ingestion, data storage, multi-modal

  • Anomaly Detection for Computer Systems Health Monitoring, including fraud detection and credit risk management, computer vision, edge networks, explainability and ML/neural networks

  • Digital Biomarker Platform, including visualization and prediction capabilities

  • Urban Informatics with geospatial representation

  • Human as an IoT Sensor including feedback, camera data and bio data

  • High Dimensional data visualization and pattern recognition

  • Predictive Analytics for ED volume prediction and syndromic surveillance

Artificial Intelligence

Inventions and new uses of artificial intelligence (AI) and machine learning (ML) to a variety of domains.  Including subareas of AI and ML, such as search, knowledge representation, planning, reasoning, natural language processing, robotics and perception, multi-agent systems, statistical learning and deep learning.


Typical areas of interest would be:

  • Algorithms: Active Learning; AutoML; Boosting and Ensemble Methods; Classification; Clustering; Collaborative Filtering; Components Analysis (e.g., CCA, ICA, LDA, PCA); Density Estimation; and many more.

  • Deep Learning: Adversarial Networks; Attention Models; Biologically Plausible Deep Networks; CNN Architectures; Deep Autoencoders; Efficient Inference Methods; and many more.

  • Reinforcement Learning and Planning: Decision and Control; Exploration; Hierarchical RL; Markov Decision Processes; Model-Based RL; Multi-Agent RL; Navigation; Planning; Reinforcement Learning.

  • Neuroscience and Cognitive Science: Auditory Perception; Brain Imaging; Brain Mapping; Brain Segmentation; Brain--Computer Interfaces and Neural Prostheses; Cognitive Science; Human or Animal Learning; Language for Cognitive Science; Memory; Neural Coding; and many more.

  • Applications: Activity and Event Recognition; Audio and Speech Processing; Body Pose, Face, and Gesture Analysis; Communication- or Memory-Bounded Learning; Computational Biology and Bioinformatics; Computational Photography; Computational Social Science; Computer Vision; and many more.


Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents.

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems?


Typical areas of interest would be:

  • Integrating ethics into system behavior

  • Software development methods etc. supporting implementation of ethics

  • Standards etc. that ensure the integrity of developers and users


Cybersecurity is a critical area of research as it addresses society’s increasing reliance on the connections between the physical world, people, hardware, software, networks, data, privacy and ethics. As these interconnections increase, so do vulnerabilities, which can expose the fragile nature of these systems and if exploited, cause irreparable damage


Typical areas of interest would be:

•  Risk and mitigation related to the field of Artificial Intelligence with potential emphasis on defending trained models against model  inversion, membership inference and developing countermeasures against techniques that may exploit supervised and unsupervised learning models.


•  Adoption of advanced techniques to enable warning and prediction of significant cyber events before they occur, leveraging predictive analytics, AI, ML and inputs or sensors from not typically associated with the cyber domain


•  Multi-dimensional threat-detection leveraging combinations of AI techniques


•  Leveraging advanced cryptography for Privacy Preservation while deploying AI techniques across multiple parties for cognitive decision-making use case

Human Centric

When all senses are tuned to innovating mode, we tend to forget the human perspective. We are underestimating the value of humans as core data source. Humans provide a host for the most advanced IoT sensors ever created and we need to understand the best approach to leverage our brains capacity to perform real-time intelligence as well as researching how we integrate human responses along with evolving artificial sensors.

Typical areas of interest would be:

•  Biofeedback: Real-time feedback using example light wearable biosensors. End-user energy levels, performance measurement, happiness, different stress levels, recovery.


•  Bio profiling: Like fingerprints, human bio profiles are unique. It is must to create profile before we can get reliable data for real-time feedback including light wearables.


•  People Flow Analytics: Using indoor positioning of people to measure interactions, space usage, and work types.

•  Sound Analytics: Moods, interaction types and collaboration. Can be used together with video analytics to get better reliable data.


•  Digital Interactions between End-users: Analyze collaboration between people in different platforms like email, intranet, chat, and group chat communities.


•  Real-time Analytics of Written Feedback (intranet or social media): Virtual real-time feedback box that organizes feedback to different stakeholders and can identify urgency levels. Future prediction from weak signals. End-user experience real-time.


•  Touch and Gesture Analytics: IoT and other building automation hardware used for collecting feedback or do adjustments (Example: air quality thermostat controllers).