About us

At IBM Research – Ireland our scientists and engineers are helping clients and partners make better decisions using an array of cognitive IoT technologies and expertise. Together we are testing new technologies on real business problems and discovering new growth opportunities.

We are focused on Cognitive IoT, Cognitive Integrated Healthcare, Interactive Reasoning, Data Centric Computing, Cloud and Privacy. Our research teams are collaborating with academic and industrial partners on several projects including research programs established by the European Union Horizon 2020 as well as pioneering collaborative projects developed side by side with University College Dublin scientists in our collaboratory.

AI and data

Cognitive Data Science

Cognitive Data Science

Cognitive data science team photo

The team behind “One Button Machine project”: (L-R):Francesco Vigliaturo, Thanh Lam Hoang, Ambrish Rawat, Francesco Fusco, Valentina Zantedeschi, Maria-Irina Nicolae, Minh Tran, Vincent Lonij and Mathieu Sinn.

We are working on next-generation data science services and acceleration of real-world data science projects by 10x-100x. Our team’s foundations lie in Data Mining, Machine Learning, Signal Processing, and Physics. We build systems that can handle huge amounts of spatiotemporal data, combined with geographic information and unstructured data such as text or images. Application areas include the Internet-of-Things (IoT), Retail, and Energy & Utilities. Our past research project with Vermont Electric Company (VELCO) was nominated as finalist for the Edison Electrical Institute Awards, the US-Ireland Research Innovation Awards.

IoT Data curation services

According to Forbes, data scientists spend more than 70% of their time on collecting, cleaning and organizing data. For IoT applications, the effort is particularly high because of the volume, complexity and volatility of the data. To support data scientists in developing and maintaining IoT applications, we develop data curation services enabling automated data exploration and self-healing data services, which can automatically detect, diagnose and resolve data inconsistencies.

Automated data exploration leverages machine learning models to contextualize IoT sensor data, semantic models to capture domain-specific context and probabilistic data fusion techniques to ultimately derive a coherent view of the world from the heterogeneous, often conflicting data streams. A layer of self-healing data services is then derived, consisting of data processing flows which can automatically answer user queries by providing cleaned and consistent data. Where gaps or conflicts in the data cannot be resolved automatically, data curation services enter a dialogue with domain experts and update their semantic model of the domain.

Automated feature engineering

Another key step in data science projects is the transformation of raw data into features that can be used as inputs for machine learning models. Often the raw data are stored across multiple tables in a relational database and need to be combined in various ways. This process is called feature engineering. Besides data curation, this is the most tedious and time-consuming data science task.

In our One Button Machine project, we build a system which automates the feature engineering process at the “push of a button”. The One Button Machine traverses the graph defined by the entities (tables) and relations (primary/foreign keys) of a relational database. Along the graph traversal, the One Button Machine computes aggregate features that can be used as input for machine learning models.

We successfully applied the One Button Machine in various data science competitions where it outperformed the majority of human teams and ranked among the top 25-36% of participants. In a client project with a social service provider from the U.S., the One Button Machine helped improving the accuracy on a complex classification task (involving a database with more than 20 tables) from 57% to 64%.

Read blog “Automating low-level tasks for data scientists

Deep learning

Over the past few decades, Machine Learning and in particular Deep Learning, has had phenomenal success in data-driven modelling, achieving close-to-human performance on a variety of cognitive tasks such as image classification or speech recognition. Of particular significance is the deployment of such models in critical applications like health care, autonomous cars, and security. Such applications require answers to far-reaching questions around trust, robustness, security and privacy of Deep Learning models. We pursue both fundamental and applied research in the following related areas:

  • Generalization beyond training classes: In real world applications, visual recognition systems will inevitably face input that was not available, or not anticipated during training. We developed methods to interpret even unseen and entirely unknown classes.
  • Adversarial Attacks: Traditional deep learning algorithms can be fooled. This is particularly concerning in critical applications like autonomous cars and security. We work on making deep networks safe and robust against adversarial perturbations.
  • Generative adversarial networks: One of the ways to understand data is to recreate the underlying generative process. GANs offer a framework to achieve this in a zero-sum game setting. We study fundamental questions about GANs and their applications.
  • Applications in Industry: We also used deep learning method to find the, often complex, relationships between data and industry process. For example, we studied the impact of weather on traffic accident risk which can enhance safety in smart vehicles.

AI and IoT

Cognitive buildings

Our scientists have created a Cognitive Campus building upon the IBM Watson IoT platform and using more than 3,000 sensors.

Buildings are evolving to learn and adapt actively, making them aware of their own energy performance and balancing this with the comfort of its occupants. Our scientists have created a Cognitive Campus building on the IBM Watson IoT platform. Several IoT use cases are being tested on Dublin’s IBM Technology Campus buildings and data centre which are enabled with more than 3,000 sensors. These uses cases are driving predictive maintenance and integrating new kinds of immersive virtual and augmented reality interfaces.

Cognitive buildings are the next generation of sustainable building systems as they automatically integrate, analyse and learn from the vast amount of IoT generated data within a building and its environment. As a result, the building itself becomes an assistant and strives to improve user satisfaction, driving down costs and enabling new innovative collaborative services.

Read Perpeteum Magazine section called " IBM Cognitive Buildings" page 18"

AI and IoT

Cognitive IoT

Cognitive IoT: Improving our understanding of the world

Using Cognitive IoT our researchers are teaching buildings to be self aware and connected cars to be a driver’s cognitive personal companion.

IBM Research IoT Ireland

Current projects

Connected Car and mobility

Cars are undergoing a huge transformation: From mobility devices with the single purpose of getting people from an origin to a destination, to becoming interconnected, intelligent and informed devices, which can be used to deliver shared services. Together with University College Dublin, IBM Research – Ireland is developing a context-aware in-car reasoning system. This system constitutes a cognitive personal companion, that debates and reasons with the driver, and mitigates personalized risks for the car. It is envisioned that the companion will become a fundamental core technology for future connected and autonomous cars.


A cognitive in-car companion to help us enjoy the journey

Helping industries use weather analytics to make informed decisions

At IBM Research, we are creating cognitive solutions that help industries make better use of weather data

Every business is different; different industries are impacted by weather in different ways. The terabytes of information available in a weather forecast need to be refined to the essential components that impact your business. IBM’s acquisition of The Weather Company brings accurate, timely, high-resolution weather forecasts to all businesses. The challenge for business and industries is to effectively use weather forecasts to be prepared and take action. Here at IBM Research, we are creating cognitive solutions that help industries make better use of these forecasts.

Our scientists are developing systems that interpret weather forecasts to predict optimal actions on airline delays, rescheduling and/or cancelling of flights to mitigate the impact of weather forecasts and the uncertainty in their interpretation. Our model blending research can significantly improve the precision of renewable energy forecasts by combining forecasts produced from different models over a range of scales. This also gives us the ability to generate an uncertainty forecast. We are helping to identify and refine relationships between weather data and large amounts of real-time data from millions of sensors, such as those deployed in electrical grids to help utility companies better manage electricity systems.



Data Centric Computing and Cloud

Data Centric Computing and Cloud

Our researchers are addressing challenges that conventional cloud and edge data centres are facing, more so in light of patterns and requirements in computing created by trending cognitive workloads and use-cases .

IBM Research ireland data

Current projects

Edge Computing for Cognitive IoT

Our researchers are working on materialising the vision of offering general purpose computing and analytics services at the "edge" of the cognitive Internet of Things value chain. By doing so, the anticipation is to enable cognitive computing services whose sustained value is conditioned on the ability of near real-time analytical processing, while still having cloud services play their vital role for aggregation, batch processing and further data monetisation. Also, in addressing edge-to-cloud communication costs and privacy requirements.

IBM Research edge computing

As an example, our researchers have built a demo called “Edge IoT Analytics”, showcasing an end-to-end solution around hospitality. The use case is about privacy-preserving customer-satisfaction insights. It involves geographically distributed edge devices employing Watson services to process interactions between guests and concierge at hotel reception and understand hidden tones of their conversation. The idea is that the devices only store anonymized tones data, and the conversations are not stored either on the Cloud or the devices, thus preserving guests' privacy. To complete the picture, the application is integrated with a Cloud service that is able to query, aggregate and present on a simple Web dashboard cognitive results coming from the devices spread around the globe.


A guide to Edge IoT analytics

New Cloud Architectures and Stacks

In today’s cloud datacenters, physical systems comprise individual server units that contribute processing, memory, accelerators and storage resources. The challenge is for these resources to be more efficient, flexible and agile. We are developing a vertically integrated “datacenter-in-a-box” prototype to showcase the superiority of disaggregation in terms of scalability, efficiency, reliability, performance and energy reduction.


We are also experimenting with a universal microserver architecture and software ecosystem which will address the challenges of performance, power consumption and reliability in both conventional cloud and edge data centres.


Data Centric Cognitive Systems

Our researchers are working on system software for sustained efficiency and productivity of next generation systems, as driven both from data centric patterns exhibited by trending workloads and/or the need to grow to unprecedented levels of capability computing. We do so by applying findings and derived solutions in various industries that employ computational methods to respond to pressing environmental, social, commercial and scientific challenges, such as e.g. controlling the impact of oil spills, advanced manufacturing and understanding physical phenomena.


We are also focusing on making High Performance Computing more consumable in several industries that can see tremendous benefit by shifting from manual labour intensive processes to robust and adaptive computational appliances. One area we are specifically focusing our cognitive computational appliances is in formulated product design, thus yielding faster R&D times, technically superior and more sustainable formulations.

Selected techniques, methods and implementing software artefacts coming out of our research in data centric systems have made it into state of the art machines, such as JURON.

IBM Research ireland data



Interactive Reasoning

Interactive Reasoning

Our scientists are building cognitive systems that can assist humans to make smarter choices using techniques such as Recommender Systems, Network Analysis, Artificial Intelligence, Natural Language Processing,Data Mining and Machine Learning.

IBM Research reasoning

Current projects

Automated Argumentation for Complex Decisions

IBM Research is ushering in a new era of Cognitive Computing through our debating technologies project. IBM Researchers in the Haifa, India and Dublin labs are developing algorithms that can assist humans to reason, make decisions, or persuade others. Specifically, this multi-year cognitive computing project explores decisions scenarios where there isn't only one singlet answer. Through computational argumentation techniques, we have demonstrated the ability to generate pro and con arguments for a given topic by drawing from a given body of knowledge (e.g. Wikipedia).


Cognitive Sales Team

The Cognitive Sales project harnesses the complex network of client organisations and the expertise of sellers and teams to assist in identifying clients, provide an enriched understanding of the client and products of interest along with a solution for building the appropriate sales team that can work together to drive the opportunity forward.


Applying AI to Drug Discovery

When chemists design a new drug, they not only need to design a target molecule or compound, but they also need to look at the reaction pathways to synthesise that target molecule or compound. Providing an automated solution to reaction pathway discovery is not a trivial task, and has yet remained unsolved; At this point, it remains mostly a manual task, often time consuming, repetitive and that can result in sub-optimal solutions or even failure in finding reaction pathways due to human cognitive limitations.

Our scientists see this challenge of drug discovery as an important way to help chemists discover reaction pathways and chemical reaction sequences for generating target chemical components. Artificial intelligence-driven discovery of chemical synthesis could have a wide-ranging impact across not only the pharmaceutical industry, but also food, chemical, and materials industries.



Integrated health and social care

Integrated health and social care

IBM Research – Ireland is working on new ways to use technology to support, improve and revolutionise the delivery of care. We are addressing the full continuum of patient care, including the support of clinical and social care staff, using state of the art technologies in semantic data management, machine learning and artificial intelligence. The group is currently working across three research themes and in collaboration with research institutes and universities across Europe.

IBM Research Healthcare Ireland

Current projects

Integrated Care and Person-Centric Care Management

IBM is exploring how context-based analytics can help address challenges in Integrated Care. We are working on tools to support decision making and care delivery by looking at the individual as a whole, rather than a collection of issues. Getting the full picture requires integrating information from disparate enterprise systems, publicly available data and data from the Internet of Things. This information is processed using IBM cognitive analytics and provides a consolidated view of the individual, key insights and most appropriate actions, ultimately creating a digitally integrated care management system. In turn, this system can be used to drive behavioural change and improve patient outcomes.

IBM Research – Ireland, in collaboration with IBM Watson Health, has won the prestigious 2017 US-Ireland Research Innovation Award


Finding ways to identify privacy risks

One of the major concerns when collecting and storing vast amounts of health data is privacy. This is important for the individual patient but also for the service providers and the trust that users have in them. Services and institutions are also increasingly interested in sharing datasets and tools that support and protect these data are needed. Our group is researching ways to identify privacy risks and anonymise sensitive datasets. The team has designed and implemented PRIMA (Privacy Risk Identification, data Masking and Anonymization), a toolkit for identifying and protecting privacy vulnerabilities that is provided both as a library and a service model. The toolkit covers a broad list of privacy tasks with the end goal to automate the decision making process for data owners .

Learn more about the identification and protection of privacy vulnerabilities


Behavioural Change

Our researchers are working on a project which will revolutionise the way evidence around behaviour change interventions (BCIs) is synthesised and applied to advance understanding of human behaviours and behaviour change and has potential impact in diverse areas beyond public health. It seeks answers for: “What works, how well, for whom, in what setting, for what behaviours - and why?” by developing and applying cognitive technologies to the rapidly growing corpus of human behaviour change research literature. This technology will assist researchers and practitioners in the field while informing public policy decisions.


Visiting IBM Research – Ireland


IBM Dublin Technology Campus, Building 3, Damastown Industrial Estate, Mulhuddart Dublin 15, Ireland

Directions to IBM Research – Ireland:

Take Exit 6 off M50 (signposted Cavan / Blanchardstown). Take Exit of N3 for Clonee / Damastown Industrial Estate. Take right at top of slip road and proceed straight for approx 500m. Upon entry to IBM Technology Campus, IBM Research – Ireland Building 3 will be signposted on the right hand side of the campus, just past the campus roundabout.

Upon Arrival to Building 3 Main Entrance:

Building 3 is the building with blue trim on windows. Park and enter by the main entrance. Press buzzer and ask the security officer to release the door for you. Check-in at our reception by advising the security guard who you are visiting and/or event you are attending and he will provide a visitor security badge. 

By public transport

Dublin Bus 38B serves the campus from Dublin City Centre. Also Dublin Bus 38D (which has a limited service) is non-stop after O’Connell Street to the IBM Campus.
There is an Expruss Bus Route 870, see schedule.

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