Autor:
Andero Kalju

IT Academy Research Summit

To mark the end of the IT Academy research programme (2018-2023), we invite you to the IT Academy Research Summit. In a single event, you can meet all research teams established in three streams of Artificial Intelligence, Big Data, and Robotics. The two-day summit is packed with highlight talks, poster sessions, free discussions, and speed-dating opportunities.

Plenary sessions

During two plenary sessions, the researchers funded by the programme will give highlight presentations on their achievements and ongoing research. In addition, a couple of additional keynotes will take place.

Poster and demo sessions

Poster sessions bring together all the researchers and their teams, giving you an excellent opportunity to deep-dive into the research of all research groups in full detail.

Speed Dating

Pre-arranged 1-to-1 meetings with research team leaders from the IT Academy programme for company representatives. Please register here: https://ita-sd.cs.ut.ee 

Important details:

  • When? 18-19 May at the University of Tartu Delta Centre 
  • Who is invited? Company researchers and managers, public and academics interested in IT research and applications
  • Participation: Free of charge
  • Registration: https://forms.gle/oNb1rVjXqmQ5Yc9f7  (please, register as soon as possible, by May 15th latest)
     

Programme

Thursday, May 18th

12:00 - 13:00 Gathering, lunch, set-up

13:00-15:00 Plenary session I

  • Prof Jaak Vilo, Introduction, overview, and impact of the ITA programme
  • Assoc Prof Jaan Aru, Natural and Artificial Intelligence Lab
  • Assoc Prof Huber Flores, Distributed and Pervasive Systems Group
  • Assoc Prof Raivo Kolde, Health Informatics Group
  • Research Fellow Arnis Paršovs, Applied Cyber Security Group

15:00-15:30 Coffee break 

15:30-18:30 Poster session, lab demos, networking

19:00 Reception and Dinner 

Friday, May 19th 

9:15 - 11:15 Plenary session II

  • Assoc Prof Arun Kumar Singh, Collaborative Robotics and Robotic Computing Group
  • Assoc Prof Eduard Ševtšenko, Digital Supply Chain Group
  • Assoc Prof Naveed Muhammad,  Autonomous Driving Group
  • Mart Toots, Enterprise Estonia, formerly also ITL R&D Manager 
  • Dan Bogdanov, PhD, Cybernetica, Chief Scientific Officer, ITA Scientific Advisory Board member.
  • Prof Petteri Nurmi, University of Helsinki, ITA Scientific Advisory Board member.

11:15 - 11:30 Coffee break

11:30 - 13:00 Speed-dating for knowledge transfer purposes 

  • A speed-dating fair with industry and researchers 
  • Each PI will have up to six slots up for registration (first come, first served)
  • Please register here: https://ita-sd.cs.ut.ee

13:00 - 14:00 Lunch

14:00 - 16:00 Posters, demos, Birds of Feathers meetings, networking. 

14:00 - 16:00 A joint three-SAB meeting (including private Zoom for remote members)

  • All three thematic areas should be summarized in a brief SAB report

16:00 Session for ITA researchers, management and SAB only: Final feedback from SAB and closing, farewell. 


 

Additional information about the research fields

Artificial Intelligence and Machine Learning

The focus of this research is health informatics and data science in the health area, or more specifically on how to develop methods to extract greater value from health data. The group develops computational methods and tools for health data research. To support the method development and promote the use of Estonian health data, the group also works on cleaning and standardization of Estonian health data and participates actively in local and international clinical studies. 

Research directions: health informatics, health data science.
ProfileETIS, Scholar, twitter
WebsiteResearch group of Health Informatics

The research focuses on building better artificial intelligence through understanding natural intelligence. The research is mainly centered on 4 main research directions: iterative inference (building AI algorithms that consider alternatives and refine them iteratively), interference from other minds (building AI systems that can infer what other agents/humans think), AI’s role in education (whether and how AI should be used in education), creativity (how to help people find creative solutions).

Research directions: artificial intelligence, computational neuroscience, cognitive science.

ProfileETIS, Scholar, twitter
WebsiteNatural and Artificial Intelligence Lab 

Research focuses on music analysis and generation, representation learning and disentanglement, music similarity and explainable AI.   

Research directions: music information retrieval, music generation, music emotion recognition
ProfileETIS, Scholar, twitter

The focus of the research is transformer models with the special focus on language process models to solve problems from different domains - solving image processing and differential equations, automated theorem improvement. 

Research directions: Deep Learning, Natural Language Processing, Chat Bots, Algorithmic Bias, Explainable Artificial Intelligence for Cancer Imaging, Mean Field Games.
ProfileETIS, Scholar, twitter

WebsiteEvolutionary Deep Learning

Group conducts research in the broad area of artificial intelligence. From a theoretical perspective, the research aims to formalize more efficient and transparent computing schemes. Techniques are being developed for a variety of applications in a joint interdisciplinary effort. Research topics include soft computing, machine learning, and decision-making. The application fields include agri-food, environment, and health. 

Research directions: artificial intelligence, fuzzy logic, soft computing, soft decision making. 
Profile
ETIS, Scholar
Website
Intelligent Systems

The aim is to seek out areas in which AI can be applied to help to overcome the current limitations and hindrances of those fields and through this drastically improve the state of art results, efficiency and safety of potentially many industries, services and processes. The research areas in development at the moment are: nuclear energy and energy generation; agricultural sciences and ontology learning.

Research directions: artificial intelligence, machine learning, deep learning, data science.
ProfileETIS, Scholar, twitter

 

 

Data Science and Big Data

The research leverages the power of extended reality (virtual, augmented, and mixed reality) and IoT frameworks to visualize, control, and monitor complex industrial systems. Additionally, it facilitates exceptional teaching and advising experiences using engineering and design thinking methodologies, with the goal of sharing best practices that other educators can implement. We also experiment with teleoperation in virtual reality of local toy cars to allow more immersive remote operation of complex systems from anywhere in the world.

Research directions: Digital Twins, IoT frameworks, teaching environments facilitated by hands-on use of extended reality and IoT, teleoperation in virtual reality.
ProfileETISScholartwitter
WebsiteComputer Graphics and Virtual Reality Study Lab

Research connects information systems and software engineering with psychology and social sciences. The main emphasis is on human-centric computing, focusing on holistic requirements engineering, which addresses emotional requirements in addition to functional and quality requirements, embedding human values in requirements and software, social robotics, and social simulations.

Research directions: emotional requirements and human values, technology acceptance, e-government services, emotional chatbots, ontologies, agent-based simulations.
ProfileETISScholartwitter
WebsiteHuman-Centric Information Systems

 

The research involves shuffling protocols, (preprocessed) secure multiparty computation, function secret sharing, non-interactive zero-knowledge, and class group-based cryptography. 

Main research directions: secure computation, zero-knowledge.
ProfileETISScholar

WebsiteCryptography 

 

The research lies in the field of data management and engineering, working with all data types, including structured, semi-structured, graph, streaming, and data analytics. The group aims to engage and contribute to building the next generation of efficient, scalable, and insightful data systems in centralized and decentralized deployment architectures. 

Main research directions: Big Data management, Cloud Computing, Edge Intelligence, IoT, High-performance Data Analytics, data privacy and security, and Federated Learning
ProfileETIS, Scholar
WebsiteData Systems 

Focuses on evaluation and assessment technology solutions used in everyday life. The aim is to develop competencies so that the group can act as an independent authority evaluating the security assurance of technology, products and services provided by the government, industry and other institutions.

Main research directionssecurity engineering, applied cryptography, and system security.
ProfileETIS
Website
Applied Cyber Security 

The research addresses the connection between Industry 4.0 technologies and the integrated lean and agile strategies despite literature backing the complementary nature of the two SCM strategies. Analysis on how Industry 4.0 technologies impact LEAN and AGILE SC practices and the impact on Estonian manufacturing and digital solution providing companies. 

Main research directions: Customer journey map, Digital Supply Chain, Digital Twin, Reclamations and Returns.
ProfileETIS

Robot-human collaboration and Internet of Things in the industrial processes

The group conducts cutting edge research that focuses on challenges that are in the intersection of distributed systems; and pervasive and mobile computing domains. Of particular interest is the design, development, and evaluation of systems and applications that collect a variety of sensor data and proactively produce autonomous responses to users and the surrounding environment.  

Research directions: pervasive computing, mobile computing, pervasive data science, explainable AI, autonomous vehicles, autonomous drones.
Profile: ETISScholartwitter
Website: 
Distributed and Pervasive Systems 

Research focuses on autonomous driving that has many promises such as making roads safer (reduction in accidents caused by human error), more efficient transportation, better mobility, ease and comfort, as well as freeing up time.

Research directions: motion prediction of road agents, GNSS-free localization, high-definition (HD) maps, and exteroceptive perception.
ProfileETISScholar
WebsiteAutonomous Driving 

Focuses on developing cutting-edge solutions to tackle urban mobility challenges. This involves but is not limited to, utilising advanced computational techniques, such as machine learning, network science, optimisation, and probabilistic modelling, to analyse spatiotemporal data. The ultimate goal is to identify and solve problems in urban environments to promote the development of smarter, more sustainable, and more livable cities. 

Research directions: Human Mobility; Active Mobility; Simulation and Calibration; Urban Computing, Smart Cities, Mobility Digital Twin; Real-Time Systems; Intelligent Transportation Systems. 

ProfileETISScholar
WebsiteIntelligent Transportation Systems 

Focuses on developing cutting-edge solutions to tackle urban mobility challenges. This involves but is not limited to, utilising advanced computational techniques, such as machine learning, network science, optimisation, and probabilistic modelling, to analyse spatiotemporal data. The ultimate goal is to identify and solve problems in urban environments to promote the development of smarter, more sustainable, and more livable cities. 

Research directions: Human Mobility; Active Mobility; Simulation and Calibration; Urban Computing, Smart Cities, Mobility Digital Twin; Real-Time Systems; Intelligent Transportation Systems.

ProfileETISScholar
WebsiteIntelligent Transportation Systems 

The group performs research into algorithmic and applied aspects of robotics (motion planning, control, and state estimation) and machine learning as applied to robotics. Our research covers a wide set of applications ranging from human-robot collaboration, robotic manipulation to autonomous driving.

Research directions: motion planning, feedback control, learning based control, optimization for robotics, planning and control under uncertainty.
ProfileETISScholar, twitter

WebsiteCollaborative Robotics and Robotic Computing 

 

Organizers of the event:

Contact us: itasummit@ut.ee 

Prof. Jaak Vilo
Head of Data Science Chair
Coordinator of IT Academy research programme at the University of Tartu

Eeva Kilk
Coordinator of Doctoral Studies and Projects

Marianne Liiv
Marketing and Communication Specialist

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About IT Academy research measure

The IT Academy research measure was initiated to develop research and teaching capacity in new research areas jointly agreed upon with the government and IT company representatives. 

Project activities are funded by the European Social Fund.


 

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