Current HIIT Researchers
HIIT provides funding for two types of researchers: Postdoctoral Researchers and Research Fellows.
HIIT Postdoctoral Fellow positions are intended for researchers who have recently completed their doctoral degrees. HIIT Postdoctoral Fellows are not hired to work on externally funded projects, but are intended to support one or more of the HIIT strategic focus areas. These positions provide opportunities for development as a researcher within a research group from one of the four departments conducting ICT research in Aalto University and University of Helsinki.
The initial contract period is normally for two years with the possibility of a one year extension up to a total of three years.
HIIT Research Fellow positions support the career development of excellent advanced researchers who already have some postdoctoral research experience. While HIIT Research Fellows have a designated supervisor at University of Helsinki or Aalto, they are expected to develop their own research agenda and to gain the skills necessary to lead their own research group in the future. HIIT Research Fellows should strengthen Helsinki’s ICT research community either through collaboration or by linking ICT research with another scientific discipline. In either case, excellence and potential for impact are the primary criteria for HIIT Research Fellow funding.
The initial contract period is normally for three years with the possibility of a two year extension up to a total of five years.
Please find presentations of the HIIT Postdoctoral Fellows and HIIT Research Fellows below. For presentations of former HIIT Postdoctoral fellows and HIIT Research Fellows, please click here.
HIIT Research Fellow 1.11.2019-31.8.2022
Docent Antti Hyttinen obtained his Master’s degree in Information Technology in Tampere University (of Technology) in 2004 and his PhD in Computer Science in 2013 from the University of Helsinki. His doctoral studies were supervised by Docent Patrik Hoyer. Dr. Hyttinen did research at California Institute of Technology during 2014. Dr. Hyttinen has also obtained the competitive personal post-doc funding from the Academy of Finland (2016). Dr. Hyttinen received the title of Docent in February 2020. Currently, Dr. Hyttinen works as a HIIT Research Fellow in the Sums of Products research group, collaborating with several research groups at the department.
Hyttinen’s research focuses on causal inference and probabilistic graphical models. Dr. Hyttinen has developed theory and structure learning algorithms for several different types of probabilistic graphical models. Many of these are causal models that allow also for latent confounding and feedback. These methods build on many different kinds of techniques such as Branch and Bound, MIP, MaxSAT, ASP, and dynamic programming. In addition, Dr. Hyttinen has developed methods for causal effect identification, experiment selection and combining different types of data sources for the aforementioned tasks.
During 2021, Dr. Hyttinen published papers on particularly in causal effect identification and structure estimation. Dr. Hyttinen lectured the probabilistic graphical models course in the data science master program. He also participated in research and thesis supervision at Ph.D. and M.Sc. levels.
Sample of publications in 2021:
 K. Rantanen, A. Hyttinen, M. Järvisalo. Maximal Ancestral Graph Structure Learning via Exact Search. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.
 S. Tikka, A. Hyttinen, J. Karvanen. Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach. Journal of Statistical Software, Articles, Volume 99, Issue 5, 2021.
 J. Karvanen, S. Tikka, A. Hyttinen. Do-search – a tool for causal inference and study design with multiple data sources. Epidemiology (journal), Volume 32, Issue 1, p. 111-119, 2021.
HIIT Postdoctoral Fellow 1.11.2021-31.12.2023
Dr. Xiaoli Liu received her PhD degree in mathematics from the University of Helsinki in December 2017. Her doctoral studies were supervised by Professor Mats Gyllenberg. After graduating she worked as a Postdoctoral researcher in the Biomimetics and Intelligent System Group (BISG) at the University of Oulu. She worked with Prof. Pan Hui’s group as a visiting scholar in 2019 and Polar Electro Oy with collaboration of University of Oulu in 2018. In November 2019, she joined the research group lead by Prof. Sasu Tarkoma in the Department of Computer Science at University of Helsinki as a Postdoctoral researcher. Dr. Xiaoli Liu currently works as a Postdoctoral fellow within Helsinki Institute for Information Technology (HIIT).
Dr. Xiaoli Liu’s research interests focus on distributed systems and edge intelligence, and her participated projects are related to IoT, federated learning, opportunistic learning, differential privacy, 5G and Augmented reality (AR). Dr. Xiaoli Liu has published 19 peer-reviewed journal and conference articles. She publishes papers in top venues, such as IEEE Internet Computing Magazine, IEEE Transactions on Intelligent Transportation Systems, and ACM Transactions on Sensor Networks.
Her recent research in 2021 contains providing solutions for scalable air quality monitoring by combing edge intelligence, 5G, and sensor calibration techniques ; utilizing context-aware AR together with 5G edge for path planning ; extending the previous Bayesian federated learning research work to have a model cluster instead of assuming a global model; opportunistic learning for air quality monitoring; and proving the feasibility of putting independent Component Analysis (ICA) under the distributed setting. Meanwhile, she also works for two survey papers: outdoor low-cost sensor and sensor calibration for air quality monitoring  and COVID-19 in Public Transportation: Transmission Risk, Mitigation and Prevention.
She is actively involved in funding application, workshop organizing, and teaching in 2021, for example, the participated project application “NSF joint call with the collaboration of Yale University” submitted in April 2021 has got the positive decision. She is the course advisor for bachelor thesis in the spring semester and co-teaching Advanced Course in Deep Learning (5cr) for in the autumn semester and she will be the main responsible teacher from 2022. Meanwhile, she serves as a board member of the Junior Faculty Club in University of Helsinki to provides support for PhD students and Postdoctoral researchers
Sample of publications in 2021:
 X. Su, X. Liu, N.H. Motlagh, J. Cao, P. Su, P. Pellikka, Y. Liu et al. “Intelligent and Scalable Air Quality Monitoring with 5G Edge.” IEEE Internet Computing 25, no. 2 (2021): 35-44.
 J. Cao, X. Liu, Xiang Su, Sasu Tarkoma, Pan Hui, “Context-Aware Augmented Reality with 5G Edge”, IEEE Global Communications Conference, Madrid, Spain, 7 -11 December 2021.
 C. Francesco, J. Mineraud, E. Lagerspetz, S. Varjonen, X. Liu, K. Puolamäki, P. Nurmi, and S. Tarkoma. “Low-cost outdoor air quality monitoring and sensor calibration: A survey and critical analysis.” ACM Transactions on Sensor Networks (TOSN) 17, no. 2 (2021): 1-44.
 Z. S. Liu, G. Xiong, Z. B. Wei, Y. Zhang, M. Zheng, X. Liu, S. Tarkoma, M. Huang, Y. S. Lv, C. H. Wu. “Trip Purposes Mining From Mobile Signaling Data.” IEEE Transactions on Intelligent Transportation Systems (2021).
HIIT Research Fellow 1.1.2021-31.7.2025
Alan Medlar is a member of the Exploratory Search and Personalization (ESP) research group lead by Prof. Dorota Głowacka. Alan has undergraduate degrees in Computer Science from University College London (UCL), UK and in 2012 was awarded a PhD in Bioinformatics and Genetics from UCL Medical School. After arriving in Finland, he worked as a postdoctoral researcher in Dr Ari Löytynoja’s Evolutionary Sequence Analysis group and in Prof. Liisa Holm’s Bioinformatics group. In 2019, he moved to the Department of Computer Science where he is currently a university researcher and HIIT research fellow.
Alan’s research interests are related to information seeking, interactive systems and user experience. In particular, he is interested in how we can support users performing challenging search tasks, such as exploratory search, and how to model users’ subjective preferences as they relate to interactive systems, such as AI-based systems and video games.
Alan’s recent research in 2021 included work on novel interfaces for exploratory scientific literature search , using Gaussian Process bandits to model user search intents over GANs , and using permutation-based statistics to make inferences about semantic shift robust to noise .
Sample of publications in 2021:
 A. Medlar, J. Li, D. Głowacka. Query Suggestions as Summarization in Exploratory Search. Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (CHIIR) 2021.
 I. Kropotov, A. Medlar, D. Głowacka. Exploratory Search of GANs with Contextual Bandits. Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM) 2021.
 Y. Liu, A. Medlar, D. Głowacka. Statistically Significant Detection of Semantic Shifts using Contextual Word Embeddings. Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) 2021.
HIIT Postdoctoral Fellow 1.1.2022-31.12.2024
Hans Moen obtained his PhD degree in Information Technology in 2016. This was a joint degree (cotutelle) between the Norwegian University of Science and Technology and the University of Turku. Afterwards he has worked as a postdoc researcher in the TurkuNLP group at the University of Turku, Department of Computing. Since 2018 he has been funded by the personal postdoc funding from the Academy of Finland. He now works as a HIIT Research Fellow in the Machine Learning for Health (ML4H) group and the Probabilistic Machine Learning (PML) group at Aalto University. He is primarily working in projects focusing on the data from the Hospital District of Helsinki and Uusimaa (HUS), Turku University Hospital (TYKS), and the FinRegistry project.
His main research field is natural language processing (NLP), with a special focus on clinical text (clinical NLP) and other health-related data. A central focus of his research is in utilizing machine learning and language technology to support health care personnel and other users in managing the information documented and stored in electronic health records. The aim is to explore new methods and applications that can ultimately help save time, effort, and resources, to improve the quality of care, and enable new ways of interacting with and generating such information. This work encompasses tasks like free-text search, information classification and extraction, and automatic summarization, where computational semantics and language representations are central focus areas. This work includes the use of (deep) neural networks for textual data, such as recurrent neural networks and transformer-based neural networks.
Moen has been an active member and coordinator in the IKITIK consortium at the University of Turku since 2015. He had a research visit to the Department of Computer and Systems Sciences at Stockholm University in 2021. He has been involved in organizing the Nordic Conference on Computational Linguistics (NoDaLiDa) in 2019 and 2021. He is an active member of the Student and Emerging Professionals Special Interest Group under the International Medical Informatics Association (IMIA SEP SIG), who received the IMIA working group of the year award in 2019 and 2020. He is a regular referee in the “Natural Language Processing” section of the annual IMIA Yearbook.
Sample of publications:
 H. Moen, K. Hakala, L. Peltonen, H. Matinolli, H. Suhonen, K. Terho, R. Danielsson-Ojala, M. Valta, F. Ginter, T. Salakoski & S. Salanterä. Assisting Nurses in Care Documentation: from Automated Sentence Classification to Coherent Document Structures with Subject Headings. Journal of Biomedical Semantics vol. 11, no: 10. 2020.
 H. Moen, L. Peltonen, H. Suhonen, H. Matinolli, R. Mieronkoski, K. Telen, K. Terho, T. Salakoski, S. Salanterä. An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora.Proceedings of the 22nd Nordic Conference on Computational Linguistics, 2019.
 L. Uronen, S. Salanterä, K. Hakala, J. Hartiala, H. Moen. Combining Supervised and Unsupervised Named Entity Recognition to Detect Psychosocial Risk factors in Occupational Health Checks. International Journal of Medical Informatics. Volume 160. April 2022.
HIIT Postdoctoral Fellow 3.7.2021-30.4.2022
Leysan’s formal education was a transition from industrial engineering to computer science and later human-computer interaction. She obtained a Master’s degree in Automation and Control systems engineering from Kazan National Technological University, Russia and a Master’s degree in Computer Science from the University of Trento, Italy. During her doctoral studies at the University of Trento, she focused on human factors in digital health systems. In 2019, she obtained a PhD in Computer Science from the University of Trento. Afterwards, she joined the Health Technology Design group at Trinity College Dublin as an ALECS Marie Skłodowska-Curie postdoctoral fellow. In July 2021, she moved to Finland to join Prof. Lindqvist’s Usable Security and Privacy research group as a postdoc at the Computer Science Department at Aalto University.
Leysan’s research is grounded in the discipline of Human Factors with a specific interest in user-centred design and usable security and privacy of socio-technical systems. She explores the challenges and opportunities of designing secure user-centred information systems that respect users’ privacy: from information design and technological support of work practices in place to interaction design and user experience. Her work aims to understand how to achieve a better fit between various stakeholders, organisational context, and technology within the healthcare context and beyond. She mostly employs mixed-method user studies with a focus on qualitative approaches.
Leysan is an action researcher, and since her Master’s, she has been collaborating with software companies such as NearForm, SilverCloud Health, and CBA Group.
Sample of publications in 2021:
 L. Nurgalieva, S. Ryan, G. Doherty. Riku Laine, Antti Hyttinen, Michael Mathioudakis. Attitudes towards COVID-19 contact tracing apps: a cross-national survey. IEEE Access, 2021.
HIIT Postdoctoral Fellow 1.5.2021-30.4.2024
Dr. Taneli Pusa obtained his Master’s degree in Applied Mathmatics from the University of Helsinki in 2015 and his PhD in Bioinformatics from the University of Lyon in 2019. During his PhD he was co-supervised by Marie-France Sagot and Alberto Marchetti-Spaccamela. He completed a postdoc at the Luxembourg Centre for Systems Biomedicine (LCSB) at the University of Luxembourg in 2020. He joined Juho Rousu’s Kernel Methods, Pattern Analysis and Computational Biology (KEPACO) group in May 2021 as a postdoctoral fellow.
Taneli’s research interests focus on the combination of mathematics, computer science, and biology. In his research on metabolic networks, he focuses on their integration with ‘omics data. Currently, he is collaborating with the Suomalainen-Wartiovaara lab at the University of Helsinki which studies different health related factors and their possible influence metabolism. Concurrently, Taneli contributes to the COBREXA software project which is a toolbox for next-generation metabolic modelling.
Sample of publications in 2021:
 Mariana Ferrarini, Avantika Lal, Rita Rebollo, Andreas Gruber, Andrea Guarracino, Itziar Martinez Gonzalez, Taylor Floyd, Daniel Siqueira de Oliveira, Justin Shanklin, Ethan Beausoleil, Taneli Pusa, Brett Pickett, Vanessa Aguiar-Pulido, Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis, Communications biology, 2021
 Miroslav Kratochvíl, Laurent Heirendt, St Elmo Wilken, Taneli Pusa, Sylvain Arreckx, Alberto Noronha, Marvin van Aalst, Venkata P Satagopam, Oliver Ebenhöh, Reinhard Schneider, Christophe Trefois, Wei Gu, COBREXA.jl: constraint-based reconstruction and exascale analysis, Bioinformatics, 2021