Members of the Machine Learning in Biomedicine group

Esa Pitkänen

Esa Pitkänen is a FIMM-EMBL Group Leader and Academy Research Fellow at the Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE)  and the Applied Tumor Genomics Research Program in the Research Programs Unit, University of Helsinki.

Riku Katainen

I am a postdoctoral researcher in the Tumor Genomics (Aaltonen) and Machine Learning in Biomedicine (Pitkänen) groups. My research focuses mainly on next-generation sequencing-powered disease genetics, which involves analyses of inherited and somatically acquired genetic variants of all types as well as software development. I got my formal education in the University of Helsinki where I received degrees in computer science (BSc), bioinformatics (MSc), and cancer genetics (PhD). 

Yrjö Koski

Yrjö is a Master’s student in Life Science Technologies program at Aalto University, majoring in Bioinformatics and Digital Health. He obtained his BSc in Bioinformation Technology from Aalto University. Alongside his studies, he has worked as a project researcher at Finnish Red Cross Blood Service doing predictive modeling of blood donors’ hemoglobin levels and at the University of Helsinki as a research assistant. He has also been an active student advocate in Aalto in order to maintain the high-quality of studies without compromising the well-being of students. Currently, he is doing his Master’s thesis on detecting DNA-modifications and adducts from Nanopore sequencing data with deep learning methods in Aaltonen and Pitkänen groups.

Anna Kuosmanen

Anna Kuosmanen is the bioinformatics team lead in the iCAN data team. She obtained her PhD in computer science (bioinformatics) from University of Helsinki in 2018, worked as a postdoc in Professor Aaltonen's Tumor Genomics research group 2018-2020, and joined iCAN data team in August 2020. In the data team she's responsible for leading the development of the bioinformatics side of the iCAN data platform.

Laura Langohr

I am a postdoctoral researcher in the iCAN project, Outi Kilpivaara's and Ulla War­tiovaara-Kautto's Hematological Genetics lab, and Esa Pitkänen's Machine Learning in Biomedicine group. My research focuses on understanding
how leukemias arise from premalignant conditions utilizing single-cell sequencing data analysis methods amongst others. I received a PhD in computer science in the field of data mining (University of Helsinki),
and a MSc and BSc in bioinformatics (Friedrich Schiller University in Jena, Germany).

Katri Maljanen

I am a PhD student in the ILS program. I received my Bachelor’s in biology and Master's in Life Science Informatics from University of Helsinki. Previously I have worked as a research assistant at the University of Helsinki. My current research focuses on looking into how genomic and epigenomic features affect cancer mutagenesis. In my research I am especially interested in more detailed knowledge on how these features affect the mutagenesis of specific mutations or in different cancer types. My overall interest in research is in applying machine learning methods on DNA sequence data to gain novel biological insights.

Parisa Mapar

Parisa is a PhD student investigating the identification of cancers and their subtypes through Deep Machine Learning methods applied to cell-free DNA data. She obtained her BSc in Computer Engineering from Azad University in Tehran, Iran, and her MSc degree in Computer, Communication and Information Sciences from Aalto University, receiving a minor in Machine Learning and Data Mining. Prior to joining the Institute for Molecular Medicine Finland (FIMM), Parisa has worked on predicting the promiscuity state of enzymes using Kernel methods at the Department of Computer Science, Aalto University. When not geeking out, Parisa spends her time traveling, volunteering for the Finnish Red Cross, baking cakes and playing lacrosse.

Tomi Määttä

I am a postdoc in Pitkänen group, and also in Jouhten group at Aalto university. My background is in biochemistry (master’s degree from university of Oulu) and mass spectrometry-based proteomics (PhD degree from EMBL Heidelberg). During my doctoral training, I became fascinated about data-driven approach on solving complex biological problems. Therefore, after spending some time in industry, I happily joined the Pitkänen group to explore the linkage between metabolic networks and genomic structure in the context of Darwinian evolution.  When not working, one can most often find me doing sports, playing guitar or reading something.

Prima Sanjaya

Prima Sanjaya is currently a doctoral student in the fields of artificial intelligence in biomedicine, at the Institute for Molecular Medicine Finland. His research is centered around the development of explainable deep learning techniques in multimodal genomics and medical image data for clinical use.
He received his MSc degree from Dongseo University, South Korea in computer science--working in the Machine Learning Research Lab. Then, he worked in Department of Radiology, Seoul National University Hospital as a medical imaging researcher.

He loves to share ideas, thoughts and innovations. His passion in basic science and computer technology brought him to interdisciplinary work.

Veronika Suni

Veronika is a bioinformatician with an interest in ‘omics technologies and large-scale data processing. She is currently involved in the development of an iCAN data platform to connect, analyze and store clinical and research cancer data.

Prior to joining the data team in the iCAN Digital Precision Cancer Medicine project, she worked at the Mary MacKillop Institute for Health Research (Australia), and the Medical Bioinformatics Centre at Turku Centre for Biotechnology. Veronika was awarded her PhD in bioinformatics by University of Turku in 2019, where she developed computational methods and tools for mass-spectrometry based phosphoproteomics studies. She earned her MSc in Computer Science from her native St. Petersburg State Electrotechnical University (Russia) in 2008.

Ara Taalas

Ara Taalas is a PhD student in the field of biomedical machine learning at the Institute for Molecular Medicine Finland (FIMM), with a MSc in Bioinformation Technology from Aalto University. He is currently splitting his time between Terveystalo and FIMM in order to pursue research on predictive diagnostics from complex longitudinal data in the clinical domain. He holds previous experience in the modelling of stem cell differentiation processes and novel drug candidate discovery, and enjoys playing excessively complex board games.

LinkedIn profile

Arina Tagmazian

I am a Doctoral student at the Helsinki Institute of life science. My long way to FIMM student started in Moscow, where I have got my bachelor’s degree in biology in Russian State Agrarian University and a master’s degree in bioinformatics in High School of Economics. The combination of biology and data science seems to me the most fascinating direction in science at the moment. 
My previous researches had quite a wide spectrum of interests, from exploring the Bovine leukemia virus resistance in cattle to an analysis of alternative splicing in African non-biting midges, Polypedilum vanderplanki. Currently, I am interested in neuroscience research combining image processing, deep learning and epigenetic analysis.
In my free time, I'm practicing meditation, learning to play the ukulele, drawing by watercolor, spending time in nature, meeting new people, and opening for myself new activities.

Veera Timonen

Veera Timonen received her BSc degree in Biology/Genetics from Oulu University in 2017 and her MSc degree in Bioinformatics from Tampere University in 2019, working in the field of bioimage informatics. She has worked with e.g. breast and lung cancer. Currently she is a doctoral student at the Institute for Molecular Medicine Finland (FIMM) working on applying machine learning methods to biomedical data, with an emphasis on deep learning and multimodality of data.

Bulat Zagidullin

Bulat Zagidullin is a PhD student in Machine Learning in Biomedicine group at FIMM, and in Network Pharmacology for Precision Medicine group, Faculty of Medicine, University of Helsinki. He applies machine learning methods to open problems in cancer research, focusing on design of combinatorial therapies and outcome prediction. Bulat got his BSc degree in Biochemical Engineering from Jacobs University Bremen and the MSc degree in Pharmaceutical Biotechnology from MLU Halle-Wittenberg. He tries to keep his work open and reproducible. In his free time he could be found somewhere outside, away from the screens.