Members of the Machine Learning in Biomedicine group
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.
Lisa is a rotation student who is currently completing her third rotation at FIMM. Her background is originally in biomedicine (studied Biomedical science in bachelor and master) but with an additional complementary study in computer science. Therefore her interest in computer scientific solutions to biomedical problems started already at the university. With a bioinformatics master thesis, she combined her two interests and hopes to gain further computer scientific expertise in her doctoral studies, as well as to reconcile her two interests in biomedical science and computer science.
Dovydas is a Master's thesis student in Life Science Informatics programme in University of Helsinki. He received his Bachelor's degree in Bioinformatics from Vilnius University, Lithuania. Before pursuing the next step in higher education, Dovydas had an internship in Molecular Neurooncology Laboratory in The Lithuanian University of Health Sciences, where he used machine learning methods with miRNA expression data to classify Glioblastoma Multiforme (GBM) tumors. Currently, he is researching novel methods for mutational signature discovery in Mustonen and Pitkänen groups as part of his Master's thesis project.
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 is a data engineer in the iCAN data team. She obtained her PhD in computer science from University of Helsinki in 2018. Her research during her PhD focused on developing novel algorithms for analysing RNA-seq data. She did her first postdoc in Professor Aaltonen's Tumor Genomics research group, where her work focused on developing and maintaining sequence data analysis pipelines.
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 is a Master’s thesis student in the Life Science Informatics program at the University of Helsinki. She received her Bachelor’s in biology from University of Helsinki in 2018. Previously she has worked as a research assistant at the University of Helsinki. Now she is researching driver mutations in cancer and methods for their identification and discovery with a focus on deep learning methods for her Master’s thesis project. Her current interest in research is applying machine learning methods on sequence data to gain novel biological insights.
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.
Biranjan is currently working as a Data Engineer in iCAN. He has MSc in Information and Service Management from Aalto University. In his previous jobs he has been developing and implementing machine learning solutions to solve various kinds of industrial problems. He likes to make data and machine learning accessible and useful through developing systems that are simple, reliable and transparent for both developers and end users.
In his free time he likes to work on his personal github projects. He also enjoys swimming, cycling and cooking spicy foods.
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.
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.
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 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.
Jori Blomqvist holds a Master’s degree in cell- and molecular biology from University of Jyväskylä (Finland). Jori started his PhD at the Institute of Biotechnology (University of Helsinki, Finland) studying chromatin organization with various microscopy approaches. Now he is a part-time doctoral student at FIMM, studying bioimage analysis with machine learning methods. The other part of his time is spent programming laboratory automation systems.
Karen is a visiting PhD student from University of Chile studying clinical and mutational data in cancer patients with machine learning techniques.