Ziaurrehman Tanoli completed his PhD at the Pakistan Institute of Engineering and Applied Sciences at the end of 2013. During his PhD, he developed machine learning (ML) based methods to classify G protein-coupled receptors, one of the biggest protein families targeted by approved rugs. During his first postdoc at the University of Sannio, Italy, 2014-2015, he developed ML-based pipelines to identify long noncoding RNAs in human, mouse, and zebrafish. From 2015-2023, he pursued several postdocs at the University of Helsinki, where he developed several in-silico methods and tools supporting drug discovery and repurposing. In 2023, Ziaurrehman Tanoli launched his independent career as a principal investigator at FIMM.
Khalid defended his PhD thesis based on precision systems medicine to improve therapeutic options in urological cancers in 2018 from FIMM. His postdoctoral project at the Hematology Research Unit during 2018-2019 was to identify genetic vulnerabilities of cancer cells towards natural killer cells and CART cells using genome-wide CRISPR libraries. In 2019 joined functional genomics centre (FGC) Cambridge UK as senior scientist where his interest is to identify novel therapeutic targets and mechanisms of drug sensitivity and resistance to facilitate the development cancer medicines through functional genomic (CRISPR) screens.
Aron Schulman completed his B.Sc. (Tech.) in Bioinformation Technology in 2021 and his M.Sc. (Tech.) in Life Science Technologies in 2024 at Aalto University. His Master's thesis was supervised by Prof. Juho Rousu, with Dr. Ziaurrehman Tanoli as his advisor. After graduating, Aron continued his work as a research assistant in Tanoli's research group. Aron specializes in drug-target binding affinity prediction with deep learning methods and has further experience in Transformer-based natural language processing approaches. He is interested in expanding his contribution to computational drug discovery and repurposing by developing artificial intelligence methodologies for targeted polypharmacology.
Umut completed his Bachelor's degree in Genetics and Bioengineering from Yeditepe University, Turkey, in 2016 and his Master's degree in Bioinformatics from Hacettepe University, Turkey, in 2022. In 2023, he started his PhD journey at the In silico Drug Discovery research group at the University of Helsinki. He focuses on developing innovative computational approaches for precision medicine, particularly in HLA typing, using transformer-based methods and harmonized drug response datasets. He has developed an AI-based method to predict drug responses using multi-omics. With expertise in HLA typing using various sequencing datasets, Umut aims to leverage AI-based analysis to improve personalized medicine and drug discovery. Umut actively collaborates with researchers and is committed to bridging computational analysis with experimental validation.
Garima Tripathi has received her M.Sc. in Chemistry, majoring in Organic Chemistry from the Aligarh Muslim University, India. In 2022, she joined the Petri Auvinen group (DNA Sequencing and Genomics Lab) at the University of Helsinki (Viikki campus). She worked with the next-generation and third-generation sequencing of DNA, RNA, amplicon, etc. There, she also worked on two projects relating to the study of denovo transcriptome assembly of Dendrohyrax (a novel and endangered species of Tanzania) and exploring molecular factors responsible for SARS-CoV-2 infection using transcriptomics and proteomics data. In 2024, she joined the Tanoli Lab in collaboration with the Frilander Lab, to understand RNA splicing dysregulation. In her current research, she is trying to identify drugs targeting the key players of minor spliceosome functions to potentially target multiple cancer types, particularly in MDS/AML, using an attention-based deep learning approach.
Aleksandr Kushnir graduated from St. Petersburg College of Information Technology in 2020 and joined Tanoli's group as a technical assistant in January 2023. His research focus is to develop interactive web portals for drug repurposing applications. He is also developing machine learning-based models for different drug discovery applications.
Shiva holds a bachelor's degree in Applied Mathematics from Isfahan University in Iran. Pursuing further academic endeavors, she completed her master's in Bioinformatics and Digital Health at Aalto University in 2021. Shiva is also actively engaged as a software developer in addition to her academic pursuits. During her master's program, she embarked on research within Tanoli's group at the University of Helsinki, focusing on Adverse Drug Reaction (ADR) prediction using deep learning models. Her thesis centers on developing a multi-label deep learning model to forecast ADRs for various compounds. Shiva's work exemplifies a commitment to advancing computational techniques in pharmacology and contributes to the ongoing evolution of drug discovery.