Find Academic Supervisors

Francesco GentileView profile

Assistant Professor

• Computer-Aided Drug Discovery
• Deep Learning in Drug Discovery
• Structure-Based Virtual Screening
• +3 more

Francesco Gentile is an Assistant Professor in the Department of Chemistry and Biomolecular Sciences at the University of Ottawa, Faculty of Science. His research focuses on computational strategies for drug discovery, leveraging AI and deep learning to accelerate virtual screening and target unconventional drug targets such as protein-protein interactions. He leads the Gentile Lab, which investigates cancer drug resistance and develops tools like DockBox and Deep Docking for structure-based drug discovery. Education: PhD in Computational Physics from the University of Alberta (2014-2019), postdoctoral training at the Vancouver Prostate Centre under Art Cherkasov. Awards include Alberta Innovates Scholarship and CIHR Fellowship. Research interests span AI-driven drug discovery, chemical space exploration, and synergistic therapies for cancer. The lab collaborates with the Ottawa Institute of Systems Biology and AI4Science@uOttawa. Key publications include work on SARS-CoV-2 protease inhibitors, LRRK2 domain targeting, and AI in early-stage drug discovery. Over 20 students and postdocs have been advised, with active projects in PROTAC design and computational modeling. Lab members include postdocs, graduate students, and undergraduates from diverse backgrounds. Current tools developed include Pose Ensemble GNNs and PROTACable pipelines.

Veit SchwämmleView profile

Associate Professor

• Computational Proteomics
• Bioinformatics
• Mass Spectrometry
• +10 more

Veit Schwämmle is an Associate Professor in Computational Proteomics and Bioinformatics at the Department of Biochemistry and Molecular Biology, University of Southern Denmark (SDU), where he leads the Computational Proteomics Group. His research focuses on developing computational solutions for large-scale omics data analysis, particularly in proteomics and post-translational modifications. PhD in Physics, University of Stuttgart (2006) Postdoctoral Fellowships: Centro de Pesquisas Fisicas (Rio de Janeiro), ETH Zürich Postdoctoral Researcher and Assistant Professor, University of Southern Denmark Research interests include software and workflow development for protein mass spectrometry data analysis, chromatin biology through histone modifications, tools for omics data quantification and interpretation, and the application of deep learning methods to improve proteomics data processing. His work bridges physics-based modeling and biological data science, with a strong emphasis on open, reproducible research. His recent publications highlight trends in machine learning for proteomics, benchmarking of analysis workflows, and community-driven bioinformatics standards. He is actively involved in international initiatives like EuBIC-MS and bio.tools, promoting software interoperability and training. Member of the European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Contributor to the bio.tools registry for life sciences software Advocate for open science and reproducible workflows He supervises researchers and students in computational proteomics and has contributed to numerous collaborative projects in systems biology and biomedical mass spectrometry. His group develops tools such as VIQoR, CrossTalkMapper, and MetaboLink, supporting the broader scientific community. Visit the group’s webpage: http://computproteomics.bmb.sdu.dk

Yardena SamuelsView profile

Professor

• Cancer Immunogenomics
• Immunopeptidomics
• Melanoma
• +7 more

Prof. Yardena Samuels is a leading researcher in cancer immunogenomics at the Department of Molecular Cell Biology, Weizmann Institute of Science, Faculty of Biological Sciences. Her lab focuses on deciphering the immunopeptidome in melanoma to identify targetable neoantigens and develop personalized immunotherapies. She is actively engaged in high-impact research, frequently publishing in top-tier journals and presenting at major conferences such as AACR. Research Interests: Her work spans melanoma biology, tumor heterogeneity, neoantigen discovery, immunopeptidomics, and biomarker identification for immunotherapy. She investigates how translational dysregulation, post-translational modifications, and genomic instability contribute to antigen presentation and immune evasion. The most recent publications highlight a strong trend in computational and functional genomics approaches to predict and validate immunogenic neoantigens, understand immune evasion mechanisms, and explore the role of the microbiome and intratumoral bacteria in shaping anti-tumor immunity. Her research increasingly integrates multi-omics data to build comprehensive models of tumor-immune interactions. Invited Plenary Speaker, AACR Annual Meeting 2025 Prof. Samuels leads a vibrant research group, mentoring students and postdocs, and securing major grants from the ERC, ISF, MRA, and other international foundations. Her lab utilizes a combination of experimental models, peptidomics, CRISPR screens, and computational tools to advance melanoma immunotherapy. She is involved in collaborative efforts to create a comprehensive atlas of cancer-specific antigens.

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +4 more

Ross King is a Professor in the Department of Systems Biology at Chalmers University of Technology. He specializes in leveraging artificial intelligence, machine learning, and automation to advance scientific research. His work focuses on developing autonomous laboratory systems and AI-driven methodologies to accelerate discoveries in drug development, systems biology, and metabolic modeling. Key projects include Genesis, a database for autonomous systems, and AutonoMS for metabolomic analysis. His research emphasizes reproducibility, hypothesis generation via large language models, and integrating robotics with computational tools. King's contributions span interdisciplinary fields, combining computational methods with experimental validation. He leads efforts to automate scientific workflows, exemplified by his Robot Scientists (Adam and Eve), which autonomously design and execute experiments. His recent work explores extrapolation challenges in machine learning, ethical AI in science, and personalized medicine through predictive modeling. He has published extensively in journals like PNAS , Nature Machine Intelligence , and Scientific Reports , with a focus on AI ethics, closed-loop discovery systems, and translational applications in healthcare. His research bridges theoretical computer science with practical biomedical and environmental challenges.

Aristidis LikasView profile

Professor

• Neural Networks
• Machine Learning
• Data Mining
• +5 more

Dr. Aristidis Likas is a Professor at the Department of Computer Science and Engineering, University of Ioannina, Greece. His research spans neural networks, machine learning, data mining, and intelligent systems with applications in medicine and biology. He has contributed to image processing, video analysis, and scientific computing, with a focus on algorithm innovation and biomedical applications. His work has been published in journals such as IEEE Transactions on Neural Networks , Pattern Recognition , and Bioinformatics . He has also served as Associate Editor for IEEE Transactions on Neural Networks (2008-2012) and co-chaired conferences including ECML/PKDD 2011 and SETN 2014. Key projects include VIDEOSUM: Video Summarization PRotEin S-Sulfenylation Server (PRESS) Global k-means algorithm Dip-means clustering

Johannes LevinView profile

Professor

• Parkinson's Disease
• Alzheimer's Disease
• Movement Disorders
• +3 more

Prof. Johannes Levin is a Professor of Clinical Neurodegeneration and Head of the Outpatient Clinics for Cognitive Neurology and Movement Disorders at the LMU Munich Neurological Clinic. He specializes in Parkinson's disease, Alzheimer's disease, and neurological intensive care. His clinical qualifications include Neurology specialist certification, Neurological Intensive Care Medicine, genetic counseling in Neurology, EEG certification, and Botulinum Toxin Therapy certification. Research focuses on synucleinopathies (Parkinson's/Multiple System Atrophy), tauopathies (Progressive Supranuclear Palsy/Corticobasal Syndrome), Alzheimer's pathology (especially genetic forms), and developing disease-modifying therapies. Key initiatives include the Early Clinical Trial Unit (ECTU), Neurology Winter School (NWS), and collaborative research networks like the Dominantly Inherited Alzheimer Network. Publications span biomarker development, drug trials (e.g., anle138b), and clinical diagnostic criteria for neurodegenerative disorders. His work bridges clinical practice with translational research, emphasizing early intervention and precision medicine approaches.

Tozammel HossainView profile

Assistant Professor

• Bioinformatics
• Health Informatics
• Social Media Analytics
• +3 more

Tozammel Hossain is an Assistant Professor at the University of North Texas. His research focuses on Bioinformatics, Health Informatics, Social Media Analytics, Event Forecasting, and Cybersecurity. He holds a Ph.D. and M.S. from Virginia Tech and a B.S. from Bangladesh University of Engineering and Technology. Education: Ph.D., Virginia Tech M.S., Virginia Tech B.S., Bangladesh University of Engineering and Technology Research Interests: Hossain’s work spans interdisciplinary areas including healthcare analytics, geopolitical event prediction, cybersecurity, and machine learning applications. He develops models for continuous blood glucose prediction, gene expression analysis, and ransomware attack detection. His methods often integrate social media data and temporal modeling for public health and security applications. Research Trends: His articles highlight advancements in LSTM networks for healthcare and event forecasting, federated learning in healthcare data, and hybrid systems for geopolitical analysis. Themes include personalized medicine, conflict prediction, and leveraging social media for syndromic surveillance. Labs & Teams: While specific lab affiliations are not listed, his projects suggest collaboration with interdisciplinary teams in healthcare informatics, cybersecurity, and computational biology.

Giulia FisconView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Network Medicine
• +5 more

Giulia Fiscon is an Assistant Professor in Bioengineering at the Department of Computer, Automation, and Management Engineering (DIAG) of Sapienza University of Rome. She holds a summa cum laude degree in Biomedical Engineering from Campus Bio-Medico University (2012) and a PhD in Computer Science from Sapienza (2016, with top honors). Her research focuses on bioinformatics, computational biology, and network medicine, particularly in drug repurposing, cancer genomics, and systems pharmacology. She has authored 65+ publications with an h-index of 24 (Scopus). Current roles include teaching Bioinformatics courses and leading research on network-based approaches for drug discovery. Her work integrates transcriptomics, interactomics, and computational tools like SWIM and SAveRUNNER to address complex diseases. Recent projects include predicting drug responses in cancers, analyzing drug-toxicity interactions, and developing ontologies for Alzheimer’s data. Her research highlights include: Network medicine applications in oncology and neurodegenerative diseases Development of computational tools for RNA analysis and drug repurposing Investigation of microRNA-mRNA interactions in cancer progression Her articles emphasize drug repositioning strategies, precision medicine, and systems biology frameworks. She has collaborated with institutions like the National Research Council and the Foundation for Personalized Medicine.

• Computational Biology
• Computational Structural Biology
• Computational Geometry
• +7 more

Frédéric Cazals is a Senior Scientist at Inria Sophia Antipolis and a Chairholder at the 3IA Côte d’Azur Institute, where he leads research in Artificial Intelligence for Molecular Studies (AIMS). He is affiliated with the ABS research team at Inria, focusing on the integration of computational geometry, topology, and machine learning in structural biology and biophysics. His research interests lie at the intersection of computer science and molecular biology. He develops algorithmic methods to analyze and model large biomolecular systems, with a focus on extracting biologically meaningful features from proteins and their complexes. His work enables insights into biological function at the atomic level, supporting applications in protein engineering and drug design. The overarching theme of his recent work involves leveraging AI and geometric modeling to simulate molecular systems over biologically relevant time scales. This includes developing scalable algorithms grounded in computational topology and geometry, adapted for high-dimensional biological data. Senior Scientist, Inria Sophia Antipolis Chairholder, 3IA Côte d’Azur Institute – AI for Computational Biology and Bio-inspired AI Lead, AIMS (Artificial Intelligence for Molecular Studies) Frédéric Cazals mentors doctoral and postdoctoral researchers through the 3IA program and contributes to interdisciplinary research initiatives. He is actively involved in advancing computational methods in biology through his leadership in the ABS team at Inria. His work is supported by institutional research funding and collaborative grants within the 3IA framework, though specific grants and advising details are not listed in the source text. He is associated with key research infrastructures including the ABS laboratory at Inria and participates in the 3IA Doctoral and Postdoctoral Seminar series, contributing to the training of next-generation researchers in AI and computational biology.

• Lung Cancer
• Precision Radiotherapy
• Biomarker Discovery
• +4 more

Kristina Viktorsson is a Senior Research Specialist at Karolinska Institutet's Department of Oncology-Pathology , leading a research team focused on precision cancer medicine and precision radiotherapy in lung cancer. Her work spans preclinical, translational, and clinical trials, particularly investigating stereotactic body radiotherapy (SBRT) for lung tumors and molecular mechanisms of resistance. Research Highlights: Exploring biomarkers in tumor/liquid biopsies for radiation/pharmaceutical response Developing fine-needle aspiration (FNA) molecular profiling for real-time diagnostics Studying Eph/Ephrin signaling in radiotherapy sensitivity and cancer progression Collaborations: Nordic SBRT Group Royal Institute of Technology (KTH) RISE Research Institute of Sweden Publications demonstrate expertise in extracellular vesicles , drug resistance , and combination therapies . Her work includes grants from Karolinska Institutet, Swedish Cancer Society, and Radiumhemmet.

• Bioinformatics
• Computational Statistics
• DNA Replication Analysis
• +5 more

Uri Keich is an Associate Professor at the School of Mathematics and Statistics , University of Sydney . His research bridges Bioinformatics , Computational Statistics , and Proteomics , with a focus on statistical methods for tandem mass spectrometry analysis , DNA replication origin mapping , and motif finding significance . He collaborates extensively with biologists and computational researchers. Education : PhD in Mathematics (Courant Institute, NYU), MSc in Mathematics (Technion, Israel), BSc in Computer Science and Mathematics (Hebrew University). Professional Experience : 2009–present at University of Sydney; 2003–2009 at Cornell University; prior roles at UCSD, UCR, Caltech, and NYU. Research Interests Keich's work centers on false discovery rate (FDR) control in high-throughput biological data, particularly for peptide identification and motif analysis . He develops exact statistical tests using large-deviation theory and FFT-based algorithms, with applications to proteomics and genomic sequence analysis . His research also explores DNA replication origins in yeast through collaborations. Scientific Awards Best Paper Award, RECOMB 2015 for A Symmetric Length-Aware Enrichment Test . Teaching and Supervision Teaches advanced courses in Probability and Statistics (e.g., STAT 2911 , MATH1905 ) and specialized topics like convolution and FFT . Supervises PhD students in computational statistics and bioinformatics.

• Cancer Epidemiology
• Biostatistics
• Genetic Variants
• +3 more

Colin B. Begg is a Professor of Biostatistics in the Population Health Sciences department at Weill Cornell Medical College. His research focuses on cancer epidemiology, genetic variant analysis, and clinical trial methodology. Key Research Areas: Melanoma survival genetics, tumor clonality testing, cancer risk prediction models, and clinical trial design optimization. Recent Contributions: Developed mutation context analysis frameworks, validated population-based clinical trial data sources, and advanced statistical methods for molecular epidemiology studies. Publication Trends: Recent articles emphasize biostatistical approaches to cancer genomics, including driver mutation identification in melanoma, vitamin D-binding protein variants in oncology, and improved variable selection techniques for clinical models. His work bridges computational methods with clinical application across oncology and epidemiology.

Lucy SkrabanekView profile

Assistant Professor

• Computational Biology
• Genomics
• Bioinformatics
• +3 more

Lucy Skrabanek is an Assistant Professor of Computational Biomedicine at the Weill Cornell Medicine Graduate School of Medical Sciences . She holds joint affiliations with the Institute for Computational Biomedicine and the Physiology, Biophysics & Systems Biology division. Her work bridges computational biology, genomics, and ethical considerations in biomedical data usage. Education: Read Genetics at Trinity College Dublin PhD in Genetics (2000) from Trinity College Dublin Research Interests: Her research centers on rigor and reproducibility in experimental design , emphasizing transparency in computational pipelines and lifecycle management of biomedical data. She explores ethical and privacy challenges in the genomic era , including genetic engineering implications and data security. Additionally, she investigates the role of eIF4E in cellular growth , focusing on nuclear mRNA export mechanisms and oncogenic properties. Her work integrates computational methods with biological systems to address high-throughput data complexities. Teaching & Outreach: Lucy founded the Tri-Institutional Biomedical Computing Workshop series in 2004, teaching Unix, R, and RNA-seq data analysis. She co-developed online learning materials for R and GitLab, and contributes to graduate courses like Scientific Computing in Biomedicine and Quantitative Understanding in Biology . Labs & Initiatives: Member of the Applied Bioinformatics Core and Institute for Computational Biomedicine Publication Trends: Her recent work (2016-2023) focuses on eIF4E’s regulatory roles in RNA processing, while earlier studies (2008-2012) highlight bioinformatics tool development (Scan2S, PPI prediction) and autoimmune disease genetics. Articles from 2006-2009 establish her foundational contributions to understanding eIF4E’s oncogenic properties.

• Theoretical and Computational Biological Physics
• Chemical Physics
• Nanoscience
• +6 more

Dr. Nicolae Viorel Buchete is an Associate Professor at the University College Dublin (UCD) School of Physics . He serves as Vice Principal for Graduate Studies in the College of Science and leads UCD's Computational Physics postgraduate program . Buchete holds academic degrees from Boston University (PhD) , Al. I. Cuza University , and University of Patras . Current appointments: 2022-Present (Vice Principal), 2014-Present (MSc Program Director) Previous roles: NIH Research Fellow (2003-2008), Visiting Assistant Professor at Boston University (2009-2010) His research focuses on theoretical and computational biological physics with applications in nanoscience , molecular dynamics of biomolecular systems , and multiscale modeling of complex fluids . Recent work includes conformational kinetics of oncogenic proteins , physics-based modeling of nanocarriers , and amyloid peptide dynamics in Alzheimer's disease . Scientific Contributions: Developed advanced Markov State Models and Milestoning frameworks for long-time MD simulations Identified novel salt bridge mechanisms in kinase activation and drug resistance Explored piezoelectric properties of diphenylalanine nanostructures Teaching Innovation: Advocates for research-oriented teaching at both undergraduate and MSc levels. Coordinates modules including Computational Biophysics and Thermodynamics & Statistical Physics at UCD.

Jörg SchaarschmidtView profile

Researcher

• Materials Science
• Computational Biology
• Data-Driven Modeling
• +4 more

Jörg Schaarschmidt is a researcher at the Karlsruhe Institute of Technology (KIT) within the Institute of Nanotechnology , specializing in the Multiscale Materials Modelling and Virtual Design research unit. His work bridges materials science and computational biology. Current affiliation: Karlsruhe Institute of Technology (KIT) Research focus: Materials data spaces, workflow frameworks, and integrative biomolecular modeling His research interests span Materials Science , Computational Biology , and Data-Driven Modeling , emphasizing semantic data accessibility and workflow engineering. Recent projects include BATTERY 2030+ for sustainable energy materials and HADDOCK2.4 for biomolecular complex modeling. Publications highlight advancements in workflow frameworks , data-centric materials science , and protein-ligand interaction prediction . While his articles demonstrate expertise in integrating computational tools with experimental data, no explicit awards or student advising details are provided in the text. He is actively involved in labs and teams focused on Virtual Materials Design and Structural Biology , contributing to digital transformation initiatives in research software sustainability.