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Thomas VillmannView profile

Professor

• Computational Intelligence
• Machine Learning
• Vector Quantization
• +7 more

Thomas Villmann is a Professor of Computational Intelligence and Techno-Mathematics at Mittweida University of Applied Sciences in Germany. He serves as Deputy Spokesperson for the Mathematics Department, AI Coordinator at the university, Director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM), and President of the German Chapter of the European Neural Network Society (GNNS). His academic credentials include the German 'Dr. rer. nat. habil.' designation, indicating both doctoral and habilitation qualifications in natural sciences. Professor Villmann's research focuses on computational intelligence with particular emphasis on vector quantization, learning vector quantization, neural networks, and interpretable machine learning. His work spans theoretical developments in mathematical foundations of machine learning algorithms as well as practical applications in bioinformatics, remote sensing, medical diagnostics, and autonomous systems. He has developed mathematically sound methods for data-based analysis (clustering), decision support systems, data-based prediction models, and visualization of complex data. His recent publication record demonstrates a strong trend toward interpretable and explainable AI, with emphasis on vector quantization techniques applied across diverse domains including medical diagnostics (particularly breast cancer detection), fairness in machine learning, satellite remote sensing, and autonomous vehicle systems. Villmann's work consistently bridges theoretical mathematics with practical applications, maintaining a focus on making machine learning models more transparent, reliable, and ethically sound. As Director of SICIM and leader of the Computational Intelligence Research Group, Professor Villmann oversees an integrated research ecosystem focused on problem-oriented intelligent data analysis. His institutes aim to stimulate interest in computational intelligence among students and young researchers while supporting public institutions, authorities, and companies in data analysis both regionally and internationally. His leadership extends to organizing academic events and workshops, including serving as president of the German Chapter of the European Neural Network Society.

Utku NormanView profile

Researcher

• Human-Robot Interaction
• Artificial Intelligence
• Educational Technology
• +4 more

Dr. Utku Norman is a Researcher at the Institute for Technology Assessment and Systems Analysis (ITAS), part of the Karlsruhe Institute of Technology (KIT) . His work focuses on Human-Robot Interaction and Artificial Intelligence applications in educational contexts, particularly through collaborative learning systems. Since 2023, he has contributed to projects like the Real-World Laboratory for Robotic Artificial Intelligence and JuBot , exploring the intersection of technology, cognition, and social impact. Ph.D. , Computer and Communication Sciences, EPFL, Switzerland (2023) M.Sc. , Computer Engineering, Bilkent University, Türkiye (2018) B.A. , Philosophy, Middle East Technical University, Türkiye (2018) B.Sc. , Electrical and Electronic Engineering, Middle East Technical University, Türkiye (2016) Norman's research integrates Technology Assessment and Systems Analysis to study how autonomous, self-learning AI systems interact with humans in practical settings. His work spans disciplines like Computational Thinking , Educational Robotics , and Machine Ethics , often combining empirical studies with algorithmic development. His publications highlight trends in Human-Robot Interaction , focusing on collaborative learning, robot perception in children, and ethical frameworks for autonomous systems. Notably, he contributed to establishing empirical methods for analyzing robot-mediated educational activities and developed tools like the ST-Steiner algorithm for genetic research in earlier career stages. Awards : Marie-Skłodowska-Curie PhD Fellowship (2018–2022) Norman has collaborated with institutions such as EPFL , Télécom Paris , and Bilkent University , participating in EU-funded projects like ANIMATAS . His current work involves analyzing social and psychological dimensions of robotics and developing empirical research methods in human-machine interaction. Contact: utku.norman@does-not-exist.kit.edu

Karsten BorgwardtView profile

Professor

• Machine learning
• Systems biology
• Bioinformatics
• +3 more

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

Karl DuderstadtView profile

Professor

• DNA replication
• Structural biology
• Single-molecule imaging
• +4 more

Karl Duderstadt is a Professor of Experimental Biophysics at Technische Universität München and an Independent Research Group Leader at the Max Planck Institute of Biochemistry since 2015. His research focuses on understanding the Structure and Dynamics of Molecular Machines involved in DNA replication, particularly the replisome and its coordination with chromatin dynamics. He employs advanced single-molecule imaging and biophysical techniques to address challenges in genome integrity and replication mechanics. B.A. in Physics, Oberlin College (2004) Ph.D. in Biophysics, University of California, Berkeley (2011) Postdoctoral Fellow, Rijksuniversiteit Groningen (2015) His research interests span chromatin replication , topological stress resolution , and replisome stability . Current projects include ReplisomeBypass (ERC-funded) and ChromoMemInMotion (ERC Consolidator Grant). His work has produced groundbreaking insights into replisome dynamics and nucleosome inheritance , with implications for genome stability and epigenetic regulation. Scientific awards include the ERC Starting Grant (2018) and ERC Consolidator Grant (2023) . The group utilizes novel single-molecule platforms to study replication under stress conditions and develop tools like the MARS software for bioimage analysis. Collaborative efforts involve characterizing molecular barriers in DNA replication and understanding how protein complexes coordinate dynamic events during chromosome duplication.

Anton G. HenssenView profile

Professor

• Pediatric Oncology
• Hematology
• Cancer Genetics
• +3 more

Prof. Dr. med. Anton G. Henssen is the Head of Preclinical Research and an Attending Physician in the Department of Pediatric Oncology and Hematology at Charité - University Medicine Berlin. His work focuses on the role of extrachromosomal DNA (ecDNA) in pediatric cancers, particularly neuroblastoma and rhabdomyosarcoma. Research Interests: Extrachromosomal DNA in oncogenesis, genomic instability, molecular targeted therapy, tumor heterogeneity, and therapy resistance mechanisms. Publications: 15 most recent studies highlight ecDNA dynamics, MYCN-amplified cancers, and advanced sequencing methodologies (e.g., scEC&T-seq, Decoil, ec3D).

Jens Horst GundlachView profile

Professor

• Experimental Gravitational Physics
• Precision Mechanical Measurements
• Biophysics
• +5 more

Jens H. Gundlach is a Professor in the Department of Physics at the University of Washington, where he has been a faculty member since 1993, progressing from assistant professor (1993-1998) to associate professor (1998-2004) and full professor (2004-present). He is a member of the University of Washington's Center for Experimental Nuclear Physics and Astrophysics (CENPA) since its founding in 2000 and also participates in major collaborations including LIGO and LISA (Laser Interferometer Space Antenna). Gundlach's research spans two major fields: experimental gravitational physics and biophysics. In gravitational physics, he is renowned for developing precision torsion balance techniques to measure the gravitational constant and test fundamental principles of gravity. His 2000 measurement of G became the basis for the CODATA value since 2006. His work with the Eöt-Wash Group has tested the equivalence principle and searched for deviations from Newtonian gravity that might indicate new physics. Since approximately 2002, he has pioneered research in biophysics, particularly developing nanopore sequencing technology using the MspA protein. His group demonstrated DNA sequencing capabilities with this technology in 2008 and 2012, enabling single-nucleotide resolution analysis. His recent publications show continued activity in both fields, with gravitational physics work focusing on dark matter detection and equivalence principle tests, while his biophysics research advances nanopore technology for DNA sequencing, protein analysis, and single-molecule enzyme studies. His nanopore work has evolved into a sophisticated platform called SPRNT (Single-molecule Picometer Resolution Nanopore Tweezers) that can track molecular movements at unprecedented resolution. Scientific Awards Francis M. Pipkin Award of the American Physical Society (2001) Elected Fellow of the American Physical Society (2009) for "contributions to precision mechanical measurements and our quantitative understanding of the strength of gravity" Breakthrough Prize in Fundamental Physics (2021) jointly with Eric Adelberger and Blayne Heckel for "precision fundamental measurements that test our understanding of gravity, probe the nature of dark energy, and establish limits on couplings to dark matter" Gundlach leads an active research group that bridges physics and biology, securing grants from both physical science and biomedical funding agencies. His laboratory work combines precision mechanical measurements with single-molecule biophysics, creating innovative tools that have applications across multiple scientific disciplines. The group maintains strong connections with both the physics and bioengineering communities at the University of Washington. The Gundlach laboratory operates state-of-the-art facilities for both gravitational physics experiments (including torsion balances operating in high vacuum) and biophysics research (advanced nanopore instrumentation). His team collaborates extensively with other research groups at the University of Washington and internationally, particularly in the LIGO and LISA collaborations for gravitational physics work, and with biotechnology companies for nanopore sequencing applications.

Shmuel AvidanView profile

Professor

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +3 more

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Tamir TullerView profile

Professor

• Computational Biology
• Bioinformatics
• Systems Biology
• +7 more

Professor Tamir Tuller is a Full Professor in the Department of Biomedical Engineering at Tel Aviv University's Faculty of Engineering, where he leads the Laboratory of Computational Systems and Synthetic Biology. He also maintains affiliations with the Edmond J. Safra Center for Bioinformatics. His research spans computational biology, bioinformatics, and systems biology with a focus on developing mathematical models of gene expression and biological systems. Prof. Tuller's research interests include computational modeling of gene expression, engineering of gene expression systems, deciphering the gene expression code, evolutionary systems biology, computational study of molecular evolution, and gene expression in diseases. His work particularly focuses on developing computational predictive models to mathematically analyze and simulate gene translation processes, devising approaches for engineering gene expression for biotechnological objectives, and analyzing large-scale genomic data to understand how gene expression is encoded in transcripts. Analysis of his 15 most recent publications reveals a strong trend toward computational approaches for understanding and engineering biological systems. His work integrates mathematical modeling, machine learning, and large-scale genomic analysis to address challenges in virology, cancer research, synthetic biology, and genome editing. Key themes include the relationship between RNA structure and viral pathogenesis, computational prediction of CRISPR efficiency, AI-driven analysis of evolutionary patterns in codon usage, and the development of novel tools for gene expression modeling. Prof. Tuller leads an active research laboratory focused on computational systems and synthetic biology. His team develops comprehensive computational models to study intracellular processes, particularly mRNA translation dynamics, and applies these models to problems in biotechnology, medicine, and agriculture. The lab's work bridges theoretical computational approaches with experimental validation, as evidenced by numerous publications demonstrating practical applications of their computational models. His research has significant implications for vaccine development (particularly for viruses like Zika and Hepatitis C), cancer diagnostics and treatment, synthetic biology applications, and improving genome editing technologies. The lab's EXPosition tool for CRISPR-Cas9 sgRNA evaluation represents a practical application of their computational models that has potential to enhance genome editing projects across multiple fields.

• Robotics
• Algorithmic Foundations
• Structural Molecular Biology
• +6 more

Oliver Brock is a Professor at the Technische Universität Berlin (TU Berlin) , holding the Alexander von Humboldt Professorship since 2009. His research focuses on algorithmic foundations for robotic agents to perform complex tasks in dynamic and unstructured environments , with cross-disciplinary applications in molecular structural biology (e.g., protein folding, docking). Education: Ph.D. in Computer Science, Stanford University (2000) M.S. in Computer Science, Stanford University (1994) Diploma in Computer Science, TU Berlin (1993) Research Trends: His work spans robotics , motion planning , soft robotics , and machine learning , with recent emphasis on acoustic sensing , adaptive behavior representation , and separating learning from modeling in manipulation tasks. Publications highlight probabilistic methods , human-robot interaction , and biomimetic systems . Scientific Awards: Notable recognitions include the 2016 RSS Best Systems Paper Award , 2015 Amazon Picking Challenge First Place , and the 2006 NSF CAREER Award . Advising & Grants: As a Humboldt Professor, he has led programs like the Science of Intelligence and Cognitive Systems tracks at TU Berlin. Professional service includes editorial roles in Autonomous Robots and International Journal of Robotics and Research . Labs & Teams: He leads the Robotics and Biology Laboratory (RBO) at TU Berlin, focusing on robotic manipulation , sensor design , and biological data integration .

Christian MartinView profile

Researcher

• Machine Learning
• Statistical Learning
• Life Science & Medicine
• +7 more

Christian Martin is a Researcher at the University of Leipzig since 2019, affiliated with the ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Dresden/Leipzig. His work focuses on integrating Machine Learning and Biomedical Data Analysis for applications in Life Science & Medicine , particularly in Personalized Medicine and Medical Imaging . Current affiliation: ScaDS.AI Center, University of Leipzig Previous roles: Software Developer & Research Associate at Loeser/Meierhofer Medizintechnik GmbH (2008-2019), Research Associate at Biodata Mining Group, University of Bielefeld (2003-2008) His research spans Deep Learning , Radiomics , and Image Processing for medical applications, with notable projects like: GRAMMY (Integrative analysis of gastric cancer, microenvironment, and patient outcomes) MIRACLE (Machine learning for lung cancer relapse prediction) SaxoCell Omics (Systems biology for radiomics classification) He has supervised multiple master's students including: Mohammad Issa (Automotive lane detection) Wael Assy (Soft tissue sarcoma classification) Julius Ellermann (MRI image prediction) Jan Philipp Zimmer (Cervix cancer classification) Marlene Mertens (Cervical cancer analysis) Contact: christian.martin@informatik.uni-leipzig.de

Muzlifah HaniffaView profile

Professor

• Immunology
• Dermatology
• Single-cell Genomics
• +3 more

Professor Muzlifah Haniffa holds dual appointments as Professor of Dermatology and Immunology at Newcastle University and Senior Group Leader/Head of Cellular Genetics at the Wellcome Sanger Institute. She serves as Biological Network Co-Coordinator for the Human Cell Atlas and Deputy lead PI for Newcastle's Immunology and Inflammation theme, pioneering single-cell genomics applications to decode immune system development and disease pathogenesis. Her academic foundation includes: Medical Degree: Cardiff University Postgraduate Clinical Training: Cambridge and Newcastle PhD: Newcastle University Post-doctoral Fellowship: Singapore Research focuses on single-cell resolution mapping of human tissue ecosystems, with core themes: Human Cell Atlas development for embryonic and tissue-specific immunity Immune dysfunction mechanisms in otitis media and cutaneous diseases Multi-omics profiling of COVID-19 responses iPSC-derived skin organoid models for developmental studies Her lab champions "Strength through diversity" and reproducible science with public data sharing. Analysis of 2024-2025 publications reveals dominant trends in spatiotemporal immune mapping across skin, meninges, and prenatal tissues. Key methodological advances include MintFlow microenvironment modeling, scTRAM trajectory benchmarking, and multi-omics integration for disease classification, spanning immunology, dermatology, and cancer biology. Major recognitions: Fellow of the Academy of Medical Sciences (FMedSci) Wellcome Senior Research Fellowship Lister Institute Prize Fellowship 2019 Foulkes Foundation Medal for immunology contributions As a dedicated mentor, she supervises six PhD students including Antony Rose (hepatocellular carcinoma microenvironment) and Jacqueline Boccacino (cancer genomics). Current grants include Wellcome Trust support for her senior fellowship and Human Cell Atlas leadership, alongside UK-CIC consortium funding for pandemic response research. The Haniffa Lab operates across Sanger Institute and Newcastle's Biosciences Institute with 25+ members including clinicians, computational biologists, and wet-lab scientists. Their "Inclusion and Innovation" ethos drives collaborative projects on skin wound healing atlases, macrophage heterogeneity in graft-versus-host disease, and spatial transcriptomics of inflammatory skin conditions.

Erhardt BarthView profile

Professor

• Computer Vision
• Machine Learning
• Biological Vision

Prof. Erhardt Barth is the Deputy Director at the Institute of Neuro- and Bioinformatics (INB), University of Lübeck . His research focuses on Computer Vision and Machine Learning , drawing inspiration from biological vision systems to develop hybrid human-machine solutions. Education : PhD in Electrical Engineering (1994), Technical University of Munich Positions : Research Associate (Munich), Visiting Fellow (Melbourne), Klaus-Piltz Fellow (Berlin), NASA Vision Science Group member His work bridges computer vision and biological vision , exploring how neural mechanisms can inform algorithm design. Recent projects include deep learning applications in medical imaging (CT scans, radiographs) and bio-inspired neural architectures like FP-Nets and Min-Nets. Publications span medical diagnostics , image processing , and network efficiency . Key trends include explainable AI in healthcare, compact network design , and unsupervised learning techniques. Scientific distinctions : Recipient of the Schloessmann Award (Max Planck Society, 2000) Held prestigious Klaus-Piltz Fellowship (Institute for Advanced Study, Berlin) Prof. Barth has pioneered technology transfer initiatives, founding companies like GazeCom and gestigon while advancing automated diagnostic frameworks in clinical settings.

• Bioinformatics
• Computational Biology
• Genomics
• +3 more

Mr. Leonard Bohnenkämper, M.Sc., is a researcher affiliated with the Faculty of Engineering at Bielefeld University . He is actively involved with the Genome Informatics Group and the Center for Biotechnology (CeBiTec) , focusing on computational genomics and genome rearrangement analysis. His work spans algorithm development for genomic distance metrics and natural genome halving. His research interests include: Computational modeling of genome rearrangements Development of algorithms for natural genome analysis Genomic distance metrics and epidemiology applications Bioinformatics tool creation for large-scale genomic datasets Mr. Bohnenkämper contributes to academic infrastructure through his membership in the Institute for Bioinformatics Infrastructure (BIBI) board and management. His work aligns with Bielefeld University's strategic focus on socio-technical systems and interdisciplinary research at the intersection of computer science, biology, and technology. He is involved in: IT support for research groups Academic infrastructure development Training and education in bioinformatics

• Bioinformatics
• Genome Analysis
• Computational Biology
• +3 more

Dr. Marília Dias Vieira Braga is an active researcher at the University of Bielefeld, holding appointments across multiple departments with primary affiliation in the Faculty of Engineering's Genome Informatics Group and the Center for Biotechnology (CeBiTec). She maintains active research connections with both the Algorithmic Cheminformatics Group and BiSEd members of the Faculty of Education, demonstrating interdisciplinary engagement across engineering, biotechnology, and educational domains. Her research interests focus on computational approaches to biological data analysis, particularly in genome informatics and algorithmic methods for cheminformatics. This work aligns with University of Bielefeld's strategic research area in The Material World , which examines structures and processes at the interfaces of physics, chemistry, biology and bioinformatics. Her expertise bridges theoretical computational methods with practical biological applications, contributing to the university's emphasis on interdisciplinary research that transcends traditional academic boundaries. Dr. Dias Vieira Braga works within CeBiTec (Center for Biotechnology), one of Bielefeld's Central Academic Institutes that pursues research on an interdisciplinary basis by combining expertise from multiple faculties. Her position situates her within the university's strong tradition of collaborative research, particularly in areas connecting mathematics, computer science, and biological sciences.

• Bioinformatics
• Genome Informatics
• Computational Biology
• +3 more

Dr. Kassian Kobert is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering, the Institute for Bioinformatics Infrastructure (BIBI), and the Center for Biotechnology (CeBiTec). His primary research base is within the Genome Informatics Group, where he focuses on computational approaches to genomic analysis and biological data processing. He maintains an active research presence with recent publications extending to 2022. Dr. Kobert's research interests center on bioinformatics and computational biology, with particular expertise in algorithm development for genomic data analysis. His work bridges computer science and biological applications, focusing on creating efficient computational methods for handling large-scale genomic datasets. His research spans phylogenetic inference, sequence alignment, viral evolution, and the development of specialized software tools for next-generation sequencing data processing. The interdisciplinary nature of his work connects computer science theory with practical biological applications, particularly in evolutionary biology and genomics. Analysis of Dr. Kobert's publication record reveals a consistent focus on developing computational methods for biological data analysis. His work demonstrates a trajectory from fundamental algorithm development to practical software implementation, with notable contributions including the PEAR read merger and ExaBayes phylogenetic analysis tool. His research shows strong emphasis on computational efficiency, particularly for handling the massive datasets generated by modern genomic technologies. The publications indicate expertise in both theoretical computer science aspects (algorithm design, computational complexity) and practical biological applications (viral evolution, phylogenetic analysis). Dr. Kobert appears to have significant involvement in software development for bioinformatics applications, with several publications describing tools that have become standard in genomic analysis workflows. His work on parallel computing approaches suggests engagement with high-performance computing environments necessary for modern genomic research.