Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.
Prof. Knut Drescher is an Associate Professor at the Biozentrum, University of Basel , leading a research group focused on bacterial biofilms , swarming , and microbial multicellularity . Previously, he served as a Professor of Biophysics and Max Planck Research Group Leader at Philipps-Universität Marburg (2015-2021) and conducted postdoctoral research at Princeton University. Research Interests: Physical and biological mechanisms of biofilm formation Cell-cell interactions in microbial communities Antibiotic resistance in biofilms Hydrodynamics of bacterial swarms Evolution of cooperation in multispecies biofilms Development of bioimaging software (BiofilmQ, BacStalk) Scientific Awards: 2023: SNSF Consolidator Grant 2019: Heinz Maier-Leibnitz Prize (DFG), VAAM Research Prize, IUPAP Young Scientist Prize 2016: ERC Starting Grant Advising & Grants: Advises PhD and Master's students in microbiology, biophysics, and bioinformatics Secured major grants from ERC , HFSP , and DFG
Zhe Ji is an Assistant Professor in the Department of Biomedical Engineering at McCormick School of Engineering and the Department of Pharmacology at Feinberg School of Medicine, Northwestern University. His research integrates computational and experimental genomics to study gene transcription and RNA translation in cell fate commitment and oncogenic processes, aiming to develop precision medicine strategies. **Education**: Postdoctoral Fellow in Cancer Systems Biology, Harvard Medical School Postdoctoral Fellow in Computational Biology, Broad Institute of MIT and Harvard Ph.D. in Computational Genomics, Rutgers University B.S. in Biotechnology, Nanjing University, China **Research Focus**: Keywords include Data Science, Computational Biology, Functional Genomics, RNA, Cancer, Inflammation, and Machine Learning. The lab explores regulatory mechanisms underlying disease, with a focus on translational control, cancer metastasis, and inflammatory networks. **Grants & Advising**: No specific grants or student advisees listed. The lab emphasizes collaborative projects and computational-experimental approaches. **Lab Affiliations**: Zhe Ji’s lab is part of Northwestern’s interdisciplinary environment, bridging engineering and medicine to advance genomic technologies and therapeutic strategies.
Benjamin Machta is an Assistant Professor of Physics at Yale University, affiliated with the Department of Physics and the QBio Institute. He holds a BS from Brown University and a PhD from Cornell University, followed by a postdoctoral fellowship at Princeton University. His research focuses on applying theoretical physics to understand biological systems, particularly leveraging statistical physics and information theory to study biological membranes near critical points and the energetic constraints of biological signaling. Education: BS in Physics (Brown University), PhD in Physics (Cornell University), Postdoc at Princeton University (Lewis-Sigler Theory Fellow). Research Interests include: membrane criticality, phase transitions in biological systems, information-theoretic limits in organism function, and energy dissipation in biological processes. His work often bridges theoretical models with experimental data, such as collaborations with Sarah Veatch’s lab on membrane phase behavior. Publications highlight themes like membrane criticality, protein phase separation, and energy constraints in signaling. His group’s current projects explore cochlear mechanics, thermodynamic control in biological systems, and the role of criticality in sensory systems. Awards: 2019 Simons Investigator Award. Lab Affiliations: QBio Institute and Department of Physics at Yale, located in YSB-C164. Group members include postdocs Isabella Graf and Michael Abbott, and graduate students Asheesh Momi, Mason Rouches, and others.
Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .
Prof. Dr. Deniz Tasdemir is a Full Professor (W3) of Marine Natural Products Chemistry at GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel and serves as Director of the GEOMAR-Biotech center and Head of the Marine Natural Product Chemistry Research Unit. Her career spans institutions including the National University of Ireland Galway and UCL School of Pharmacy. PhD in Pharmacy, ETH Zurich (1997) Post-doctoral work, University of Utah (2001) Dr. Helmut Legerlotz Fellowship, University of Zurich (2002-2025) Her research focuses on marine chemical ecology , metabolomics , and bioprospecting for bioactive compounds from sponges, algae, and marine microbiomes. Recent work explores seagrass pathogen reduction, microbiome interactions, and aquafeed applications. Scientific awards include: Waters Award for Natural Products Innovation (2016) Egon Stahl Silver Medal (2005) Pierre Fabre Prize (2004) ETH Zurich Medal (1997) She leads collaborative projects on ocean sustainability and marine drug discovery, with editorial roles in Marine Drugs , Planta Medica , and Phytochemistry Letters .
Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Hugo de Boer is a Professor at the Copernicus Institute of Sustainable Development , Faculty of Geosciences, Utrecht University. He serves as scientific lead for the Delta Climate Center in Vlissingen and coordinates MSc programs in Water Science and Management and Water Management for Climate Adaptation . His research explores climate-ecosystem interactions, focusing on plant ecophysiology, ecosystem dynamics, and nature-inclusive climate adaptation in deltas. Research Themes: Future Deltas, Pathways to Sustainability, Integrative Bioinformatics Projects: LEMONTREE, CloudRoots, 'From losers to winners' (ancient plant lineages under elevated CO2) Teaching Expertise: System thinking for sustainability, quantitative statistics, plant ecophysiology Research Trends emphasize interdisciplinary approaches to climate change impacts on ecosystems, with publications spanning plant-cloud processes, CO2 acclimation, and eco-evolutionary optimality models. His work bridges biogeochemistry, land-atmosphere interactions, and sustainable development frameworks. Projects & Collaborations include experimental studies on ancient plant lineages (Equisetum) and integrative field campaigns in Amazon and temperate forests. He contributes to modeling climate-vegetation feedbacks, pesticide emission scenarios, and social-ecological system transitions.
Karl Griswold is a Professor of Engineering at Dartmouth College's Thayer School of Engineering, where he leads the Griswold Research Group focused on protein engineering and biotherapeutics development. His interdisciplinary work spans chemical, biological, and engineering disciplines to address critical challenges in drug-resistant infections and protein therapeutics. Education: BS in Chemistry from Southwest Texas State University (1995) PhD in Chemistry from the University of Texas at Austin (2005) Research Focus: Professor Griswold's laboratory specializes in protein engineering, directed evolution, and biotherapeutics development , creating novel biomolecules with superior functionality compared to natural proteins. The group develops high-throughput screening methods, protein deimmunization strategies, enhanced expression systems, and antibacterial agents targeting drug-resistant infections. Their work has significant translational potential for treating conditions like MRSA and Pseudomonas aeruginosa infections. Scientific Recognition: Wallace H. Coulter Foundation Early Career Translational Research Award in Biomedical Engineering (2008) NIH Biotechnology Training Grant (2000-2003) Royston M. Roberts - Regents Fellowship, University of Texas (1999) DOW Chemical Foundation Scholar, Texas State University (1991-1995) Senior Fellow, National Academy of Inventors Research Leadership: As co-founder and CEO of Stealth Biologics, Professor Griswold translates academic research into commercial applications. His work has secured NIH funding and Dartmouth Innovations Accelerator support, with research featured in Chemical & Engineering News, Vermont Public Radio, and the New Hampshire Union Leader for developing alternatives to traditional antibiotics. Research Environment: The Griswold Research Group maintains extensive collaborations across disciplines including clinical medicine, immunology, structural biology, and chemical engineering. This interdisciplinary approach prepares trainees for careers at the intersection of multiple scientific fields, with research focusing on Biomolecular Antimicrobial Therapies, Deimmunizing Protein Therapeutics, and Gene Library Construction Technologies.
Yasushi Sakurai is a Professor in the Department of Translational Datability at Osaka University's Institute of Scientific and Industrial Research, co-leading the Sakurai and Matsubara Laboratory within the Center for Industrial Science and AI. His research mission focuses on transforming society through real-time prediction of natural and social phenomena using large-scale data analytics, with emphasis on practical technological implementation. His research spans time-series big data analysis, dynamic learning systems, and real-time information provision. Key areas include tensor stream mining, EEG-based healthcare applications, cybersecurity anomaly detection, and multi-omics cancer subtyping. The lab specializes in developing deployable technologies that optimize social activities through predictive modeling of evolving data streams. Recent publications (2023-2025) reveal concentrated innovation in time-series data stream processing, with dominant themes in tensor analytics, frequency-domain forecasting, and causal modeling. His team produces high-impact work accepted at premier AI venues (ICLR, AAAI, KDD, WWW), consistently featuring oral presentations that highlight technical novelty and societal relevance. Scientific Awards: FY2024 Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (Research Category) for dynamic learning and real-time data stream analysis Professor Sakurai mentors graduate students including Naoki Chihara (DEIM2024 Outstanding Paper Award winner), Yuka Tamura (DEIM2024 Student Presentation Award winner), and Ren Fujiwara. His lab maintains active industry-academia partnerships focused on practical technology deployment, with research directly addressing real-world challenges in healthcare monitoring and cybersecurity. The Sakurai and Matsubara Laboratory operates as a dynamic research unit within Osaka University's Center for Industrial Science and AI, structured around specialized teams for tensor stream analysis, medical data mining, and network dynamics. Current projects emphasize real-time prediction systems with immediate societal applications, supported by strong industry collaboration frameworks.
Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.
Kesen Ma is an Associate Professor and Undergraduate Advisor in Biology at the University of Waterloo, specializing in hyperthermophile physiology and enzymology. His research explores metabolic processes at extreme temperatures for biofuel production, with affiliations at the Centre for Bioengineering and Biotechnology and Waterloo Centre for Microbial Research. His educational background includes: Ph.D. in Microbial Biochemistry from Philipps-Universität Marburg, Germany (1989) M.Sc. in Microbiology from Academia Sinica, Taiwan (1984) B.Sc. in Microbiology from Wuhan University, China (1982) Dr. Ma's research spans Microbiology , Biochemistry , and Renewable Energy , focusing on protein thermostability, novel metabolic pathways in extremophiles, and industrial applications of thermostable enzymes. His work bridges fundamental enzymology with practical biocatalysis for pharmaceutical and biofuel industries. Analysis of his 2017-2024 publications reveals consistent focus on alcohol dehydrogenases and pyruvate decarboxylases from thermophiles, emphasizing enzyme characterization, thermostability mechanisms, and high-temperature fermentation systems for ethanol production. His scientific recognition includes: Research Fellow at Max-Planck Institute, Philipps-Universität Marburg (1985) Graduate Scholarship from Academia Sinica, Beijing (1982) As Undergraduate Advisor for Biology, Dr. Ma mentors students while leading patented research on thermostable biocatalysts (US patent 8476051). His work has direct industrial applications in bioethanol production and stereo-specific compound synthesis. He actively collaborates through the Waterloo Centre for Microbial Research, focusing on extremophile enzymology for sustainable bioprocessing solutions.
Lucas Paoli is a Researcher and Course Instructor at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Paoli Lab (Microbiome Immunity and Ecology) within the Global Health Institute (GHI) under the School of Life Sciences (SV). His roles include scientific collaboration and teaching responsibilities in life sciences engineering. Paoli holds a PhD in Microbiome Research from ETH Zürich (2018-2023), an M.Phil. in Environmental Policy from the University of Cambridge (2017-2018), and an M.Sc. in Ecology and Evolution from École normale supérieure (2015-2017). Research Focus: His work bridges microbial immunity and ecology, investigating how microbes defend against viral infections across ecosystems like oceans and human microbiomes. Techniques include global-scale metagenomics and functional genomics to study immune strategies and ecological interactions. Key themes involve microbial community dynamics, viral defense mechanisms, and the impact of environmental factors on microbial immunity. Lab & Education: The Paoli Lab explores microbiome-immunity interactions with a focus on ecological contexts. Paoli supervises PhD students in Life Sciences Engineering. His research outputs span microbial ecology, metagenomic tool development, and marine microbiome studies. Contact: lucas.paoli@epfl.ch, AAB 1 39, +41 21 693 16 99.
