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André LamúriasView profile

Assistant Professor

• Biomedical Information Extraction
• Natural Language Processing
• Machine Learning
• +6 more

André Lamúrias serves as an Assistant Professor in the Department of Computer Science at NOVA School of Science and Technology, Universidade Nova de Lisboa, Portugal. His academic journey includes a postdoctoral position at Aalborg University's Department of Computer Science (DKWE group) and prior work at Priberam Labs. He completed his PhD at LASIGE, Faculdade de Ciências, Universidade de Lisboa. His research specializes in biomedical information extraction from scientific papers, electronic health records, and genomics data using machine learning algorithms integrated with domain ontologies. Current projects include metagenomic binning with graph neural networks and low-resource biomedical document processing. His work bridges computational methods with biological applications through extensive GitHub development. Lamúrias teaches Bioinformatics, Algorithms and Data Structures for Biology, Artificial Intelligence Techniques for Biology, Databases, and Artificial Intelligence courses at both MSc and BSc levels. His publication record shows consistent output in top bioinformatics journals including BMC Bioinformatics, Frontiers, and Journal of Cheminformatics, with research evolving from chemical entity recognition to advanced metagenomic analysis. He actively supervises MSc students on projects spanning information extraction in low-resource scenarios, cell type classification, genome recovery, and document retrieval systems. His GitHub profile demonstrates ongoing technical contributions with 28 commits in the last year across multiple repositories.

• Environmental Cheminformatics
• Non-Target screening
• Transformation products
• +1 more

Emma Helena Palm is a Doctoral researcher at the Luxembourg Centre for Systems Biomedicine (LCSB) within the Department of Environmental Cheminformatics at the University of Luxembourg. Her research focuses on environmental cheminformatics, non-target screening, transformation products, and the study of persistent, mobile, and toxic compounds. Key research topics include developing methods for structural annotation of chemicals using LC/HRMS, assessing persistent pollutants through grouping strategies, and analyzing gas-phase reactivity of PCBs. She has contributed to tools like ShinyTPs for curating transformation products via text mining and frameworks for predicting retention times in chromatography. Her work emphasizes FAIR principles and open science, particularly in studying PFAS and environmental contaminants. She collaborates with interdisciplinary teams to advance analytical methodologies and environmental risk assessment. Contact: emma.palm@uni.lu

Mario EdenView profile

Professor

• Process Systems Engineering
• Process Synthesis
• Process Design
• +4 more

Mario Eden serves as Dean of the Samuel Ginn College of Engineering and the Joe T. and Billie Carole McMillan Professor in Chemical Engineering at Auburn University. Appointed Dean in 2023 after leading the Chemical Engineering department, he holds Ph.D. and M.S. degrees from the Technical University of Denmark. Education: Ph.D. in Chemical Engineering, Technical University of Denmark M.S. in Chemical Engineering, Technical University of Denmark Research Focus: Dr. Eden's work centers on Process Systems Engineering , specializing in Process Synthesis, Design, Integration and Optimization and Product/Molecular Design . His research integrates machine learning, optimization algorithms, and sustainability principles to develop efficient chemical processes and green products, with particular emphasis on process intensification and solvent design. Publication Trends: Recent publications (2022-2024) reveal strong focus on sustainable process design through machine learning applications, ethylene oxide process intensification, and green solvent development. Key innovations include hybrid optimization frameworks for heat exchanger networks, interpretable toxicity prediction models, and NLP-based molecular property prediction. Scientific Recognition: Fellow of the American Institute of Chemical Engineers (AIChE) Author of top 1% most cited article in Industrial & Engineering Chemistry Research Leadership Impact: As Department Chair, he elevated Chemical Engineering to its highest U.S. News ranking, added 17 tenure-track faculty, and achieved record enrollment with top-tier student metrics. As Dean, he oversees the college's record $117.7 million FY24 research portfolio and spearheads major initiatives including the Gulf Coast Engineering Research Station. Research Infrastructure: His group contributes to Alabama's bioplastics initiative using agricultural waste and develops computational tools like ProCAFD for sustainable process synthesis, with research directly supporting the college's strategic focus on coastal environment studies through the new Orange Beach facility.

Chen ChenView profile

Lecturer

• Medical Image Analysis
• Artificial Intelligence
• Machine Learning
• +1 more

Dr. Chen Chen is a Lecturer in Computer Vision at the University of Sheffield's Department of Computer Science, with affiliations at Oxford BioMedIA and Imperial College London. She holds a Ph.D. in Advanced Computing from Imperial College London, where she specialized in medical imaging and artificial intelligence. Her research develops robust machine learning algorithms for medical data analysis, focusing on cardiac, brain, and prostate imaging. She has received multiple recognitions including IEEE TMI Gold-level Distinguished Reviewer and MICCAI Outstanding Reviewer awards. Dr. Chen leads projects on multi-modal data integration for cardiac care and has published extensively on adversarial data augmentation, domain generalization, and medical vision-language models. She serves as Associate Editor for the Journal of Visual Communication and Image Representation and organizes workshops on data augmentation in medical imaging.

Nicolas MoitessierView profile

Professor

• Medicinal Chemistry
• Computer-Aided Drug Design
• Asymmetric Synthesis
• +1 more

Nicolas Moitessier is a Professor in the Department of Chemistry at McGill University, specializing in computational and experimental approaches to drug discovery. He earned his Maîtrise (1994) and Ph.D. (1998) from the University of Nancy, France, followed by postdoctoral research at the University of Montreal (1998-2000) and a Research Senior position at CNRS Nancy (2001-2003). His research integrates computer-aided drug design with organic synthesis, focusing on developing predictive software platforms (FITTED, IMPACTS, FORECASTER) for drug binding, metabolism prediction, and asymmetric catalyst design. Current projects include covalent inhibitor development for viral proteases and RNA-targeted therapeutics. Professor Moitessier teaches CHEM 222 (Introductory Organic Chemistry 2) and CHEM 552 (Physical Organic Chemistry). His interdisciplinary work bridges chemical biology, catalysis, and computational chemistry to accelerate molecular discovery.

Gavin WilliamsView profile

Professor

• Chemical Biology
• Synthetic Biology
• Enzyme Engineering
• +4 more

Gavin Williams is a Professor and Department Head in the Department of Chemistry at North Carolina State University , holding the LORD Corporation Distinguished Scholar title. His research focuses on integrating organic chemistry and biology to create novel organic molecules through enzyme engineering, biocatalysis, and synthetic biology, particularly in drug discovery. He leads the Williams Lab, which innovates platforms for natural product diversification and polyketide synthesis. Education: B.Sc. Biochemistry, University of Wales (1998) Ph.D. Chemical Biology, University of Leeds (2002) Research Interests: Williams specializes in leveraging synthetic biology tools like transcription factor biosensors, genetically encoded reporters, and directed evolution to reprogram biosynthetic pathways. His work targets fluorinated polyketides, hemiterpene analogues, and non-natural extender units, with applications in antibiotic development and drug lead generation. Key innovations include the SIME cheminformatics system for macrolide library design and engineered polyketide synthases with expanded substrate specificity. Awards: LORD Corporation Distinguished Scholar GLBT Advocate (2020-2021) Grants & Labs: His lab actively pursues funding for projects in synthetic biology and combinatorial biosynthesis. Collaborative efforts include developing biosensors for real-time pathway monitoring and creating synthetic metabolic pathways decoupled from native systems. Location: Dabney Hall 850, Raleigh, NC 27695

• Organic Chemistry
• Supramolecular Chemistry
• Catalysis
• +7 more

Dr. Keith Andrews is an Assistant Professor in the Department of Chemistry at Durham University. With a research focus on enzyme mimics and supramolecular chemistry , his work bridges synthetic organic chemistry with molecular recognition and catalysis. He teaches "Enzyme Mimics in Organic Synthesis" at the Year 4 level. MChem from Imperial College London PhD from University of Nottingham (2013-2016) Post-doctoral work at University of Oxford (2016-2023) His research develops soluble organic cages through chemical self-assembly, aiming to desymmetrize these structures for atomic precision in molecular sensing and catalysis. Current projects include enzyme mimic catalysts for biomolecule transformations and polymer degradation. Notable scientific achievement: Royal Commission 1851 Research Fellowship awarded in 2019. Publications highlight breakthroughs in non-symmetric cage synthesis , acyl transfer catalysis , and silane reagent applications . Primary advisee: Betty Raynal Research group established at Durham University in 2023

• Data Science
• Cheminformatics
• Computational Biology
• +7 more

Dr. Stephan C. Schürer is a Professor at the University of Miami , affiliated with the Department of Molecular & Cellular Pharmacology. He serves as Director of Digital Drug Discovery at the Institute for Data Science & Computing and Associate Director of Data Science at Sylvester Comprehensive Cancer Center. Education: PhD in Chemistry (Technical University Berlin), MS in Chemistry (Humboldt University) His research focuses on systems drug discovery, integrating data science, cheminformatics, computational biology, and medicinal chemistry to develop small molecules for precision medicine. Key initiatives include FAIR data principles, ontologies (BioAssay Ontology), and software tools for drug discovery. Recent publications highlight expertise in SARS-CoV-2 wastewater monitoring, cancer risk prediction models using genomic data, and computational approaches in PROTAC ternary complex prediction. Scientific Awards: Zubrod Award, Roche Symposium participation, multiple national scholarships He leads the Schürer Systems Drug Discovery Research Group and contributes to national research consortia (MLP, LINCS, BD2K, IDG, RADx, MorPhiC).

Patrick Aloy CalafView profile

Research Professor

• Structural bioinformatics
• network biology
• network medicine
• +3 more

Dr. Patrick Aloy Calaf is an ICREA Research Professor and Principal Investigator at the Institute for Research in Biomedicine (IRB Barcelona) , leading the Structural Systems Biology lab. His academic journey includes a BSc in Biochemistry and an MSc in Biotechnology from the Autonomous University of Barcelona (UAB) , followed by a postdoctoral and staff scientist role at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany. His research merges structural bioinformatics , network biology , and systems pharmacology to analyze macromolecular assemblies using 3D structures. He has pioneered Structural Systems Pharmacology , integrating chemical and structural data for drug development, and developed tools like 3did , IntSide , and NetAligner to model protein interactions and networks. Key research areas: 3D interactomes Peptide-mediated interactions Network medicine His 150+ publications in journals like Nature , Cell , and PLoS reflect broad impact, with over 17,500 citations. His work spans from yeast interactome modeling to human disease networks, emphasizing structural insights in biological complexity. The Structural Systems Biology lab under his leadership focuses on bridging computational models with experimental validation, advancing structural predictions for protein complexes and pathways.

• Bioinformatics
• Deep Learning
• Biosensors
• +5 more

Yang Zhang is a Professor at Tianjin University and has held academic positions at the Harbin Institute of Technology (2015-present) and The University of Tokyo (Visiting Professor, 2024). Education : PhD in Pathology from University of Cambridge (2012-2015) MPhil from University of Hong Kong (HKU-Pasteur Research Center, 2009-2011) His research bridges computational biology and experimental microbiology , focusing on: AI-powered microscopic imaging for disease diagnostics Deep learning analysis of multi-omics data (proteins, RNAs, etc.) Development of biosensors for pathogen and cancer detection Research Trends from 2018-2025: Consistent focus on deep learning applications Expanding from parasitology to broader cancer research Integration of electrochemical and optical biosensors Scientific Recognition : Elected Fellow of The Royal Society of Chemistry (2025) Elected Fellow of the Royal Society of Biology (2023) His work combines computational approaches (AI, protein language models) with experimental techniques (high-throughput sequencing, mass spectrometry) to address fundamental questions in disease mechanisms at the molecular level.

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

Arthi JayaramanView profile

Professor

• Materials Science
• Polymer Science
• Machine Learning in Materials Characterization
• +3 more

Arthi Jayaraman is a full professor at the University of Delaware, holding dual appointments in the Departments of Chemical & Biomolecular Engineering and Materials Science and Engineering. She directs an NSF-funded NRT traineeship program on computing and data science for materials innovation. Her research focuses on computational design of soft materials using machine learning, polymer physics, and molecular simulations. Education: Ph.D. in Chemical Engineering from North Carolina State University (2000s), postdoctoral research at University of Illinois-Urbana Champaign in Materials Science and Engineering. Her work integrates AI with experimental techniques to solve challenges in materials characterization and industrial applications. Research interests include ML-driven analysis of materials data, polymer composites design, and bioinspired materials. Key projects involve developing algorithms like CREASE (Computational Reverse-Engineering Analysis for Scattering Experiments) to interpret nanoparticle structures. She has pioneered methods for predicting phase behavior in polymer blends and nanocomposites. Her awards span ACS PMSE Fellowship (2025), APS Fellow (2020), AIChE IMPACT Award (2021), and DOE Early Career Award (2010). Over 100 peer-reviewed publications address topics like polymer solution structures, nanoparticle self-assembly, and collagen-mimetic materials. Leadership includes advising graduate students through the NRT program, collaborating with industry on material design problems, and serving as an editorial board member for ACS journals. Her lab employs multiscale modeling strategies to bridge molecular-scale phenomena with macroscale material properties.

Kas HouthuijsView profile

Researcher

• Spectroscopy
• Glycosylation
• Mass Spectrometry
• +3 more

Kas Houthuijs is a Researcher at the Faculty of Science, Department of Chemistry for Environment & Health at Vrije Universiteit Amsterdam. His research focuses on spectroscopic methods, glycosylation mechanisms, and environmental analysis. He leads collaborations involving advanced mass spectrometry instrumentation like the FELIX facility. Key interests include microplastic detection in biological systems, infrared ion spectroscopy for metabolite identification, and machine learning applications in chemical analysis. Research Interests: Spectroscopic techniques (FTICR MS, IRMPD spectroscopy) Glycosylation reaction mechanisms Environmental pollutant analysis (microplastics, occupational exposure) Development of AI-driven spectral analysis tools Media Contributions: Panelist for documentary 'Plastic People' (August 2024) Radio interview discussing plastic pollution impacts (August 2024) Labs/Teams: Active in the FELIX facility for infrared ion spectroscopy research, collaborating with groups in analytical chemistry and metabolomics.

• Materials Informatics
• Polymer Science
• Artificial Intelligence
• +4 more

Chris Kuenneth is an Assistant Professor at the University of Bayreuth, specializing in Materials Informatics and Polymer Science. His research focuses on AI-driven approaches for materials discovery, with particular emphasis on polymer informatics and computational chemistry. He contributes actively to open-source projects like RDKit, ensuring cross-platform availability of cheminformatics tools. Key areas of expertise include machine learning applications in materials science, data-driven polymer property prediction, and high-throughput screening for novel materials. Developed polyBERT, a chemical language model for polymer informatics. Advances in AI-assisted discovery of battery electrodes and dielectric materials. His work bridges computational methods with practical applications, addressing challenges in sustainable materials and energy storage technologies. Collaborations involve interdisciplinary teams and open-source communities.

Amanda BarnardView profile

Professor

• Applied machine learning and artificial intelligence
• Data science and eResearch
• Computational methods for physical sciences
• +2 more

Professor Amanda Barnard is a Senior Professor of Computational Science at the ANU College of Engineering and Computer Science. She leads research in computational modeling, high-performance supercomputing, and AI applications in materials science. With a BSc (Hons) in applied physics (2000) and PhD in theoretical condensed matter physics (2003) from RMIT University, she has held prestigious roles including Distinguished Postdoctoral Fellow at Argonne National Lab (USA) and Violette & Samuel Glasstone Fellow at Oxford University (UK). Board member at BioViS (Garvan Institute), CTCMS (AIBN), Our Health in Our Hands (ANU), and NeSI (New Zealand eScience). Former Chair of the Australian National Computational Merit Allocation Scheme (NCMAS) and current Chair of the Australasian Leadership Computing Grants (ALCG). Research focuses on materials informatics, nanoinformatics, and AI-driven material discovery. Awards include the 2009 Malcolm McIntosh Physical Scientist of the Year, 2014 Feynman Prize in Nanotechnology, and 2019 AMMA Medal. Her work bridges computational science with real-world applications in energy storage, carbon removal, and hydrogen economy technologies. Collaborates with industry through ChoiceFlows Inc. and Data61 (CSIRO).