Stefan Kremer is a Professor at the University of Guelph. His research focuses on learning to recognize, categorize, and generate structural patterns in complex data, utilizing artificial neural networks, support vector machines, deep belief networks, hidden Markov models, evolutionary algorithms, and deep learning. His lab emphasizes problem-driven approaches, particularly in domains like biology. Methods include deep learning, spatio-temporal pattern recognition, and bioinformatics. Contact: skremer@uoguelph.ca
Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems
Arnold Polanski is an Associate Professor in Economics at the School of Economics, University of East Anglia (UEA), where he is an active member of the Applied Econometrics and Finance, Economic Theory, and Statistics research groups. He is currently accepting PhD students and supervising research in socio-economic networks, game theory, financial economics, and financial tail risk. His academic journey includes a PhD from the University of Alicante, postdoctoral research at the University of Minnesota, and prior teaching at Queen’s University Belfast. PhD in Economics, University of Alicante (2004) Postdoctoral Studies, University of Minnesota (2005) Postgraduate Certificate in Higher Education Teaching, Queen’s University Belfast (2007) Arnold Polanski's research focuses on socio-economic networks , game theory , information economics , and financial tail risk , with a growing emphasis on integrating machine learning into economic modeling. His work explores how network structures influence cooperation, information diffusion, and financial interdependencies, particularly during extreme market events. He investigates the role of homophily, influence, and strategic behavior in shaping economic outcomes. His recent publications (2019–2025) reveal a consistent trend toward analyzing tail risk interdependence , network stability , and information flows using advanced econometric and computational methods. Many of his articles apply machine learning and axiomatic frameworks to bargaining and financial risk, published in journals like Journal of Economic Theory , Journal of Applied Econometrics , and Computational Economics . His work bridges theoretical economics with empirical and computational approaches. Arnold Polanski has received research funding from prestigious institutions including the British Academy and the Institut Europlace de Finance Louis Bachelier . He leads the Economic Theory Group at UEA and serves in key administrative roles such as Plagiarism Officer and Chair of the Faculty Appeals and Complaints Panel. He actively contributes to the academic community as co-organizer of an annual international workshop on the economics of networks. His research supervision includes PhD projects on socio-economic networks, game theory, and financial tail risk. He collaborates with scholars such as E. Stoja, F. Vega-Redondo, and J. Sikora, and his work often involves interdisciplinary methods combining economics, statistics, and computer science. Arnold Polanski is involved in the Economic Theory Group and contributes to collaborative research within UEA’s School of Economics. His projects emphasize network-based modeling, financial risk analysis, and the application of machine learning in economic contexts. He fosters academic exchange through organizing international workshops and leading research initiatives focused on the intersection of networks and economic behavior.
Professor Kenneth Payne is a Professor of Strategy at King's College London's Defence Studies Department, part of the Faculty of Social Science & Public Policy. His research focuses on the intersection of political psychology, strategic studies, and artificial intelligence. He has authored influential books like I, Warbot (2021) and Strategy, Evolution, and War (2018), exploring AI's transformative impact on conflict and decision-making. Payne has advised governments, NATO, and appeared before parliamentary committees in the UK and Netherlands. His work bridges evolutionary theory, modern warfare, and AI ethics, with recent contributions to debates on autonomous weapons and reliable AI in defense. Key affiliations include the Cyber Security Research Group (CSRG) and King's Cybersecurity Centre. His awards include a Visiting Fellowship at Oxford University's Department of International Relations (2008). He teaches strategic studies, AI's role in conflict, and has supervised PhD students in related fields. Payne's research projects include studies on geopolitics, post-traumatic stress in combat troops, and counterinsurgency strategies in Iraq.
Jun-Ki Choi is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Dayton’s School of Engineering. He holds the O. Jack and Opal Anderson Faculty Fellowship in Engineering Innovation and serves as Director of the University of Dayton Industrial Training and Assessment Center (UD-ITAC), a U.S. Department of Energy-funded program providing energy efficiency assessments to small and mid-sized manufacturers. His research interests include: Sustainable Manufacturing Energy Efficiency Design for Environment Life Cycle Assessment Circular Economy Sustainable Energy Infrastructure Economic Systems Modeling Energy and Environmental Policy His recent publications focus on industrial energy efficiency, life cycle assessment, sustainable manufacturing, and the application of machine learning in energy systems. The research spans topics such as photovoltaic integration, desalination, refrigeration, compressed air systems, and building energy modeling, often combining techno-economic and environmental evaluations. Dr. Choi has received multiple awards for his work, including the U.S. Department of Energy Excellence in Applied Energy Engineering Research Award (2022), the ASHRAE Technical Paper Award (2017), and multiple recognitions as a Center of Excellence from the U.S. Department of Energy (2003, 2015). He has secured over $8 million in research funding as PI or Co-PI and has mentored numerous students through the UD-ITAC program, which has conducted over 1,000 energy assessments. He teaches courses such as Manufacturing Process, Heat Transfer, and Design for Environment at both undergraduate and graduate levels. Dr. Choi leads the UD-ITAC, a key research and outreach center focused on improving manufacturing competitiveness through energy savings, productivity improvements, and waste reduction. The center has received national acclaim for its impact and professionalism.
Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.
John Huelsenbeck is a Professor of Integrative Biology at the University of California, Berkeley, affiliated with the Center for Computational Biology. His research focuses on computational and evolutionary biology, addressing fundamental problems in phylogeny reconstruction and genetic adaptation. Research Fields: Biostatistics, Clinical Data Analysis, Evolutionary Biology and Phylogenetics, Genomics and Genetics, Machine Learning and Algorithms, Population Genetics Contact: johnh@berkeley.edu
Yun Song is a Professor at the University of California, Berkeley, affiliated with the College of Engineering and holding joint appointments in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics. He serves as Director of the Center for Computational Biology and works as a Principal Investigator. Research Interests: Biophysics, Biostatistics, Epigenomics, Evolutionary Biology, Phylogenetics, Gene Expression and Regulation, Genomics, Genetics, Machine Learning, Algorithms, Population Genetics, Structural Biology He advises doctoral students including Milind Jagota and PhD student Yun Deng. Contact: yss@berkeley.edu | Lab Webpage
Rafael Capilla is a Professor of Software Engineering at Rey Juan Carlos University of Madrid and an Adjunct Professor at LUT School of Engineering Sciences. His research focuses on software architecture, architectural knowledge, software product line engineering, dynamic variability, technical debt, software sustainability, and Industry 4.0 architectures. He has conducted international collaborations across Europe, Latin America, and Asia, with over 80% of his collaborators from foreign institutions. Research Interests : Software Architecture, Technical Debt, Dynamic Variability, Software Sustainability, Industry 4.0 Quality Evaluation Recent Publications highlight trends in sustainable software practices, Industry 4.0 systems, and AI-assisted architectural decision-making. His work explores intersections between technical debt identification, serverless computing economics, and runtime variability models.
Christopher H Remien is an Associate Professor in the Department of Mathematics and Statistical Science at the University of Idaho's College of Science. He is affiliated with multiple interdisciplinary research initiatives, including the Initiative for Bioinformatics and Evolutionary Studies, Institute for Interdisciplinary Data Sciences, and the Institute for Modeling Collaboration and Innovation, all within the Office of Research and Economic Development. PhD in Mathematics (University of Utah, 2012) MSc in Mathematics (University of Utah, 2008) BA in Mathematics and Russian Language (St. Olaf College, 2005) His research spans mathematical biology, epidemiology, and systems biology, focusing on modeling biological systems across scales. Key projects include: Developing transmissible vaccines for zoonotic disease control Studying microbial phenotypic heterogeneity under stress Creating predictive models for viral spillover prevention Analyzing microbiome population dynamics using generalized Lotka-Volterra equations Investigating stable isotope biogeochemistry for ecological and forensic applications Recent publications demonstrate expertise in epidemiological modeling, vaccine design, and microbial systems biology. His work often integrates stochastic modeling, population dynamics, and interdisciplinary approaches to address public health and ecological challenges.
Mohammad Poursina is an Associate Professor at the Department of Engineering Sciences , University of Agder, Norway. His research spans multibody dynamics , robotics , and celestial mechanics , with a focus on dynamic modeling, control systems, and biomechatronics. He has held prior academic roles at the University of Arizona and Rensselaer Polytechnic Institute. Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2011) M.Sc. and B.Sc. in Mechanical Engineering, University of Tehran (2006, 2003) His work addresses complex systems through interdisciplinary approaches, including robotic rehabilitation , flexible manipulators , and molecular dynamics . Recent publications highlight advancements in trajectory optimization , VR/AR surgical training , and asteroid system modeling . He serves on the editorial boards of Multibody System Dynamics and Journal of Computational and Nonlinear Dynamics , and has organized ASME conference sessions. Key research groups: Artificial Intelligence, Biomechatronics, and Collaborative Robotics Projects: Reality-Connected Machine Simulation of Heavy Machinery (RealSim)
Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.
Martha L. Bulyk is a Professor of Medicine and Pathology at Harvard Medical School, with appointments in the Division of Genetics at Brigham & Women's Hospital. She serves as Co-Chair of the Harvard Biophysics Graduate Program and holds associate memberships at the Broad Institute of MIT and Harvard and the Dana-Farber Cancer Institute Center for Cancer Systems Biology. Her laboratory is located in the New Research Building at Harvard Medical School in Boston. Her research focuses on decoding transcriptional regulation through the study of transcription factors (TFs) and DNA regulatory elements. Using integrated experimental and computational genomics approaches, her lab investigates: High-throughput analysis of TF-DNA binding specificities Cis-regulatory codes in eukaryotic genomes Effects of protein interactions and DNA structure on TF function Molecular determinants of protein-DNA binding specificity and evolution Impact of genetic variation on human transcription factors Pioneer factor interactions with nucleosomes Regulatory functions of alternative TF isoforms in breast cancer Quantitative information encoding in transcriptional enhancers/silencers Recent publications demonstrate a strong focus on mechanistic studies of transcriptional regulation, with recurring themes in chromatin dynamics, cancer biology (particularly neuroblastoma and breast cancer), developmental gene regulation, and computational method development. Her work frequently integrates structural, evolutionary, and functional genomics approaches. The Bulyk Laboratory currently comprises 2 graduate students, 3 postdoctoral fellows, 4 technicians, and 2 undergraduate students. The lab participates in multiple Harvard University programs including the Biophysics PhD Program, Biological & Biomedical Sciences (BBS) PhD Program, Systems/Synthetic/Quantitative Biology PhD Program, Bioinformatics & Integrative Genomics (BIG) PhD Program, and the School of Public Health Program in Quantitative Genomics.
Sebastian Zollner is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health, with a secondary appointment in the Department of Psychiatry. His research bridges statistical genetics, computational biology, and psychiatric disease analysis. Education PhD in Biology, University of Munich (2001) MSc in Mathematics, University of Munich (1997) Research Focus : Zollner develops statistical methods to study genome variation, population history, and disease variant mapping. His work addresses challenges in copy number variation, rare variant analysis, and psychiatric genetics, particularly focusing on bipolar disorder and germline mutation patterns. Scientific Contributions : Recent publications highlight advancements in mutation signature analysis, rare variant modeling, and coalescent algorithms, demonstrating expertise in integrating mathematical theory with genomic data. Collaborations : Active in interdisciplinary teams like the Prechter Bipolar Clinical Research Collaborative and BRIDGES Consortium, applying statistical tools to large-scale genomic datasets.
Dr Nataliia Sergiienko is a Senior Lecturer at the School of Electrical and Mechanical Engineering, University of Adelaide. Her research focuses on wave energy conversion, floating offshore wind turbines, and hybrid renewable energy systems, with a particular emphasis on power optimization, coastal erosion mitigation, and autonomous underwater vehicles (AUVs). Research Interests: Wave energy converter design and control Hybrid wind-wave energy systems Coastal protection using offshore wave farms Deep learning for marine energy forecasting Hydrodynamic optimization of floating structures Supervision & Collaboration: Dr Sergiienko co-supervises multiple Honours and PhD projects with colleagues like Prof Ben Cazzolato and Prof Maziar Arjomandi, focusing on floating wind turbines, distributed propulsion aircraft, and CubeSat technology.
