Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science
Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.
Ala Trusina is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Biocomplexity and Biophysics . Her research integrates coarse-grained modeling to study complex biological systems. Key Research Areas Stress Response Systems (diabetes, aging, cancer, inflammation) Stem Cell Differentiation (cell fate coordination, reversibility of states) Complex Systems (species coexistence, epidemics, CRISPR-phage interactions) Methodologies Theoretical: Agent-based modeling, in-silico simulations Experimental: Quantitative single-cell imaging, RNA/protein profiling Collaborations Joshua Brickman (ES cells), Anne Grapin-Botton (pancreas), Feroz Papa (diabetes), Else Kai Hoffman (p53 dynamics) Thomas Mandrup-Poulsen (inflammation), Savas Tay (spatio-temporal regulation) Teaching Physics of Molecular Diseases Numerical Methods in Physics
Professor Michael Breakspear is an internationally recognized leader in computational neuroscience, brain imaging, and translational neurotechnology at the University of Newcastle's School of Psychological Sciences. His research bridges complex systems theory, mathematical modeling, and clinical neuroscience to advance understanding of brain dynamics in health and disease through interdisciplinary collaboration across mathematics, physics, neuroimaging, psychiatry, and artificial intelligence. Professor Breakspear holds a Doctor of Philosophy from the University of Sydney, along with multiple undergraduate degrees including a Bachelor of Medicine and Bachelor of Surgery. His academic journey includes professorial appointments at the University of Sydney (School of Physics), University of Queensland (School of Psychiatry), and University of Western Sydney (School of Psychiatry), where he progressed from Post-doctoral Research Fellow to Associate Professor. Current: Professor, University of Newcastle, School of Psychological Sciences 2017-present: Principal Research Fellow, National Health & Medical Research Council 2017-present: Senior Scientist and Head, QIMR Berghofer Medical Research Institute 2012-2017: Professor (adjunct), University of Sydney, School of Physics 2011-present: Professor (adjunct), University of Queensland, School of Psychiatry 2007-2012: Associate Professor, University of Western Sydney, School of Psychiatry Professor Breakspear's research program integrates expertise across mathematics, physics, neuroimaging, psychiatry, and artificial intelligence. His core expertise includes computational neuroscience (modeling brain dynamics using nonlinear systems theory), neuroimaging and connectomics (pioneering methods to analyze brain networks), brain disorders and mental health (applying computational models to disorders like schizophrenia and bipolar disorder), and neurotechnology and AI (developing machine learning techniques for imaging biomarkers). His recent publications demonstrate a strong focus on brain dynamics, neuroimaging techniques, and applications to psychiatric and neurological disorders. His work spans theoretical frameworks to clinical applications, with particular emphasis on understanding the neural basis of mood disorders, Alzheimer's disease, and psychosis, employing advanced computational approaches to uncover fundamental principles of brain organization and dysfunction. Senior Researcher Award (2017) Principal Research Fellow, National Health & Medical Research Council (2017-present) Professor Breakspear actively collaborates with clinical researchers, engineers, and technology developers to translate theoretical frameworks into practical diagnostic and therapeutic innovations. He provides leadership in training programs at the nexus of neuroscience, mathematics, and data science, fostering the next generation of interdisciplinary researchers through mentorship and collaborative projects that bridge theoretical and clinical domains.
Dr. Daniel Blanco-Melo is an Assistant Professor at Fred Hutchinson Cancer Research Center, holding dual appointments in the Vaccine and Infectious Disease Division and Public Health Sciences Division . He is also a member of the Translational Data Science Integrated Research Center (TDS IRC). PhD in Biological Sciences (Rockefeller University, 2016) Postdoctoral Fellow (Icahn School of Medicine at Mount Sinai, 2016-2021) BS in Genomics (UNAM, Mexico, 2008) His research focuses on evolutionary virology, analyzing how animal hosts and viruses co-evolve through molecular biology, genomics, and bioinformatics. Key themes include: Antiviral immune strategy evolution Paleovirology of ancient pathogens Computational modeling of host-virus interactions Translational therapeutic design Recent work explores Influenza A virus NS1 protein dynamics and cross-species host response prioritization. Awards include Science News' SN10: Scientists to Watch (2024) and CZI funding (2023) . The lab actively engages in international collaborations and data science integration.
Julie Ahringer is Professor of Genetics and Genomics at the University of Cambridge and Director of the Wellcome Trust/Cancer Research UK Gurdon Institute. She leads a research group investigating chromatin structure and gene regulation using C. elegans as a model system. Her work integrates genomics, super-resolution microscopy, and computational approaches to understand epigenetic controls in development and disease. She holds fellowships from the Royal Society (FRS) and Academy of Medical Sciences (FMedSci). Research Focus: Her laboratory studies chromatin regulation mechanisms including heterochromatin formation, Polycomb domain function, genome architecture, and enhancer/promoter interactions. Key approaches include single-cell multiomics, high-throughput genomics, and super-resolution microscopy to analyze developmental trajectories. Research areas span: H3K27me3 domain formation and Polycomb repression Constitutive heterochromatin organization Regulatory element characterization 3D genome architecture via ARC-C technology Single-cell resolution developmental mapping Awards & Honors: Fellow of the Royal Society (FRS) Fellow of the Academy of Medical Sciences (FMedSci) Wellcome Senior Research Fellowship Academic Leadership: She mentors PhD students and postdoctoral researchers, with funding from Wellcome, MRC, and CRUK. Her lab develops open-source bioinformatics tools (VplotR, periodicDNA) and maintains the genome-wide C. elegans RNAi feeding library. Lab & Collaborations: The Ahringer Lab is based at the Gurdon Institute and collaborates widely on chromatin dynamics, nuclear organization, and developmental genomics projects across model organisms.
Christian Wolf is a Professor of Mathematics at The City College of New York (CCNY) and a member of the doctoral faculty at the CUNY Graduate Center. He serves as the Executive Officer of the Ph.D. program in Mathematics at the Graduate Center. Wolf’s research spans the fields of ergodic theory, dynamical systems, thermodynamic formalism, computability theory, and applications to mathematical biology. Research Trends: His publications highlight intersections between dynamical systems and computability, with a focus on topological pressure, rotation sets, entropy, zero-temperature measures, and geometric properties of symbolic and smooth systems. Topics include phase transitions, localized equilibrium states, and algorithmic analysis of dynamical invariants. Scientific Awards: Simons Foundation Collaboration Grant for Mathematicians #637594 Grants and Advising: Wolf’s research on computability in dynamics and thermodynamic formalism has been supported by the Simons Foundation. He mentors students in research and independent study projects at CCNY and the Graduate Center.
Tobias Andermann serves as an Assistant Professor at Uppsala University's Department of Organismal Biology, specializing in Systematic Biology. He leads the Biodiversity Data Lab, an interdisciplinary research group combining ecology, molecular biology, geomatics, and machine learning to address the biodiversity crisis through innovative computational approaches. His research focuses on quantifying biodiversity loss using AI-driven analysis of environmental DNA, remote sensing data, and fossil records. Key interests include modeling extinction rates across geological timescales, developing standardized biodiversity assessment methods, and predicting species distribution changes under anthropogenic pressures. His work demonstrates current extinction rates are 2000-10,000 times higher than natural background levels, comparable to historical mass extinction events. Methodologically, Andermann integrates machine learning with large-scale environmental DNA datasets and high-resolution remote sensing to develop predictive models of biodiversity distribution. His lab pioneers field sampling protocols for environmental DNA collection and AI frameworks that translate remote sensing data into biodiversity metrics for unsurveyed sites. The Biodiversity Data Lab maintains a dynamic, non-hierarchical research environment focused on high-impact solutions to the biodiversity crisis. Current projects include developing environmental DNA protocols for fungi and insects, analyzing land-use impacts on species communities, and creating neural network models for cross-scale biodiversity forecasting. The lab emphasizes practical applications for conservation policy, notably supporting the UN's 30% protected area target established at COP15.
Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.
Sarah Cobey is a Professor in the Department of Ecology and Evolution at the University of Chicago. Her research focuses on the coevolution of pathogens and host immunity , particularly influenza , using computational and mathematical models . Education: AB from Princeton (2002), PhD from University of Michigan (2009), Postdoc at Harvard School of Public Health (2013) Research Interests : The Cobey Lab studies adaptive immunity dynamics , including antibody repertoire evolution , vaccine effectiveness , and immune-mediated pathogen competition . Key areas include influenza evolution , vaccination strategies , and B cell response predictability . Publication Trends (2023–2025): Recent work examines longitudinal immune modeling , influenza antigenic diversity , and cross-reactive antibody dynamics . Collaborations span immunology , virology , and public health . Scientific Awards : NIH New Innovator Award (2014) James S. McDonnell Scholar (2014) Neubauer Fellowship (2016) NSF GRF (2005) Grants : Past and current NIH funding includes U01AI187063 (2025–2029) on Adaptive Immunity to Influenza and R01AI170116 (2022–2027) on Influenza Vaccine Response Variability . Labs & Collaborations : Leader of the Cobey Lab , with collaborations at Harvard, NIH, and WHO. Projects often involve multi-scale modeling linking individual immune responses to population-level viral evolution .
Dr. Aydan Bulut-Karslıoğlu is a Research Group Leader at the Max Planck Institute for Molecular Genetics in Berlin, where she leads the Bulut-Karslıoğlu Lab focused on gene-environment interactions in stem cells and development. Her work has significantly advanced our understanding of embryonic diapause and stem cell state transitions. Dr. Bulut-Karslıoğlu's educational background includes: B.Sc. in Chemical Engineering (major) and Biology (minor) from Middle East Technical University, Ankara, Turkey (2006) M.Sc. in Molecular Biology and Genetics from Bilkent University, Ankara, Turkey (2008) Ph.D. from Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany (2013) Her research focuses on mechanisms regulating stem cell state transitions and fate commitment, particularly how cells communicate signals from their surroundings to the gene expression machinery. She has pioneered work on mammalian embryonic diapause - a reversible dormant state that gives embryos extra time to develop. Her lab uses a combination of functional perturbation methods in stem cells and early mouse embryos with omics, imaging, and biochemistry to reveal how genetic networks adjust to the status of the embryo. Analysis of her recent publications reveals a strong focus on the epigenetic and metabolic regulation of embryonic diapause, with particular emphasis on mTOR signaling, lipid metabolism, and DNA methylation dynamics. Her work bridges developmental biology, stem cell research, and metabolism, demonstrating how environmental cues like oxygen levels and nutrient availability influence developmental timing and cell fate decisions. Her notable scientific achievements include: Sofja Kovalevskaja Award (2018) ERC Starting Grant (2023) ERC Proof of Concept Grant (2025) GSCN Young Investigator Award (2025) Dr. Bulut-Karslıoğlu actively mentors the next generation of scientists, currently supervising multiple PhD students including Persia Akbari-Omgba, Anastasios Balaskas, Heleen Mallie, and Gunwant Patil. Her lab has received substantial funding through prestigious grants, including the Sofja Kovalevskaja Award, ERC Starting Grant, and ERC Proof of Concept Grant, enabling her team to pursue innovative research at the intersection of developmental biology and metabolism. The Bulut-Karslıoğlu Lab maintains a vibrant research environment with both computational and experimental scientists working together to unravel the mysteries of embryonic diapause and stem cell regulation. The lab actively participates in the International Max Planck Research School for Biology And Computation (IMPRS-BAC), contributing to the training of doctoral candidates at the interface of molecular life sciences and computational sciences.
Robert Laubacher is a Research Fellow at the MIT Center for Collective Intelligence within the Sloan School of Management , focusing on how technological innovations like generative AI and large language models are reshaping organizational practices, work structures, and social patterns. Education: B.A. in American Studies, Northwestern University M.A., Doctoral Coursework in Modern History, Harvard University His research explores: Developing ontologies for work activities inspired by biological taxonomy Enhancing human creativity through AI collaboration frameworks Building collective intelligence systems for global challenges like climate change and sustainable development Identifying foundational elements of collective intelligence Historical analysis of IT's impact on employment relationships Recent publications focus on human-AI co-creation , crowdsourced problem-solving , and organizational adaptation to digital transformation. His work has been featured in Harvard Business Review , Sloan Management Review , and ACM conference proceedings.
Dr. Sarah Wolf serves as Head of the Junior Research Group 'Mathematics for Sustainability Transitions' at Free University of Berlin's Department of Mathematics and Computer Science and as a Senior Researcher and Board Member at the Global Climate Forum (GCF). Her dual affiliation bridges rigorous mathematical modeling with real-world sustainability policy, focusing on complex socio-ecological systems through an interdisciplinary lens since joining GCF's Green Growth initiative in 2012. Wolf earned her PhD in Mathematics from Freie Universität Berlin in 2010 with the thesis 'From Vulnerability Formalization to Finitely Additive Probability Monads,' developed during interdisciplinary work at the Potsdam Institute for Climate Impact Research. Her academic foundation combines pure mathematics with applied climate impact research, establishing her unique approach to formalizing sustainability concepts. Her research centers on agent-based modeling of socio-technical systems, with core expertise in sustainability transitions , green growth mechanics , and sustainable mobility . She develops mathematical frameworks to clarify vulnerability concepts while embedding simulations in stakeholder dialogues through innovations like the 'Decision Theatre Triangle.' This work uniquely positions mathematics as both analytical tool and communication medium for climate policy. Analysis of her 15 most recent publications reveals an evolutionary trajectory from foundational vulnerability formalization (2009-2012) toward applied stakeholder-integrated modeling (2021-2023). Her work consistently bridges mathematical rigor with policy relevance, showing increasing emphasis on participatory approaches while maintaining computational sophistication in agent-based systems. No scientific awards are documented in the source material, though her leadership in the MATH+ junior research group indicates competitive funding attainment. As group head, she directs research strategy and likely mentors junior researchers, though no formal student advisees are listed. Wolf leads the 'Mathematics for Sustainability Transitions' junior research group within FU Berlin's Biocomputing Group, collaborating with institutions like the Potsdam Institute. Her team develops computational frameworks for green growth transitions, emphasizing stakeholder co-creation through platforms like the Decision Theatre while maintaining strong ties to GCF's global policy networks.
Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.
Dr. Denis Jacob Machado is an Assistant Professor in the Department of Bioinformatics and Genomics at UNC Charlotte and a key member of the CIPHER Center. Starting in August 2022, he established the Phyloinformatics Lab, focusing on computational intelligence and phylogenetics to address challenges in One Health through multi-omics integration. Research areas: Pathogen evolution, zoonotic disease risks, and biorepository data solutions Labs: Phyloinformatics Lab (CIPHER Center) His work intersects computational biology, evolutionary frameworks, and public health, with additional service roles in campus safety (Green Dot strategy) and diversity advocacy as part of UNC Charlotte’s Latinx/Hispanic Faculty and Staff Caucus. Awards: Willi Hennig Society Fellow (2024)
