Professor Steve G Burrow is a faculty member at the School of Civil, Aerospace and Design Engineering at the University of Bristol. His research focuses on energy harvesting, vibration control, and environmental sensing, particularly in aerospace and glaciological contexts. Professor of Aircraft Systems Member of the Cabot Institute for the Environment Active in Dynamics and Control research themes His work in energy harvesting emphasizes electromagnetic transducers and nonlinear resonant structures, while environmental sensing involves deploying sensors under ice sheets to study glacial hydrology. Recent articles highlight inerter-based suspension systems, vibration absorber optimization, and broadband energy harvesting techniques. Collaborations span nonlinear mathematics, glaciology, and structural dynamics. No scientific awards were explicitly mentioned, but his research outputs demonstrate extensive contributions to power electronics and sustainable technologies.
Margaret Maaka is a Professor in Curriculum Studies at the University of Hawaiʻi at Mānoa's College of Education, with a distinguished career spanning Indigenous educational psychology, leadership, and policy development. Her work centers on decolonizing educational frameworks and advancing Indigenous knowledge systems. Her academic credentials include a PhD in Educational Psychology (1992) from the University of Hawaiʻi at Mānoa, a Master of Education (Honors) with English Literature focus (1978) from the University of Waikato, New Zealand, a Diploma of Teaching in Elementary and Secondary Education (1978) from New Zealand's Department of Education, and a Bachelor of Education (Honors) with English Literature specialization (1977) from the University of Waikato. Maaka's research explores Indigenous leadership complexities, multiliteracies, and cognitive development within Māori and Hawaiian contexts. She investigates how language revitalization intersects with educational policy to foster Indigenous advancement, emphasizing place-based pedagogies and self-determination in schooling systems. Her scholarship consistently challenges Western epistemological dominance in education. Her publications since 2009 reveal a trajectory prioritizing Indigenous research sovereignty, with recurring themes of decolonization, community-led leadership models, and culturally grounded curriculum design. Key contributions examine contested spaces in Indigenous schooling and the politics of knowledge production. Her teaching excellence has been recognized through: Board of Regents’ Medal for Excellence in Teaching (1998) Presidential Citation for Meritorious Teaching (1996) Graduate Student Organization Outstanding Graduate Assistant Teaching Award (1991) While specific advising details are not documented, Maaka's mentorship is reflected in her collaborative publications with emerging Indigenous scholars. Her work demonstrates sustained commitment to transforming educational paradigms through Indigenous epistemologies.
Dr. Monica P. Colaiacovo is a Professor of Genetics at Harvard Medical School, where she leads research in the Department of Genetics within the Blavatnik Institute. Her laboratory is located in the New Research Building in Boston, Massachusetts. Dr. Colaiacovo's research focuses on the molecular mechanisms of meiosis, chromosome dynamics, and DNA repair in the Caenorhabditis elegans model system. Her work examines how environmental toxicants impact germline function and reproductive health, with particular emphasis on chromosome segregation, recombination, and the synaptonemal complex. Her publications reveal a consistent research trajectory examining critical aspects of meiotic chromosome behavior, including double-strand break formation and repair, crossover designation, and chromosome movement during prophase I. Her laboratory has made significant contributions to understanding how environmental exposures like bisphenol A and phthalates disrupt normal meiotic progression and lead to germline dysfunction. Dr. Colaiacovo's work demonstrates strong interdisciplinary connections between basic chromosome biology, environmental health sciences, and reproductive medicine. Her research group employs advanced genetic, molecular, and imaging techniques to dissect the complex mechanisms ensuring accurate chromosome segregation during gamete formation. Her laboratory actively collaborates with researchers studying aging, DNA repair pathways, and environmental toxicology, as evidenced by publications spanning multiple high-impact journals including Nature, PLoS Genetics, and Genetics.
Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.
Dorina Siebert is a Researcher at the Chair of Metal Construction within the School of Engineering at the Technical University of Munich. She has been working as a research assistant at the Chair since 2019, contributing to various research projects related to steel and aluminum construction, fracture mechanics, and additive manufacturing in construction. Education: M.Sc. in Civil Engineering from Technical University of Munich (2012-2019) Affiliation: Chair of Metal Construction, School of Engineering, Technical University of Munich Contact: dorina.siebert@tum.de, Room 0101.Z1.038, +49 (89) 289-22527 Dorina's research primarily focuses on the fatigue strength of aluminum structures, fracture mechanics in railway bridges, and the application of additive manufacturing techniques in construction. Her work on powder bed-based laser beam melting of metal has significant implications for modern construction methods. She also investigates safe operating time intervals for historic steel bridges and has contributed to the development of a mobile vehicle barrier, demonstrating the practical applications of her theoretical work. Her publication record shows a strong trend toward computational and experimental analysis of material behavior under stress, particularly in aluminum alloys and steel structures. She has published extensively on fatigue properties, fracture mechanics calculations, and additive manufacturing applications, with a clear progression toward more complex modeling techniques and practical engineering solutions. Her work bridges theoretical computational models with real-world infrastructure challenges. Dorina teaches courses including 'Constructing with aluminum' for the Summer semester 2025 and 'Fracture mechanics and fatigue' for the Winter semester 2024/25. She also leads a seminar on plate buckling and steel bridge construction, sharing her specialized knowledge with engineering students. Her teaching directly reflects her research expertise, creating a strong connection between theoretical knowledge and practical application for her students.
Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.
Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley within the College of Letters and Science. His research spans the intersection of mathematical statistics, probability theory, and statistical learning theory with particular focus on high-dimensional data analysis and non-parametric inference. His research interests include mathematical statistics, applied probability, statistical learning theory, high-dimensional data analysis, non-parametric inference, and artificial intelligence/machine learning. Zhivotovskiy's work addresses fundamental questions in statistical learning theory, including risk bounds, algorithmic stability, and convergence rates, with applications spanning multiple domains including robust statistics and private learning. His recent publications (2021-2025) demonstrate significant contributions to theoretical machine learning, particularly in statistical learning theory, risk bounds, high-dimensional statistics, and algorithmic stability. These works appear in top venues including NeurIPS, COLT, and FOCS, reflecting the theoretical depth and importance of his contributions to the field. Among his notable achievements is a Best Paper Award at the Conference on Learning Theory (COLT) in 2020 for his work on 'Proper Learning, Helly Number, and an Optimal SVM Bound.' Zhivotovskiy has also contributed to the theoretical foundations of PAC learning, risk minimization, and statistical aggregation. Prior to his current position, Zhivotovskiy was a postdoctoral researcher at ETH Zürich (2021-2022) and Google Research (2019-2020). He completed his PhD at Moscow Institute of Physics and Technology in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov.
Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.
Dr. Robert J. Teather is an Associate Professor and the Director of the School of Information Technology at Carleton University in Ottawa, Canada. He previously served as an interim Director of the School of Information Technology during the 2022-23 academic year. His academic journey includes a PhD in Computer Science from York University (2013) and a postdoctoral fellowship at McMaster University (2015). Dr. Teather's educational background includes: PhD in Computer Science from York University (2013) Master's Thesis: "Comparing 2D and 3D Direct Manipulation Interfaces" from York University (2008), which was awarded the Joseph Liu Thesis Award Dr. Teather's research broadly falls under the field of human-computer interaction, with specialization in 3D user interfaces, virtual reality, and user interfaces for computer games. His work establishes methods for direct comparison of 2D and 3D interfaces for conceptually equivalent tasks, such as selection and manipulation interfaces. He investigates factors influencing human performance in VR, including stereo 3D graphics, haptic feedback, and head-tracking. His research also evaluates novel user interfaces like tilt control or touchscreens, and examines human performance with game input devices in complex tasks involving navigation, selection, and manipulation of objects in game environments. His research has been published extensively in top venues including IEEE VR, ACM SUI, and Graphics Interface. Among his notable scientific achievements are: NSERC Postgraduate Scholarship during his PhD studies Ontario Graduate Scholarship during his PhD studies Best Paper Honourable Mention at the ACM Symposium on Applied Perception 2020 Best Demo Award for SUI 2017 Joseph Liu Thesis Award (2008) Dr. Teather actively supervises graduate students at Carleton University, currently overseeing multiple PhD and Master's students in the areas of human-computer interaction and interactive digital media. His research is supported by NSERC and the Canada Foundation for Innovation, providing funding for his students and laboratory equipment. His students have produced research spanning VR as a persuasive tool to improve vaccine confidence, selection performance using smartphones in VR, and text entry methods in virtual reality environments. Dr. Teather leads a well-equipped CFI-supported lab focused on virtual and augmented reality research. His team works collaboratively on projects related to interactive virtual reality systems, computer game user interfaces, and input devices for 3D interaction. The lab environment fosters interdisciplinary research with opportunities for students to work on cutting-edge VR/AR technologies and contribute to the growing field of spatial computing.
Dr. Patrick J. McNamara is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University's College of Engineering. He directs the McNamara Research Group, which focuses on understanding how chemicals from consumer products impact public health and the environment once they pass through water treatment systems. His research bridges environmental engineering and microbiology to address critical water quality challenges facing modern infrastructure. Dr. McNamara's educational background includes: Ph.D., 2012, Civil Engineering, University of Minnesota, Twin Cities M.S., 2008, Environmental and Water Resources Engineering, University of Texas at Austin B.S., 2006, Civil Engineering (Minor - Spanish for the Business Professions), Marquette University His research program investigates how consumer product chemicals impact engineering treatment processes that rely on healthy bacteria to treat water. The McNamara Research Group develops non-traditional treatment processes to remove these chemicals from water and mitigate their environmental effects. His work spans antibiotic resistance in water systems, micropollutant removal technologies, pyrolysis of biosolids, PFAS contamination, and electrochemical treatment processes. Specific areas include the impact of corrosion inhibitors on antibiotic resistance, removal of chemicals via drinking water treatment, environmental antibiotic resistant bacteria, beneficial biosolids reuse, and pyrolysis applications. Dr. McNamara's publication record demonstrates a strong focus on emerging water quality challenges, particularly the intersection of chemical contaminants and antibiotic resistance. His recent work examines corrosion inhibitors' impact on antibiotic resistance in drinking water, PFAS mitigation through advanced treatment processes, and environmental drivers of antibiotic resistance in stormwater systems. His research combines fundamental microbiology with practical engineering solutions to address complex water quality issues. Dr. McNamara has received numerous honors and awards: 2022 OCOE Outstanding Researcher Award from Marquette University Marquette University's Campus 2020 KEEN Rising Star Faculty Scholar Award from Provost Office (2019) Central States Water Environment Association Bill Boyle Outstanding Educator Award (2018) Way Klingler Young Scholar Award (Marquette University, 2018) Excellence in Review Award – Environmental Science & Technology (2017) Dr. McNamara has secured significant research funding as Principal Investigator on multiple projects, including NSF grants focused on mitigating antibiotic resistance in drinking water and studying the environmental impacts of quaternary ammonium compounds. His current research portfolio includes projects on PFAS removal through novel electrocoagulation-peroxidation processes, designing green stormwater infrastructure to combat antibiotic resistance, and removing contaminants from greywater using electrocoagulation technology in collaboration with industry partners like Kohler Company. The McNamara Research Group at Marquette University maintains strong collaborations with researchers across multiple institutions and works closely with water utilities and industry partners to translate research findings into practical solutions for water treatment challenges. Their work addresses critical infrastructure needs while protecting environmental and public health through innovative engineering approaches.
Jonathan Nangle is a Lecturer in Music Technology and Electro-Acoustic Composition at the Royal Irish Academy of Music. His work spans acoustic/electro-acoustic composition, live electronics, installations, and improvisation. Educated at Trinity College Dublin under Donnacha Dennehy, Rob Canning, and later privately with Kevin Volans, his music has been performed by ensembles like RTÉ National Symphony Orchestra and Crash Ensemble. Research interests include spatial music, interactive installations, and hybrid acoustic/digital forms. Notable works include Pause (Ergodos, 2017), Iridescent Cobalt Glow (2015), and our headlights blew softly... which earned a 2009 Rostrum commendation. Awards include dual Irish Rostrum representations (2009/2011) and the 2007 AIC/Music 21 Prize. Recordings span solo piano works to ensemble commissions, often integrating innovative electronic elements. His work has been broadcast internationally and choreographed for stage/film. Current roles include academic teaching and ongoing creative exploration at the intersection of technology and traditional composition.
Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.
Professor John Wildman is a research-active faculty member at Newcastle University specializing in public health economics and health inequalities, with particular focus on social prescribing interventions and socioeconomic determinants of health in the North East of England. His core research interests include: Public Health Health Economics Health Inequalities Social Prescribing Epidemiology Health Policy Wildman's recent work demonstrates methodological rigor through quasi-experimental designs and longitudinal analyses to evaluate complex health interventions, particularly examining how austerity policies, welfare reforms, and community-based programs like social prescribing impact vulnerable populations. His publications consistently address real-world health disparities with policy-relevant findings, especially regarding deaths of despair in deindustrialized communities, maternal mental health, and chronic disease management. No scientific awards are documented in the available publication records. He maintains extensive collaborative networks within Newcastle University's public health research ecosystem, notably with Dr Josephine Wildman and Professor Clare Bambra, securing research funding for projects investigating social prescribing efficacy, health inequality drivers, and pandemic-related health outcomes. His work directly informs NHS commissioning practices and health equity initiatives in deprived UK communities. Wildman contributes to Newcastle's research infrastructure through involvement in multidisciplinary teams focused on health services evaluation and socioeconomic determinants of health, with particular emphasis on translating evidence into community-level interventions for marginalized populations.
Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.
Angel Saz Carranza is a Professor and Associate Professor at the Department of Strategy and General Management in the Esade Business School, Ramon Llull University. His research focuses on global governance, intergovernmental organizations, regulatory networks, and public-private partnerships. He contributes to the UN Sustainable Development Goals through his work on institutional frameworks and policy design. He leads research projects funded by the EU and Spanish Ministry of Science, including 'EU-VALUES' (2023-2026) and 'LegitGov' (2022-2025). His work explores accountability mechanisms in EU regulatory networks and governance structures of intergovernmental organizations under the UN system. Key research outputs analyze board design in intergovernmental organizations, network tasks in regulatory frameworks, and determinants of public-private partnership policies. His projects often involve semantic big data analysis and configurational approaches. He is part of the Grup de Recerca en Lideratge i Innovación en la Gestión Pública (GLIGP), a research group focusing on public management innovation. Current and past projects address global governance legitimacy, strategic foresight for Spain's foreign policy, and regulatory network governance.
