Dr. Edoardo Gallo is a University Associate Professor at the Faculty of Economics, University of Cambridge, and a member of Magdalene College. His research focuses on Experimental Economics , Networks , and Behavioural Economics . Harvard University: A.B. in Physics and Mathematics University of Oxford: M.Phil. and D.Phil. in Economics Gallo's research explores how network structures influence economic behavior, cooperation, and decision-making. His work spans topics like cybercrime disruption , social exclusion , COVID-19 behavioral responses , and unethical behavior in rivalry . Gallo's recent publications (2020-2025) analyze networked social dynamics , including cooperation mechanisms , monetary exclusion , and pandemic-induced behavioral changes . His experiments often employ interactive online platforms like UbiquityLab. Unicredit Foundation Best PhD Thesis Award CRASSH Early Career Fellowship Gallo supervises PhD students such as Rebecca Heath and Christian Höhne , with interests in online social networks and network cognition . He has received grants from the International Foundation for Research in Experimental Economics and the Isaac Newton Trust .
Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.
Prof. Dr. Kai Cieliebak is a Professor of Mathematics at the University of Augsburg, where he holds the Chair of Analysis and Geometry within the Institute of Mathematics under the Faculty of Mathematics, Natural Sciences, and Materials Engineering. He has been at Augsburg University since 2012, following a professorship at Ludwig-Maximilians-Universität München from 2001-2012. His research group includes several researchers and postdocs working on symplectic geometry and related fields. Dr. Cieliebak earned his Diplom in mathematics summa cum laude from Ruhruniversität Bochum in 1992, with thesis on "Pseudo-holomorphe Kurven und periodische Orbits auf Cotangential Bündeln" under advisor H. Hofer. He completed his PhD in mathematics at ETH Zürich in 1996, with thesis "Symplectic boundaries: closed characteristics and action spectra," also advised by H. Hofer. His academic journey included positions at Harvard University, Stanford University, and research at IBM Zürich before his professorships in Munich and Augsburg. Prof. Cieliebak's research focuses on symplectic and contact geometry , with significant contributions to understanding symplectic manifolds, Lagrangian and Legendrian knots, Stein manifolds, and string topology. His work in Hamiltonian dynamics explores variational methods, periodic orbits, and celestial mechanics problems, particularly the restricted three-body problem. In global analysis , he investigates solution spaces of elliptic PDEs and symplectic field theory. His approach often bridges differential geometry, topology, and dynamical systems, with applications to mathematical physics. Over the past decade, Prof. Cieliebak's publications reveal a consistent focus on symplectic homology, Floer theory, and their applications to geometric problems. His work shows increasing integration of algebraic structures with geometric methods, particularly in cyclic homology and string topology. Recent research demonstrates strong collaboration with Urs Frauenfelder on celestial mechanics problems, applying symplectic techniques to the restricted three-body problem and related orbital dynamics. Prof. Cieliebak has secured significant research funding throughout his career, including multiple DFG grants under project codes CI 45/1 through CI 45/12, NSF grants, and participation in European Science Foundation networking programs. His most notable grants include "Foundations of Symplectic Field Theory" (2009-2015) and the current "Rabinowitz Floer Homology" project (since 2023), both in collaboration with U. Frauenfelder. He has mentored numerous researchers and maintains an active research group at Augsburg University, including postdocs and collaborators working on symplectic geometry problems. His team includes researchers such as Dr. Filip Broćić, Zhen Gao, Dr. Hanna Häußler, Emilia Konrad, Shuaipeng Liu, Dominik Meidert, Dr. Airi Takeuchi, Dr. Evgeny Volkov, Milan Zerbin, and PD Dr. Lei Zhao. Prof. Cieliebak has also organized numerous workshops on symplectic geometry, including the annual "Symplectic Field Theory" workshop series.
Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .
Ben Green is the Waynflete Professor of Pure Mathematics at the University of Oxford and a Fellow of Magdalen College. His work spans additive combinatorics, analytic number theory, harmonic analysis, ergodic theory, discrete geometry, and group theory, with a focus on interdisciplinary approaches. Research Interests: Additive combinatorics and its applications to primes Analytic number theory (prime distribution, L-functions) Harmonic analysis (Fourier methods, spectral theory) Ergodic theory and its combinatorial applications Discrete geometry (ordinary lines, convex structures) Group theory (approximate groups, expansion) Article Trends: His recent work emphasizes multiplicative functions, Ramsey-type problems in number theory, expansion in finite groups, and extremal set theory. Themes include prime gaps, arithmetic progressions, and interactions between analysis and algebra. Scientific Awards: Clay Research Award (2004) Ostrowski Prize (2005) Whitehead Prize (2005) Leverhulme Prize (2007) European Mathematical Society Prize (2008) Royal Society Fellow (2010) Sylvester Medal (2014) Senior Whitehead Prize (2019) Advising: Ben has supervised numerous D.Phil students across additive combinatorics, analytic number theory, and related fields. Past students hold postdoctoral and academic positions globally.
Jason Foster is an Assistant Professor at the Faculty of Engineering, University of Toronto, specializing in Engineering Education and Philosophy of Engineering . His work bridges rigorous academic inquiry with practical applications in engineering pedagogy. His research focuses on Research Through Design , aiming to redefine how design and education intersect. Key projects include analyzing the utility of design tools in small enterprises, developing coherent engineering requirements models, and creating open-source lab equipment for budget-constrained institutions. Recent publications highlight trends in engineering education, such as integrating multidisciplinary design, flexible project planning, and addressing intersubjective grading dynamics. His work emphasizes interdisciplinary collaboration, sustainable development, and systems thinking in curricula. He supervises graduate students through a junior colleague/collaborator model, prioritizing adaptability and critical engagement. No awards or formal honors are mentioned in the provided texts.
Fabian Fritz holds an M.Sc. degree and works at the Technical University of Munich (TUM) within the Chair of Aerodynamics and Fluid Mechanics . His research focuses on computational fluid dynamics (CFD) and numerical simulation of multiphase flows, particularly using Smoothed Particle Hydrodynamics (SPH) . He collaborates on projects like PBF-LB/M (additive manufacturing) and contributes to Lagrangian fluid mechanics benchmarking frameworks. Research Trends: His publications emphasize numerical methods (SPH, level-set, finite-volume), multiphase flow modeling , heat transfer , and thermoacoustic stability . Recent work includes hardware-agnostic code optimization and adaptive mesh refinement techniques. Education: Completed a master’s thesis on Diffusive-Interface Modeling of Multiphase Flows with Surface-Tension Effects , supervised by P.D. Dr.-Ing. habil. Stefan Adami.
Raphaël Beuzart-Plessis is a CNRS Research Fellow affiliated with Aix-Marseille University and the Institute of Mathematics of Marseille (I2M) at Luminy Campus. He specializes in advanced areas of mathematics, including harmonic analysis, automorphic forms, representation theory, and number theory, with a focus on unitary groups and conjectures like Gan-Gross-Prasad. His work bridges algebraic geometry, differential geometry, and operator theory. Arithmetic, Geometry, Logic and Representations Group (AGLR) 2022-2027 ERC RELANTRA grant recipient Research interests span automorphic representations, L-functions, periods of automorphic forms, and harmonic analysis on real spherical spaces. He works extensively on the local and global Gan-Gross-Prasad conjectures, endoscopy, and supercuspidal representations. His recent publications analyze Plancher1el formulas, spherical characters, and congruences of automorphic forms. His 15 most recent publications (2014-2022) address topics such as the Gan-Gross-Prasad conjecture, Jacquet-Rallis's fundamental lemma, and the Asai Rankin-Selberg integrals. These works reflect his expertise in automorphic forms, representation theory, and number theory, often involving collaborations with leading mathematicians. 2016-2017 Peccot Prize for young mathematicians under 30 2022-2027 ERC RELANTRA grant for research in automorphic forms and representation theory Beuzart-Plessis has no listed students or laboratory teams but participates in the AGLR-RGR (Reduction Group Representations) team and has been an invited speaker at the 2022 International Congress of Mathematicians. His career includes guest lectures at Collège de France, including four sessions on Period factorizations and Plancherel formulas in 2017.
Samia Khan is a Professor in the Department of Curriculum & Pedagogy at the University of British Columbia's Faculty of Education, where she also serves as Associate Dean of Research. Her academic work bridges educational technology, science education, and teacher preparation across K-16 contexts. Dr. Khan earned her PhD from the University of Massachusetts. Her educational background informs her interdisciplinary approach to learning sciences and technology integration. Her research centers on how digital technologies transform science learning , with emphases on model-based teaching , visualization tools , and equitable participation in STEM . She investigates simulation technologies, future-state modeling, and strategies to broaden science engagement through interpretive and mixed-methods research. Her work particularly examines teacher epistemologies, scientific reasoning development, and socio-cultural factors in technology-mediated learning environments. Analysis of her recent publications reveals three dominant trends: (1) International comparative studies of science curricula across Southeast Asia, (2) Efficacy of digital tools (PhET, GeoGebra, Symbolab) in conceptual understanding, and (3) Pre-service teacher development in model-based science instruction. Her research spans diverse contexts from Canadian classrooms to Rwandan and Vietnamese educational settings. Dr. Khan's contributions have been recognized through: New Scholar Award from the Canadian Society for Study in Education Prime Minister’s Award of Canada for Teaching Excellence in Science, Technology, and Mathematics As former MET Director (2021-2022) and author of foundational courses ETEC 530/533, she has significantly shaped UBC's educational technology programs. Her Faculty Associate role at the Institute of Resources, Environment, and Sustainability demonstrates cross-disciplinary engagement with sustainability education. Her research appears in leading journals including Journal of Technology and Teacher Education, Computers and Education, and Educational Technology Research and Development, with consistent citation as field-shaping work in educational technology.
Marcia C. Linn is the Evelyn Lois Corey Professor of Instructional Science in the Berkeley School of Education at the University of California, Berkeley. She serves as Chair of the Graduate Group in Science and Mathematics Education (SESAME) and has made significant contributions to the field of science education for over five decades. Dr. Linn is a member of the National Academy of Education and a Fellow of multiple prestigious organizations including the American Association for the Advancement of Science (AAAS), the American Psychological Association (APA), the Association for Psychological Science (APS), the American Educational Research Association (AERA), and the International Society of the Learning Sciences (ISLS). Dr. Linn earned her B.A. in Psychology and Statistics (1965), M.A. in Educational Psychology (1967), and Ph.D. in Educational Psychology (1970) from Stanford University, where she worked under Lee Cronbach. Her early career included working with Jean Piaget at the Institute Jean Jacques Rousseau in Geneva, Switzerland (1967-68), serving as a Fulbright Professor at the Weizmann Institute of Science in Israel (1983), and conducting research at University College in London. She has been a fellow at the Center for Advanced Study in Behavioral Sciences three times and a Writing Resident at the Rockefeller Foundation Bellagio Center twice. Dr. Linn's research focuses on how students learn science and how technology can be used to improve science education. She developed the Knowledge Integration framework, which has become widely used in science education. Her work explores the intersection of cognitive science and educational practice, with particular attention to how students develop understanding of complex scientific concepts. She has pioneered the use of technology in science education, developing the Web-based Inquiry Science Environment (WISE) and directing the NSF-funded Technology-Enhanced Learning in Science (TELS) center. Dr. Linn's recent publications demonstrate a clear trajectory toward integrating artificial intelligence with science education. Her work increasingly focuses on how AI can support knowledge integration, facilitate science learning opportunities, and promote equitable educational experiences. She examines how technology can help students develop deeper understandings of scientific concepts through inquiry-based learning while addressing issues of social justice in science education. Scientific Awards and Honors National Association for Research in Science Teaching Award for Lifelong Distinguished Contributions to Science Education American Educational Research Association Willystine Goodsell Award Council of Scientific Society Presidents first award for Excellence in Educational Research Fulbright Professor (1983) Apple Wheels for the Mind grant (1985) National Institute of Education grant (1983) Throughout her career, Dr. Linn has secured significant funding for educational research, including multiple National Science Foundation grants. She directed the NSF-funded Technology-Enhanced Learning in Science (TELS) center and has led numerous projects investigating the cognitive consequences of computer environments for learning. She has advised countless students and researchers in the field of science education, shaping the next generation of educational researchers and practitioners. Dr. Linn directs the Web-based Inquiry Science Environment (WISE) project and has been instrumental in developing technology-enhanced learning environments for science education. Her laboratory has been at the forefront of creating and testing innovative learning technologies that support students in developing deep understanding of scientific concepts through inquiry-based approaches.
Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.
Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.
Timothy Bretl is a Professor of Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Severns Faculty Scholar position since 2021. He also serves as Associate Head of the Aerospace Engineering department. His research focuses on robotics, control systems, rehabilitation robotics, and engineering education. Bretl earned his Ph.D. from Stanford University (2005), with prior degrees from Swarthmore College. He holds affiliate roles across multiple departments, including Neuroscience, Coordinated Science Laboratory, and Computer Science. Education: Ph.D. in Aeronautics and Astronautics, Stanford University (2005) B.A. in Mathematics and B.S. in Engineering, Swarthmore College (1999) His research spans engineering education innovations, robotic manipulation, and brain-machine interfaces. Notable awards include the NSF CAREER Award (2010), Best Manipulation Paper (2012), and multiple teaching honors like the Rose Award for Teaching Excellence (2016). Bretl’s work integrates theoretical foundations with practical applications in prosthetics, autonomous systems, and educational technology. He has advised numerous projects on robotics, control systems, and human-robot interaction. His lab explores advanced topics like elastic rod manipulation, magnetic positioning, and curriculum reform in STEM education. Collaborative projects include partnerships with industry and interdisciplinary teams at the Beckman Institute.
CHONG Yidong is a Professor in the Division of Physics and Applied Physics at Nanyang Technological University (NTU), Singapore. He leads the Centre for Disruptive Photonic Technologies and holds positions in the School of Physical and Mathematical Sciences. His research focuses on theoretical photonics, topological systems, and non-Hermitian physics, with contributions to photonic crystals, topological insulators, and coherent perfect absorbers. He has been recognized with awards including the President's Science Award (2020) and the National Research Foundation Fellowship (2012). Education: Ph.D. (Physics), Massachusetts Institute of Technology (2005–2008) B.Sc. (Physics) and B.Sc. (Mathematical & Computational Sciences), Stanford University (1999–2003) Research Interests: Topological photonics, non-Hermitian systems, photonic topological insulators, PT-symmetric structures, and applications in quantum optics and acoustics. Recent Articles: Focus on experimental realizations of topological lasers, exceptional points in non-Hermitian systems, and higher-order topological phenomena in acoustic and photonic platforms. His work bridges theory and experiment, with collaborations in materials science and electrical engineering. Awards: Extensive recognition for both research and education, including Nanyang Research and Education Awards. Teaching: Courses in mathematical methods for scientists, quantum mechanics, and computational physics, emphasizing numerical techniques and wave phenomena.
Vicki L. Plano Clark is a Professor in the Research Methods area of the School of Education at the University of Cincinnati, where she advises students in the Quantitative and Mixed Methods Research Methodologies (QMRM) concentration of the Educational Studies doctoral program and the Applied Research Methods (ARM) track of the Educational Studies master's program. She joined the University of Cincinnati in 2012 after serving as the director of the Office of Qualitative and Mixed Methods Research at the University of Nebraska-Lincoln. Dr. Plano Clark earned her Ph.D. in Quantitative and Qualitative Methods in Education from the University of Nebraska-Lincoln (2005), M.S. in Physics from Michigan State University (1993), and B.A. in Physics from Kalamazoo College (1990). Her academic journey transitioned from physics education to research methodology, bringing a unique interdisciplinary perspective to her work. As a leading methodologist specializing in mixed methods research, Dr. Plano Clark's scholarship focuses on delineating useful designs for conducting mixed methods research, examining procedural issues associated with these designs, and exploring the contexts for the adoption and use of mixed methods. Her research spans diverse application areas including cancer pain management, STEM graduate student identity development, teacher professional development, and the well-being of rural low-income families. Her work demonstrates how mixed methods approaches can effectively address complex research questions across multiple disciplines. Dr. Plano Clark has made significant contributions to the field through her editorial leadership and publications. She was the founding Managing Editor for the Journal of Mixed Methods Research and currently serves as an Associate Editor. In 2011, she co-led the development of Best Practices for Mixed Methods in the Health Sciences for NIH's Office of Behavioral and Social Sciences Research. In 2012, she became a founding co-editor of the Mixed Methods Research Series with Sage Publications. She has authored numerous influential books including 'Designing and Conducting Mixed Methods Research' (now in its 3rd edition) and 'Mixed Methods Research: A Guide to the Field.' Founding Managing Editor for the Journal of Mixed Methods Research Co-developer of NIH's Best Practices for Mixed Methods in the Health Sciences (2011) Founding co-editor of the Mixed Methods Research Series with Sage Publications (2012) Chair of the Mixed Methods Research Special Interest Group of AERA As an active researcher, Dr. Plano Clark has secured multiple grants including a Department of Education grant evaluating Ohio Network of Education Transformation (ONET) Schools (as Principal Investigator) and a UC University Research Council grant on reducing mass incarceration by improving public defense (as Collaborator). Her recent publications continue to advance methodological understanding in mixed methods research, with a focus on integration techniques, terminology challenges, and applications across health sciences and education. Dr. Plano Clark maintains an active role in the research community through invited presentations and workshops worldwide, helping to train the next generation of researchers in mixed methods approaches and contributing to the ongoing development of methodological standards and practices.
