Alis Oancea is Professor of Philosophy of Education and Research Policy at the University of Oxford's Department of Education, part of the Social Sciences Division. She holds additional roles including ESRC Centre for Global Higher Education Deputy Director and Social Sciences Division Advocate for Responsible Engagement. With dual doctoral degrees (DPhil from Oxford and PhD from Bucharest), she also received a Doctor Honoris Causa from University of the West Timisoara. Her research focuses on meta-research, research policy, and higher education governance. Key areas include research assessment frameworks, impact measurement, ethics, open knowledge practices, and teacher education. She co-edited the 2024 Handbook of Meta-Research and leads international studies on research cultures and policies. Education: DPhil (Oxford), PhD (Buc), DipLATHE (Oxford), Dhc (UWT) Roles: Director of Research (2016-20), REF2021 Coordinator, ESRC CGHE Deputy Director Leadership: Over 30 funded projects including the RKEEI initiative and BERA Observatory Her 150+ publications span research policy, impact evaluation, and education systems. Awards include FAcSS Fellowship and numerous international advisory roles across Europe and Norway. Advises on global education reforms and chairs panels for EU, UK, and Nordic research councils. Current doctoral supervision focuses on research policy, higher education systems, and teacher education innovations. Active in editorial roles for Oxford Review of Education and Review of Education .
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Scott Kerlin is a Senior Lecturer in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds an M.S. and B.S. in Computer Science from the University of North Dakota. Prior to academia, he worked at the Mayo Clinic on medical software systems and IBM as a build master for enterprise products. His career spans roles including network administrator, lab manager, and Undergraduate Director at UND. Dr. Kerlin's research focuses on bridging industry experience with academic curriculum, particularly in computer science education, project-based learning, and cybersecurity. He emphasizes practical applications of theoretical concepts, such as integrating 3D printing and scanning technologies with security systems for small satellites. His work also explores student efficacy in AI courses and scalable software project management methodologies. He has taught at multiple institutions, including the University of Minnesota and Augsburg University, and held roles at Michigan Tech as Senior Security Engineer. His 2024 Engineering+ Outstanding Teaching Award highlights his commitment to pedagogical innovation. Current projects include in-space 3D printing, solar energy systems, and cryptographic solutions for satellite communications. Key areas of contribution include: 3D printing/Scanning: Material characterization, key replication, and aerospace applications Cybersecurity: Intrusion detection, satellite communications security, and chaotic cryptosystems Educational Innovation: Active learning frameworks, PBL implementation, and student performance modeling His interdisciplinary approach connects computer science fundamentals with real-world engineering challenges, emphasizing sustainability and industry relevance in curricula.
Sascha Struwe serves as a Postdoctoral Researcher at Aalborg University Business School within the Faculty of Social Sciences and Humanities, actively contributing to the International Business Research Group. Based at Fibigerstræde 11 in Aalborg Øst, Denmark, Struwe operates at the intersection of service innovation theory and business practice with international focus. Research expertise centers on service innovation , value co-creation , and digital servitization , particularly examining B2B contexts and open banking ecosystems. Work consistently explores institutional influences through German-Chinese case studies, addressing how cultural and regulatory frameworks shape service design. Recent trajectories reveal evolution from foundational service design challenges (2019-2021) toward digital transformation implications (2022-2023), with emphasis on value co-destruction mechanisms in financial ecosystems. Struwe led the PhD project Innovating the Invisible and Intangible: Value Creation in B2B service (2018-2021) investigating co-creation capabilities across industrial sectors. Academic engagement includes conference participation at EIBA and CICALICS events, plus a visiting researcher appointment at Fudan University's Nordic Centre (2019-2020). Current work continues through the International Business Research Group, focusing on digital literacies and service ecosystem resilience.
Shanshan Xu is a Dame Kathleen Ollerenshaw Fellow and Academic Lecturer in Catalysis at the Department of Materials, University of Manchester, since January 2025. She specializes in heterogeneous catalytic systems for sustainable chemical reactions, including hydrogen production, nitrogen fixation, and CO2 conversion, employing operando X-ray spectroscopy and DRIFTS techniques to study catalytic mechanisms. Previously, she worked on the EU-funded Laurelin project, focusing on CO2 conversion to renewable methanol using nonthermal plasma catalysis. She earned her PhD in Chemical Engineering (2021) and MSc in Materials Science and Engineering from the University of Manchester. Her research interests span catalyst design (metal oxides, porous materials like zeolites and MOFs), operando spectroscopy (XAS, XPDF, IR), and sustainable chemistry. She leads the UoMaH research group at the University of Manchester-Harwell, collaborating internationally. Xu is actively mentoring PhD students and supervising projects in catalysis, with funding opportunities through scholarships like the President’s Doctoral Scholarship and the University of Manchester-CSC joint program. Notable awards include the Dame Kathleen Ollerenshaw Fellowship (2024), Dean’s Doctoral Scholarship (2017), and First Prize in the China ShaoXing Innovation Competition (2023). Her work aligns with UN Sustainable Development Goals, contributing to clean energy and sustainable industrial processes. Xu’s lab focuses on advancing catalyst design through operando studies, with emphasis on nonthermal plasma systems. She collaborates on projects like the UoMaH initiative, exploring nanoparticle behavior and catalytic materials for industrial applications.
Chao Wang is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the Whiting School of Engineering at Johns Hopkins University. He also serves as the Director of the Nano Energy Laboratory and the department’s Master’s Admissions Director. His research focuses on sustainable energy systems and nanomaterials for CO2 capture and conversion, electrocatalysis, thermocatalysis, and green chemical engineering. Education: Bachelor’s degree, University of Science and Technology of China (2004) Doctorate, Brown University (2009) Wang’s research targets efficient energy conversion and storage via nanomaterials with tailored atomic structures, emphasizing catalytic activity, selectivity, and stability. His group explores electrochemical and thermochemical processes for reduced carbon footprints, including CO2 and methane conversion, ammonia recovery, and phosphorus/nitrogen nutrient recycling using zeolite-based systems. Recent publications (2021–2024) highlight his work in high-entropy alloys, solid-state battery materials, CO2 electroreduction, and biomedical nanotechnologies. Collaborative efforts span catalysis, nanoparticle dynamics, and environmental applications. Grants include a $1M DOE award for multi-university research, a $625K DOE grant for electrified transportation systems, and $3M in startup funding for carbon-removal technology commercialization. Alumni under his mentorship include Ph.D. graduates like Michael J. Manto (2018) and Master’s students like Mitchell Keller (2018), with notable achievements in catalyst development for ammonia/phosphorus recovery and industry placements at Grace & Co. and GEA Engineering.
Mark Martinez-Klimov is a researcher in the Department of Chemical Engineering at Åbo Akademi University, Faculty of Science and Engineering. His work focuses on catalysis for sustainable energy and renewable fuel production, with an emphasis on heterogeneous catalysis, biomass conversion, and CO2 utilization. He is actively involved in experimental and kinetic studies of catalytic processes. Research Interests: His primary research areas include hydrodeoxygenation, dry methane reforming, combustion synthesis, and catalytic upgrading of bio-oil and biomass derivatives. He investigates catalyst design, deactivation mechanisms, and process optimization using advanced characterization techniques such as X-ray diffraction, scanning electron microscopy, and thermogravimetric analysis. His work supports the development of cleaner energy technologies and circular chemical processes. The analysis of his recent publications (2021–2025) reveals a consistent focus on sustainable catalytic processes, particularly in renewable jet fuel production, hydrogenation of sugars, and CO2 valorization. His research spans both fundamental catalyst development and applied reaction engineering, often in continuous flow systems such as trickle bed reactors. The work integrates material science with chemical engineering principles to address challenges in energy transition. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students or grants are not listed, his collaborative publication pattern with senior researchers like Dmitry Murzin and Pavel Mäki-Arvela suggests involvement in major research projects, likely funded by national or EU-level grants. He appears to contribute to team-based research in catalysis and sustainable technologies, potentially mentoring junior researchers and PhD students within the group. Labs and Teams: Mark is part of a prominent catalysis research group at Åbo Akademi University, specializing in sustainable chemical processes. The team leverages advanced synthesis methods (e.g., solution combustion, impregnation) and characterization tools to develop novel catalysts for energy and environmental applications. Their work is highly collaborative, involving both national and international partners in the field of green chemistry and renewable fuels.
Ulrik Schroeder is a Universitätsprofessor (Full Professor) at RWTH Aachen University, leading the Chair of Learning Technologies within the Faculty of Computer Science. His research focuses on the intersection of educational technology, learning analytics, and immersive technologies with particular emphasis on practical implementations in higher education settings. Professor Schroeder's research spans multiple interconnected domains in educational technology. His primary interests include Learning Analytics implementation (particularly using xAPI standards), Virtual Reality applications for education, Open Educational Resources development and conversion, and gamification approaches for programming education. He has developed several notable tools including convOERter for OER conversion, WebWriter for creating explorable explanations, and various xAPI-based learning analytics infrastructures. His work consistently bridges theoretical frameworks with practical educational applications, often focusing on computer science education contexts. Analysis of his recent publications reveals a strong trend toward integrating Learning Analytics with immersive technologies, particularly Virtual Reality environments. His research demonstrates a systematic approach to educational technology development, with emphasis on scalability, interoperability through standards like xAPI, and practical implementation in real educational settings. The work increasingly focuses on personalized learning paths, quality assurance for educational resources, and privacy-conscious data collection. Co-editor of 21. Fachtagung Bildungstechnologien (DELFI) (2023) Co-editor of Hochschuldidaktik der Informatik HDI 2018 Co-editor of DeLFI 2018 conference proceedings Professor Schroeder has supervised numerous doctoral and postdoctoral researchers who frequently appear as co-authors on his publications, indicating an active research group. His projects often involve interdisciplinary collaborations across computer science, education, and psychology. Current major initiatives include the AIStudyBuddy project for study path analysis and the development of VR classroom simulations for teacher training. His research group, the Learning Technologies Innovation Lab, develops open research tools that support various aspects of educational technology research and implementation.
Rina Foygel Barber is the Louis Block Professor in the Department of Statistics at the University of Chicago, where she also serves as Co-chair of the Committee on Community, Diversity, and Inclusion (CCDI) and is a member of the Committee on Computational and Applied Mathematics (CCAM). Her educational background includes: PhD in Statistics, University of Chicago (2012), advised by Mathias Drton and Nati Srebro MS in Mathematics, University of Chicago (2009) ScB in Mathematics, Brown University (2005) NSF postdoctoral fellow, Stanford University Department of Statistics (2012-13), supervised by Emmanuel Candès Professor Barber's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference, particularly in high-dimensional settings where classical methods may not be reliable. She specializes in distribution-free inference methods such as conformal prediction, multiple testing methods, algorithmic stability, and shape-constrained inference. Her work also extends to modeling and optimization problems in medical imaging reconstruction. Her recent publications demonstrate a strong focus on distribution-free inference, with particular emphasis on conformal prediction, false discovery rate control, and algorithmic stability. Her work bridges theoretical statistics with practical applications, especially in the medical imaging domain. Professor Barber has received numerous prestigious awards: Elected to National Academy of Sciences (2025) MacArthur Fellowship (2023) IMS Fellow (2023) COPSS Presidents' Award (2020) Peter Gavin Hall Early Career Prize (2020) She actively mentors students and collaborators, with many co-authored publications across statistics, machine learning, and medical imaging. Her research has been supported by significant grants that enable her work on theoretical foundations of statistical inference and practical applications in medical imaging. Professor Barber also co-organizes the International Seminar on Selective Inference. Her research group focuses on developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems. They work on false discovery rate control, distribution-free inference, and applications in medical imaging reconstruction.
Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.
Amanda Stockton is an Associate Professor at the School of Chemistry and Biochemistry, Georgia Institute of Technology. Her research focuses on the development of analytical instruments for planetary exploration and the study of terrestrial analog environments to understand conditions suitable for life emergence. She leads the Stockton Lab, which specializes in microfluidics, biosignature detection, and astrobiological applications. Education: B.S. in Chemistry and Aerospace Engineering, Massachusetts Institute of Technology (2004) M.A. in Chemistry, Brown University (2006) Ph.D. in Chemistry, University of California Berkeley (2010) Stockton’s work bridges planetary science and analytical chemistry, targeting extraterrestrial life detection through technologies like the FELDSPAR and IMPOA projects. Her research explores sea spray aerosols, icy moon penetrators, and microfluidic systems for environmental and medical diagnostics. Research Highlights: Instrument development for Europa and Enceladus missions Microfluidic tools for origin-of-life experiments Terrestrial applications in environmental monitoring and point-of-care diagnostics Collaborative studies in Icelandic and Antarctic analog environments The Stockton Lab’s publications reveal expertise in biosignature preservation, Raman spectroscopy, and planetary habitability, with a focus on Mars and ocean worlds. Her team has pioneered low-cost microfluidic platforms like GLUE and modular CE-LIF systems.
Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.
Wolfgang Porod is a Professor of Electrical Engineering and holder of the Frank M. Freimann Chair at the University of Notre Dame. He is also a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS) since 2009, affiliated with the Nanoimprint and Nanotransfer Focus Group led by Paolo Lugli. His research focuses on nanoelectronics and quantum devices, notably co-inventing the Quantum-Dot Cellular Automata (QCA) framework, a molecular-scale information processing paradigm. Porod earned his Diplom (M.S.) and Ph.D. from the University of Graz, Austria, and held postdoctoral roles at Colorado State and Arizona State Universities before joining Notre Dame in 1986. He directs Notre Dame’s Center for Nano Science and Technology, advancing interdisciplinary nanotechnology research. His academic accolades include Fellowships from the AAAS (2005) and IEEE (2001), and teaching awards such as the Kaneb Teaching Award (2005) and the Ruth and Joel Spira Award (2000). Porod’s work bridges quantum physics and engineering, with applications in low-power computing, spintronics, and nanoscale device fabrication. At TUM-IAS, he collaborates on nanoimprint lithography and nanotransfer technologies, advancing scalable nanomanufacturing methods.
Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.
Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.
