Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.
Dr Dawn Elizabeth Cavanagh is a Researcher at Manchester Metropolitan University, affiliated with the Faculty of Health and Education. Her work focuses on healthcare equity for individuals with intellectual disabilities and autism, particularly addressing systemic issues like restrictive practices and long-term segregation. She holds a PhD from the University of South Wales, where her research explored annual health checks and self-management for people with learning disabilities. Dr Cavanagh’s research has included NIHR-funded studies on psychotropic medication decision-making and the impact of the pandemic on people with learning disabilities. She is currently evaluating the HOPE(S) Programme to End Long-Term Segregation, focusing on mothers of children detained in secure hospitals. Her advocacy extends to campaigning against institutional segregation through initiatives like Stolen Lives Wales and her role as a trustee of the Paul Ridd Foundation. Her research interests intersect personal advocacy, as she is autistic and the parent of an adult son with multiple disabilities whose experiences informed her work. She co-chaired the 2023 RRN conference and has been recognized with awards for her contributions to restraint reduction and disability advocacy. Education: PhD in Intellectual Disability Healthcare (University of South Wales) Dr Cavanagh’s articles emphasize ethical healthcare practices, policy reform, and co-produced solutions for marginalized groups. Her work bridges academic research with grassroots advocacy, aiming to dismantle systemic barriers in healthcare access and institutional care. Awards: Restraint Reduction Network’s 2024 Outstanding Contribution Award, Cardiff Parents’ Federation 2024 Trustee Award She collaborates with organizations like the Rightful Lives Team and leads Stolen Lives Wales, advocating for systemic change in healthcare provision for vulnerable populations.
Sebastian Pfotenhauer is a Carl von Linde Professor for Innovation Research at Technical University of Munich (TUM) and Deputy Head of the STS Department. He specializes in the societal dimensions of innovation, including governance of emerging technologies, regional innovation cultures, and responsible innovation practices. His work bridges academic research and policy, with leadership roles in major initiatives like the €50M Munich Cluster for the Future of Mobility (MCube) and the EU-funded SCALINGS project on co-creation in innovation. He holds a PhD in Physics from the University of Jena and postdoctoral training at MIT and Harvard. Research interests include innovation policy, science governance, and the interplay between technology and societal change. Notable projects include comparative studies of innovation cultures, governance frameworks for AI and neurotechnology, and global partnerships in science and innovation. He advises governments and international bodies like the OECD and the German Engineers’ Association (VDI). Publications span journals like Research Policy , Social Studies of Science , and Nature Biotechnology , addressing topics from regulatory sandboxes to neurotechnology ethics. Awards include the Leading Technology Policy Fellowship (MIT) and NSF grants for studying complex international partnerships.
Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.
Mel Ainscow is a Professor in Education at the University of Glasgow and holds adjunct and emeritus roles at Queensland University of Technology and the University of Manchester. He is recognized globally for his work on inclusion and equity in education, particularly through systemic reforms and collaborative research with schools. His career includes roles as a head teacher, adviser, and researcher, emphasizing strategies to make schools effective for all students. Key projects include leading the Greater Manchester Challenge (a £50M initiative for school improvement) and Schools Challenge Cymru (a Welsh Government program focusing on disadvantaged students). He advises UNESCO and the Organization of American States on equity in education. His research focuses on systemic change, policy analysis, and inclusive practices, with over 90 publications including books like Struggles for Equity in Education and articles in journals like Journal of Educational Change and Prospects . Dr. Ainscow's awards include a CBE for services to education (2012). His work bridges theory and practice, advocating for equity through collaboration, ethical leadership, and evidence-based strategies. He has collaborated with international networks in Australia, Portugal, Spain, and Latin America, emphasizing global approaches to educational challenges.
Aditya Prakash is an Associate Professor and Associate Chair for Academic Affairs in the School of Computational Science and Engineering at Georgia Tech. He holds a PhD from Carnegie Mellon University (2012) and a B.Tech from IIT Bombay (2007). His research focuses on data science, machine learning, and AI applied to epidemiology, healthcare, security, and urban computing. His work has led to impactful tools used by organizations like CDC, ORNL, and Walmart. Notable awards include the NSF CAREER Award (2018) and IEEE's 'AI Ten to Watch' (2017). Education: PhD (Computer Science, CMU, 2012), B.Tech (IIT Bombay, 2007) Research Interests: Epidemic forecasting, network analysis, healthcare informatics, and large-scale data-driven solutions for societal challenges. He has authored over 80 papers and holds two patents. His lab develops methods for disease modeling, urban infrastructure analysis, and cybersecurity. Key projects include the NSF-funded BEHIVE initiative for pandemic prediction and collaborations with MIDAS network for infectious disease modeling. Awards: NSF CAREER, Facebook Faculty Award, IEEE AI Recognition, and multiple best-paper awards. His group advises students across PhD, MS, and undergraduate levels, with notable alumni in academia and industry. Labs & Affiliations: Core faculty at ML@GT (Machine Learning Center) and IDEaS (Institute for Data Engineering and Science). Active in organizing conferences like AAAI, KDD, and SIGMOD.
Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.
Professor Keita Takayama is a prominent academic in education studies at the University of South Australia’s UniSA Education Futures. His work focuses on global education policy, comparative education, and teacher education methodology. He actively engages with transnational policy processes, decolonial research frameworks, and critical analyses of international assessments like PISA. As editor of the Asia-Pacific Journal of Teacher Education (APJTE), he emphasizes rethinking teacher subjectivity and pedagogical practices. His research critiques the politics of knowledge production and advocates for methodological innovations like 'Asia as Method.' Recent work addresses contradictions in education export policies, linguistic imperialism in scholarship, and the role of education in resisting authoritarianism. Education Background: Doctorate in Education (focus on comparative policy studies) Advanced training in decolonial methodologies and policy analysis Research Interests: Professor Takayama’s work bridges global and local educational contexts. Key themes include: Policy mobilities & transnational education governance Reimagining comparative education through 'negative' comparative frameworks Ethics of academic publishing and knowledge dissemination Teacher education’s role in shaping democratic societies Labs/Teams: Leads the APJTE editorial collective and co-founded the iTKNe transnational knowledge exchange platform for teacher educators. Active in global academic networks addressing East Asian education stereotypes and postcolonial educational research.
Arjan F. Kirkels is an Assistant Professor and University Lecturer in the Department of Technology, Innovation & Society within the School of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). His work focuses on sustainability assessments, decision-making in transitions, and interdisciplinary education for sustainable innovation. His research interests include Life Cycle Assessment (LCA), Multi-Criteria Assessment (MCA), sustainability transitions, industrial ecology, and systems engineering. He applies these to emerging technologies such as batteries, hydrogen systems, and heat pumps, with a strong emphasis on integrating stakeholder values and addressing deep uncertainties in sustainability decisions. The recent articles highlight a consistent focus on assessing emerging technologies, particularly in renewable energy and circular economy applications. Trends include prospective LCA, experience curves in solar technology, socio-technical impact assessment, and educational innovations in sustainability. His work bridges technical analysis with social and governance dimensions of sustainability transitions. Best Teacher Award, 1st year Bachelor Sustainable Innovation, for course 0SV20 'Industrial Ecology' (2013) Best Teacher Award, 1st year Bachelor Sustainable Innovation, for course 0SV20 'Industrial Ecology' (2018) Best Teacher Award, 1st year Bachelor Sustainable Innovation, for course 0SV20 'Industrial Ecology' (2020) Educational award nomination, bachelor Sustainable Innovation, course 0SK30 Inter-University Sustainability Challenge (2023) Education Award 2024 - master Innovation Sciences (2025) Arjan F. Kirkels has supervised over 100 thesis students and more than 100 interdisciplinary student groups. He has developed and taught over 20 courses and co-authored 4 textbooks and 7 publications on educational innovation. He is currently supervising four researchers: Huiying Liu, Prapti Maharjan, Rishi Bhatnagar, and Seyed Mousavi. His research is supported by projects such as the EU-funded cVPP (Community-based Virtual Power Plant) and studies on Dutch sustainability transitions. He is actively involved in educational initiatives, including MOOC development and challenge-based learning, and contributes to public discourse through media appearances and workshops on sustainability education and systems thinking.
Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.
Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.
Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.
Du Changwen is a Researcher (Professor) at the Nanjing Institute of Soil Science, Chinese Academy of Sciences, serving as Deputy Director of the National Engineering Laboratory for Soil Nutrient Management. He supervises doctoral and master's students in soil science and agricultural technology development. His academic journey includes: Bachelor's degree from Huazhong Agricultural University's College of Resources and Environmental Science (1997) Master's degree from Huazhong Agricultural University's Trace Element Laboratory (2000) PhD from Nanjing Institute of Soil Science, Chinese Academy of Sciences (joint program with Technion - Israel Institute of Technology) (2003) Dr. Du's pioneering research focuses on precision fertilization technologies, particularly polymer-coated controlled-release fertilizers developed through model membrane and water-based reaction film-forming techniques. His work integrates Fourier Transform Infrared spectroscopy (ATR and PAS modes) with engineering mathematics to monitor nutrient release dynamics, soil chemistry processes, and plant nutrition in real-time. This interdisciplinary approach bridges agricultural chemistry, materials science, and environmental engineering to optimize fertilizer efficiency while minimizing ecological impact. Analysis of his 2015-2017 publications reveals a consistent emphasis on spectroscopic methods for soil-plant system analysis, with dominant themes in controlled-release fertilizer development, soil organic matter characterization, and in-situ nutrient monitoring. His work demonstrates strong cross-disciplinary integration between agricultural technology, analytical chemistry, and environmental science. His scientific recognition includes: Special Award of the First China Agricultural Science and Technology Innovation and Entrepreneurship Competition First Prize of Jiangsu Science and Technology Award First Jiangsu Youth Entrepreneurship Award Second Prize of Chinese Academy of Sciences Science and Technology Contribution Award First Prize of China Agricultural Science and Technology Award Dr. Du has secured major research funding including National Natural Science Foundation projects (key, general, youth), National '973' Basic Research Program, '13th Five-Year' R&D Plan sub-projects, '863' High-tech Program sub-projects, and Jiangsu Provincial Science and Technology Support Plan initiatives. His leadership in the National Engineering Laboratory for Soil Nutrient Management drives innovation in fertilizer technology, with significant outputs including 198 academic papers (89 SCI, 35 EI), 6 monographs, 1 international patent, 8 national patents, and 2 software copyrights. His laboratory specializes in advanced spectral analysis of soil-plant systems, utilizing FTIR-ATR and FTIR-PAS technologies for real-time monitoring of nutrient dynamics and polymer membrane reactions. Current research focuses on next-generation controlled-release fertilizers, machine learning-enhanced spectral analysis, and precision nutrient management systems for sustainable agriculture.
Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.
Steve Hailes is a Professor of Wireless Systems at the Department of Computer Science, University College London. He has served as Head of Department since 2019 and Deputy Head from 2005. His research spans wireless networks, computational trust, AI, and sensor systems for health, ecological, and environmental applications. Education: PhD and undergraduate degree from Cambridge University Appointment: Joined UCL in 1991 (postdoc), Lecturer in 1992 Research interests include: Trust and security in networked systems (co-founder of computational trust) Security of industrial control systems AI/ML applications in security and causal discovery Multi-agent reinforcement learning and moral behavior modeling Gas sensor fabrication and deployment for diverse applications Recent publications focus on feature selection for cybersecurity , moral alignment in LLM agents , trust-based consensus algorithms , and causal discovery using reinforcement learning . Collaborations include co-authors like Westphal, Musolesi, and Tennant. Applications span healthcare (dementia, JIA), ecology (endangered species in Botswana), and environmental monitoring (CO distribution, meth lab detection).
