Bissan Ghaddar is a Professor in the Department of Technology, Management and Economics at Technical University of Denmark (DTU). Her work focuses on robust optimization, edge computing, and sustainable energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. She supervises PhD projects on sector coupling in energy models and quantum computations for power systems. Her research interests include optimizing energy consumption in electric vehicle routing and application placement in edge computing under uncertainty. She has published influential papers in journals like Transportation Research Part C and Omega , addressing latency and efficiency challenges in dynamic systems. Current projects include modeling large-scale sectoral energy systems using smart-linking approaches (2024–2027) and secure power system operation leveraging quantum computations (2021–ongoing). She collaborates internationally with experts in operations research and telecommunications.
Erik Scheme is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB), and serves as Associate Director of the Institute of Biomedical Engineering (IBME). He holds a PhD and is a Professional Engineer (PEng). His roles include advising the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship, emphasizing innovation in biomedical technologies and healthcare systems. His research focuses on advanced human-machine interaction through biomedical engineering, with a strong emphasis on myoelectric prosthetics, wearable sensors, and machine learning applications. Key areas include improving neuroprosthetic control via incremental learning, gait analysis using underfoot pressure sensors, and developing robust EMG-based gesture recognition systems. His work bridges clinical needs with technological innovation, addressing challenges in rehabilitation, activity monitoring, and user-centric design. Recent publications highlight advancements in adaptive control systems, sensor fusion, and ethical data practices in healthcare. His contributions span both theoretical frameworks (e.g., self-supervised learning models) and applied technologies (e.g., gold-plated 3D-printed electrodes). Dr. Scheme collaborates across disciplines, integrating robotics, signal processing, and clinical validation to create impactful solutions. His lab, affiliated with IBME, actively explores emerging areas like exhaled breath analysis for disease detection and federated learning in healthcare data analytics.
William Parnell is a Professor of Applied Mathematics at the University of Manchester's School of Mathematics. His research focuses on continuum mechanics, metamaterials, and industrial composites, with applications in soft tissue mechanics and acoustic wave manipulation. He leads the Mathematics of Waves and Materials (MWM) group and co-founded the Manchester Materials Modelling Centre (M3C). He has held roles including EPSRC Fellowship 'NEMESIS' (2014-2019) and its extension, contributing to transformative materials science. Education: BSc Mathematics (First Class), University of Bristol (1996-1999) MSc Mathematical Modelling and Scientific Computing (Distinction), University of Oxford (1999-2000) PhD in Applied Mathematics, University of Manchester (2001-2004) His research interests span elastic wave propagation, cloaking, and viscoelastic modeling. He has pioneered hyperelastic cloaking techniques and developed mathematical methods for metamaterials. His work contributes to UN Sustainable Development Goals related to advanced materials and digital innovation. Key achievements include the 2019 Whitehead Prize and over 80 publications. His grants include funding for microstructured material design and collaborations with Thales UK and the National Physical Laboratory. Grants & Awards: EPSRC Fellowships (NEMESIS and extension) Whitehead Prize (2019) Labs/Teams: MWM Group (focusing on waves and materials) M3C (Manchester Materials Modelling Centre)
Bryan S. Graham is a Professor of Economics at the University of California, Berkeley. He specializes in econometrics, focusing on network formation, social interactions, and panel data analysis. His research explores topics such as peer effects, poverty traps, and small sample properties of econometric methods. Graham holds a Ph.D. from Harvard University (2005) and has held visiting positions at Harvard, CEMFI (Spain), and NYU. He is an elected Fellow of the International Association of Applied Econometrics. Education highlights include a Rhodes Scholarship (1997–2000) at Oxford University, a Fulbright Scholarship (1997–1998) at the Australian National University, and a B.A. in Quantitative Economics from Tufts University (1993–1997). His work has been published in top journals like Econometrica and the Review of Economic Studies . Key awards include NSF grants (multiple), the Review of Economics Studies Tour, and the Daniel Ounjian Prize. Graham’s research has practical applications in policy analysis, particularly in education and social spillover effects. He also actively contributes to academic service, including editorial roles at Review of Economics and Statistics and Journal of Econometrics .
Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.
Kieron Burke is a Distinguished Professor in the Department of Chemistry and Department of Physics at the University of California, Irvine (UCI), where he also leads the Burke research group. His academic contributions focus on advancing density functional theory (DFT), a cornerstone of computational quantum mechanics. He collaborates with institutions like Google Accelerated Science and DeepMind to integrate machine learning into DFT, enhancing its predictive power for materials and chemical systems. His research spans theoretical and computational physical chemistry, materials science, and quantum mechanics. Notable achievements include pioneering density-corrected DFT and exploring DFT applications in extreme conditions like planetary interiors and fusion reactors. Prof. Burke's work is internationally recognized, with over 25,000 annual citations, and he holds prestigious fellowships from the American Physical Society and British Royal Society of Chemistry. Prof. Burke's educational initiatives include teaching a popular graduate course on machine learning for scientists and advocating for interdisciplinary training. His research group includes students from chemistry, physics, math, computer science, and engineering, reflecting his belief in cross-disciplinary approaches to scientific challenges. Awards: Fellow of the American Physical Society, British Royal Society of Chemistry, AAAS; Member of International Academy of Quantum Molecular Sciences Labs/Teams: Burke Research Group, focusing on DFT development and machine learning applications
Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.
Sophie Othman is a Lecturer at the University of Franche-Comté, affiliated with the Centre de Linguistique Appliquée (CLA) and the DEFLET department. She is actively engaged in research and teaching in language didactics, digital education, and innovative pedagogical practices. Her work spans multiple roles in research coordination, program leadership, and international academic collaboration. Research Interests: Her primary research areas include digital technology in language teaching and learning, design of online and distance learning environments, digital engineering in education, pedagogical innovation, comodal and hybrid training models, MOOCs, e-portfolios, and teacher training in ICT. She explores how digital tools transform language education, especially in multilingual and multicultural contexts. The recent scholarly output shows a consistent focus on post-pandemic educational transformation, AI integration in learning systems, and the design of flexible pedagogical scenarios. Her publications appear in journals such as ALSIC and in international conference proceedings, reflecting a strong engagement with digital pedagogy and language education innovation. Leadership and Service: Vice-President, Scientific and Pedagogical Commission, CLA – University of Franche-Comté (2021–present) Co-Responsible, TIPED Research Program, ELLIADD (2017–present) Scientific Coordinator, Innov'FLE Thematic Thursdays (2022–present) Former Coordinator, Master 2 FLE 'Political and Digital Environments' and FLE Track at CTU Besançon (2018–2020) Scientific Committee Member for international conferences (e.g., ADCUEFE, CEDIL, UBEST, EIAH) Expert reviewer for language didactics journals and collective works International trainer for the French Ministry and OIF in over 15 countries Projects and Grants: #ApproprIA: AI Appropriation in Education (2025–...) UNPEAA: Digital Use by Allophone Adults (2024–...) Innov'FLE: Innovation in FLE Teaching (2022–...) HUMANE: Digital Humanities for Education (2019–2022) ANR-IDEFI Innovalangues (2013–2016) Advising and Grants: While no formal students are listed, she has supervised research teams and mentored junior researchers through collaborative projects and editorial roles. She has secured national and international funding, notably through the ANR-IDEFI program, and contributes to large-scale educational initiatives supported by governmental and Francophone institutions. Labs and Teams: She is affiliated with the ELLIADD research laboratory (University of Franche-Comté) and was previously associated with LIDILEM (Université Grenoble Alpes). She leads and participates in interdisciplinary research groups focused on digital language education, teacher training, and innovation in didactics.
Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.
Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.
Gernot Grabher is a Professor at HafenCity University Hamburg, where he is affiliated with the School of Urban Planning. He is a leading scholar in economic geography and urban studies with extensive research on innovation, platform economies, and project ecologies. His work bridges theoretical and empirical approaches to understanding contemporary economic transformations. Grabher's research interests focus on the intersection of economic geography, urban planning, and digital transformation. He explores how platformization reshapes industrial production, how urban futures are made through professional agency, and how project ecologies cope with ignorance and uncertainty. His work combines theoretical innovation with empirical rigor, particularly in studying temporary organizations and their role in economic development. His recent publications reveal a strong focus on platform economies, showing how digital platforms transform industrial production, urban governance, and knowledge creation. The research demonstrates a consistent interest in the spatial dimensions of economic processes, particularly how digital transformations interact with geographical contexts. His work often takes a critical perspective on technological disruption, revealing how new forms of platformization are embedded in existing social and economic structures rather than representing clean breaks from the past. Best Paper Award 2024 of the Project Management Journal Fellow of the Regional Studies Association (FeRSA) Top 2% of the most cited researchers worldwide in his field (Elsevier and Standford University Data Base) Member of the Selection Committee for the Alice Amsden Award (SASE) Most cited economic geographer in German-speaking area (ZitArt 2020 ranking) Professor Grabher has successfully supervised numerous PhD students whose work spans urban biodiversity, former industrial neighborhoods, global garment production, smart city participation, and creative processes. He leads the DFG-Research Training Group 'Urban future-making: Professional agency across time and scales' and frequently organizes international workshops on creativity and innovation. His research has been supported by significant grants including DFG funding for projects on the temporal structuring of creative processes. Grabher is actively involved in major international conferences, regularly hosting special sessions at the Global Conference of Economic Geographers and other venues. He serves on editorial boards and selection committees for prestigious academic organizations, reflecting his standing in the field of economic geography and urban studies.
Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.
Pedro Jorge Martins Coelho is a Professor in the Mechanical Engineering Department at Instituto Superior Técnico, University of Lisbon, Portugal. His academic career spans several decades with a focus on advanced thermal sciences and computational methods. His research has significantly contributed to the understanding of radiative heat transfer phenomena in complex systems. Dr. Coelho's educational background includes a Ph.D. in Mechanical Engineering, which has provided the foundation for his extensive research in thermal sciences. His work demonstrates a strong theoretical foundation combined with practical applications across various engineering domains. His primary research interests encompass radiative heat transfer, turbulence-radiation interaction, combustion modeling, and numerical methods for thermal systems. Recent work has expanded into biomedical applications of thermal radiation, particularly in laser-tissue interactions for cancer detection and treatment. His publications reveal a consistent focus on developing and refining computational methods for solving complex heat transfer problems, with particular emphasis on the radiative transfer equation in various media and geometries. Analysis of his recent publications shows a clear evolution toward more complex and interdisciplinary applications, including biomedical thermal applications, advanced turbulence modeling, and thermal management of electrical systems. His work consistently bridges fundamental theoretical developments with practical engineering applications, particularly in combustion systems, energy recovery, and thermal management. Dr. Coelho has served on editorial boards for prestigious journals including Heat Transfer Research, Computational Thermal Sciences, and International Journal of Energy for a Clean Environment, demonstrating his standing in the thermal sciences community. His research collaborations span numerous institutions and researchers worldwide, as evidenced by his extensive publication record with various co-authors across different countries. He has also been involved in conference organization, serving as Associate Editor for major international heat transfer conferences.
Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.
Maryellen L. Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She serves as Vice-Chair of Radiology (Basic Science Research) and was the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/Chair of the Committee on Medical Physics. Her career spans over 30 years of pioneering research in computer-aided diagnosis, machine learning, and deep learning applications in medical imaging. Dr. Giger's research focuses on computational image-based analyses for cancer risk assessment, diagnosis, prognosis, and response to therapy, particularly in breast cancer, lung cancer, prostate cancer, lupus, bone diseases, and more recently, COVID-19. Her work has evolved from developing computer-aided diagnosis systems to utilizing 'virtual biopsies' in imaging genomics association studies for discovery. She has made significant contributions to quantitative imaging, radiomics, and AI applications in medical imaging, with emphasis on translating research into clinical practice. Her publication record shows a clear trajectory from foundational work in computer vision for medical imaging to cutting-edge AI and deep learning applications. The recent publications demonstrate her leadership in large-scale collaborative efforts like the Medical Imaging and Data Resource Center (MIDRC), focus on health equity through AI analysis, and expansion into diverse applications including gynecological imaging, lung cancer screening, and trauma assessment. Her work consistently bridges technical innovation with clinical relevance. Dr. Giger has received numerous prestigious honors including membership in the National Academy of Engineering, the William D. Coolidge Gold Medal (the highest award from AAPM), and being named one of the 50 most impactful medical physicists in the last 50 years. She is a Fellow of multiple professional societies including AAPM, AIMBE, SPIE, SBMR, and IEEE. Her 2019 TIME magazine recognition for QuantX, the first FDA-cleared machine-learning-driven system for cancer diagnosis, highlights her translational impact. As an educator and mentor, Dr. Giger has guided over 100 graduate students, residents, and medical students throughout her career. She has secured substantial research funding including NIH R01 grants and serves as contact PI for the NIH NIBIB-funded & ARPA-H-funded Medical Imaging and Data Resource Center (MIDRC). Her leadership extends to former presidencies of the American Association of Physicists in Medicine and SPIE, and she was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging. Dr. Giger co-founded Quantitative Insights, Inc. through the University of Chicago's New Venture Challenge, which developed QuantX - the first FDA-cleared AI system for cancer diagnosis. She leads the Medical Imaging and Data Resource Center (MIDRC), a critical resource for AI development in medical imaging that received the 2023 DataWorks Prize. Her research laboratory bridges engineering, physics, and clinical medicine to develop and validate quantitative imaging biomarkers and AI tools for precision medicine.
