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Gideon ChristianView profile

Associate Professor

• Artificial Intelligence and Law
• Racial Bias in AI
• Environmental Impact of Emerging Technologies
• +3 more

Gideon Christian is an Associate Professor and University Excellence Research Chair in AI and Law at the University of Calgary’s Faculty of Law. He holds a Ph.D. from the University of Ottawa, an LLM specializing in Law and Technology, and an LLB from the University of Lagos. Prior to his current role, he worked as Legal Counsel at the federal Department of Justice and as an adjunct professor at the University of Ottawa. He currently serves on the boards of CanLII and Lexum. Education: Ph.D., University of Ottawa (2014) LLM (Law & Tech), University of Ottawa (2008) LLB, University of Lagos (2002) Research Interests: Dr. Christian focuses on AI’s legal implications, particularly addressing racial bias in technologies. His work includes analyzing algorithmic racism in facial recognition systems, ethical use of Generative AI in law, and AI’s environmental impacts. He has advised parliamentary committees on AI in immigration decisions and its societal effects. Awards: 2024: Calgary Herald’s Top 20 Compelling Calgarians 2023: Alberta Newcomer Recognition Award 2023: NCC Grant ($916,000) and OBA Fellowship 2022: Howard Tidswell Teaching Excellence Award Teaching and Grants: Teaches courses like eLitigation, Civil Procedure, and Ethical Lawyering. Has secured grants totaling over $1.2M for research on AI ethics, cybersecurity, and legal frameworks. His work bridges law, technology, and social justice. Labs/Teams: Collaborates with CanLII and Lexum on open legal information. Engages in interdisciplinary projects addressing AI’s societal impact through partnerships with NGOs and government bodies.

• Optimization and optimal control
• Inverse problems
• Mathematical imaging
• +5 more

Karl Kunisch is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz and serves as Scientific Director of the Radon Institute of the Austrian Academy of Sciences in Linz. With a distinguished career spanning several decades, he has established himself as a leading researcher in optimization and control theory. Prof. Kunisch completed his PhD and Habilitation at the Technical University of Graz in 1978 and 1980, respectively. His academic journey includes significant positions at Brown University's Lefschetz Center for Dynamical Systems, INRIA Rocquencourt, Universite Paris Dauphine, and he previously served as a professor of numerical mathematics at the Technical University of Berlin. Research Interests: Prof. Kunisch's research focuses on optimization and optimal control, inverse problems and mathematical imaging, numerical analysis and applications, with current emphasis on life sciences applications. His specific areas include Optimal Control of Partial Differential Equations, Nonsmooth Optimization in Function Spaces, and Applications of Optimization and Control in the Life Sciences. His work bridges theoretical mathematics with practical applications across various scientific domains. His recent publications demonstrate a continued focus on advancing the theoretical foundations of optimal control while developing practical numerical methods. Key trends include work on infinite horizon control problems, feedback stabilization techniques, applications to PDE-constrained optimization, and the integration of machine learning approaches with traditional control theory. His research group actively explores connections between theoretical developments and applications in the life sciences. Scientific Recognition: W.T. and Idalia Reid Prize 2021 SIAM Fellow (2017) European Research Council Advanced Grant (2015) Alwin Walther Medaille (2008) SIAM Outstanding Paper Prize (2006) Prof. Kunisch has made substantial contributions to the mathematical community through his editorial work, serving as editor for prestigious journals including SIAM Journal on Control and Optimization, SIAM Journal on Numerical Analysis, and the Journal of the European Mathematical Society. He leads the Research Group on Optimization and Optimal Control at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) and is involved in the ERC-Project OCLOC "From Open to Closed Loop Control".

Ruan ElliottView profile

Senior Lecturer

• Nutritional Metabolism
• DNA Damage and Repair
• Nutrigenomics
• +4 more

Dr Ruan Elliott is Dean of the Doctoral College and Senior Lecturer in Nutrition at the University of Surrey's School of Biosciences. He holds a PhD in Nutritional Metabolism (2017) and BA in Natural Sciences from Cambridge (1988). His research focuses on nutritional modulation of DNA repair mechanisms, functional genomics in nutrigenomics, and micronutrient homeostasis of iron, copper, selenium, and zinc. He has led studies on vitamin D efficacy, food bioavailability, and oxidative stress biomarkers. Education: PhD (University of Surrey, 1992), BA (University of Cambridge, 1988) Previous Roles: Senior Scientist (Institute of Food Research, 1994-2009), Postdoctoral Fellow (University of British Columbia, 1992-1994) His research integrates molecular biology and nutrition to address health impacts of dietary components. Recent work explores oat-based milk alternatives' sensory profiles, vitamin D supplementation efficacy, and DNA repair pathways in cardiovascular disease. He collaborates internationally on projects like the D2-D3 Study and NuGO initiatives. Publications emphasize translational research, including systematic reviews on insect protein efficacy and meta-analyses of vitamin D supplementation. His work bridges lab-based models (e.g., Caco-2 cells for iron studies) and clinical applications. No scientific awards were explicitly listed in the provided texts. He advises on doctoral training programs and has contributed to grant-funded projects like the University of Surrey's food and infection biosciences initiative (2024). Collaborations include studies on probiotics' immune effects and transcriptomic analysis of metabolic conditions.

Seth FreyView profile

Associate Professor

• Computational Social Science
• Governance Institutions
• Online Communities
• +3 more

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis. His research focuses on computational social science, exploring governance institutions and complex human decision-making through computational methods, large datasets, and web-based experiments. He examines online communities as models of governance, with expertise in computational approaches to institutional design and strategic behavior analysis. Education: Ph.D. in Cognitive Science and Informatics, Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey's work integrates data science, lab experiments, and computational modeling to study human organizations and communication. Key areas include governance technology, cognitive mechanisms of social outcomes, and institutional evolution. His projects span online games, sports, and open-source software communities, emphasizing topics like collective action, self-governance, and cooperative behavior. Publications & Funding: His work appears in journals like PNAS and Nature Scientific Reports, and has been funded by NSF, NASA, and the Ford Foundation. He contributes to the Ostrom Workshop at Indiana University and directs the Computational Communication Lab at UC Davis. Teaching: Frey teaches courses on data visualization, simulation methods, and online data analysis in the social sciences.

Ashish CherukuriView profile

Associate Professor

• Optimization
• Control Systems
• Multi-agent Systems
• +7 more

Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.

Christoph T. KochView profile

Professor

• Electron Microscopy
• Materials Science
• Condensed Matter Physics
• +3 more

Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.

• Open Education Resources
• Active Learning Pedagogies
• Environmental Policy
• +5 more

Aselia Urmanbetova is a Teaching Professor in the School of Economics at Georgia Institute of Technology. She serves as the Undergraduate Teaching Coordinator and advisor to GT Econ Club and the Georgia Tech chapter of Omicron Delta Epsilon (ODE). Her roles include managing Graduate Student Instructors, TAs, and 30+ Undergraduate TAs, emphasizing active learning and equitable education. She holds a PhD in Public Policy and multiple advanced degrees from Georgia Tech and Emory University. Her research focuses on open education resources, active learning pedagogies, environmental policy, and transportation economics. She also explores globalization's impact on local industries and higher education policy. Courses taught include Global Economy, Cost-Benefit Analysis, and SAS Coding/Data Analytics. Her publications analyze U.S. manufacturing trends, China's rural labor markets, and pulp/paper industry economics. She actively contributes to campus initiatives on sustainability, diversity, and mental health. Outside academia, she practices yoga and dance, reflecting her commitment to holistic well-being.

• Artificial Intelligence
• Natural Language Processing
• Computational Semantics
• +5 more

Luis Espinosa-Anke is a Senior Lecturer at Cardiff University's School of Computer Science and Informatics. His academic journey includes working as a Natural Language Processing (NLP) scientist at Savana Médica, a Madrid-based healthcare AI company, prior to joining Cardiff. He completed his PhD at Pompeu Fabra University in Barcelona while working at Savana. Dr. Espinosa-Anke's research focuses on Artificial Intelligence and NLP, with particular emphasis on meaning representation, computational semantics, multilingual NLP, and computational lexicography. His work spans theoretical and applied aspects of language technology, with applications in healthcare, social media analysis, and multilingual systems. His recent publications reveal a strong trend toward analyzing language model behavior, bias detection in AI systems, and creating resources for semantic analysis. The publications show increasing focus on practical applications of NLP in healthcare, social media, and cross-lingual settings, with notable contributions to datasets like WIKITIDE and 3D-EX that support definition extraction and semantic understanding. laCaixa Fellow Fulbright scholarship recipient Erasmus Mundus program participant Dr. Espinosa-Anke has secured research funding including a Kaggle Open Research grant ($2,000 USD) as PI for the 'Don't Patronize Me!' project, a Snap Inc. grant ($10,000 USD) as CO-I for modeling meaning shift in social media, and a £90,000 Welsh Government grant for English-Welsh bilingual embeddings research. He currently supervises five PhD students working on meaning representations, contextual word embeddings, NLP for healthcare applications, and metaphor identification.

Eduardo GildinView profile

Professor

• Reservoir Modeling and Simulation
• Control and Optimization of Dynamical Systems
• Model Reduction Techniques
• +5 more

Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Chuan KhooView profile

Lecturer

• Interaction Design
• Physical Computing
• Tangible Embodied Interaction
• +3 more

Dr. Chuan Khoo is a Lecturer in the Department of Design at Monash University, specializing in interaction design, digital media, and physical computing. He holds a PhD from RMIT University and an MFA from the Rhode Island School of Design. His work explores the dual edges of technology through projects like Touchstone (interactive public art) and Lumogram , which interrogate the relationship between humans and digital systems. Khoo’s research focuses on 'slow data'—design perspectives that critically engage with digital augmentation. He has exhibited internationally and developed tools like the mote mini , an IoT microcontroller for design students. His academic roles include teaching at RMIT and heading the Interaction Design program at Nanyang Polytechnic, Singapore. Key projects include When Echoes Find Light (2019), an interactive public bench installation, and Slow Data in the Age of Digital Things , his PhD work critiquing tech velocity. He co-designed the Alfred Health Universal Design Principles project (2024) and received a 2024 DIA Award shortlist for Lumogram . Khoo maintains Technologies for Interactivity , an open-source resource for physical computing, and collaborates globally on projects blending art, tech, and critical design.

Arpan GujaratiView profile

Academic

• Real-Time Systems
• Distributed Systems
• Cyber-Physical Systems
• +7 more

Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.

Xiucai DingView profile

Associate Professor

• Mathematical Statistics
• Statistical Learning Theory
• Random Matrix Theory
• +10 more

Xiucai Ding is a tenured Associate Professor in the Department of Statistics at the University of California, Davis, starting in 2025. He is also affiliated with the Graduate Group in Applied Mathematics (GGAM) at UC Davis. Previously, he was an Assistant Professor in the same department from 2020 to 2025 and a Research Associate at Duke University from 2018 to 2020. PhD in Statistics, University of Toronto (2014–2018), advised by Jeremy Quastel Research Associate, Duke University (2018–2020), with Hau-Tieng Wu Assistant Professor, UC Davis (2020–2025) Associate Professor (tenured), UC Davis (starting 2025) His research focuses on mathematical statistics and statistical learning theory, particularly applied random matrix theory, high-dimensional statistics, non-stationary and functional time series analysis, statistical optimal transport, and the statistical foundations of machine learning algorithms. His methodological work emphasizes nonparametric and sieve-based estimation, inference under complex dependencies, and applications to noisy, high-dimensional data. The recent publications and software tools (such as RMT4DS, Sie2nts, SIMle) reflect a consistent trend in developing theoretically grounded, computationally feasible tools for analyzing complex time series and high-dimensional covariance structures. His work bridges theoretical statistics with practical data science. His research has been supported by the National Science Foundation (NSF). Estimation and inference for precision matrices of nonstationary time series (2020) Auto-regressive approximations to non-stationary time series (2021) On the partial autocorrelation function for locally stationary time series (2022) He advises students and researchers through his role in the Department of Statistics and GGAM. He has taught courses such as STA 108 (Regression Analysis), STA 137 (Applied Time Series Analysis), STA 135 (Multivariate Data Analysis), STA 221 (Big Data & High Performance Statistical Computing), and STA 250 (Topics in Applied and Computational Statistics) at UC Davis. He previously taught at Duke University and the University of Toronto. He has developed several open-source R packages for statistical methodology: RMT4DS : Random matrix tools for data scientists (CRAN/GitHub) Sie2nts : Sieve methods for non-stationary time series (CRAN/GitHub) SIMle : Estimation and inference for nonlinear and non-stationary regression (CRAN/GitHub) UHDtst : Two-sample tests for high-dimensional covariance matrices (GitHub)

David Orrego-CarmonaView profile

Associate Professor

• Translation Studies
• Audiovisual Translation
• Subtitling
• +7 more

David Orrego-Carmona is an Associate Professor in Translation Studies at the University of Warwick, where he also serves as Deputy Director of Graduate Studies. He is affiliated with the School of Modern Languages and Cultures, specifically within the Translation and Transcultural Studies team. Prior to joining Warwick in 2022, he held academic positions at Aston University and was a visiting lecturer at the University of Warsaw. He maintains a research associate position at the University of the Free State in South Africa. His academic background includes a PhD and MA in Translation and Intercultural Studies from Universitat Rovira i Virgili, Spain, and a BA in English-French-Spanish Translation from Universidad de Antioquia, Colombia. David Orrego-Carmona’s research centers on the intersection of translation, technology, and users. He investigates how translation technologies empower both professional and non-professional translators, and how the democratization of these tools transforms users into active participants in translation processes. His work employs mixed-methods approaches, including eye-tracking experiments, to study subtitle reception, machine translation adoption, and the societal implications of AI in media localization. He is particularly interested in how translation fosters social equity and inclusion in multilingual societies. His recent publications reflect a strong trend in empirical and technological aspects of audiovisual translation. Key themes include AI-powered media localization, viewer comprehension under various subtitling conditions, the quality of machine-translated subtitles, and the methodologies for studying subtitle reading through eye-tracking. His research bridges academic inquiry with real-world applications, often involving collaboration with industry and public sectors. He is actively involved in the academic community, serving as treasurer of the European Association for Studies in Screen Translation (ESIST), associate editor of Translation Spaces , and deputy editor of JoSTrans, the Journal of Specialised Translation . He also contributes to organizing academic events such as IPCITI 2025 at the University of Warwick. David teaches modules on digital translation, audiovisual translation, translation technologies, and professional aspects of translation. He welcomes PhD proposals aligned with his research interests. He supervises students in areas related to subtitling, machine translation, and translation technology, though specific student names are not listed in the provided texts. He has led interdisciplinary initiatives to connect academic research with private and public sector challenges in translation and communication. He leads research projects focused on media localization, translation reception, and the impact of AI, often involving collaboration with international scholars. His lab and team activities emphasize experimental methods like eye-tracking and mixed-methods research in audiovisual translation contexts.

David O'ReillyView profile

Lecturer

• Metaphoric Competence in First and Second Languages
• Vocabulary Knowledge
• Language Play/Creativity
• +4 more

Dr. David O'Reilly is a Lecturer in Education (Research and Teaching) at the University of York's Department of Education since 2022. Previously, he held roles as Lecturer (2021–2022) and Associate Lecturer (2017–2021). His academic journey includes a PhD in Applied Linguistics from the University of York (2013–2017), an MA in Applied Linguistics (2012–2013), and a BA in English Language and Literature from the University of Manchester (2003–2006). His research focuses on metaphoric competence in first and second languages, language play/creativity, test design, and quantitative methods. He leads projects like OASIS (Open Accessible Summaries in Language Studies) and contributed to the IRIS repository for second language research materials. He is a Fellow of the Higher Education Academy and holds editorial roles, including Review Editor at Metaphor and the Social World . Key Projects: OASIS (ESRC/British Academy funded), IRIS Repository Awards: RaAM Early Career Research Paper Prize (2021), Vice-Chancellor’s Teaching Award (2021) Professional Roles: Secretary of RaAM (2020–2022), Member of RaAM Executive Committee (2014–2020) Dr. O'Reilly teaches undergraduate and postgraduate modules in educational research methods, language assessment, and applied linguistics. He actively promotes open research practices and digital literacy in academia.