جست‌وجوی استاد و پژوهشگر

Zhenzhen ZHAOمشاهده پروفایل

دانشیار

• Human-Computer Interaction (HCI)
• Digital Marketing
• Consumer Behavior
• +۶ مورد دیگر

Dr. Zhenzhen ZHAO is an Associate Professor at the Digitalisation Academy of SKEMA Business School in Paris. She holds a PhD in Computer Science from Télécom Paris (France) and has held visiting researcher positions at institutions including Tsinghua University (China) and Vrije Universiteit Brussel (Belgium). Her research focuses on the intersection of Human-Computer Interaction (HCI) and digital economy, particularly exploring how platform design influences consumer behavior in areas like mobile applications, gamification, and innovation management. Education: PhD in Computer Science, Télécom Paris, France (2012) MSc in Information Systems and Communications, Beijing University of Posts and Telecommunications, China (2008) Research Interests: Dr. Zhao’s work examines digital marketing strategies, consumer behavior in online platforms, and design thinking for innovation. She investigates topics such as crowdsourcing for organizational knowledge, the impact of social presence on privacy, and the adoption of disruptive technologies like self-quantification devices. Her research bridges HCI principles with business applications, emphasizing practical design frameworks for branded apps and marketing strategies. Recent Trends in Publications: Recent studies focus on smart object adoption drivers, privacy-utility tradeoffs in location-based apps, and the role of uniqueness in innovation acceptance. Her work combines empirical analysis with theoretical models like the SECI knowledge creation framework, contributing to both marketing and technology management fields. Academic Activities: She has reviewed for journals like Information & Management and conferences such as the American Marketing Association. Her advisory roles include serving on PhD thesis juries and review committees for international conferences. Labs/Teams: Affiliated with SKEMA’s Digitalisation Academy, her research group collaborates on projects involving digital innovation, consumer behavior analysis, and platform design for sustainability.

• Formal Methods
• Privacy Engineering
• Probabilistic Programming
• +۳ مورد دیگر

Raúl Pardo Jimenez is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen, affiliated with the Software Quality Research (SQUARE) group. His research focuses on formal methods and privacy engineering, with particular emphasis on probabilistic programming and privacy risk quantification in data analytics. His research spans three interconnected domains: Formal verification of privacy compliance systems like GDPR implementation Probabilistic modeling for quantitative privacy risk assessment Open source sustainability impacts on software quality metrics Recent publications demonstrate a consistent focus on developing rigorous computational methods for privacy preservation, with works appearing in Springer's Lecture Notes in Computer Science and Empirical Software Engineering journals. He leads the REIDENT project (2018-2022), funded by Villum Fonden, which developed Bayesian probabilistic programming approaches for reidentification risk assessment in sensitive datasets.

David Mercierمشاهده پروفایل

استاد

• Information Fusion
• Belief Functions
• Uncertainty Management
• +۳ مورد دیگر

David Mercier is a Professor at the University of Artois, France, affiliated with the LGI2A laboratory and the IUT de Béthune's Department of Networks and Telecommunications. He holds a HDR (Habilitation à Diriger des Recherches) from 2015 and a PhD from the University of Technology of Compiègne (UTC), France (2006). His research focuses on information fusion, belief functions, and uncertainty management, with applications in electrical engineering, transportation systems, and machine learning. Education DEA (M.S. Equivalent) in Artificial Intelligence, University Paul Sabatier, Toulouse (2003) PhD in Information Fusion for Postal Address Recognition, UTC (2006) HDR (Research Habilitation), University of Artois (2015) Research & Awards Notable contributions include work on contextual belief function corrections, vehicular network information fusion, and multi-target tracking. He received a co-best student paper award at BELIEF 2016. His research spans electrical machines diagnosis, stochastic simulations, and deep learning integration with Dempster-Shafer theory. Students & Involvement Supervised PhD theses include studies on evidential deep learning and motor fault diagnosis. Active in the Belief Functions and Applications Society (board member since 2010). Co-edited special issues in International Journal of Approximate Reasoning and authored book chapters on belief function applications. Labs & Teams Primary affiliation with LGI2A laboratory, collaborating with institutions like LSEE and industrial partners such as Solystic and SNCF. Research integrates theoretical advances with practical applications in smart mobility and fault detection.

• Decision support systems
• Computational intelligence
• Recommender systems
• +۴ مورد دیگر

Distinguished Professor Jie Lu is Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where she also serves as Director of the Australian Artificial Intelligence Institute (AAII). With over two decades of academic service at UTS, she has held progressively senior roles including Professor since 2007, Head of School of Software (2011-2014), and Director of the Centre for Artificial Intelligence (2017-2020) before assuming her current leadership positions. Professor Lu earned her PhD from Curtin University in 2000 and joined UTS the same year. Her academic journey progressed from Lecturer (2000-2001) to Senior Lecturer (2002-2004), Associate Professor (2005-2007), and ultimately Professor (2017-present). Professor Lu's research spans computational intelligence with particular expertise in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. Her work bridges theoretical innovation with practical applications across transportation, telecommunications, healthcare, and education sectors. She has pioneered approaches in data-driven decision making that deliver tangible economic benefits and risk management solutions for industry partners. Her recent publications demonstrate continued leadership in AI research, with a focus on addressing challenges in non-stationary environments, out-of-distribution detection, cross-domain recommendation, and healthcare applications. Her work increasingly integrates large language models with specialized domain knowledge for enhanced decision support. Officer of the Order of Australia (2022) 2023 NSW Premier's Prize for Excellence in Engineering or Information & Communication Technology Australian Laureate Fellow (2019) IEEE Fellow for contributions to fuzzy machine learning and decision making (2018) Fellow of International Fuzzy Systems Association (2017) Australia's Most Innovative Engineer Award (2019) Multiple IEEE Transactions Outstanding Paper awards Professor Lu has supervised over 50 PhD students to completion, with half pursuing academic careers and half working in industry. She has secured over A$10 million in research funding as lead Chief Investigator, including 10 highly competitive ARC Discovery Projects. Her industry collaborations include significant projects with Optus, Sydney Trains, Domain Holdings, and healthcare organizations. As Director of the Australian AI Institute, Professor Lu leads Australia's largest AI research hub with over 250 researchers and PhD students. Under her leadership, AAII has secured 47 ARC grants and over 110 industry projects since 2017. She also serves as Editor-in-Chief of Knowledge-Based Systems, a leading journal in the field.

• Renewable Energy Systems
• Forecasting
• Optimization under Uncertainty
• +۶ مورد دیگر

Professor Pierre Pinson is the Chair of Data-centric Design Engineering at the Dyson School of Design Engineering, Imperial College London (UK). He also serves as a Chief Scientist at Halfspace (Denmark), Editor-in-Chief of the International Journal of Forecasting, and holds affiliated roles at Technical University of Denmark and Aarhus University. His research focuses on generating societal value from data through interdisciplinary approaches combining mathematics/statistics with applications in energy, logistics, and business analytics. Notable contributions include probabilistic energy forecasting, market design innovations, and optimization under uncertainty. Research interests span forecasting methodologies, stochastic optimization, game theory (with energy applications), and data markets. He has held visiting roles at institutions like the University of Oxford and the European Centre for Medium-Range Weather Forecasts. Awards include the prestigious INFORMS Edelman Award (2024) and IEEE Fellow status. Prof. Pinson actively advises PhD candidates with strong applied mathematics/programming backgrounds. His work bridges academia and industry, addressing challenges in renewable energy integration, grid management, and decentralized market mechanisms. Collaborative projects include the Smart4RES initiative for advanced renewable forecasting. Awards: INFORMS Edelman Award (2024), IEEE Fellow, Simons Fellowship (2021) Labs/Teams: Dyson School Research Group, Halfspace R&D Team, CoRE (Aarhus University) Grants: Imperial College President's PhD Scholarship, EU Smart4RES Project

Richard Zemelمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Statistics
• +۶ مورد دیگر

Richard Zemel is the Trianthe Dakolias Professor of Engineering and Applied Science in the Department of Computer Science at Columbia University. He leads the NSF AI Institute for ARtificial and Natural Intelligence (ARNI). His research focuses on machine learning, artificial intelligence, statistics, neuroscience, and cognitive science with recent emphasis on robust AI, few-shot learning, algorithmic fairness, and continual learning. Education details are not explicitly listed, but his career includes roles as director of an NSF institute and extensive academic contributions. His research group includes active and former students/postdocs now in academia and industry roles at institutions like Google, DeepMind, NVIDIA, and universities globally. Key Research Themes: Fairness in AI, causal inference, generative models, and neural network architectures. Labs/Teams: ARNI Institute (NSF-funded), Zemel Research Group at Columbia. Advising and grants include supervision of over 40 students/postdocs and leadership in multi-institutional AI initiatives. His work spans theoretical foundations to real-world applications in healthcare, ethics, and computational neuroscience.

Panos Parpasمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Computational Mathematics
• Scientific Computing
• +۳ مورد دیگر

Panos Parpas is a Reader in Computational Mathematics at the Department of Computing, Imperial College London. His research focuses on mathematical optimization algorithms, computational mathematics, and scientific computing, with applications in science, engineering, and finance. He previously held roles as a postdoctoral fellow at MIT and a quant at Credit Suisse. Key research interests include dynamical systems, numerical methods for optimization, and stochastic processes. He teaches courses such as Computational Optimization and Computational Finance with C++. His work bridges theoretical optimization with practical applications in areas like water distribution networks, financial modeling, and machine learning. Panos has authored numerous publications on topics like multilevel optimization methods, stochastic mirror descent algorithms, and the analysis of saddle points using Witten Laplacians. His research emphasizes algorithmic efficiency and scalability, particularly in high-dimensional and distributed settings. Notable collaborations include projects on optimizing water distribution systems (e.g., pump scheduling and pressure management) and developing robust algorithms for financial and engineering systems. His contributions span journals like Automatica , SIAM Journal on Scientific Computing , and Journal of Optimization Theory and Applications .

• Distributed Machine Learning
• Edge Computing
• Federated Learning
• +۲ مورد دیگر

Christos Anagnostopoulos is Reader (Associate Professor) in Distributed Computing & Data Engineering Systems at the University of Glasgow, where he directs the MSc Information Technology and MSc Software Development programmes. His research focuses on distributed computing, federated learning, and edge intelligence for large-scale systems. Research interests span: Distributed ML/AI for edge environments Federated learning optimization Proactive data management in pervasive systems Edge-centric predictive analytics Funded projects include EU Horizon grants TERRA, COIN-3D, ELLIE, and TRACE, focusing on intelligent platforms for climate services, 3D VLSI reliability, cultural heritage technologies, and logistics optimization. Additional support comes from EPSRC, Royal Academy of Engineering, and industry partners including BMW and NXP. Recent publications (2024-2012) demonstrate innovations in edge-based task allocation, federated learning efficiency, distributed query processing, and mobile edge offloading. Article trends show consistent development of resource-aware distributed intelligence algorithms. Serves as Editor-in-Chief of Open Computer Science and Associate Editor of IEEE Access, and General Chair of IEEE ICDCS 2025. Leads the Knowledge and Data Engineering Systems research group.

Muffy Calderمشاهده پروفایل

استاد

• Formal Methods
• Digital Twinning
• Responsible AI
• +۳ مورد دیگر

Dame Muffy Calder is Professor of Formal Methods at the University of Glasgow and Senior Visiting Fellow at CETaS. She previously served as Vice-Principal and Head of the College of Science and Engineering (2015-2025). Her research develops computational modeling and automated reasoning for complex systems, with applications in responsible AI, privacy protection, and national security. She pioneers formal methods for verifying autonomous systems and assessing AI impacts on privacy. Honors include Fellowship of Royal Societies, OBE, and membership in advisory bodies like the Prime Minister's Council for Science and Technology. Her work bridges theoretical computer science and societal challenges in digital technologies.

Jialin Liمشاهده پروفایل

استادیار مهمان

• Uncertainty Quantification
• Stochastic Optimization
• Statistical Learning
• +۱ مورد دیگر

Jialin Li is a Visiting Assistant Professor at the Department of Mathematics and Statistics, University of Massachusetts Amherst. Previously, he served as a Postdoc Research Fellow at the Joseph L. Rotman School of Management, University of Toronto, under Professors Ningyuan Chen, Ming Hu, and Sheng Liu. He earned his PhD in Applied Mathematics (AMSC program) from the University of Maryland, College Park, advised by Ilya Ryzhov. His research focuses on methodologies for decision-making under uncertainty, emphasizing societal impact through stochastic optimization, uncertainty quantification, and statistical learning. His work has been recognized, including selection for the February 2025 Applied Probability Collection for his paper on Gaussian processes. He has secured grants such as the Graduate Student Summer Research Fellowship (University of Maryland, 2019), TD Management Data and Analytics Lab Research Grant (2023), and New Pilot Postdoc Funding (Rotman School of Management, 2022). His teaching is noted for award-winning excellence. Current affiliations include the Lederle Graduate Research Tower, UMass Amherst. Research interests span theoretical advancements and applications, including data privacy in pricing (as explored in ongoing work). No lab/teams are explicitly listed, but collaborations are implied through his interdisciplinary work.

• Computer Vision
• Machine Learning
• Control Engineering
• +۶ مورد دیگر

Dr. Dimity Miller is a Lecturer at the Faculty of Engineering , Queensland University of Technology , specializing in Computer Vision , Machine Learning , and Robotics . With a Doctoral Degree and Bachelor of Engineering (Mechatronics, Robotics) from QUT, her research focuses on uncertainty quantification, open-set recognition, and robust object detection systems. Her work spans interdisciplinary domains including Internet of Things , Adversarial AI Defense , and Autonomous Systems . Recent publications analyze topics such as probabilistic object detection , backdoor attack mitigation , and electric vehicle charge prediction . She explores methods like dropout sampling , neural radiance fields , and vision-language models . Key themes in her research include: Model Robustness in Open-Set Conditions Uncertainty Propagation in IoT Human-AI Collaboration in Image Analysis As an active researcher in QUT Centre for Robotics , Dr. Miller contributes to advancing AI reliability in safety-critical applications through both theoretical and applied studies.

• Digital Communications
• Information Theory
• Signal Processing
• +۳ مورد دیگر

Prof. Amos Lapidoth is a Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, leading the Signal and Information Processing Lab. He holds a B.A. (summa cum laude) in Mathematics and B.Sc. (summa cum laude) in Electrical Engineering from the Technion, along with an M.Sc. from Technion and Ph.D. from Stanford University. Previously, he was an Associate Professor at MIT and held the KDD Career Development Chair in Communications and Technology. His research focuses on Digital Communications and Information Theory , with contributions to coding theory, channel modeling, and signal processing. He has conducted R&D in wireless communications at the IDF Signal Corps Labs, earning two Creative Thinking Awards. His awards include the NSF CAREER Award and Rothschild Fellowship. Recent work explores state-dependent channels with helpers, feedback-assisted communication, and identification over noisy channels. His lab develops theoretical frameworks for reliable communication in complex systems, with applications in coding, modulation, and network design. Labs/Teams: Head of Signal and Information Processing Lab at ETH Zurich Key Contributions: Pioneering work on helper channels, zero-error capacity, and Rényi entropy applications

Barbara J. Woldمشاهده پروفایل

استاد

• Transcriptional Networks
• Molecular Biology
• Genomics
• +۵ مورد دیگر

Barbara J. Wold is the Bren Professor of Molecular Biology and Merkin Institute Professor at the California Institute of Technology (Caltech), where she has held continuous faculty appointments since 1978. Her leadership roles include Director of the Beckman Institute (2001-2011) and Director of the Merkin Institute (2019-2023), reflecting her significant institutional impact. Her academic journey began with a B.S. from Arizona State University (1973) followed by a Caltech Ph.D. (1978). She advanced through Caltech's faculty ranks from Research Fellow (1978) to Assistant Professor (1981-1988), Associate Professor (1988-1996), Professor (1996-2002), Bren Professor (2003-), and Merkin Institute Professor (2024-). Dr. Wold's research centers on transcriptional networks in mouse and human systems, integrating molecular biology, cellular biology, developmental biology, biochemistry, and biophysics . Her work pioneers single-cell and spatial transcriptomics approaches to decode gene regulation across tissues and developmental stages, with particular emphasis on cellular heterogeneity and regulatory dynamics. Analysis of her 2022-2025 publications reveals dominant themes in large-scale collaborative genomics , including leadership in the Human Cell Atlas and ENCODE projects. Key methodological contributions span spatial transcriptomics for kidney disease modeling, cross-strain mouse genomics, long-read RNA-seq validation, and ethical frameworks for international data sharing, demonstrating her dual focus on technical innovation and responsible science. Her distinguished career is marked by two named professorships: the Bren Professorship (2003) and Merkin Institute Professorship (2024), recognizing her transformative contributions to molecular biology. As a decades-long Caltech faculty member and institute director, Dr. Wold has mentored numerous graduate students and secured substantial research funding, though specific advisee names and grant details are not publicly listed. Her current leadership in the Merkin Institute continues to drive interdisciplinary biomedical research. Her laboratory operates at the nexus of the Beckman Institute and Merkin Institute ecosystems, leveraging Caltech's strengths in engineering and quantitative biology to advance transcriptional network research through cutting-edge genomic technologies and computational approaches.

Guang Wangمشاهده پروفایل

استادیار

• Cyber-physical systems
• Spatiotemporal data mining
• Human-centered computing
• +۵ مورد دیگر

Dr. Guang Wang is an Assistant Professor at the Department of Computer Science at Florida State University (FSU). He holds a Ph.D. in Computer Science from Rutgers University and was a Postdoctoral Research Associate at MIT. His research focuses on cyber-physical systems, spatiotemporal data mining, and human-centered AI, addressing societal challenges in mobility, energy, disaster resilience, and healthcare. He leads projects on fairness-aware ride-hailing systems, equitable post-disaster power restoration, and AI-enhanced logistics systems. Education: Ph.D. in Computer Science, Rutgers University Postdoctoral Research, MIT (with Prof. Sandy Pentland) Research Interests: Generative AI for trajectory prediction and logistics Trustworthy machine learning in urban systems Fairness-aware optimization for mobility and energy systems Data-driven decision-making in disaster response Recent Article Trends: Focus on fairness-aware algorithms, spatiotemporal AI, and large-scale graph mining. Recent work includes optimizing post-disaster power restoration, improving ride-hailing fairness, and developing generative models for asynchronous trajectories. Scientific Awards: FSU CS Department Faculty Research Award (2025) National AI Research Resource Pilot Award (2024) CPS Rising Star (2022) Outstanding Paper Award at IEEE RTSS 2021 Advising & Grants: Advising 4 PhD, 2 MS, and 3 undergraduate students in AI and data science Lead PI on NSF-funded projects on sustainable mobility and disaster resilience Recipient of FSU Sustainability & Climate Solutions Grant ($150K) Labs & Collaborations: Directs the EV Simulation Lab and collaborates on projects like the National AI Research Resource. Leads the Data Science for Smart Cities initiative at FSU.

• Cybersecurity
• Automotive Systems
• Formal Methods
• +۴ مورد دیگر

Dr Hoang Nga Nguyen is an Associate Professor in Cyber Security at Swansea University, UK. Affiliated with the School of Mathematics and Computer Science, his work focuses on cybersecurity for automotive and autonomous systems, formal verification, and embedded systems security. He leads research projects funded by EPSRC, RSSB, InnovateUK, and Digital Catapult, including AutoCHERI (2022-2024) and QRNG for CAV . Current research agenda: Cybersecurity in automotive systems Specialization: Model-based and simulation-based verification techniques Projects: VRBMAS, ACID, Ditto, Secure CAV His research contributions span: Cybersecurity frameworks for automotive systems Formal methods in railway safety Embedded systems threat landscapes Resource-bounded agent logic Android app collusion detection Scientific Awards and honors: None explicitly mentioned in the provided text. As a supervisor , he guides PhD students in topics like: Addressing Automotive User-Centric Privacy and Security Model-Based Security Testing Approaches for Hardware Security He also teaches modules including Software Security Engineering (CS-239) and Embedded Systems Security (CSCM68, CSCM868) . Nguyen works at the Computational Foundry on Bay Campus, Swansea, contributing to the Verification & Validation of Autonomous Systems network and the European Technical Working Group on Formal Methods in Railway Control.