جست‌وجوی استاد و پژوهشگر

Tianhua Xuمشاهده پروفایل

دانشیار

• Optical Communication Networks
• Optical Sensing Systems
• Optoelectronics
• +۴ مورد دیگر

Dr. Tianhua Xu is a Reader in the School of Engineering at the University of Warwick and an Honorary Lecturer in the Department of Electronic and Electrical Engineering at University College London (UCL). He holds a Ph.D. in Optical Communications and Intelligent Signal Processing from KTH Royal Institute of Technology in Sweden, followed by postdoctoral and research roles at KTH, RISE Acreo, DTU, and UCL. His research focuses on optical communication systems, intelligent signal processing, machine learning, optical sensing, and advanced energy systems. He has secured major grants, including a €1.8M EU Horizon Europe project (SPAR) and a £285K UK National Grid initiative. Dr. Xu serves as an Associate Editor for IEEE Transactions on Communications and the Journal of the European Optical Society-RP, and chairs technical groups in the Optical Society of America. He has authored over 200 publications, including invited book chapters, with a Google Scholar h-index of 32. Current projects involve smart photonic sensing, energy storage systems, and deep learning in optical networks. His lab oversees six PhD students and multiple postdoctoral researchers. He actively recruits students through global scholarships and oversees vibrant research teams in optical communications, sensing, and energy systems.

Zhen Liuمشاهده پروفایل

استادیار

• Generative Models
• 3D Representation Learning
• Computer Vision
• +۲ مورد دیگر

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +۶ مورد دیگر

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Jie Xuمشاهده پروفایل

پژوهشگر

• Polymer Electronics
• Flexible Electronics
• Sustainable Materials
• +۴ مورد دیگر

Jie Xu is a Scientist at Argonne National Laboratory and a CASE Affiliated Scientist at the University of Chicago, Pritzker School of Molecular Engineering . Her research focuses on engineering durable, scalable, and sustainable polymer semiconductors for skin-like electronics and autonomous material discovery. Education : PhD in Chemistry (Nanjing University), Postdoctoral Fellow (Stanford University) Her research bridges polymer physics , self-driving laboratories , and AI-guided material synthesis to address challenges in stretchable electronics, recyclable polymers, and energy-efficient manufacturing. She pioneered polymer circuits that remain conductive under extreme deformation and developed the first roll-to-roll mass-production method for stretchable semiconductors. Her 15 most recent articles highlight advancements in AI-driven polymer discovery , biodegradable electronics , and multi-modal energy dissipation . Key themes include autonomous experimentation , hydrogen-bonded polymer systems , and machine learning for conjugated polymers , with applications in wearable medical sensors , soft robotics , and human-computer interfaces . Scientific accolades include the Materials Research Society Postdoctoral Award , MIT Technology Review’s Innovators Under 35 , and recognition as a Scialog Fellow . She serves on editorial boards for APL Machine Learning and Flexible Electronics , and her team at Argonne includes postdocs and students working on self-driving labs and degradable polymers .

• Computational Fluid Dynamics
• Turbomachinery
• Aerodynamics
• +۴ مورد دیگر

Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.

Brian Surgenorمشاهده پروفایل

استاد

• Mechanical Engineering
• Machine Vision Systems
• Autonomous Vehicles
• +۳ مورد دیگر

Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.

Lu Suمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +۷ مورد دیگر

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.

Muhammad Shujaat Mubarikمشاهده پروفایل

دانشیار

• Supply Chain Management
• Sustainability
• Operations Management
• +۶ مورد دیگر

Muhammad Shujaat Mubarik is an Associate Professor and Head of Research (Marketing & Operations Department) at Heriot-Watt University's Edinburgh Business School. His expertise spans logistics, supply chain management, operations management, and sustainability. He focuses on integrating digital technologies like blockchain and digital twins into supply chain frameworks, emphasizing intellectual capital's role in resilience and sustainability. His research methods include Structural Equation Modeling, Multi-Criteria Decision Making (AHP, ANP), and hybrid modeling. He holds leadership roles, including prior positions as Dean of the College of Business Management (IoBM) and Dean of the Faculty of Business Administration & Social Sciences (Mohammad Ali Jinnah University). He has secured grants from UKRI-EPSRC, NSERC, and HEC Pakistan, leading projects on supply chain resilience, logistics decarbonization, and SME internationalization strategies. His research collaborations span institutions like the University of Cambridge, Stanford University, and Copenhagen Business School. He teaches postgraduate courses in supply chain strategies, systems analysis, and green logistics. He actively engages in academic governance and industry partnerships, contributing to policy bridging. His work aligns with UN SDGs related to sustainable consumption, climate action, and responsible consumption. He has published over 100 papers, including 45 in high-impact journals, authored five books, and received the 2023 Best Researcher Award at the institute level. His funded projects address challenges like blockchain in oil and gas sectors and circular economy adoption barriers.

• Industrial Organization
• Political Economy
• Applied Microeconomics
• +۳ مورد دیگر

Juha Kasperi Tolvanen is an Assistant Professor in the Department of Economics and Finance at Tor Vergata University of Rome. He specializes in applied microeconomics, focusing on the role of information and communication in shaping market structures and political outcomes. His research bridges industrial organization and political economy, examining topics such as information asymmetry in markets and the strategic use of ambiguity by anti-establishment political parties. Dr. Tolvanen holds a PhD from Princeton University (2016). He teaches courses including Game Theory and Industrial Organization, Microeconomics 2, and contributes to graduate programs in Philosophy and Economics. His research combines theoretical frameworks with empirical data from corporate and political contexts. His recent publications explore themes like quantity contracts in decentralized markets, the impact of algorithmic recommendations on strategic behavior, and the empirical analysis of demand using managerial responses to pricing algorithms. He collaborates actively with firms to quantify the economic implications of communication strategies and information flows. Dr. Tolvanen’s work has appeared in leading journals such as the RAND Journal of Economics , Management Science , and American Political Science Review . His research agenda emphasizes bridging theory with real-world applications, particularly in understanding how information dynamics influence economic and political systems.

Olga Vitekمشاهده پروفایل

استاد

• Statistical Science
• Machine Learning
• Mass Spectrometry
• +۵ مورد دیگر

Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology

Sonia Falconieriمشاهده پروفایل

استاد

• Corporate Finance
• Corporate Governance
• Financial Markets
• +۳ مورد دیگر

Professor Sonia Falconieri holds a faculty position in Corporate Finance at the Bayes Business School, City St George's, University of London. She previously held roles at institutions including the Einaudi Institute for Economic and Finance (Rome), the University of Amsterdam, Tilburg University, and Brunel University (London). Her research focuses on corporate finance, IPO design, gender diversity, and corporate governance. She contributed to the 2019 Hampton-Alexander review and has been featured in Forbes and People Management. Education: BSc/MSc in Economics from Bocconi University (Milan, Italy), PhD in Economics from the University of Toulouse (France). Research interests include IPO underpricing, gender quotas in corporate boards, and the role of information theory in corporate finance. She has supervised multiple PhD students, including 1st supervisions of Chiara De Amicis and Chen Xu. Professional activities include serving as Associate Editor of the European Journal of Finance , organizing conferences, and media engagements on IPOs and corporate governance. Notable awards include the Senior Fellow of the Higher Education Academy.

• Catalytic Reaction Engineering
• CO2 Hydrogenation
• Methanation Processes
• +۵ مورد دیگر

Prof. Robert Güttel is the Director of the Institute for Chemical Engineering at the University of Ulm. He is a member of the Ulm Center for Thermal and Environmental Technology (UZWR) since 2016 and serves on its executive board. His work focuses on catalytic reaction engineering, particularly in CO2 utilization, methanation, Fischer-Tropsch synthesis, and process intensification. Research emphasizes catalyst design, reaction kinetics, and reactor modeling under dynamic conditions. Educations : Not explicitly stated in the provided text. His academic career is highlighted through his leadership roles and publications. Research Interests : Güttel’s research spans heterogeneous catalysis for CO/CO2 hydrogenation, transient kinetic analysis, polymeric reactors for extraterrestrial applications, and AI-driven reactor design. His team develops advanced catalysts (e.g., Ru/TiO2, cobalt@silica core-shell structures) and investigates novel reactor concepts like fibrous structured catalysts and sorption-enhanced processes. Recent work explores in-situ methanation on Mars and the impact of light on catalytic selectivity. Publications Trends : His 2024–2025 articles focus on low-temperature methanation beyond Earth, AI-based residence time analysis, and deactivation mechanisms. Studies highlight both experimental and computational approaches to optimize catalytic systems for sustainability and industrial relevance. Awards : No specific scientific awards mentioned in the text. Grants/Labs : Leads the Institute for Chemical Engineering at Ulm, collaborating on EU-funded projects related to power-to-X technologies and extraterrestrial chemistry. Active in developing lab-scale reactor systems and industrial partnerships for catalyst testing. Labs/Teams : Directs research groups focused on catalytic reactor design, transient kinetic methods, and sustainable process engineering. Collaborates with institutions on Mars habitat resource utilization and green hydrogen production systems.

Joseph Alejandro Gallego Mejiaمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۱۲ مورد دیگر

Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.

• Fluid Dynamics
• Particle-Laden Flows
• Machine Learning
• +۴ مورد دیگر

Dr Xinchen Zhang is a Grant-Funded Researcher (A) at the University of Adelaide's Department of Mechanical Engineering within the School of Electrical and Mechanical Engineering. His research focuses on integrating machine learning with computational fluid dynamics (CFD) to enhance predictive capabilities for multiphase flow solutions, particularly in sustainable energy applications like decarbonization technologies. He holds a PhD (2022) with a Dean's Commendation for Doctoral Thesis Excellence, emphasizing fluid and particle dynamics in particle-laden flows. His work addresses challenges in net-zero industrial processes such as limestone calcination and hydrogen production via methane pyrolysis, leveraging advanced CFD and ML-augmented methodologies. Key research areas include turbulence modeling, particle dispersion in jets, and flow regime analysis in horizontal particle-laden pipe systems. He is eligible to supervise Masters and PhD students as a co-supervisor. Dr Zhang's publications span 2018–2024, with recent trends focusing on physics-informed machine learning for turbulence modeling and multiphase flow optimization. His contributions advance computational efficiency and accuracy in predicting complex fluid-particle interactions.

Charley Wuمشاهده پروفایل

استاد

• Computational Cognitive Science
• Human Learning Under Uncertainty
• Generalization and Inference
• +۷ مورد دیگر

Charley Wu is an Independent Research Group Leader and W3 Professor of Computational Cognitive Science, currently transitioning from the University of Tübingen to Technische Universität Darmstadt. He leads the Human and Machine Cognition Lab (HMC Lab), jointly funded by the Excellence Cluster 'Machine Learning for Science' and the Tübingen AI Center, soon to be based at TU Darmstadt under a LOEWE Start Professorship and an ERC Starting Grant. University of Tübingen (former affiliation) Technische Universität Darmstadt (current/transitioning to) Human and Machine Cognition Lab (HMC Lab) Excellence Cluster 'Machine Learning for Science' Tübingen AI Center Charley Wu's research lies at the intersection of cognitive science and artificial intelligence, focusing on how humans learn and make decisions under uncertainty. Using computational models, statistical learning, and virtual reality experiments, he investigates the cognitive shortcuts and strategies people use to generalize and explore efficiently in complex environments. His work also explores social learning and collective intelligence through biologically inspired multi-agent systems. His recent publications, including a key paper in Nature Human Behaviour on generalization guiding exploration, reflect a strong trend toward integrating machine learning techniques with human behavioral data. The research emphasizes efficient inference, compressed representations, and compositional structures in cognition, bridging gaps between human and artificial intelligence. Notable scientific awards include: ERC Starting Grant: C⁴: Compositional Compression in Cognition and Culture LOEWE Start Professorship Dr. Wu is actively mentoring and expanding his research group, currently recruiting three fully-funded PhD students and one postdoctoral researcher. His lab is supported by competitive grants and institutional funding, indicating strong research momentum and future directions in computational models of cognition, AI-human alignment, and collective learning. He collaborates with leading researchers such as Fiery Cushman and Sam Gershman from his postdoctoral work at Harvard University. The Human and Machine Cognition Lab (HMC Lab) is a dynamic research environment focused on understanding the computational principles of human learning. As it transitions to TU Darmstadt, the lab will continue to explore fundamental questions in cognition using cutting-edge methodologies, including online experiments, multi-agent simulations, and AI-driven modeling.