جست‌وجوی استاد و پژوهشگر

Mark J. Hillمشاهده پروفایل

مدرس

• Digital Humanities
• Computational Social Science
• Intellectual History
• +۶ مورد دیگر

Dr Mark J. Hill is a Lecturer in Cultural Computation at King's College London's Department of Digital Humanities within the Faculty of Arts & Humanities. He holds a DPhil from the University of Oxford, an M.Sc. in Political Theory from the London School of Economics, and a B.A. in Political Science from Concordia University. His interdisciplinary work bridges digital humanities, computational social science, and intellectual history. Research Focus: Social network analysis, public discourse analysis via large datasets, quantitative text analysis, and critical evaluation of digital methods. Current Projects: Investigating discourse patterns across historical and contemporary contexts, including Early Modern Nonconformist networks and digital discourse around football fandom. He collaborates with institutions like the University of Helsinki and engages with public sectors on digital research projects. Teaching includes digital research methods and critical thinking in the digital age. His affiliations include the Computational Humanities Research Group and the Centre for Digital Culture at King's College London.

• Biostatistics
• Public Health
• Medical Imaging
• +۳ مورد دیگر

Jingjing Zou is an Assistant Professor in Residence at the Herbert Wertheim School of Public Health & Human Longevity Science, University of California San Diego. Her research focuses on integrating statistical methodologies with medical imaging and public health challenges, particularly in cancer prevention, physical activity analysis, and radiomics. Education: PhD in Statistics, Columbia University MA in Statistics, Columbia University BS in Statistics, Peking University Research Interests: Dr. Zou's work emphasizes functional data analysis, accelerometer-based physical activity monitoring, and MRI techniques for cancer treatment evaluation. She develops statistical models to analyze longitudinal health data, with applications in cardiovascular disease prevention and oncology. Her studies often bridge clinical outcomes with advanced imaging biomarkers. Grants: NIH/NHLBI (Principal Investigator): Examining Longitudinal Changes in Accelerometer-Measured Physical Activity in Preventing Cardiovascular Disease (2024-2028) NIH R37CA249659 (Co-Investigator): Advanced diffusion MRI for cervical cancer treatment evaluation (2021-2026) NIH R01CA255780 (Co-Investigator): Whole-body radiomics for gynecologic cancers (2020-2024) Labs/Teams: Collaborates with interdisciplinary teams in radiology, oncology, and biostatistics, including co-authors like Loki Natarajan (UCSD) and Loren Mell (UCSD).

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

• Geotechnical Engineering
• Seismic Analysis
• Soil Mechanics
• +۳ مورد دیگر

Youssef M A Hashash is the W. W. Grainger Chair and Professor in the Department of Civil and Environmental Engineering at the University of Illinois. His research focuses on geotechnical and earthquake engineering, with emphasis on seismic site response analysis, soil-structure interaction, and advanced computational methods like the Discrete Element Method (DEM). He has led projects on infrastructure resilience, including studies of buried water reservoirs, railway systems, and post-earthquake reconnaissance. Hashash has developed influential models for site amplification in Central and Eastern North America, contributing to seismic hazard assessments. His work integrates experimental centrifuge testing, numerical simulations, and field data. Notable contributions include guidelines for implementing NGA-East ground motion models and advancements in pore-water pressure generation models for liquefaction evaluation. Key Research Areas: Ground movement, seismic response, soil dynamics, and geotechnical data systems Major Projects: NGA-East Geotechnical Working Group, Beirut Explosion Analysis, and LA Metro Tunnel Projects Recipient of prestigious awards including the NAE Membership (2022), PECASE (2000), and Walter L. Huber Prize (2006), he collaborates internationally on earthquake engineering and geotechnical innovations. His lab develops tools like the DEEPSOIL software for nonlinear site response analysis and explores AI applications in geotechnical data interpretation.

Garg Vikasمشاهده پروفایل

استادیار

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

Michael Thompsonمشاهده پروفایل

استاد

• Semiconductor Physics
• Materials Science
• Laser Processing
• +۷ مورد دیگر

Professor Michael Thompson is a tenured faculty member in the Department of Materials Science and Engineering at Cornell University's College of Engineering. He holds the Dwight C. Baum Professorship in Engineering and specializes in advanced materials processing, particularly semiconductor materials under pulsed laser exposure. His research focuses on transient thermal processing (nanosecond to sub-second timescales) for material property modification and characterization, with applications in semiconductors, EUV lithography, and photonic materials. Thompson has authored over 120 papers and 20 patents, emphasizing industrial challenges like front-end junction formation and flexible electronics. Education: B.S. in Physics (CalTech, 1979), M.S./Ph.D. in Physics (Cornell, 1982/1984). He has received prestigious awards including the North American Award for Technical Contribution to the Semiconductor Industry (2009), multiple Cornell Excellence in Teaching Awards, and the Stephen H. Weiss Presidential Fellow designation (2021). His teaching focuses on thermodynamics and electronic properties, with a commitment to making abstract concepts accessible through real-world examples. Service: Leads curriculum development, ABET accreditation, and industry outreach. His lab develops novel methods for autonomous materials discovery using AI-driven approaches. Current projects include laser spike annealing for semiconductor doping and high-throughput synthesis of metastable materials.

Tom Beuclerمشاهده پروفایل

استادیار

• Geo-Environmental Data Science
• Climate Informatics
• Atmospheric Physics
• +۵ مورد دیگر

Tom Beucler is a Conditional Pre-Tenure Assistant Professor in Geo-Environmental Data Science at the University of Lausanne’s Institute for Earth Surface Dynamics (IDYST). He holds a Master’s degree in Science and Mechanics from École Polytechnique (2014) and a PhD in Atmospheric Science from MIT (2019). Postdoctoral research at Columbia University and UC Irvine focused on machine learning applications in climate science under Professors Pierre Gentine and Michael Pritchard. Research Interests: Climate informatics, atmospheric physics, fluid dynamics, tropical meteorology, and integrating machine learning into climate models for extreme weather prediction and hydrological cycle modeling. Collaborations: Works with environmental scientists and computer engineers to improve climate models using neural networks and causal discovery methods. Initiatives: Organizes weekly brainstorming sessions to promote machine learning adoption in environmental sciences. Publications span climate-invariant machine learning, data-driven parameterizations, and hybrid AI-climate modeling frameworks like ClimSim. His work emphasizes causal consistency and generalizability across climate conditions.

Abubakar Belloمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Cyber Crime
• Cyber Criminology
• +۳ مورد دیگر

Dr. Abubakar Bello is a Senior Lecturer in Criminal Justice and Program Leader at Edge Hill University's School of Law, Policing, and Criminal Justice. Previously, he held roles at Western Sydney University, including Academic Program Advisor and Lecturer in Cyber Security and Behaviour. He holds a PhD in Cyber Criminology, an MBA in Business Law and Technology, and degrees in Computer Science. His research focuses on interdisciplinary approaches to cyber security risks, threat intelligence models, and behavioral aspects of cyber crime. Education: PhD (Cyber Criminology, Murdoch University), MBA (Business Law & Tech, Western Sydney University), MSc & BSc (Computer Science, University of Wolverhampton). Research Interests: Combating cyber crime through AI and machine learning, secure systems design, and behavioral cybersecurity. Key areas include ransomware defenses, social engineering, and cybersecurity frameworks for diverse populations. Grants & Projects: Awarded funding for initiatives such as 'Social Engineered Payment Diversion Fraud' (NSW Cyber Security Network), 'Brain-Inspired Algorithm for Network Anomaly Detection' (DST Group), and 'Cyber Security Awareness Framework' (ECR Grant). Awards: 'Award for Teaching and Learning Contributing to Public Good.' Active in professional networks like the International Centre on Racism and Centre for Applied Criminal Justice Research. Labs & Collaboration: Engages in cyber investigations, forensics, and community outreach through initiatives like Western Cyber Aid. Serves as a consultant for corporate espionage cases and a speaker on ransomware and AI in law enforcement.

Roozbeh Razavi-Farمشاهده پروفایل

استادیار

• Machine Learning
• Cybersecurity
• Federated Learning
• +۵ مورد دیگر

Roozbeh Razavi-Far is an Assistant Professor at the Faculty of Computer Science and the Canadian Institute for Cybersecurity at the University of New Brunswick. His research focuses on machine learning, big data analytics, and cybersecurity of cyber-physical systems and IoT devices. He has authored/co-authored over 150 publications and is listed by Stanford as among the top 2% of most cited researchers (2022). His work spans federated learning, transfer learning, quantum machine learning, and dependable AI systems. He serves as an Associate Editor for Neurocomputing, Machine Learning with Applications, and IEEE Transactions on Industrial Cyber-Physical Systems, among others. As an IEEE Senior Member, he chairs IEEE Computational Intelligence and Systems, Man, and Cybernetics Societies. Previously, he directed the Learning System and Cybernetics Group at the University of Windsor (2016–2022). His research interests emphasize security in non-stationary environments, adversarial machine learning defenses, and real-time analytics for smart grids. Awards include NSERC-DG, NSERC-ECR, and USRG grants. He has mentored students who received NSERC Alexander G. Bell, MITACS, and Ontario Graduate Scholarships. His recent publications highlight advancements in privacy-preserving split learning, blockchain-based federated learning security, and graph-based malware detection. He also explores quantum computing applications in AI and cybersecurity frameworks for cyber-physical systems.

• Corporate Finance
• Corporate Governance
• Financial Technology (Fintech)
• +۹ مورد دیگر

Gilles Chemla is a Professor of Finance at Imperial College Business School and Co-Director of the Centre for Financial Technology. He holds affiliations with the Centre National de la Recherche Scientifique (CNRS), the Centre for Economic Policy Research (CEPR), and the Rimini Centre for Economic Analysis (RCEA). His academic journey includes a PhD in Economics from the London School of Economics, an MSc from Paris School of Economics, and an engineering degree from Ponts Paristech. Chemla’s research focuses on corporate finance, fintech innovation, causal inference methodologies, and corporate governance. His work bridges theoretical economics with practical applications in finance, energy markets, and policy. Recent articles explore AI adoption in finance, crowdfunding dynamics, and executive compensation dynamics. He has contributed to top journals like Journal of Financial Economics and Journal of Empirical Finance . Education: PhD in Economics, London School of Economics (1996) MSc in Economics, Paris School of Economics (1992) Engineering Degree in Mathematics and Economics, École des Ponts ParisTech (1992) Awards: Multiple teaching prizes (unspecified). Professional Roles: Non-executive director roles in corporations and financial institutions Associate Editor, Journal of Empirical Finance Chemla’s research also addresses interdisciplinary topics such as medical trial biases and policy-relevant experiments. He advises on fintech innovation, financial regulation, and energy market dynamics, leveraging mathematical modeling and data science expertise.

• Human-Algorithm Interaction
• AI Ethics
• Data-Driven Design
• +۳ مورد دیگر

Dr. Dave Murray-Rust is an Associate Professor in Human-Algorithm Interaction Design at TU Delft's Faculty of Industrial Design Engineering. He explores the intersection of humans, data, and AI through design research, focusing on ethical AI systems and sociotechnical interactions. His work bridges computer science, design theory, and digital sociology, addressing challenges like algorithmic fairness and human-AI collaboration. He leads initiatives such as the AI Futures Lab and Data-Centric Design Lab, advancing methods for leveraging behavioral data in design processes. His research emphasizes experiential AI frameworks, metaphors for designers, and the legibility of AI systems. He has been honored with awards including Best alt.HRI 2024 and a CHI 2023 Best Paper Award for contributions to fairness perceptions in algorithmic decision-making. Murray-Rust teaches courses like the Speculative Design Studio and collaborates on projects like DCODE (Designing the Future of AI) and the BrightSky Project. His work extends to public engagement through installations like GeoPact and explorations of blockchain's societal impact. He holds an Honorary Fellowship at the University of Edinburgh.

• Public Law
• Algorithmic Decision-Making
• Constitutional Law
• +۵ مورد دیگر

Adam Harkens is a Lecturer in Public Law at the University of Strathclyde. He holds a PhD in Law from Queen's University Belfast, funded by the Leverhulme Interdisciplinary Network on Cybersecurity and Society (LINCS). His research focuses on the intersection of public power, digital decision-making tools, and constitutionalism, addressing issues like algorithmic policing, surveillance, and AI governance. He is a founding member of the Strathclyde Centre for Doctoral Training (SCDT) in Human Rights-Based Decision-Making and the LEADS Lab at the University of Birmingham. Teaching includes modules on constitutional law, administrative law, and digital regulation at both undergraduate and postgraduate levels. Professional activities include advising bodies like the Office for AI and the Dutch Raad van State. He co-leads the SCDT project (2023–2027) and contributed to the FATAL4JUSTICE? Programme (2019–2022). His upcoming monograph, The Algorithmic State: Power, Constitutionalism and the Future of Administrative Law , explores these themes further. Research interests emphasize preventing power abuse via algorithmic systems, engaging legal theory, human rights, and interdisciplinary collaboration. Recent projects include workshops on predictive policing regulation and EU AI accountability frameworks.

Andrea Santilliمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +۱۰ مورد دیگر

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.

Chenang Liuمشاهده پروفایل

دانشیار

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +۲ مورد دیگر

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)