جست‌وجوی استاد و پژوهشگر

• Adaptive Experimentation
• Reinforcement Learning
• Statistical Inference
• +۳ مورد دیگر

Kelly W. Zhang is an Assistant Professor at Imperial College London's Mathematics Department (statistics section) and a faculty member in the I-X interdisciplinary AI initiative. Her research focuses on adaptive experimentation, reinforcement learning, and statistical inference with applications in healthcare and clinical trials. She holds a PhD from Harvard University and was a Postdoctoral Fellow at Columbia Business School. Notable awards include the Siebel Scholar (2023) and NSF Graduate Fellowship. Education: PhD in Computer Science from Harvard University (2023), advised by Susan Murphy and Lucas Janson; internships at Apple, Facebook AI, and eBay. Her work bridges statistical theory and practical applications in digital health interventions, with deployments in oral health and cannabis use trials. Research Interests: Reinforcement Learning Algorithms for Digital Interventions Statistical Methods in Adaptive Experimentation Clinical Trial Design and Monitoring Machine Learning Theory and Applications Awards: Siebel Scholar (2023) NSF Graduate Fellowship (PhD support) Presentations at NeurIPS 2021/2020 and Econometric Society Conference 2024 Grants and Funding: Her work has been supported through interdisciplinary initiatives at Imperial College and prior fellowships. She co-leads sessions on statistical reinforcement learning at conferences like INFORMS and JSM. Professional Activities: Co-organizer of sessions at INFORMS 2025, IMS-Bernoulli 2024, and workshops on Deployable RL. Active in academic outreach, including speaking at Amazon Berlin's StatML workshop.

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +۶ مورد دیگر

Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.

• Machine Learning
• Artificial Intelligence
• Genomics
• +۴ مورد دیگر

Haohan Wang serves as Assistant Professor at the School of Information Sciences, University of Illinois Urbana-Champaign, with additional appointments as Affiliate at the Carl R. Woese Institute for Genomic Biology and Assistant Professor at the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges machine learning, genomics, and AI security, focusing on trustworthy systems for biomedical applications and foundational AI research. Wang's research centers on robust and secure artificial intelligence, with emphasis on large language model vulnerabilities (jailbreaking, safety evaluation), federated learning personalization, and genomic data analysis. He develops techniques for privacy-preserving dataset distillation, confounding factor correction in genome-wide studies, and multi-agent frameworks for scientific discovery. His fingerprint highlights expertise in Machine Learning (94%), Linear Mixed Models (87%), and Confounding Factor Correction (41%), reflecting his focus on methodological rigor in complex data environments. Analysis of his 2025 publications reveals dominant trends in AI security (jailbreak evaluation frameworks like GuardVal, adversarial attacks such as InfoFlood), biomedical AI (transcriptomic analysis, wearable data privacy), and foundational methods (federated learning optimization, synthetic data generation). These works consistently address real-world challenges in model trustworthiness while advancing computational techniques for genomics and healthcare. Through NCSA's high-performance computing resources and the Institute for Genomic Biology's collaborative ecosystem, Wang integrates supercomputing capabilities with biological research to tackle data-intensive problems in disease modeling and AI safety testing, as evidenced by media coverage of his team's AI security testing methods.

Monowar Bhuyanمشاهده پروفایل

دانشیار

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +۶ مورد دیگر

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

Leila Methnaniمشاهده پروفایل

پژوهشگر

• Artificial Intelligence Ethics
• Explainable AI (XAI)
• Trustworthy AI
• +۵ مورد دیگر

Leila Methnani is a doctoral student at the Department of Computing Science at Umeå University . Her research focuses on ethical and sociotechnical challenges in artificial intelligence, particularly in areas such as AI alignment , trustworthy AI , and human-AI collaboration . She has published extensively on topics including Explainable AI (XAI) , MLOps , and hybrid human-AI systems . Her recent work explores: Trustworthy AI in variable autonomy robotic systems Explainable and counterfactual-based AI interfaces for industrial operators Operationalizing AI ethics through socio-technical assessments Real-time reactive planning with social norms in multi-agent systems She has co-authored publications in journals such as Ethics and Information Technology , ACM Computing Surveys , and Frontiers in Artificial Intelligence . She is reachable via email at leila.methnani@umu.se .

Maozhen Liمشاهده پروفایل

استاد

• High Performance Computing
• Big Data Analytics
• Artificial Intelligence
• +۱۳ مورد دیگر

Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.

Benjamin Rechtمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Statistics
• +۵ مورد دیگر

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

• Energy Systems
• Machine Learning
• Reinforcement Learning
• +۱۲ مورد دیگر

Marco Badami is a Full Professor at the Department of Energy (DENERG) , Polytechnic of Turin. He serves as Scientific Director for national and EU-funded research projects in energy systems and has been a Course Lecturer for Energy Systems and Industrial Use of Energy since 2010. He supervises PhD students in Energetics and Electrical Engineering . Research Interests: His work spans energy systems optimization, machine learning applications for industrial energy efficiency, smart grids, cogeneration scheduling, blockchain-based energy data immutability, and predictive maintenance algorithms for photovoltaic plants. Current projects focus on AI-driven energy audits, optimized control systems for industrial microgrids, and digital twin architectures. Collaborations: He works with Trigenia Srl, Stogit SpA, and international institutions on commercial research contracts. His scientific contributions include 15+ publications on topics like LSTM forecasting for solar energy, deep reinforcement learning for multi-energy systems, and decentralized peer-to-peer energy trading platforms.

Maria Rita D’Orsognaمشاهده پروفایل

استاد

• Mathematical Biology
• Statistical Mechanics
• Applied Mathematics
• +۴ مورد دیگر

Maria Rita D’Orsogna is a Professor of Mathematics at California State University, Northridge (CSUN) and holds an Adjunct Associate Professor appointment in the Department of Computational Medicine at UCLA. She earned her PhD in Theoretical Physics from UCLA in 2003 and has since bridged mathematical modeling with interdisciplinary research in biology, social dynamics, and criminology. Her work utilizes statistical mechanics and applied mathematics to study collective behavior, viral dynamics, and societal challenges. Her research spans Biological swarming and self-organization Crime pattern modeling and policy analysis Drug addiction relapse dynamics Environmental activism against offshore oil drilling Recent publications focus on Medical decision-making optimization Age-specific overdose mortality forecasting Radicalization and social network dynamics Criminal career empirical studies Hematopoiesis modeling . She has secured funding from the NSF and Army Research Office. Teaching experience includes differential equations, multivariable calculus, and mathematical biology at CSUN and UCLA. She has mentored students through RIPS, IPAM, and PUMP programs. As Associate Director of UCLA’s Institute for Pure and Applied Mathematics (2018–2021), she promoted interdisciplinary research. Her environmental advocacy in Italy led to national policy changes banning coastal oil drilling, earning her recognition as the "Erin Brockovich of Italy".

• Wireless Communication Systems
• Satellite Communication Networks
• Spectrum Sharing
• +۳ مورد دیگر

Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.

Roman Garnettمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Active Learning
• +۳ مورد دیگر

Roman Garnett is an Associate Professor in the Department of Computer Science & Engineering at the McKelvey School of Engineering, Washington University in St. Louis. He joined WashU in January 2015 and holds affiliations with the Division of Computational & Data Sciences and the Synthetic Biology Manufacturing of Advanced Materials Research Center (SMARC). His work focuses on Bayesian experimental design and active learning to automate scientific discovery across domains like materials science, drug discovery, and personalized medicine. Garnett earned his PhD from the University of Oxford (2010), and dual degrees (AB and MS) from Washington University in St. Louis (2004). Research Interests: Garnett’s research emphasizes Bayesian active learning methodologies, with applications in natural sciences and engineering. He develops frameworks for large-scale geospatial exploration, reinforcement learning systems, and AI-driven solutions for environmental and social challenges. His long-term goal is to democratize machine learning through fully automated, robust active learning systems. Grants & Collaborations: He leads or contributes to initiatives like a $3M NSF Research Traineeship program (2023) and collaborates with experts in astronomy, surface science, and synthetic biology. His lab actively explores interdisciplinary applications of AI, reflected in recent projects such as nature-inspired materials research and AI for geospatial search. Affiliations & Recognition: Garnett’s work is showcased through his lab website and Google Scholar profile. He has been featured in university news for contributions to advanced materials research and AI-driven problem-solving.

• Artificial Intelligence
• Supply Chain
• Sustainability
• +۶ مورد دیگر

Evelyn Xiaoyue Gong is an Assistant Professor of Operations Management at the Tepper School of Business , Carnegie Mellon University . She holds a PhD in Operations Research from MIT (2023) and a BS in Honors Mathematics and Interactive Media Arts from New York University (2017) . Research Interests : Developing artificial intelligence solutions for supply chains and sustainability with provable performance guarantees. Online assortment optimization, pure exploration in reinforcement learning, and data-driven decision-making . Recent Work Trends : Her publications focus on applying reinforcement learning and optimization algorithms to supply chain sustainability, server deployment under uncertainty, and resource management. Key themes include AI-driven environmental impact reduction and theoretical advancements in online inventory models . Scientific Recognition : Best Dissertation Prize , Supply Chain Conference (2023) Accenture Fellowship (2022-2023) Bayer Women in Operations Research Scholarship (2021) Professional Service : Reviewer for Management Science , NeurIPS 2025 , and IPCO 2025 . Committee member for INFORMS Public Sector Operations Research Best Paper Award (2025).

• Swarm Robotics
• Artificial Intelligence
• Collective Decision Making
• +۵ مورد دیگر

Nicolas Cambier is a Visiting Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Artificial Intelligence department and the Network Institute. His research focuses on swarm robotics, collective behavior, and evolutionary systems. He explores topics like emergent communication, modular robotics, and prosociality in robotic swarms. Key areas include task-driven language evolution, adaptive decision-making, and environmental interaction in constrained environments. His work bridges theoretical models with practical implementations, emphasizing self-organization and cultural evolution in synthetic systems. Recent contributions address challenges in heterogeneous swarms, skill acquisition in modular robots, and decision-making without prior knowledge. He collaborates widely, with publications in IEEE Robotics and Automation Letters, Nature Communications, and top conferences like GECCO and Distributed Autonomous Robotic Systems. Research interests span robotics, artificial intelligence, and evolutionary computation, with applications to both theoretical frameworks and real-world robotic systems. His studies often involve agent-based simulations and embodied evolution approaches to understand complex collective phenomena.

M.Z. Naserمشاهده پروفایل

استادیار

• Machine Learning
• Structural Engineering
• Fire Engineering
• +۵ مورد دیگر

Dr. M.Z. Naser is an Assistant Professor in the Glenn Department of Civil Engineering at Clemson University. His research focuses on causal and explainable machine learning methodologies applied to structural engineering, materials science, and fire safety. He holds a PhD from Michigan State University and an M.S. from the American University of Sharjah. Naser teaches courses such as Machine Learning for Civil Engineers and Structural Fire Engineering, emphasizing interdisciplinary innovation. His work bridges data-driven analysis with domain-specific knowledge to address challenges in resilient infrastructure design, including fire-resistant materials, structural retrofits, and AI-driven decision-making. Education: PhD, Michigan State University; M.S., American University of Sharjah Research Themes: Explainable AI, Fire Engineering, Structural Materials, Causal Inference Key Projects: Developing SPINEX framework, wildfire classification models, and cognitive infrastructure systems Recent publications analyze over 1000 fire tests to uncover spalling mechanisms, explore synthetic fire tests via GANs, and benchmark automated ML platforms. His work on causal diagrams for civil engineers and firefighter algorithms highlights contributions to both theory and practical applications. Naser also advocates for integrating AI into engineering education, emphasizing ethical and transparent model deployment.

Sudip Mittalمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Cyber-Physical Systems

Dr Sudip Mittal is an Assistant Professor in Computer Science & Engineering at Mississippi State University and Associate Research Director of the PATENT Lab. His research spans cybersecurity, artificial intelligence, and cyber-physical systems, with a focus on building self-protecting systems and predictive security for unmanned vehicles. He leads the SECRETS Lab and has published over 70 papers in top venues, with work featured in The LA Times and WIRED. Research interests include: Autonomous intrusion response systems AI-driven threat detection in IoT/CPS Adversarial machine learning His publications (2019-2025) show a strong emphasis on AI security applications, particularly in malware detection, healthcare compliance, and anomaly detection using large language models. Recent articles explore MLOps security, adaptive cyber defense, and synthetic data generation for critical systems.