جست‌وجوی استاد و پژوهشگر

Clinton Fookesمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +۷ مورد دیگر

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

Juho Leeمشاهده پروفایل

دانشیار

• Bayesian deep learning
• Bayesian inference
• meta learning
• +۵ مورد دیگر

Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.

Lawrence M. Weinمشاهده پروفایل

استاد

• Operations Management
• Public Health
• Homeland Security
• +۵ مورد دیگر

Lawrence M. Wein is the Jeffrey S. Skoll Professor of Management Science at Stanford University's Graduate School of Business, where he also serves as Senior Associate Dean of Academic Affairs. He holds a PhD in Operations Research from Stanford (1988) and has taught operations management at MIT (1988–2002) and Stanford since 2003. His research spans operations management, public health, and homeland security, with notable contributions to HIV therapy optimization, bioterror response logistics, and forensic science innovations. His academic roles include Senior Fellow at Stanford’s Center for International Security and Cooperation, and editorial leadership as Editor-in-Chief of Operations Research (2000–2005). Notable awards include the Kimball Medal, Lanchester Prize, and membership in the National Academy of Engineering. Research interests emphasize applying operations research to real-world challenges, such as optimizing radiation detection systems, analyzing sexual assault kit backlogs, and improving ballistic imaging techniques. His work bridges theoretical models with practical policy, impacting areas like healthcare, counterterrorism, and criminal justice. Key contributions include developing the workload regulating release policy in semiconductor manufacturing, influencing U.S. smallpox vaccination strategy, and advancing mathematical models for cancer treatment and pandemic influenza control. His recent focus includes forensic genetic genealogy and crime-solving technologies. Grants and collaborations span industries, governments, and academic institutions, reflecting interdisciplinary impact. His teaching includes courses on operations management, stochastic networks, and homeland security applications.

Katažyna Bogdzevičمشاهده پروفایل

استاد

• Private International Law
• European Law
• Human Rights
• +۲ مورد دیگر

Katažyna Bogdzevič is a Professor at Mykolas Romeris University (MRU) Law School, specializing in Private International Law , European Law , Human Rights , and Environmental Law . She leads the MRU Human Rights LAB and contributes to the Environmental Management Research LAB, focusing on legal frameworks for ecosystem services, climate change mitigation, and stakeholder engagement. Key Research Themes: Integration of Private International Law with Environmental Law , analysis of legal conflicts in nature-based solutions, and name rights under international human rights conventions. Policy Engagement: Serves as advisor to Lithuania's Minister of Justice Ewelina Dobrowolska , emphasizing knowledge transfer to legislative and judicial practices. Email: roffice@mruni.eu

• Cryptography
• Post-Quantum Cryptography
• Network Security
• +۳ مورد دیگر

Douglas Stebila is an Associate Professor in the Department of Combinatorics and Optimization at the University of Waterloo, Faculty of Mathematics. His research focuses on cryptographic protocols, with an emphasis on post-quantum cryptography, TLS protocol security, and key exchange mechanisms. He has contributed to the design and analysis of cryptographic systems resilient to quantum computing threats, including work on hybrid key exchange methods and post-quantum TLS implementations. Stebila is involved in standards projects such as the Open Quantum Safe initiative, aiming to transition existing infrastructure to quantum-resistant algorithms. His recent work addresses security models for cryptographic protocols, including formal verification of key establishment schemes and analysis of real-world protocols like Signal and TLS. His research spans theoretical cryptography, applied protocol analysis, and implementation security. Key contributions include studies on obfuscated key exchange, verifiable decapsulation of post-quantum KEMs, and optimization of TLS handshake efficiency (e.g., TurboTLS). He also explores challenges in cryptographic protocol design, such as preventing double authentication and ensuring resistance against side-channel attacks. His work frequently bridges academic research with practical applications, emphasizing the transition of cryptographic innovations into real-world systems. Stebila collaborates with industry and academic partners on projects like the Open Quantum Safe initiative, which develops libraries for post-quantum cryptography integration. His publications often address security analyses of emerging protocols and their vulnerability to both classical and quantum adversaries. He has co-authored conference proceedings for major venues like CRYPTO and SAC, and contributed to standards documentation for protocols such as TLS and SSH.

Simon Robinsonمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Participatory Design and Co-Creation
• Conversational User Interfaces
• +۴ مورد دیگر

Professor Simon Robinson is a faculty member in the School of Mathematics and Computer Science at Swansea University, holding a position as a Professor of Computer Science. He serves as the Head of the Future Interaction Technologies (FIT) Lab and Director of the MSc year for the EPSRC Centre for Doctoral Training in Enhancing Human Interaction and Collaboration with Data-Driven Systems. His research focuses on devices and interactions designed for emergent users in low-connectivity regions, emphasizing participatory design and co-creation with underserved communities. Current projects include the EPSRC-funded UnMute initiative, which aims to empower marginalized language speakers through spoken language technologies. Key affiliations include leadership roles in the FIT Lab and the EPSRC CDT, alongside contributions to projects like Rethinking Public Technology in a Post-COVID Era, PV Interfaces, and Scaling the Rural Enterprise. His work spans ubiquitous computing, deformable devices, and inclusive interaction design for global south communities. Research interests center on Human-Computer Interaction (HCI), with a focus on low-resource settings. Recent work addresses speech technologies for unwritten languages, trust in human-robot interactions, and sustainable self-powered interfaces. His lab explores innovations like Light-In-Light-Out (Li-Lo) displays and community-driven smart materials (PV-Pix). Professor Robinson has supervised numerous PhD students across topics including AI ethics, predictive maintenance, and human-centric NLP. He teaches modules like Introduction to HCI and contributes to the Human-Centred Big Data and AI Dissertation program. His work integrates grants from EPSRC and collaborative projects with international partners, reflecting a commitment to socially impactful technology. Labs & Teams: Director of the FIT Lab, active in the EPSRC CDT, and collaborator in interdisciplinary teams focusing on rural technology, assistive AI, and sustainable interfaces.

• 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.

• Artificial Intelligence
• Machine Learning
• Fairness in AI
• +۵ مورد دیگر

Sanmi Koyejo is an Assistant Professor of Computer Science at Stanford University and holds an adjunct position as Associate Professor at the University of Illinois at Urbana-Champaign. He leads the Stanford Trustworthy AI Research (STAIR) group, focusing on fairness, robustness, and healthcare applications in machine learning. His work bridges theoretical foundations with practical systems, emphasizing ethical AI and clinical informatics. Affiliations: SAIL, HAI, CRFM, AIMI, AI Safety, and the Machine Learning Group. Research Interests: His expertise spans trustworthy AI, federated learning, and neuroimaging. He actively addresses challenges in algorithmic fairness, particularly in healthcare, where he collaborates with institutions like OSF Healthcare on projects like federated learning for clinical data. Key Contributions: Co-developed frameworks for unlearning in large language models, evaluated AI systems' societal impacts, and advanced benchmarks for medical applications. His work has been featured in venues like NeurIPS, ICML, and AAAI. Awards: NSF CAREER Award, Alfred P. Sloan Fellowship, and Terman Faculty Fellowship. Grants & Teams: Leads NSF-funded projects on domain adaptation and fairness in breast cancer risk scoring. Collaborates with interdisciplinary teams on NIH's MIDRC and NSF's AIFARMS initiative for agricultural sustainability. Labs/Teams: STAIR lab drives interdisciplinary research in ethical AI, with emphasis on real-world deployment and policy implications.

• Cybersecurity
• Digital Ecosystems
• Cloud Computing
• +۴ مورد دیگر

Kari M. Koskinen is a University Lecturer at Aalto University's Department of Information and Service Management. His research focuses on cybersecurity, digital ecosystems, and technology management with applications in healthcare, agriculture, and smart cities. He frequently collaborates with scholars like Hadi Ghanbari and Sonja Hyrynsalmi on topics ranging from ransomware mitigation to sustainable mobility solutions. His work emphasizes real-world case studies, such as analyzing the Vastaamo psychotherapy clinic data breach and examining supply chain attacks in manufacturing. Key contributions include frameworks for cloud-based integration platforms, risk management in digital ecosystems, and the socio-technical implications of autonomous vehicles. Koskinen also explores visibility labor in NFT markets and how digital platforms address development challenges in the Global South. His teaching cases, published in the Journal of Information Technology Teaching Cases, bridge theoretical concepts with practical cybersecurity scenarios.

• Information Theory
• Privacy (Data Disclosure)
• Statistical Signal Processing
• +۴ مورد دیگر

Tobias Oechtering is a Professor at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. His research focuses on information theory, privacy-preserving technologies, statistical signal processing, machine learning, and smart grid systems. He has held academic positions at KTH since 2008, advancing from Post-Doctoral Researcher to Assistant Professor (2010–2013), Associate Professor (2013–2018), and Professor (2018-present). He has supervised over 20 PhD students and contributed to numerous postdoctoral programs. Research Interests: - Network information theory and physical-layer security - Privacy mechanisms with provable guarantees - Distributed statistical inference and sensor calibration - Reinforcement learning and privacy-aware machine learning - Smart grid privacy and energy management - Wireless communication algorithms and signal processing - Networked control systems and stability analysis He currently supervises 7 PhD students and hosts 3 postdocs. His work has led to over 150 peer-reviewed publications, with recent contributions in privacy-preserving smart grid strategies, adversarial inference control, and information-theoretic security. He has served as editor for IEEE Transactions on Information Forensics and Security and held leadership roles in KTH's Digitalisation Research Platform.

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.

Christopher T. Bavitzمشاهده پروفایل

استاد بالینی

• Intellectual Property Law
• Media Law
• Technology Law
• +۵ مورد دیگر

Christopher T. Bavitz is the WilmerHale Clinical Professor of Law and Vice Dean for Experiential and Clinical Education at Harvard Law School. He serves as Managing Director of the Cyberlaw Clinic at the Berkman Klein Center for Internet & Society and is a Faculty Co-Director of the Center. His expertise spans intellectual property, media law, AI governance, and technology policy. Bavitz teaches courses like Music & Digital Media and Counseling and Legal Strategy in the Digital Age . His research focuses on algorithmic fairness, intermediary liability, and regulatory frameworks for emerging technologies. Bavitz holds a B.A. from Tufts University and a J.D. from the University of Michigan Law School. Before joining HLS, he was Senior Director of Legal Affairs at EMI Music and practiced litigation at Sonnenschein Nath & Rosenthal. Key initiatives include work on the Lumen Database for transparency in content takedowns, the AGTech Forum for state attorneys general, and policy analysis on AI ethics, facial recognition, and algorithmic risk assessment tools. He advises on startup legal strategy, digital finance, and generative AI accountability.

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.

Jayadev Acharyaمشاهده پروفایل

دانشیار

• Information Theory
• Statistical Inference
• Machine Learning
• +۷ مورد دیگر

Jayadev Acharya is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University, with graduate field memberships in Computer Science and Operations Research and Information Engineering. His research focuses on the intersection of information theory, statistical inference, algorithms, and machine learning. He explores trade-offs between data, memory, time, and robustness in learning problems, including quantum information and machine unlearning. Education: B.Tech in Electronics and Communication Engineering from Indian Institute of Technology, Kharagpur (2007) M.S. in Electrical and Computer Engineering from University of California, San Diego (2009) Ph.D. in Electrical and Computer Engineering from University of California, San Diego (2014) Research Trends: His recent work (2020-2022) emphasizes information-constrained inference, differential privacy, quantum entropy estimation, and distributed learning. Key subfields include communication complexity, local privacy, and adaptive gradient processing. His publications span NeurIPS, ICML, COLT, and IEEE Transactions on Information Theory. Awards: Kenneth A. Goldman ’71 Excellence in Teaching Award (Cornell, 2022) MIT Energy Initiative Fellowship (2014) Shannon Graduate Fellowship (UCSD, 2012) Jack Keil Wolf Student Paper Award (ISIT, 2010) Advising and Grants: He advises Sourabh Bhadane, Saravanan Kandasamy, Yuhan Liu, Ziteng Sun, and Huanyu Zhang. Research funded by NSF-CAREER, NSF-CRII, NSF-CIF small grants, and Google Faculty Research Award.

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.