جستجوی استاد راهنمای دانشگاهی

• Cryptography
• Privacy
• Secure Multiparty Computation (MPC)
• +۵ مورد دیگر

Gabe Kaptchuk is an Assistant Professor in the Computer Science Department at the University of Maryland, College Park (UMD), focusing on cryptography, privacy, and their social implications. He is affiliated with UMD's MC² (Maryland Cybersecurity Center) and UMIACS (Institute for Advanced Computer Studies). Previously, he was research faculty at Boston University and earned his PhD from Johns Hopkins University under advisors Avi Rubin and Matt Green. Research Interests: Kaptchuk's work bridges theoretical cryptography and social applications, emphasizing secure multiparty computation (MPC), zero-knowledge proofs, and steganography. He also explores the intersection of cryptography with law, policy, and human-centered design. Notable projects include Meteor (secure steganography using generative models) and Pulsar (MPC for dynamic participants). Teaching: Courses include Governing Algorithms (UMD) and Law and Algorithms (BU), focusing on algorithmic governance, privacy, and legal implications of technology. He has taught computer security and networks at Johns Hopkins and BU. Grants & Funding: Recent NSF awards include Collaborative Research: ReDDDoT Phase 2 (2024) for participatory privacy protections in AI training data. Active in deploying MPC for social good, such as analyses for Boston Women’s Workforce Council. Labs/Teams: Collaborates with researchers at Georgetown, Columbia, and institutions like the Wikimedia Foundation. Co-authors include Rachel Cummings, Elissa Redmiles, and Matthew Green on privacy and usability topics.

Chris Peikertمشاهده پروفایل

استاد

• Cryptography
• Lattice-based Cryptography
• Coding Theory
• +۲ مورد دیگر

Chris Peikert is a Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He received his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory in 2006 under the supervision of Silvio Micali. Peikert is a leading researcher in cryptography, particularly known for his foundational work in lattice-based cryptography. His research interests span cryptography, lattices, coding theory, algorithms, and computational complexity, with a particular focus on cryptographic schemes whose security can be based on the apparent intractability of lattice problems. Peikert has made significant contributions to the development and analysis of lattice-based cryptographic primitives, including ring-LWE, fully homomorphic encryption, and zero-knowledge proofs. Peikert's recent work demonstrates continued leadership in post-quantum cryptography, with publications in top venues like CRYPTO, EUROCRYPT, and STOC. His research spans theoretical foundations of lattice problems to practical implementations of lattice-based cryptographic systems, including hardware acceleration for fully homomorphic encryption. IACR Fellow (2024) Test-of-Time Award from Crypto 2008 (2023) TCC Test-of-Time Award (2017) Patrick C. Fischer Development Professor of Theoretical Computer Science (2017) Best Paper Award at Eurocrypt 2010 Best Paper Award at STOC 2009 Alfred P. Sloan Foundation Fellowship Google Research Award Peikert has been actively involved in the cryptographic research community, serving on program committees for major conferences including CRYPTO, EUROCRYPT, FOCS, and TCC (where he was program co-chair in 2021). He has also developed educational resources, including extensive lecture materials on lattice-based cryptography and teaching courses on cryptography and theoretical computer science at both the undergraduate and graduate levels.

Michael Blumمشاهده پروفایل

استاد

• Digital Health
• Health Informatics
• Preventive Cardiology
• +۷ مورد دیگر

Michael Blum, MD, is a Professor of Medicine in the Division of Cardiology at the University of California, San Francisco (UCSF), within the School of Medicine and Department of Medicine. He serves as Associate Vice Chancellor for Informatics and Chief Digital Transformation Officer at UCSF, leading strategic digital health initiatives. He is also the Executive Director of the UCSF Center for Digital Health Innovation (CDHI), where he drives innovation in digital health technologies. His research and clinical interests focus on preventive cardiology , digital health , and clinical informatics . Dr. Blum specializes in the care of patients with congestive heart failure, valvular heart disease, and general cardiology, with a strong emphasis on wellness and early detection of heart disease. His work integrates clinical care with digital transformation, including the development of clinical decision support systems and enterprise health data platforms. The available publications highlight his leadership in health informatics, particularly in electronic medical records, clinical documentation, and care transition systems. His work reflects a consistent trend toward leveraging technology to improve clinical workflows, patient safety, and care delivery innovation. AMDIS Award for Excellence and Achievement in Advancement of Clinical Information Systems Dr. Blum has led major digital health initiatives, including the enterprise deployment of Epic EHR and the development of a large-scale data warehouse supporting clinical, research, and educational missions at UCSF. He has secured and led strategic partnerships with industry leaders such as Samsung, Cisco, and GE to advance digital health innovation, including AI in healthcare. He mentors through leadership in CDHI and similar programs, though no formal students are listed. He leads the UCSF Center for Digital Health Innovation (CDHI) , which serves as an incubator for early-stage digital health technologies. The center validates innovations and partners with industry for commercialization. He also co-founded the UCSF-Samsung Digital Health Innovation Laboratory , the UCSF-Cisco Connected Health Interoperability Platform , and the UCSF-GE collaboration on artificial intelligence and deep learning in healthcare .

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Martin Nordal Petersenمشاهده پروفایل

دانشیار

• Internet of Things
• Optical Networking
• Wireless Communication
• +۳ مورد دیگر

Martin Nordal Petersen is an Associate Professor at the Department of Electrical and Photonics Engineering , Technical University of Denmark (DTU) . His work spans Internet of Things (IoT) , optical networking , and wireless communication systems, with notable contributions to LoRa , NB-IoT , and LPWAN technologies. He actively supervises PhD projects on topics such as machine learning in IoT edge devices , secure 5G communication , and smart community architectures . Active projects (2024–2027): Machine Learning in IoT Edge Devices , Deterministic and Secure 5G Communication Finished projects (2021–2024; 2018–2021; 2015–2018): Reliable M2M/IoT Communication , Smart Communities , IoT 100% , Network Slicing His research explores: IoT Reliability : Multi-RAT communication, backup systems, and signal propagation Optical Networks : Alien wavelength integration, SDN control, and network emulation platforms Wireless Innovation : GPS-free geolocation, maritime NB-IoT use cases, and multimode fiber distribution Current collaborations emphasize cross-disciplinary applications of IoT in healthcare , industrial ergonomics , and smart environments .

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

Shuvendu K. Lahiriمشاهده پروفایل

پژوهشگر

• Formal Verification
• Smart Contracts
• Program Synthesis
• +۵ مورد دیگر

Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.

Niina Zuberمشاهده پروفایل

پژوهشگر

• Ethical Software Design
• Digital Ethics
• Agile Software Development
• +۶ مورد دیگر

Niina Zuber is a Research Coordinator at the Bavarian Institute for Digital Transformation (bidt), focusing on ethical software design, agile development processes, and the intersection of digital technologies with democratic systems. Her work emphasizes integrating ethical principles into technical systems through frameworks like EDAP (Ethical Deliberation for Agile Processes). PhD in Ethics and Software Development (LMU Munich) Former roles: Research Consultant (Cognostics AG), LMU Munich, Center for Digitalization Bavaria (ZD.B) Key projects: EDAP, ReCREATIV, AI Gender Bias Dialogue Her research spans: ethical requirements management, value-sensitive design, AI's impact on creativity, facial recognition regulation, and digital responsibility frameworks. She regularly contributes to academic publications and public discourse on digital ethics. Scientific Contributions: Co-developed EDAP framework for ethical agile software processes Investigated ethical challenges in large language models (Vox ChatGPT) Explored gender bias in AI systems Studied generative AI's impact on creative industries Contributed to regulatory discussions on facial recognition Zuber collaborates across disciplines, connecting philosophy with technical implementation through projects like EDAP and publications in journals such as Philosophy & Technology and Informatics Spectrum.

Jean-François Godboutمشاهده پروفایل

استاد

• Data Science
• AI Safety
• Generative AI
• +۶ مورد دیگر

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Nour Aliمشاهده پروفایل

دانشگاهی

• Software Architecture
• Distributed Systems
• Adaptive Systems
• +۶ مورد دیگر

Dr Nour Ali is a Reader in the Department of Computer Science at Brunel University London , where she co-heads the Brunel Software Engineering Lab and serves as Vice-Dean of Education for the College of Engineering, Design and Physical Sciences. She holds a PhD in Software Engineering from Universidad Politecnica de Valencia, Spain, and a Major in Computer Science from Bir-Zeit University, Palestine. Research Interests: Software architecture for distributed and adaptive systems, integrating techniques like Model-Driven Engineering, Reverse Engineering, and Machine Learning. Teaching: Module leader for Software Project Management and supervisor of undergraduate group projects and final-year projects. Scientific Contributions: Over 70 publications in journals, conferences, and books. Key research areas include microservice architecture recovery, autonomic healthcare systems, and mobile self-adaptive architecture. Scientific Awards: Fellow of the Higher Education Academy (HEA). Membership: Deputy Editor-in-Chief for IET Software, member of multiple conference program committees, and reviewer for EPSRC, NWO, and other funding bodies.

Daehyeok Kimمشاهده پروفایل

استادیار

• Computer Networks
• Operating Systems
• Distributed Systems
• +۶ مورد دیگر

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Information Security
• Network Security
• Digital Twins
• +۹ مورد دیگر

Rasheed Hussain is an Associate Professor of Intelligent Network Security at the Smart Internet Lab and Bristol Digital Futures Institute (BDFI), School of Electrical, Electronic and Mechanical Engineering at the University of Bristol, UK. Previously, he served as a Senior Lecturer at the same institution from December 2021 to July 2025. He has held academic positions at Innopolis University, Russia, where he served as Associate Professor and Director of the Institute of Information Security and Cyber-Physical Systems, and as a guest researcher at the University of Amsterdam, Netherlands. His educational background includes a PhD in Computer Engineering from Hanyang University, South Korea (2011-2015), an MS in Computer Engineering from the same institution (2008-2010), and a B.Sc in Computer Software Engineering from the University of Engineering and Technology, Peshawar, Pakistan (2003-2007). Hussain's research focuses on network and cybersecurity, particularly future network security including 6G, the role of Digital Twins in future networks and systems security, and Responsible AI including fairness, trustworthiness, and explainability. His work spans information security, privacy, applied cryptography, vehicular networks, Internet of Things, Content-Centric Networking, cloud computing, API security, and blockchain applications. Senior member of IEEE Member of ACM ACM Distinguished Speaker Editorial board member for IEEE Communications Surveys & Tutorials, IEEE Access, and other journals His recent publications demonstrate a strong focus on the intersection of AI, networking, and security, with particular emphasis on Digital Twins, blockchain applications, federated learning, and 6G security. His research shows a clear trajectory toward addressing security challenges in emerging network architectures while incorporating responsible AI principles. Scientific Recognition: ACM Distinguished Speaker Netherlands University Teaching Qualification (Basis Kwalificatie Onderwijs, BKO) Hussain serves as a reviewer for major IEEE transactions, Springer and Elsevier journals, and participates in technical program committees for conferences including IEEE VTC, IEEE VNC, IEEE Globecom, and IEEE ICC. He is also certified as a trainer for the Instructional Skills Workshop (ISW) and contributes to the ESRC Centre for Sociodigital Futures (CenSoF) at the University of Bristol. His laboratory work centers around the Networks and Blockchain Lab, which focuses on security solutions for next-generation networks, with particular emphasis on Digital Twins security, blockchain applications, and AI-driven network security solutions. His current projects involve developing secure frameworks for future networks, trustworthy AI models, and privacy-preserving federated learning approaches.

Luo Maiمشاهده پروفایل

استادیار

• Computer Systems
• Machine Learning Systems
• Data Management
• +۳ مورد دیگر

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .