جست‌وجوی استاد و پژوهشگر

Hansjörg Kuttererمشاهده پروفایل

استاد

• Geodesy
• Geophysics
• Environmental Science
• +۳ مورد دیگر

Prof. Hansjörg Kutterer is a Professor and Dean at the KIT-Department of Civil Engineering, Geo and Environmental Sciences at Karlsruhe Institute of Technology (KIT). His primary affiliation is with KIT's Department of Civil Engineering, Geo and Environmental Sciences. He leads geodetic research initiatives focusing on Earth observation systems, atmospheric modeling, and geophysical data analysis. His research emphasizes advanced applications of GNSS, InSAR, and satellite gravimetry for monitoring climate-related phenomena such as water vapor dynamics, terrestrial water storage changes, and ground motion patterns. Key projects include developing machine learning-enhanced models for tropospheric delay corrections and integrated water vapor estimation in the Upper Rhine Graben region. Prof. Kutterer actively contributes to international geodetic frameworks like the Global Geodetic Observing System (GGOS), particularly through DA-CH regional collaborations. His work bridges geodetic methodologies with interdisciplinary challenges in climate science and environmental engineering. He oversees departmental operations as Dean, fostering innovation in geospatial education and infrastructure. His technical expertise spans geodetic deformation analysis, statistical robust estimation, and the integration of geophysical models with observational data.

Barb Morraمشاهده پروفایل

دانشیار

• Chemistry Education
• Organic Chemistry
• Green Chemistry
• +۳ مورد دیگر

Barb Morra is an Associate Professor in the Department of Chemistry at the University of Toronto, affiliated with the Faculty of Arts and Science. Her work focuses on integrating real-world applications and green chemistry principles into teaching and laboratory design. She emphasizes discovery-based learning, critical thinking, and collaboration in her courses, often using pharmaceutical synthesis and research scenarios to contextualize organic chemistry concepts. Her research explores innovative teaching methods such as the ‘Chemistry Connections Challenge,’ which encourages students to link course content to real-world problems. She has developed green chemistry-based laboratory experiments and curricula, emphasizing sustainability and modern research practices. These initiatives include solvent-free synthesis techniques and databases for instructional resources. Collaboration is central to her work, involving undergraduate and graduate students in programs like the Research Opportunities Program and Chemistry Teaching Fellowship Program. These partnerships have enhanced course quality and student engagement across multiple levels. Her contributions span journal articles on education and green chemistry, reflecting her dual focus on pedagogical innovation and chemical sustainability. Barb Morra’s efforts have shaped the Department of Chemistry’s approach to education, fostering a culture that bridges academic rigor with practical, sustainable applications. Her teaching and research highlight the importance of making chemistry accessible and relevant to contemporary challenges.

• Human-Computer Interaction
• Privacy Studies
• Social Media
• +۳ مورد دیگر

Andrea Forte is a Professor affiliated with the University of Michigan, with a former position at Drexel University. Her research focuses on Human-Computer Interaction (HCI), Privacy Studies, and Social Media, exploring topics such as privacy expertise, marginalized communities' digital experiences, and collaborative systems. She has published extensively in top venues like CHI, CSCW, and ACM Transactions on Computer-Human Interaction. Her work bridges technical, social, and ethical dimensions of technology, addressing challenges in privacy protection, online anonymity, and the impact of social media on vulnerable populations. She has contributed to understanding how technologies like Wikipedia and social platforms mediate trust, credibility, and activism. Notable projects include studies on user privacy rights in people search websites and the sociotechnical challenges faced by disability activists online. Forte collaborates with interdisciplinary teams, addressing issues such as internet-facilitated harassment of researchers and the design of privacy-enhancing technologies. Her research emphasizes equitable and inclusive approaches to technology, particularly in library and educational contexts.

• Behavioral Ecology
• Animal Behavior
• Social Systems
• +۴ مورد دیگر

Eli D. Strauss is a behavioral ecologist and Postdoctoral Researcher at the Max Planck Institute of Animal Behavior and the University of Konstanz. He will transition to an Assistant Professor position at the Department of Integrative Biology, Michigan State University in late 2025. His research focuses on the evolution of stable social systems, particularly in spotted hyenas, analyzing how individual behaviors and relationships shape group-level dynamics. Strauss employs field experiments, computational techniques, and long-term observational data to study dominance hierarchies, social inequality, and the interplay between individual experiences and collective behaviors. Research Interests : Strauss investigates how societies emerge from individual interactions, emphasizing long-term perspectives. Key themes include dominance hierarchy dynamics, social inheritance, and the ecological drivers of social behavior. His work bridges behavioral ecology with computational methods, addressing questions about social structure stability and evolutionary adaptations. Key Projects : Co-director of the Mara Hyena Project in Kenya, a long-term study on hyena behavior and ecology. Developing methodologies for longitudinal studies of dominance hierarchies, including frameworks for analyzing hierarchy dynamics across timescales. His computational tools (e.g., DynaRankR ) are widely used for dominance inference. Upcoming Initiatives : Establishing a lab at Michigan State University focusing on social behavior and collective ecology. Recruiting graduate students and postdocs to study social systems, computational ecology, and animal behavior.

• Cybersecurity
• Privacy-Preserving Computation
• Cryptography
• +۲ مورد دیگر

Dr. YANG Guomin is an Associate Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he coordinates the BSc Cybersecurity Track. His research focuses on privacy-preserving cryptography, authentication systems, and secure IoT frameworks. Research spans cryptographic protocols for cloud security, blockchain applications, and federated learning with emphases on efficiency and practical implementation. Recent publications demonstrate innovations in threshold authentication, redactable blockchains, and privacy-aware communication protocols. Advisees include LI Huilin and WANG Jiaheng, with research projects examining hardware-enhanced encryption, biometric authentication policies, and space network security. Work consistently addresses tension between security guarantees and computational efficiency in distributed systems.

Gerome Miklauمشاهده پروفایل

استاد

• Data Privacy
• Database Systems
• Differential Privacy
• +۳ مورد دیگر

Gerome Miklau is a Professor of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences (CICS). He leads the DREAM Lab and focuses on privacy, security, and equitable data management, particularly in differential privacy and fair data analysis. His work includes designing algorithms for private data synthesis, privacy-preserving SQL engines, and auditing systems like AuditGuard. He co-founded Tumult Labs to commercialize privacy technology and advised the U.S. Census Bureau on privacy for the 2020 decennial census. Education: Ph.D. in Computer Science from the University of Washington (2005), B.S. in Mathematics and Rhetoric from UC Berkeley (1995). Research Interests: Differential privacy, secure data management, fairness in algorithms, privacy-preserving data synthesis, and forensic database analysis. His lab develops tools like Ektelo and PrivateSQL, addressing challenges in privacy-accurate tradeoffs and scalable private data processing. Awards: 2006 ACM SIGMOD Dissertation Award, 2007 NSF CAREER Award, 2013 ICDT Best Paper Award, and two ACM PODS Test-of-Time Awards (2020 and 2012). Grants & Service: Co-chair of OpenDP Advisory Board, steering committee member for TPDP workshops, and organizer of the 'Data, Responsibly' Dagstuhl workshop. His service includes program committees for SIGMOD, ICML, and FAT*. He teaches courses on databases, privacy, and programming. Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and collaborations with the Center for Data Science and Cybersecurity Institute at UMass Amherst.

Griffin Weberمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Social Network Analysis
• Team Science
• +۵ مورد دیگر

Griffin Weber, M.D., Ph.D., is an Associate Professor of Medicine and Biomedical Informatics at Harvard Medical School (HMS) and Beth Israel Deaconess Medical Center (BIDMC). He directs the Biomedical Research Informatics Core (BRIC) at BIDMC. His research focuses on expertise mining, social network analysis, and biomedical informatics. Key contributions include developing Profiles RNS (an open-source research networking platform) and i2b2 / SHRINE federated query tools for clinical data. He holds MD and PhD degrees from Harvard (2007), and earlier degrees in bioengineering and computer science. Education: SB in Bioengineering (Harvard, 2000), SM/PhD in Computer Science (Harvard, 2004/2005), MD (Harvard, 2007). He served as Harvard Medical School's first Chief Technology Officer, building educational platforms for 500+ courses. His work spans DNA microarrays, breast cancer tumor modeling, and EHR bias analysis. Research Interests: Leveraging informatics to improve healthcare through federated data systems, team science dynamics, and EHR analysis. Projects include Profiles RNS for researcher networks and i2b2 for clinical data queries across institutions. He explores biases in EHR data filtering and visualizing healthcare system dynamics in biomedical data. Grant Leadership: Principal investigator on NIH grants addressing EHR biases (R01LM013345), healthcare system dynamics (U01CA198934), and scientific workforce networks (U01GM112623). Collaborator on PCORI and NIH-funded initiatives. Awards: 2020 Fellow of the American College of Medical Informatics; 2011 Top Podium Presentation (AMIA); 2007 Medical Technology Award (Massachusetts Medical Society). Labs/Teams: Leads BRIC at BIDMC, collaborates on i2b2/SHRINE, and contributes to the 4CE consortium for federated healthcare data analysis.

• Cybersecurity
• Data Encryption
• Access Control Systems
• +۳ مورد دیگر

Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.

Alexander Rodríguezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +۷ مورد دیگر

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

Al Amjad Tawfiq Isstaifمشاهده پروفایل

پژوهشگر

• Cloud and Edge Computing
• Microservices and Serverless
• Operating Systems and Middleware
• +۵ مورد دیگر

Al Amjad Tawfiq Isstaif is a Research Associate in the Systems Research Group (SRG) at the Department of Computer Science and Technology (also known as "The Computer Lab") at the University of Cambridge, working with Professor Richard Mortier. He is a member of Clare College. In 2018, he was awarded the Said Foundation Cambridge Scholarship to study the MPhil in Advanced Computer Science (ACS) at the Computer Lab. Before moving to Cambridge, he co-founded several projects for empowering engineering students in Syria, including the award-winning education enterprise: Wikilogia. His research interests include: Cloud and Edge Computing Microservices and Serverless Operating Systems and Middleware Networked and Distributed Systems Machine Learning Systems Human-Centred Computing Machine Learning and Artificial Intelligence Systems and Networking Isstaif's primary research focus is on building self-scaling network services on top of next generation infrastructure (IoT/5G/edge), leveraging lightweight virtualization techniques and distributed tracing. His work falls under the Compute First Networking research project. He has deep passion for social entrepreneurship and the role of digital infrastructure in addressing development and sustainability challenges across the globe. His technical expertise bridges the IT and telecom worlds, with emphasis on cloud-native and open source movements shaping future infrastructure. His recent publications demonstrate a strong focus on optimizing serverless computing environments, with particular attention to scheduling algorithms, resource management, and latency optimization in Linux-based systems. The trajectory of his work shows progression from database performance modeling to cutting-edge research in edge computing and network infrastructure. Notable awards and recognitions: Said Foundation Cambridge Scholarship (2018) Co-founded Wikilogia, an award-winning education enterprise for engineering students in Syria Isstaif has teaching experience in Operating Systems (LT 2021) and maintains an active research profile within the Systems Research Group. His work connects theoretical computer science with practical applications in cloud infrastructure, particularly focusing on how these technologies can serve broader societal needs in developing contexts. He is located in Room FN07 at the William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD.

Sandhya Dwarkadasمشاهده پروفایل

استاد

• Parallel and distributed computing
• Computer architecture
• Compiler/Runtime-Architecture Interaction
• +۵ مورد دیگر

Sandhya Dwarkadas is the Walter N. Munster Professor and Chair of the Department of Computer Science at the University of Virginia. Her research focuses on the intersection of computer hardware and software, particularly in parallel computing, computer architecture, and compiler/runtime-architecture interaction. She holds dual roles as department chair and active researcher, balancing leadership with contributions to energy-efficient and reconfigurable computing systems. Education: B.Tech. in Electrical Engineering, Indian Institute of Technology (1986) M.S. and Ph.D. in Electrical and Computer Engineering, Rice University (1989, 1993) Research interests emphasize parallel and distributed computing architectures, with a focus on energy efficiency, cache coherence, and security in multicore systems. Recent work explores mitigating side-channel attacks via innovations like TimeCache and RollingCache. Her publications span 30+ years, addressing both foundational and applied challenges in computer systems. Awards highlight her impact: ACM and IEEE Fellowships (2018/2017), University of Rochester’s Hajim Award (2020), and AAAS Fellowship (2024). She actively mentors students through courses like CS 6190 and leads interdisciplinary projects like TriForce. Labs/Teams: Her work is anchored in the University of Virginia’s Computer Science Department, collaborating across academia and industry to advance next-generation computing systems.

Kourosh Davoudiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

Gursans Guven Isinمشاهده پروفایل

استادیار

• Construction Automation
• Building Information Modeling
• Digital Twins
• +۲ مورد دیگر

Gursans Guven Isin is Assistant Professor in the Department of Civil Engineering at University of Manitoba's Price Faculty of Engineering. Her research develops digital technologies to improve construction processes through automation, sensing, and data integration. Research focuses on: Integration of BIM with IoT and sensing technologies Digital twins for construction monitoring AI applications in construction engineering Automated progress monitoring and quality control BIM-enabled facility management systems She leads projects creating AI-powered BIM assistants, automated progress tracking systems, and post-occupancy evaluation platforms. Her work advances sustainable construction through improved resource tracking and material efficiency analysis.

• Database Systems
• Distributed Computing
• Cloud Computing
• +۲ مورد دیگر

Bettina Kemme is a faculty member at McGill University in Montreal, Canada. Her research focuses on database systems , distributed computing , and cloud data management . She has made significant contributions to database replication, consistency models, and middleware frameworks for scalable applications. Research Themes : Database replication, distributed systems, cloud computing, and software engineering. Notable Collaborations : Jörg Kienzle, Joseph Vinish D'silva, Yunjia Zheng, and Marta Patiño-Martínez. Publications span critical areas such as graph database view management, transactional recovery in key-value stores, and latency-aware publish/subscribe systems. Her work is published in venues like VLDB , ICDE , Middleware , and SRDS .

Jeremy Edwardsمشاهده پروفایل

استاد

• Pharmaceutical Chemistry
• Quantitative Biology
• Genomics
• +۵ مورد دیگر

Jeremy Edwards serves as a Professor in the Department of Chemistry at the University of New Mexico, where he maintains an active research program at the intersection of pharmaceutical chemistry, genomics, and computational biology. His work spans multiple disciplines with a particular focus on developing innovative technologies for DNA sequencing and analysis. Professor Edwards' research interests center around Pharmaceutical Chemistry, Quantitative Biology, and Genomic Technologies. His work has significantly contributed to the fields of metabolic engineering, genome sequencing, and systems biology. He has pioneered approaches in nanopore sequencing technology and developed computational frameworks for analyzing complex biological systems. His research group has made notable contributions to understanding metabolic networks through flux balance analysis and in silico modeling, with applications ranging from bacterial metabolism to mammalian systems. Analysis of Professor Edwards' publication record reveals a strong trend toward developing cutting-edge genomic technologies and computational approaches for biological analysis. His recent work focuses on spatial transcriptomics, nanopore sequencing innovations, viral genome surveillance, and target illumination for drug discovery. The publications demonstrate a consistent trajectory from foundational metabolic modeling work toward increasingly sophisticated genomic technologies and applications in drug target identification and validation. Professor Edwards has established himself as a leader in computational genomics with an extensive publication record including highly cited papers such as "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data" (1297 citations) and "The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities" (1295 citations). His work on metabolic modeling has been particularly influential in systems biology. As an active researcher, Professor Edwards has mentored numerous students and collaborators, though specific student names aren't documented in the available materials. His research has attracted significant funding supporting the development of genomic technologies and computational approaches. His laboratory appears to focus on the intersection of bioinformatics, molecular biology, and engineering, with particular emphasis on next-generation sequencing technologies and their applications. Professor Edwards leads a research group that integrates computational modeling with experimental approaches to tackle challenges in genomic analysis and metabolic engineering. His team has developed innovative tools like the Sentieon Genomics Tools, described as "a fast and accurate solution to variant calling from next-generation sequence data." The group's work spans from fundamental research on DNA sequencing technologies to applied projects in viral surveillance and drug target identification.