جست‌وجوی استاد و پژوهشگر

Marta Ariasمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +۳ مورد دیگر

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

Dongmei Fengمشاهده پروفایل

استادیار

• Terrestrial Hydrology
• River Dynamics
• Satellite Remote Sensing
• +۴ مورد دیگر

Dongmei Feng serves as an Assistant Professor in the Department of Chemical and Environmental Engineering within the College of Engineering and Applied Science at the University of Cincinnati. Her research focuses on terrestrial hydrology, specializing in river systems through innovative integration of satellite remote sensing, process-based models, and machine learning to address freshwater resource challenges. Dr. Feng earned her Ph.D. (2018), M.S. (2013), and B.S. (2010) in Environmental Engineering from Northeastern University and Tongji University. Her research interests span terrestrial hydrology, river dynamics, satellite remote sensing applications, machine learning in hydrological modeling, freshwater ecosystem health, inland water carbon cycling, and harmful algal blooms monitoring. She develops cutting-edge methodologies to quantify river water quantity and quality while advancing understanding of complex river dynamics under changing environmental conditions. Her publication portfolio demonstrates strong focus on satellite-based river monitoring, with recent work analyzing global river discharge changes, Arctic hydrology, suspended sediment quantification, and phosphorus cycling using machine learning. This research trajectory reflects increasing integration of NASA's SWOT mission data with traditional hydrological models to address continental-scale water challenges. 2023 CEAS Faculty Development Award 2024 NASA Early Career Investigator Award 2024 CEAS Distinguished Researcher Award 2025 CEAS Research Award for Early Career Faculty As Principal Investigator, Dr. Feng has secured over $2.2 million in federal research funding since 2022, including multiple NASA grants (totaling $1.9M), USGS projects, and University of Cincinnati initiatives. Her current portfolio includes lead roles in pan-Arctic sediment mapping, Ohio River Basin phosphorus monitoring, and global inland water carbon emission studies. She directs the Terrestrial Hydrology Lab, which employs UAVs, satellite data, and machine learning to study Earth's water systems with emphasis on river dynamics and ecosystem health.

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +۴ مورد دیگر

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA) since 2023 and Vice President for Technology Transfer at ISTA since 2024. Her research focuses on efficient algorithms and data structures , particularly in dynamic settings where input data changes continuously. She also investigates privacy-preserving algorithms and the practical implementation of theoretically optimal algorithms. Education: PhD in Computer Science (1993, Princeton University, USA) Research Interests: Her work spans dynamic graph algorithms, differential privacy, and transforming theoretical algorithms into practical solutions. Key areas include minimizing computational resources, proving limits of efficiency, and ensuring data privacy through noise addition. Article Trends: Recent publications emphasize dynamic algorithms for graph problems , differential privacy , and clustering techniques . Topics such as minimum cut, bipartite matching, and edge coloring in dynamic environments are prominent, alongside privacy-preserving data analysis. Scientific Awards: 2024 Best Paper Award (SODA) 2021 Wittgenstein Award and ERC Advanced Grant 2019 Carus Medal (Leopoldina Academy) 2016 ACM Fellow 2014 ERC Advanced Grant 2004 European Young Investigator Award Advising & Grants: She advises PhD students and leads projects funded by grants such as the ERC Advanced Grant, Wittgenstein Award, and Austrian Science Fund initiatives. Her team includes postdocs and PhD candidates working on dynamic data structures and privacy. Labs & Teams: Leads the Henzinger_Monika Group at ISTA, focusing on algorithms, data structures, and dynamic graph problems. Collaborates with institutions globally on projects related to network optimization and privacy.

Simon S.Y. Shimمشاهده پروفایل

استاد

• Internet Computing
• Database Management
• Machine Learning
• +۳ مورد دیگر

Simon S.Y. Shim is a Professor in the Computer Engineering Department at San Jose State University (SJSU). He holds a PhD in Computer Science from the University of Minnesota (1997). His research focuses on Internet Computing, Database Management, Machine Learning, and Information Retrieval. He has extensive industry experience as a Director at SAP Labs, working on in-memory database systems. Shim has served on the editorial board of IEEE Computer, including a 5-year tenure as Area Editor for Internet Computing. He has guest-edited special issues on High-Speed Security (2004), CAP Theorem (2012), and Next-Gen Memory (2013). He co-authored one technical book and holds multiple patents. His professional contributions include organizing IEEE Data Engineering workshops, IEEE Mobile Commerce, and IEEE Electronic Commerce events. Shim has published over 50 peer-reviewed articles and actively participates in academic service roles. His work emphasizes interdisciplinary applications of computer science in e-commerce, distributed systems, and multimedia technologies.

Loutfouz Zamanمشاهده پروفایل

دانشیار

• User research in gaming
• Game analytics and data science
• Game evaluation and testing
• +۲ مورد دیگر

Dr. Loutfouz Zaman is an Associate Professor in the Game Development and Interactive Media department at Ontario Tech University , part of the Faculty of Business and Information Technology . He holds a PhD in Computer Science with a focus on Human-Computer Interaction from York University. His research explores visual programming interfaces, game analytics, and extended reality technologies, with a focus on practical applications such as automated bug detection and user experience optimization. Dr. Zaman has collaborated on projects funded by MITACS, NSERC, and industry partners, addressing challenges in healthcare incident management, language learning gamification, and air traffic control simulation training. He teaches courses ranging from introductory game math to graduate-level topics in human-computer interaction and machine learning for game analytics. Education: Bachelor of Science in Computer Science (Software Systems) – Concordia University Master of Science in Computer Science (Human-Computer Interaction) – York University PhD in Computer Science (Human-Computer Interaction) – York University Research Interests: Dr. Zaman’s work spans user research in gaming, game evaluation methodologies, and emerging technologies like AR/VR. His team focuses on developing tools for visual game analytics and automated testing, with recent projects including mixed reality fitness gaming interfaces and deep learning-based bug detection systems. Grants & Collaborations: His industry partnerships include projects on healthcare incident management during pandemics, CRM gamification, and pet identification systems using computer vision. He actively seeks doctoral candidates to expand research in visual analytics, XR technologies, and automated bug detection. Labs & Affiliations: Dr. Zaman is affiliated with the Software and Informatics Research Centre (SIRC) and contributes to the university’s efforts in bridging academic research with real-world applications.

Anirudh Sivaramanمشاهده پروفایل

استادیار

• Computer Networks
• Network Programmability
• Distributed Systems
• +۳ مورد دیگر

Anirudh Sivaraman is an Assistant Professor of Computer Science at New York University's Courant Institute, specializing in computer networks and programmability. He holds a PhD from MIT (2017) and a BTech from IIT Madras (2010). His research focuses on enabling ubiquitous network programmability through hardware-software systems, including compilers, runtimes, and applications like distributed consensus and financial exchanges. Education: Ph.D. in Computer Science, MIT (2012–2017) S.M. in Computer Science, MIT (2010–2012) B.Tech in CSE, IIT Madras (2006–2010) Research Interests: Network programmability (e.g., P4, Barefoot Networks) High-speed packet processing and compiler optimization Distributed systems and consensus protocols Cybersecurity in programmable networks Cloud computing and microservice autoscaling Notable Articles Trends: Focus on programmable networks (P4, NIC design) Hardware-software co-design for performance Security and isolation in multi-tenant environments Compiler-based solutions for bug detection Awards: ACM SIGCOMM Doctoral Dissertation (2017), SIGCOMM Best Paper (2017), and multiple teaching awards. Advising & Grants: PI on grants totaling $900K+ (Google, Amazon, NSF). Advises 8+ PhD/Master’s students. Serves on committees for SIGCOMM, NSDI, and HotNets. Labs/Teams: Technical advisor at Clockwork. Co-organizes workshops on programmable networks and P4 standards.

Bronwyn Carlsonمشاهده پروفایل

استاد

• Indigenous Digital Life
• Social Media Activism
• Indigenous Futurisms
• +۳ مورد دیگر

Distinguished Professor Bronwyn Carlson is the Head of Critical Indigenous Studies at Macquarie University's School of Communication, Society and Culture. She leads the Global Indigenous Futures Research Centre and serves as founding editor of the Journal of Global Indigeneity . A leading scholar in Indigenous digital engagements, her work focuses on social media activism, Indigenous futurisms, and decolonial praxis. She has secured major ARC grants and pioneered research on Aboriginal and Torres Strait Islander digital practices, including landmark publications like Indigenous People Rise Up: The Global Ascendancy of Social Media Activism . Her research interrogates colonial commemorations, digital health equity, and technology-facilitated abuse. Notable projects include the CEVAW23.WS04 research stream addressing violence against women and the Indigenous Language Hub initiative. Carlson is a 2020 Fellow of the Australian Academy of the Humanities and recipient of the 2013 Stanner Award. Key Roles: Head of Critical Indigenous Studies, Director of Global Indigenous Futures Research Centre Grants: Three consecutive ARC grants (Digital Technologies Engagement Stream) Media Impact: Over 8 media contributions addressing Indigenous representation and social justice Her work bridges academia and activism, with projects like the 'Crowther Reinterpreted' art initiative challenging colonial monuments. Current research explores AI and Indigenous data sovereignty, wearable health technologies for remote Indigenous communities, and transnational Indigenous feminisms.

Jaime Llorcaمشاهده پروفایل

استاد پژوهشی

• Network Algorithms
• Network Optimization
• Distributed Control
• +۵ مورد دیگر

Jaime Llorca is a Research Professor at the New York University (NYU) Tandon School of Engineering's Electrical and Computer Engineering Department. He holds affiliations with NYU WIRELESS, a research center focused on 6G and beyond. His work bridges network algorithms, optimization, and distributed control with applications in edge computing, cloud networks, and content delivery. Prior roles include Senior Research Scientist at Nokia Bell Labs and positions at Alcatel-Lucent Bell Labs and the University of Maryland's Center for Networking of Infrastructure Sensors. Education: M.S. and Ph.D. in Electrical and Computer Engineering from the University of Maryland, College Park (MD). Research Focus: Llorca pioneers advancements in network-coded caching, distributed cloud control, and edge-cloud integration. Notable contributions include 'cache-aided coded multicasting' and 'cloud-network flow' frameworks. His work addresses latency constraints, resource allocation, and service orchestration in next-generation networks. Key Achievements: Over 100 peer-reviewed publications and 20 patents. Awarded the 2007 IEEE Sensors Best Paper Award, 2016 IEEE ICC Best Paper Award, and the 2015 Jimmy H.C. Lin Award for Innovation. Co-developed foundational technologies for content delivery and distributed cloud networks. Advising & Grants: Advises on distributed computing networks and edge-cloud systems. His research is supported by grants focused on 6G, network optimization, and IoT integration. Collaborates closely with NYU WIRELESS on projects like the Vineyard Digital Twin initiative leveraging UAV imaging. Labs & Teams: Active member of NYU WIRELESS, advancing 6G, mmWave/terahertz networks, and cross-layer network programmability. Leads teams exploring morphable networks and end-to-end service orchestration.

Antonio Cartaمشاهده پروفایل

استادیار

• Continual Learning
• Deep Neural Networks
• Machine Learning
• +۴ مورد دیگر

Antonio Carta is an Assistant Professor in the Department of Computer Science at the University of Pisa. His primary research focuses on Continual Learning (CL) with deep neural networks, particularly addressing challenges like catastrophic forgetting and knowledge consolidation in online settings. He leads the Computational Intelligence and Machine Learning (CIML) group and contributes to the Pervasive AI Lab (PAILab) . As the lead maintainer of the Avalanche library, he develops end-to-end tools for deep continual learning. His work spans applications in computer vision, NLP, and time series analysis. Research emphasizes distributed CL, efficient knowledge transfer, and replay-free methods. Teaching includes courses on Continual Learning, Collective Machine Intelligence, and Intelligent Systems at the graduate level. Publications highlight adaptive normalization, temporal ensembles, and domain-based Bayesian approaches in CL.

• Database Systems
• Mobile and Pervasive Data Management
• Distributed Computing
• +۵ مورد دیگر

Panos K. Chrysanthis is a Professor of Computer Science at the University of Pittsburgh and an adjunct Professor at Carnegie-Mellon University and the University of Cyprus. He holds a BS in Physics with a Computer Science concentration from the University of Athens, Greece, and MS and PhD degrees in Computer and Information Sciences from the University of Massachusetts Amherst. He has been affiliated with the University of Pittsburgh since 1991, serving as Director of the Advanced Data Management Technologies (ADMT) Lab. His research focuses on database systems, mobile and pervasive data management, distributed computing, and real-time systems, with recent emphasis on data streams and energy-efficient data management for IoT and sensor networks. Chrysanthis has authored over 100 peer-reviewed publications, including a book on transaction processing and consistency in distributed databases. He has received the NSF CAREER Award (1995) and the University of Pittsburgh's Provost Award for Excellence in Mentoring (2015). He has held editorial roles at the VLDB Journal, IEEE TKDE, and DAPD, and has organized major conferences like ICDE and MDM. His work bridges theoretical database research with practical applications in urban informatics, healthcare, and sustainability. Chrysanthis has advised numerous students, contributed to grants focused on big data and healthcare analytics, and co-developed tools like the ADMT Lab's experimental platforms for urban mobility and congestion. He is a Senior Member of IEEE and an ACM Distinguished Scientist.

Ali C. Begenمشاهده پروفایل

استاد

• Multimedia Networking
• Adaptive Streaming
• Low-Latency Live Streaming
• +۷ مورد دیگر

Ali C. Begen is a Professor in the Computer Science Department at Ozyegin University in Istanbul, Turkey. He is also the founder of Networked Media , a technology consultancy specializing in IP video solutions. Prior to academia, he was a Technical Lead at Cisco in San Jose, California, and has over 40 US patents in media transport protocols. Education: PhD in Electrical and Computer Engineering (Georgia Tech, 2006); BSc in Electrical Engineering (Bilkent University, 2001) Professional Recognition: Emmy® Award for Technology and Engineering (2022); ACM SIGMM Test of Time Paper Award (2022); SVTA Fellow (2023); IEEE Distinguished Lecturer (2016-2018) Research Focus: Begen’s work bridges low-latency live streaming , media-over-QUIC transport (MOQ) , and adaptive streaming protocols (DASH, WebRTC) . His recent research explores unifying real-time communication and content delivery under a single protocol to reduce complexity and improve scalability. Publications: Recent articles analyze bandwidth prediction , QUIC prioritization , and cross-protocol collaboration , reflecting a trend toward data-driven, network-aware media delivery systems. Honors: Emmy® Award (2022) ACM SIGMM Test of Time (2022) IEEE Distinguished Lecturer (2016, re-elected 2018) SVTA Fellow (2023) Royal Society Newton Fellowship (2019) Advising & Collaborations: Begen has served on thesis committees for over 15 PhD/MS students at institutions like Ozyegin University, University of Klagenfurt, and Koc University. He actively contributes to standards bodies (IEEE, ACM) and industry consortia like the Streaming Video Technology Alliance.

• Cloud Computing
• Computer Networking
• Machine Learning
• +۱ مورد دیگر

Xiaoqian (Tiffany) Zhang is an Assistant Professor in the Department of Computer Science at the University of Nebraska at Omaha (UNO) College of Information Science & Technology. Her work focuses on cloud computing, computer networking, and cybersecurity. Education: Ph.D. in Computer Science (University of Massachusetts Boston, 2023), MS in Mathematics (New York University, 2014), BA in Mathematics and Economics (Skidmore College, 2012) Her research explores cloud computing and networking with emphasis on machine learning and cybersecurity . Recent work addresses challenges in disaggregated storage systems , edge computing , and augmented reality . She received the DOE NNSA Joule Award (2022) for her scholarship. Her publications span topics including network congestion control , distributed file systems , and audio noise filtering . She teaches courses in cloud computing and computer networking .

Graham Cormodeمشاهده پروفایل

استاد

• Data Sketching
• Streaming Algorithms
• Differential Privacy
• +۷ مورد دیگر

Graham Cormode is a Professor in the Department of Computer Science at the University of Warwick. He has held research positions at Bell Laboratories and AT&T Labs-Research and leads major research projects in algorithms for big data. He is affiliated with the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC), and the Alan Turing Institute (ATI). His research focuses on the data lifecycle , particularly data sketching , streaming algorithms , and privacy-preserving data analysis . He develops efficient summaries for massive datasets, enabling scalable analytics under constraints of space, time, and privacy. His work spans theoretical algorithm design and practical deployment, especially in differential privacy and distributed systems. His recent publications show a strong trend in private data release , quantile estimation , and interactive proofs for outsourced computation. He frequently publishes in top venues like PODS, VLDB, and STOC, demonstrating sustained impact in database theory and algorithms. Adams Prize, University of Cambridge (2017–2022) Royal Society Wolfson Research Merit Award - Small Summaries for Big Data (2014–2019) Cormode supervises PhD students and postdoctoral researchers, including Pavel Veselý and Michael Shekelyan. He has secured substantial funding from EPSRC, ERC, GCHQ, and industry partners like Microsoft and AT&T. His projects include Small Summaries for Big Data (ERC), FAIR (EPSRC), and ComPaTrIoTs . He is actively involved in organizing workshops and giving keynote talks, such as at PODC and DISC. He leads a research group focused on algorithms for big data , particularly within the ERC-funded “Small Summaries for Big Data” project. The team works on sketching, streaming, and privacy-preserving techniques, often in collaboration with DIMAP, WISC, and the Alan Turing Institute.

Dwi Rahayuمشاهده پروفایل

مدرس

• Computer Science
• Data Science
• Smart Home Technologies
• +۳ مورد دیگر

Dwi Rahayu is a Lecturer in the Department of Human Centred Computing at Monash University. She is a Chief Investigator in the 2025 project 'Integrating Equity, Diversity, and Inclusion (EDI) into ICT Education: A GenAI-Powered Approach to Curriculum Development' funded by the Australian Council of Deans of Information and Communications Technology (ACDICT). Her research spans smart energy systems, wireless sensor networks, data science, and educational technology. Her expertise contributes to UN Sustainable Development Goals, particularly in advancing equitable and inclusive education through technology. Research collaborations include work on multimodal analytics for team teaching, sentiment analysis of e-commerce reviews, and material science innovations in metallurgy. Key research trends include optimizing data routing in sensor networks, developing context-aware energy solutions, and leveraging large language models for topic and sentiment analysis. She has advised multiple projects involving energy management, healthcare practices, and corporate financial analysis. No scientific awards are explicitly mentioned in the provided texts. Her work also involves community-focused initiatives, such as improving web programming skills through Laravel frameworks and enhancing logical thinking via game development in adolescents. Projects often emphasize practical applications of technology in public administration and healthcare contexts.

Michel MAROTمشاهده پروفایل

استاد

• Networking
• Wireless Communication
• Performance Evaluation
• +۵ مورد دیگر

Professor Michel MAROT is affiliated with Telecom SudParis, where he holds the position of Professor in the NeSS department. His research focuses on networking, wireless communication systems, performance evaluation, smart grids, and machine learning applications in telecommunications. MAROT has contributed to advancements in vehicular networks (VANETs/V2V), IoT architectures (LoRaWAN), and energy-efficient protocols for wireless sensor networks (WSNs). He has led studies on coalition formation in smart grids, reinforcement learning for policy optimization, and network resource management in 6G systems. Key research areas include optimizing network performance through cross-layer design, improving QoS in mobile and vehicular environments, and deploying intelligent reflecting surfaces (IRS) for 6G. His work frequently addresses challenges in mobility management, collision avoidance, and energy efficiency in distributed systems. MAROT has co-authored influential papers in journals like Neurocomputing, IEEE Transactions on Smart Grid, and IEEE Open Journal of the Communications Society. His research group (SAMOVAR/NeSS) develops practical solutions for real-world networks, including cold chain monitoring systems using sensor networks and DNS-based optimizations for SCHC protocols. MAROT’s recent work explores machine learning embedded in LPWAN sensors and mobility-aware resource allocation in LoRaWAN.