جست‌وجوی استاد و پژوهشگر

Jodie Mozdzer Gilمشاهده پروفایل

استاد

• Freedom of Information
• Digital journalism tools
• Community news
• +۴ مورد دیگر

Jodie Mozdzer Gil is a tenured Professor in the Department of Journalism at Southern Connecticut State University (SCSU). She serves as faculty adviser to the SCSU Society of Professional Journalists (SPJ) chapter and co-adviser to the student magazine Crescent , with active involvement in promoting freedom of information and digital journalism education. Her affiliations include the Connecticut Council on Freedom of Information and the Society of Professional Journalists. Bachelor's and Master's in Journalism Faculty Development Grants recipient Active FOI law researcher Hyperlocal media advocate Research Focus : Her scholarly work centers on Freedom of Information (FOI) laws , particularly home address exemptions and privacy implications in digital journalism. She explores the intersection of open government and data privacy , with recent pandemic-related studies on virtual civic engagement and budgetary transparency . Her pedagogical research investigates digital media tools and video tutorial engagement in journalism education. Grants & Collaborations : Key funded projects include: "Faculty Training for Online Video Tutorials" ($800) "Telling local stories of World War I" ($4,400) "Curriculum Related Activities Committee Grant" ($2,000) Publications & Presentations : Her 2022 article Home Address Exemptions in State FOI Laws established critical frameworks for privacy exemptions in public records. She has presented at national conferences including AEJMC and the National Freedom of Information Coalition, with specific attention to FOI Summit 2022 and ER visits for asthma data reporting.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ مورد دیگر

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

Koushik Sinhaمشاهده پروفایل

دانشیار

• Mobile Computing
• Wireless Ad hoc Networks
• Wireless Sensor Networks
• +۶ مورد دیگر

Koushik Sinha is an Associate Professor at the School of Computing, Southern Illinois University Carbondale, where he directs the Complex Networks & Applications Lab. With extensive experience spanning academia and industry—including roles at Qatar Computing Research Institute, Hewlett-Packard Labs, and Honeywell—he brings practical expertise to his research in wireless networking, complex systems, and human-machine collaboration. His work bridges theoretical foundations with real-world applications across multiple domains. Education Ph.D. in Computer Science, Jadavpur University, India (2007) M.S. in Computer Science, Clemson University, USA (2003) B.Tech. in Computer Science, Kalyani Government Engineering College, India (2001) Research Focus Dr. Sinha's research centers on energy-efficient wireless communication protocols, 5G/6G networking, and complex network analysis. His "Communication through Silence" paradigm represents a novel approach to minimizing energy consumption in wireless networks. Current projects address critical challenges in ultra-dense 5G deployments, smart city infrastructure, and the CrowdCloud platform for hybrid human-machine computing. His work increasingly intersects with societal challenges in rural connectivity, agricultural technology, and public health. Publication Trends Analysis of Dr. Sinha's recent publications reveals a strategic progression from core wireless networking research toward interdisciplinary applications. While maintaining expertise in energy-efficient protocols and network optimization, his work now addresses smart city infrastructure, rural broadband expansion, precision agriculture, and pandemic response systems. His publications demonstrate growing integration of machine learning techniques and increasing focus on real-world impact in underserved communities. Scientific Recognition Senior Member, IEEE Grand Prize Winner of 9th OSS World Challenge for AIDR (2015) N.V. Gadadhar Memorial Award from IETE (2011) Young Scientist Award from Indian Science Congress Association (2009) Mentorship and Leadership Dr. Sinha has successfully guided multiple Ph.D. and MS students through research in wireless networking and complex systems. His leadership extends to conference organization as General Co-Chair for IEEE ANTS 2018 and TPC Co-Chair for previous editions. Within his department, he has shaped curriculum as Course Coordinator for key offerings including Distributed Systems, Mobile and Wireless Computing, and Computer Architecture. Research Infrastructure The Complex Networks & Applications Lab under Dr. Sinha's direction supports cutting-edge research in wireless protocols, network optimization, and hybrid computing systems. Current projects include 5G network optimization, ultra-dense base station placement, and the CrowdCloud platform for human-machine collaborative computing, providing students with opportunities to address real-world challenges in smart cities, rural connectivity, and emergency response systems.

Wei Wangمشاهده پروفایل

استاد

• Wireless Networks
• Multimedia Communications
• QoE/QoS Optimization
• +۸ مورد دیگر

Dr. Wei Wang is a Professor of Computer Science at San Diego State University (SDSU) within the College of Sciences . He joined SDSU in August 2014 after serving as an Assistant Professor at South Dakota State University (2010-2014). He earned his Ph.D. in Computer Engineering with Computer Science Emphasis from the University of Nebraska-Lincoln (2009), an MS in Information and Telecommunications Systems from Xian Jiaotong University (2005), and a BS in Computer Science/Engineering from the same institution (2002). Dr. Wang's research focuses on Wireless Networks , Quality of Experience (QoE)/Quality of Service (QoS) Optimization , Network Economics , Blockchain Applications , and Internet of Things (IoT) . His recent work explores Semantic Communications , Crypto-Economic Incentives for wireless systems, and Quantum Annealing Approaches for multimedia optimization. Over 15 years, he has published extensively on topics like NOMA Resource Allocation , Game-Theoretic Pricing Models , and Energy-Harvesting Wireless Sensors . Trends in Publications : His 2017-2025 work shows increasing focus on blockchain-integrated wireless systems (2024-2025), machine learning for signal processing (2023), and traditional wireless network economics (2018-2022). Scientific Awards : Excellent Educator Award, SD-SAIC Hackathon (2024) Best Paper Award, IEEE CAMAD (2022) SDSU CS Faculty of the Year (2016) IEEE Senior Member (2016) ACM Service Awards (2013-2015) Advising & Grants : Mentored 30+ students across Ph.D., M.S., and B.S. levels. Principal Investigator for $571,489 in NSF grants (2014-2023) and other industry-funded projects. Labs & Teams : Leads the Cyber Innovation Research Group at SDSU, focusing on QoE-driven wireless solutions and IoT optimization . Collaborates with IEEE MMTC (served as Director of Publicity Board) and NSF panels.

• Natural Language Processing (NLP)
• Deep Learning
• Data Science
• +۳ مورد دیگر

Bang LIU is an Associate Professor at the University of Montreal's Faculty of Arts and Science, specifically within the Department of Computer Science and Operations Research. His research focuses on Natural Language Processing (NLP), Deep Learning, and Data Science with applications in healthcare, materials science, and embodied AI systems. He leads multiple research projects funded by organizations like NSERC and MITACS, exploring causal inference, multimodal reasoning, and AI-driven material discovery. Education: PhD in Computer Science (University of Montreal). Research Highlights : Developing causal discovery frameworks (e.g., CausalNET, OCDB) Pioneering materials science applications via AI agents (MatExpert, Honeycomb) Advancing embodied instruction following systems (OPEx) Grants & Projects : NSERC Discovery Grants (2021-2027) MITACS Accelerate Projects (2022-2025) focusing on healthcare LLM trust, CAD generation, and procedural knowledge extraction He supervises Master's students in low-resource NLP tasks and serves as a lead researcher in over 15 active projects. Affiliated with the CRM and RALI labs, his work bridges theoretical AI advancements with practical industrial applications.

Rui Wangمشاهده پروفایل

دانشیار

• Computer Graphics
• Global Illumination Algorithms
• Real-Time Rendering
• +۵ مورد دیگر

Rui Wang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. His research focuses on computer graphics, including global illumination algorithms, real-time rendering, 3D scanning, and graphics hardware. He received a PhD in Computer Science from the University of Virginia (2006) and a Bachelor's from Zhejiang University (2001). He leads the Computer Graphics Research Group at UMass Amherst and holds an NSF CAREER Award (2008). He previously worked on open-source hardware projects (2013-2014) and has affiliations with the Center for Data Science and the Computer Graphics Laboratory. Education: PhD, Computer Science, University of Virginia, 2006 Bachelor's, Computer Science, Zhejiang University, 2001 Research interests include: Global illumination techniques Interactive photorealistic rendering Appearance modeling Graphics hardware optimization Key contributions involve advancing GPU-based graph algorithms, neural rendering methods, and real-time lighting effects. His work bridges theoretical computer graphics with practical applications in visualization and hardware acceleration. Awards: NSF CAREER Award (2008) Lab affiliations include the Computer Graphics Research Group and collaborations with the Center for Data Science.

Arghya Palمشاهده پروفایل

مدرس

• Causality
• Generative Models
• Computer Vision
• +۳ مورد دیگر

Dr. Arghya Pal is a Lecturer at the Malaysia School of Information Technology, Monash University. His research focuses on causality, generative models, and computer vision, with notable contributions in medical imaging optimization, bias detection in AI systems, and cryptographic applications of autoencoders. He has led or co-investigated projects such as Æinstein: Adversarial AI amongst Materials Discovery Domains (2024–2026) and Memory Efficient Fast Sampling of Diffusion Model (2023–2024). Research Interests: Causality and bias analysis in machine learning Generative models for multimedia applications Computer vision techniques in medical imaging Cryptography and AI-driven security systems Awards: Magna cum laude (2022) Grants/Projects: Primary Investigator: Memory Efficient Fast Sampling of Diffusion Model (2023–2024) Chief Investigator: Æinstein Project (2024–2026)

• Algorithms
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

David Li serves as Program Director for Data Analytics and Visualization at the Department of Graduate Computer Science and Engineering, part of the Katz School of Science and Health at Yeshiva University. His research focuses on algorithmic modeling, machine learning, and interdisciplinary applications in fields such as computational chemistry, finance, and ecology. He has published extensively on topics including MapReduce optimization, reinforcement learning frameworks, and ecological forecasting. Notable work includes the Best Regional Paper Award-winning Dual-Path Deep Learning Framework for Video Quality Assessment presented at IEEE ICCE 2025. Education: Details not provided in current text. Courses Taught: Deep Reinforcement Learning, Numerical Methods, Data Acquisition & Management, Independent Study. Grants & Collaborations: Collaborates with students on projects across algorithm design, machine learning, and interdisciplinary applications. His work bridges theoretical computer science with practical challenges in diverse domains, emphasizing scalable solutions for complex systems.

• Distributed Computing
• Cloud Computing
• Federated Learning
• +۳ مورد دیگر

Associate Professor Bing Zhou is a Theme Leader in Distributed Computing Applications at the School of Computer Science, University of Sydney. He teaches courses such as Operating Systems Internals and Parallel and Distributed Computing , and leads research on distributed systems, cloud computing, and machine learning applications in bioinformatics. Current Research Students: Sichang DING (Spatial Multi-Omics), Quan LA (Adversarial Quantum Machine Learning) Contact: bing.zhou@sydney.edu.au | J12, Sydney University Research Interests: His work spans distributed computing, cloud optimization, bioinformatics data management, and machine learning techniques. Notable projects include DCDedupe (distributed storage optimization), FRANK (network analysis), and DomNet (protein prediction systems). Recent Publications (2022-2025): Focus on online caching algorithms, edge video processing, federated learning frameworks, and adaptive scheduling. Keywords include distributed systems, reinforcement learning, blockchain analytics, and GPU optimization. Key Collaborations: Co-authorships with researchers like D. Yuan, W. Bao, and A. Zomaya across 30+ journals and conferences.

Junchen Jiangمشاهده پروفایل

استادیار

• Networked Systems
• Machine Learning
• Cloud Computing
• +۳ مورد دیگر

Junchen Jiang is an Assistant Professor of Computer Science at the University of Chicago, joining in July 2018. He holds a PhD from Carnegie Mellon University (2017) under advisors Vyas Sekar and Hui Zhang, and a B.A. from Tsinghua University's Yao Class (2011). His research focuses on networked systems and machine learning systems, aiming to enhance performance and reliability of large-scale systems through advanced ML techniques. Key research areas include Big Data, Cloud Computing, Distributed Systems, and applying ML to network optimization. He leads projects such as LMCache for LLM serving and GRACE for real-time video streaming. His work has been recognized with awards like the NSF CAREER Award (2022) and ACM EuroSys Best Paper (2025). He collaborates with the Systems Group and Machine Learning Group at UChicago, and co-leads the CERES Center for Unstoppable Computing. His teaching includes graduate courses on machine learning for networks and systems. Education: PhD in Computer Science, Carnegie Mellon University, 2017 B.A., Computer Science (Yao Class), Tsinghua University, 2011 Awards: ACM EuroSys Best Paper (2025) NSF CAREER Award (2022) Google Faculty Research Award (2019) Labs/Groups: Systems Group Machine Learning Group CERES Center Professional Activities: Program committee member for SIGCOMM, NSDI, EuroSys, and others Organizer of workshops on Edge Video and NetAI

Sanjay Krishnanمشاهده پروفایل

استادیار

• Data Science
• Databases
• Machine Learning
• +۵ مورد دیگر

Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research focuses on managing uncertain data in databases and information systems, including accuracy guarantees in incomplete databases, query evaluation under corruption, and data leakage detection. He leads the ChiDATA research group within the University of Chicago's Department of Computer Science, collaborating with researchers like Michael Franklin and Raul Castro Fernandez on large-scale data analysis and systems. His academic work spans foundational areas such as AI/ML integration with data systems, scalable data processing, and resource-efficient database design. He is affiliated with the CERES Center for Unstoppable Computing and the Systems Group, exploring resilient computing systems and software engineering challenges. His lab's research has been featured in UChicago CS news for innovations like geometric database query processing and interdisciplinary collaborations with the Museum of Science & Industry. Key contributions include frameworks like EdgeServe for decentralized model serving, CrocodileDB for resource-efficient query execution, and ActiveClean for interactive data cleaning in ML pipelines. His work bridges theoretical computer science with practical system implementations, emphasizing real-world data challenges in IoT, healthcare, and environmental monitoring.

• Computer Networks
• Internet Video Distribution
• Network Synthesis
• +۶ مورد دیگر

Sanjay Rao is a Professor in the School of Electrical and Computer Engineering at Purdue University, leading the Internet Systems Laboratory. His research focuses on network synthesis/design/verification and Internet video distribution. He holds a B.Tech from IIT Madras and a Ph.D from Carnegie Mellon University. He has held visiting roles at Google, AT&T Research, and Princeton University. Research & Awards: His work includes groundbreaking contributions like End System Multicast (winner of the ACM SIGMETRICS Test of Time Award), Oboe (ABR auto-tuning), and Veritas (causal video streaming analysis). He received the NSF CAREER Award (2010) and is an ACM Distinguished Member (2021). Teaching: Courses include Computer Networking (ECE 463), Object-Oriented Programming (ECE 39595), and Computer Network Systems (ECE 595). Service: Chair, ACM Sigcomm Doctoral Dissertation Award Committee (2022); Associate Editor, IEEE/ACM Transactions on Networking (2016–2020). Labs & Teams: Directs the Internet Systems Lab (ISL) with active projects on video streaming, network resilience, and intent-based design. Graduate students collaborate on these initiatives.

Keith Winsteinمشاهده پروفایل

دانشیار

• Networked Systems
• Computer Networks
• Data Compression
• +۶ مورد دیگر

Keith Winstein is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Electrical Engineering. His research focuses on creating innovative networked systems, particularly in communication, compression, and computing. Notable projects include Mosh (an interactive remote shell for mobile clients), Puffer (a video-streaming platform), Lepton (a compression tool), Mahimahi (network emulators), and the gg framework for distributed computing. He has received prestigious awards such as the SIGCOMM Rising Star Award, Sloan Research Fellowship, and NSF CAREER Award. Winstein's academic journey includes undergraduate and graduate studies at MIT. Before academia, he worked at The Wall Street Journal as a reporter and at Ksplice (now part of Oracle), where he held roles in product management and business development. His research spans network protocols, video streaming optimization, cloud computing, and machine learning applications in networking. His work emphasizes practical systems that bridge theoretical concepts with real-world implementation. Recent projects explore computation-centric networking, in-network performance enhancements, and low-latency video streaming. He advocates for reproducible experiments through tools like Mahimahi and has contributed to open-source software widely used in academia and industry. Key Projects: Mosh, Puffer, Lepton, Mahimahi, gg Awards: SIGCOMM Rising Star Award, Sloan Fellowship, NSF CAREER Expertise: Networked Systems, Compression Algorithms, Distributed Computing

Lisimachos P. Kondiمشاهده پروفایل

استاد

• signal processing
• communications
• image/video compression
• +۸ مورد دیگر

Lisimachos P. Kondi is a Professor at the University of Ioannina in the Department of Computer Science and Engineering, where he has established himself as a leading researcher in signal processing and communications. His academic career spans over two decades with significant contributions to video compression, wireless transmission, and image processing. Education: Diploma in Electrical Engineering, Aristotle University of Thessaloniki, Greece (1994) M.S. in Electrical and Computer Engineering, Northwestern University, USA (1996) Ph.D. in Electrical and Computer Engineering, Northwestern University, USA (1999) Professor Kondi's research focuses on signal processing and communications, with particular emphasis on image/video compression , video transmission over wireless channels , video quality assessment , sparse representations , compressive sensing , super-resolution of video sequences , image watermarking , and shape coding . His work bridges theoretical foundations with practical applications in wireless communications and medical imaging. His recent publications demonstrate a strong trend toward medical image processing and super-resolution techniques, with increasing integration of machine learning approaches. The research spans from theoretical frame construction for compressed sensing to practical applications in wireless visual sensor networks and medical imaging. Teaching: Current Courses (2022-2023): Communication Systems, Multimedia, Video Processing and Compression Previous Courses: Introduction to Programming, Signals and Systems, Topics in Digital Signal Processing, Statistical Signal Processing, Topics in Networking, Topics in Digital Image Processing Professor Kondi has authored the book 4G Wireless Video Communications (Wiley, 2009) and contributed chapters on Color Image Super-Resolution and Video Telephony to major reference works. His research has been published extensively in top-tier journals including IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing, and SPIE Journal of Electronic Imaging.

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +۷ مورد دیگر

Prof. Simona Lohan is a Full Professor at the Electrical Engineering unit of Tampere University, Finland, and a visiting professor at Universitat Autònoma de Barcelona. Her research focuses on wireless positioning, GNSS algorithms, and wearable computing, with an emphasis on privacy-aware localization and interference mitigation. She leads the Signal Processing for Wireless Positioning research group and coordinates the H2020 MSCA European Joint Doctorate A-WEAR (2019-2023). She holds a PhD in Telecommunications from Tampere University of Technology (2003), and has authored/co-authored over 250 peer-reviewed publications. Education background includes an MSc in Electrical Engineering from Polytechnics University of Bucharest (1997), a DEA in Econometrics from École Polytechnique, Paris (1998). Her research spans satellite navigation (Galileo/GPS/GLONASS), UMTS/WCDMA positioning, 5G positioning, IoT localization, and medical applications of positioning systems. She is an associate editor for the RIN Journal of Navigation and IET Journal on Radar, Sonar, and Navigation . Key projects include the H2020 A-WEAR project (wearable health tech), Academy of Finland ULTRA (2020-2022), and SJU GATEMAN (2018-2019). Her work addresses global navigation challenges through LEO-PNT constellations, low-cost positioning solutions for Africa, and fusion of 5G/mmWave radar for airport surveillance. She has pioneered datasets like TUJI1 for indoor localization and developed open-source tools like SyDR for GNSS algorithm benchmarking. Her research themes include: LEO satellite-based positioning systems Anti-spoofing techniques for GNSS RF fingerprinting for device identification Privacy-preserving localization algorithms IoT and industrial internet applications Embedded signal processing systems Her team's innovations bridge communication and navigation domains, with applications in aviation, healthcare, and smart infrastructure. Recent work emphasizes energy-efficient GNSS signal processing and multi-constellation PNT solutions for global accessibility.