جست‌وجوی استاد و پژوهشگر

Zvi Galilمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Cryptography
• +۳ مورد دیگر

Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.

Prakash Ramananمشاهده پروفایل

استاد

• Algorithms
• Database Systems
• Distributed Computing
• +۳ مورد دیگر

Prakash Ramanan is a Professor at the School of Computing within the College of Engineering at Wichita State University. His research focuses on algorithms, distributed computing, database systems, and XML processing. He holds an office at 208 Jabara Hall on the university's main campus. His academic interests include parallel algorithms, MapReduce frameworks, computational complexity, and query processing in XML databases. His work spans theoretical algorithm analysis to practical implementations in distributed systems. Ramanan's publications emphasize algorithmic efficiency, lower bounds analysis, and optimization techniques. His recent work addresses MapReduce-based matrix multiplication and XPath query evaluation over XML streams. Earlier contributions include foundational studies on tree patterns, join operations, and parallel computing. No scientific awards or grants are explicitly listed in the provided information. He currently advises no listed students or supervisees. His research activities involve collaborations in distributed systems and database query optimization.

Shikha Singhمشاهده پروفایل

استادیار

• Algorithmic Game Theory
• Algorithms & Data Structures
• Theoretical Computer Science
• +۳ مورد دیگر

Shikha Singh is an Assistant Professor of Computer Science at Williams College. She holds a PhD from Stony Brook University (2018) and an Integrated BSc & MSc from IIT Kharagpur (2013). Her research focuses on algorithmic game theory, algorithms with predictions, and data structure optimization. She has received tenure at Williams College and was recognized for her work on adaptive filters at ESA 2021 and NeurIPS 2023. Education: PhD, Stony Brook University (2018); Integrated MSc & BSc, IIT Kharagpur (2013) Research Interests: Algorithmic game theory, adaptive and I/O-efficient algorithms, data structures with predictions. She explores how incentives influence algorithm outcomes and designs efficient algorithms for big data challenges. Key Contributions: Her work on online list labeling with predictions (NeurIPS 2023) and incremental shortest path algorithms (ICALP 2025) highlights her focus on predictive algorithms. Collaborations include visits to Carnegie Mellon University and Dagstuhl seminars. Awards: COCOA 2017 Best Paper Runner-Up, NeurIPS 2023 Spotlight Advising: Advised David Lee on adaptive filters (ESA 2021). Active in program committees for Euro-Par, TAMC, and ALENEX. Labs/Teams: Collaborates with Benjamin Moseley on scheduling and data structure research. Part of Williams College’s vibrant CS department.

Christopher Barzakمشاهده پروفایل

استاد

• Revisionist and adaptive writing
• Writing about place
• Fabulist literature
• +۲ مورد دیگر

Christopher Barzak is Professor of English at Youngstown State University, teaching creative writing, fiction, and speculative literature. He earned his MFA from Chatham University and MA/BA from Youngstown State University. His research explores revisionist writing, place-based narratives, fabulist literature, mythology, and folklore. Barzak's creative works blend magical realism, queer themes, and Midwest settings, often reinventing fairy tales and gothic traditions. Barzak's publications include award-winning novels and short story collections. His novel Wonders of the Invisible World received the Stonewall Honor, while the collection Before and Afterlives won the Shirley Jackson Award. Recent work includes the novella A Voice Calling (2024) and collection Monstrous Alterations (2023). Honors include the Crawford Award, Whippoorwill Award, and multiple Nebula and Locus Award nominations. His books have been selected for CNN's Pride Month Honor List, HRC's Required Reading List, and the Choose to Read Ohio program. Barzak serves on editorial boards and has organized literary conferences, including the International Conference for the Fantastic in the Arts. His writing has been translated into multiple languages and adapted into the Sundance feature film Jamie Marks is Dead .

Aparna Vardeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۹ مورد دیگر

Dr. Aparna Varde is a tenured Associate Professor in the School of Computing at Montclair State University (MSU), NJ. She also serves as Associate Director of the Clean Energy and Sustainability Analytics Research Center (CESAC) and previously held roles as Inaugural Associate Director for Graduate Studies and Research in SoC. She has conducted research visits at the Max Planck Institute for Informatics in Germany and holds degrees from the University of Bombay (BE), Worcester Polytechnic Institute (MS/PhD). Her research focuses on AI, Machine Learning, Data Mining, Environmental Computing, and Robotics. Notable projects include GreenDSS (green data center decision support), CSK-based robotics (e.g., CSK-Detector and Robo-CSK-Organizer), and offshore wind energy analysis. She has secured over $2M in grants from PSE&G, NSF, NOAA, and NJEDA. Dr. Varde advises PhD students (e.g., Xu Du, Michael Pawlish) and serves on editorial/review boards for IEEE/ACM journals. She has received 9 best paper awards at IEEE conferences and is recognized as an outstanding researcher by USCIS. Education: BE (University of Bombay), MS/PhD (Worcester Polytechnic Institute) Awards: 9 best paper awards at IEEE conferences, NSF/NOAA grants, Fulbright mentorship Key Projects: Smart Cities policy analysis, autonomous vehicle CSK integration, drone-based environmental monitoring Her work spans 150+ publications in IEEE/ACM venues and addresses UN Sustainable Development Goals through smart living apps (e.g., food donation platforms, hydro-climate tools). Collaborations include MPII Germany, IBM Research, and Queensland University of Technology.

Zhe Zhangمشاهده پروفایل

استادیار

• Geographic Information Science
• Cyberinfrastructure
• Geospatial Artificial Intelligence
• +۴ مورد دیگر

Dr. Zhe Zhang is an Assistant Professor in the Department of Geography at Texas A&M University and an affiliate faculty member in the Department of Electrical and Computer Engineering. She leads the Cyberinfrastructure and Spatial Decision Intelligence (CIDI-Spatial) research group and holds roles such as Director of the CIDI-Spatial Lab and Council Member of the Gulf of Mexico Coastal Ocean Observing System. Her research focuses on advancing cyberinfrastructure, geospatial data science, and decision support systems to address complex human-environment interactions, sustainability, and disaster resilience. She has secured over $18 million in research grants from NSF, NOAA, NASA, and others. Education: Doctor of Science (Geoinformatics), Aalto University, Finland (2016) Master of Science (Geomatics), Helsinki University of Technology, Finland (2009) Bachelor of Environmental Engineering, Tampere University of Applied Sciences, Finland (2005) Research Interests: Dr. Zhang's work intersects GIScience, decision science, and cyberinfrastructure. Key areas include cyberGIS, geospatial AI, human-centered decision-making, spatial uncertainty modeling, and disaster management. She develops scalable decision support systems for applications like flood emergency management, sustainable fisheries, and urban resilience. Awards: NSF CAREER Award (2023) UCGIS Community Champion Award (2022) Multiple NSF grants including CyberTraining and ACES initiatives Grants & Advising: Current NSF grants include $568K for coastal resilience (CAREER) and $455K for disaster management workforce development. She actively advises graduate students in GIS, spatial analytics, and data science. Notable student achievements include winning Texas A&M data science competitions and securing roles at ESRI and GCOOS. Labs & Teams: The CIDI-Spatial Lab focuses on cyberGIS tools and spatial decision support. Collaborations include NASA-funded heat vulnerability analysis in Oklahoma City and NOAA-funded coastal resilience projects. The lab also engages in K-12 education programs like the Texas Youth Geography Network.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +۴ مورد دیگر

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

Peixiang Zhaoمشاهده پروفایل

دانشیار

• Data Science
• Network Science
• Database Systems
• +۴ مورد دیگر

Peixiang Zhao is an Associate Professor in the Department of Computer Science at Florida State University (FSU). He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) and completed his M.S. and B.S. at Peking University. His research focuses on data/network science, database systems, and graph analytics, with particular emphasis on managing and analyzing large-scale networked data. His work has been supported by AFOSR-YIP, ARO-YIP, NSF, and industry grants. He advises several doctoral and master’s students and teaches courses like Advanced Data Mining and Advanced Database Systems. His research group explores topics such as graph query optimization, graph summarization, and scalable computation in dynamic graph streams. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, Peking University (2004) B.S., Computer Science, Peking University (2001) Research Interests: Graph query processing and optimization Graph summarization and learning Scalable computation in dynamic graph streams Data-intensive systems and analytics Awards: AFOSR-YIP (2021) ARO-YIP (2020) Teaching: CAP5778 Advanced Data Mining, COP5725 Advanced Database Systems.

Angela Wuمشاهده پروفایل

استاد

• Computer Vision
• Parallel Algorithms
• Computational Geometry
• +۳ مورد دیگر

Angela Wu is a Full Professor at the Department of Computer Science, American University. She holds a PhD in Computer Science from the University of Maryland College Park (1978), an MS in Mathematics from Cornell University, and a BS in Mathematics from Villanova University. Her academic roles include serving as Conference Chair for the Annual Vision Geometry Conference (1993–2008) and Executive Council Member of Upsilon Pi Epsilon (1998–2010), including terms as International President (2002–2003, 2008–2009). She also served on the Computer Science External Advisory Board at George Washington University (2003–2005). Dr. Wu’s research focuses on computer vision, parallel algorithms, computational geometry, and their applications in image processing and pattern recognition. Her work emphasizes algorithms and data structures for geometric problems, with contributions to clustering, approximation algorithms, and parallel computing. Key contributions include co-founding the Annual Vision Geometry Conference to advance geometric techniques in computer vision. Her publications span foundational topics such as k-means clustering optimization, image convolution approximation, and parallel image processing algorithms. Awards include the American University Mellon Distinguished Award (1988) and College of Arts and Sciences Award for Outstanding Teaching (1997). Her professional activities highlight leadership in both academic and honor societies, fostering interdisciplinary collaboration through conferences and advisory roles. While no student advisees are explicitly listed, her research and conference leadership reflect significant contributions to training and guiding the field.

Syam Menonمشاهده پروفایل

استاد

• Privacy
• Data Mining
• Software Design Optimization
• +۳ مورد دیگر

Syam Menon is a Professor in the Information Systems department at the School of Management , University of Texas at Dallas. He teaches courses such as MIS 6316 Data Communications, MIS 6324 Business Intelligence Software and Techniques, and MIS 6334 Advanced Business Intelligence. PhD , University of Chicago, 1997 MBA , University of Chicago, 1996 BTech , University of Kerala, India, 1989 His research focuses on Privacy , Data Mining , and Software Design Optimization . His work addresses network congestion in peer-to-peer systems, optimization of cellular networks, and privacy-preserving techniques in shared databases. He has published extensively in journals like Information Systems Research , IEEE Transactions on Communications , and Management Science . His recent publications highlight trends in Telecommunications and Computer Science , particularly in Network Optimization , Data Privacy , and Algorithm Design . He is affiliated with professional organizations such as INFORMS , IEEE , ACM , and Association for Information Systems . Contact Syam Menon via syam@utdallas.edu or at (972) 883-4779.

Ali Hursonمشاهده پروفایل

استاد

• High-performance computing
• Pervasive computing
• Mobile databases
• +۱۱ مورد دیگر

Dr. Ali Hurson is a Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research spans high-performance computing, pervasive computing, mobile databases, personalized education, intelligent transportation systems, and cyber-physical systems. PhD, Computer Science, University of Central Florida MS, Computer Science, University of Iowa BS, Physics, University of Tehran Dr. Hurson’s research focuses on mobile data access systems, cybersecurity for critical infrastructure, and educational technologies. His work addresses challenges in data dissemination, power management, and fault propagation in heterogeneous systems. His recent publications emphasize agent-based modeling for cyber attacks, predictive analytics in education, and fault tolerance in cyber-physical systems. Trends include integration of machine learning, security frameworks, and sustainable computing. Editor-in-Chief, Advances in Computers Editor-in-Chief, Journal of Sustainable Computing and Communication Dr. Hurson has secured over $3 million in grants from NSF, DOE, DOT, and industry partners. He has held academic roles at Penn State University and the University of Oklahoma, transitioning to Missouri S&T in 2007.

Marcello Balducciniمشاهده پروفایل

دانشیار

• Knowledge Representation & Reasoning
• Ontologies
• Agent Architectures
• +۶ مورد دیگر

Marcello Balduccini is the Department Chair and Associate Professor of Decision and System Sciences at Saint Joseph's University's Erivan K. Haub School of Business. His research focuses on knowledge representation & reasoning, ontologies, agent architectures, and cybersecurity applications in cyber-physical systems (IoT) and cognitive robotics. He previously held roles as an Assistant Research Professor at Drexel University and Principal Research Scientist at Kodak Research Labs. Research Interests: Knowledge Representation & Reasoning Cyber-Security and Cyber-Analytics Ontology-Based Systems Natural Language Understanding Constraint Satisfaction Problems Trustworthiness in AI/Robotics Recent work emphasizes explainable AI (XAI) systems for Answer Set Programming (ASP), cybersecurity frameworks, and formal methods for cyber-physical systems. His over 100 publications span conferences like LPNMR and ICLP, addressing topics from actual causation to autonomous UAV mission planning. Dr. Balduccini has organized international conferences and received grants supporting AI research, including travel grants for knowledge representation conferences. His work bridges theoretical advancements with practical applications in smart grids, supply chain management, and SDG-aligned AI systems.

Haibing Luمشاهده پروفایل

استاد

• AI Fairness and Governance
• Data Security & Privacy
• Big Data Analytics
• +۴ مورد دیگر

Haibing Lu is a Professor and Department Co-Chair of Information Systems & Analytics at Santa Clara University's Leavey School of Business. He holds a Ph.D. in Information Technology from Rutgers University (2011), and B.S./M.S. in Mathematics from Xi'an Jiaotong University (2002/2005). His research focuses on AI fairness, data privacy, cybersecurity, and big data analytics, addressing real-world challenges in healthcare, transportation, and cloud computing. Education: Ph.D. in Information Technology, Rutgers University, 2011 M.S. in Mathematics, Xi'an Jiaotong University, 2005 B.S. in Mathematics, Xi'an Jiaotong University, 2002 Research interests include: AI governance and ethical frameworks Privacy-preserving technologies (e.g., differential privacy) Cybersecurity for IoT and smart grids Applications of machine learning in healthcare and transportation His work has been featured in prestigious journals like IEEE Transactions on Dependable and Secure Computing, ACM Transactions on MIS, and M&SOM, alongside media outlets such as The New York Times and WIRED. Key publications include studies on AI fairness in LLMs, privacy in cloud computing, and secure authentication protocols. Awards include: AACSB Innovations That Inspire Award (2021) M&SOM Best Paper Award Repeated SCU Leavey School Excellence Awards for Teaching/Research/Service Grants and funding sources include NSF, IBM, PARC, and industry partnerships like AKTANA and Ultimate Software. He advises on interdisciplinary projects involving data science, cybersecurity, and sustainable energy systems. Labs/Initiatives: Leads research in Information Systems & Analytics at SCU, with collaborations on metaverse-driven data management and AI governance frameworks.

• Software Engineering
• Information Retrieval
• Design Patterns
• +۳ مورد دیگر

Sargon Hasso is an Adjunct Professor in the Department of Computer Science at Illinois Institute of Technology. His work focuses on software engineering methodologies, design patterns, and information retrieval systems. He has contributed to advancing classification schemes for software patterns using linguistic and probabilistic approaches, as well as exploring the integration of behavioral models in software composition. Research interests include software design patterns, automated classification systems, and the application of linguistic principles to software architecture. His publications span topics from semantic-enriched information retrieval to theoretical frameworks for organizing design patterns. Recent research trends show a consistent focus on improving software modularity through design patterns and enhancing information retrieval models with semantic analysis. His work bridges theoretical computer science with practical software development challenges. No awards or grants are listed in the provided information. No advising records are available.

Prashant Pandeyمشاهده پروفایل

استادیار

• Large-Scale Data Systems
• High-Performance Computing
• Computational Biology
• +۳ مورد دیگر

Prashant Pandey is an Assistant Professor at Northeastern University's Khoury College of Computer Sciences, co-directing the Data Lab and affiliated with Berkeley Lab's Performance and Algorithms Research Group. He holds a PhD in Computer Science from Stony Brook University (2018), with prior postdoc research at UC Berkeley, LBL, and Carnegie Mellon University, and a year as a Research Scientist at VMware Research. His research focuses on scalable data systems with theoretical guarantees, spanning high-performance computing, computational biology, stream processing, and storage. Key projects include designing adaptive filters, GPU-accelerated data structures, and graph containers for skewed dynamic data. Recent talks include sessions on hash tables, B-trees, and graph frameworks at conferences in Bangalore, Las Vegas, and Santiago. Teaching includes courses like Advanced Database Systems and Data Structures for Scalable Computing. His work bridges systems and algorithms, emphasizing practical applications in bioinformatics and exascale computing. Awards and grants are not explicitly listed, but his contributions include foundational papers on filters, hash tables, and genomic data indexing. Ongoing efforts involve the Data Lab's exploration of high-performance filters for GPUs and collaborative projects like the BP-tree and Vector Quotient Filters. Students under his supervision focus on database systems, learned indexes, and external-memory joins.