Min Shin is a Professor and Chair of the Computer Science Department at the University of North Carolina at Charlotte (UNC Charlotte), where he also serves as Assistant Dean for Research in the College of Computing and Informatics. He earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2001. His research focuses on Computer Vision, particularly multiple object tracking and automated visual inspection of nuclear power plants. He leads the Video and Image Analysis Lab, developing user-friendly tracking software funded by NSF, NIH, DARPA, and industry grants. Notable projects include the ABCTracker.org platform, which deploys algorithms refined over 10+ years of research. Dr. Shin’s work spans tracking applications in diverse domains such as ants, bees, cells, termites, robots, and vehicles. He has mentored Ph.D. and undergraduate students extensively, emphasizing computational methods in biology and engineering. His service includes roles as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics-B, and program committees for top conferences (ICCV, CVPR, ECCV). His grants and collaborations reflect interdisciplinary strengths in vision algorithms, usability, and real-world deployment.
Liuba Shrira is a Professor of Computer Science at Brandeis University, affiliated with the Michtom School of Computer Science and the Benjamin and Mae Volen National Center for Complex Systems. Her research focuses on distributed systems, storage systems, blockchain technology, concurrent programming, and system architectures. She holds a Ph.D., M.S., and B.S. from the Technion – Israel Institute of Technology. Her work emphasizes reliable and highly available systems, including innovations in snapshot management, transactional memory, and adversarial cross-chain commerce. She has been recognized with awards such as the ACM Distinguished Scientist (2009), Lady Davis Fellowship (2010-2011), and a Best Paper Award (2020). Her research has been supported by grants from the National Science Foundation and other institutions. Recent publications highlight advancements in optimistic concurrency control, blockchain interoperability, and modular past-state systems. Shrira has also contributed to middleware design and distributed computing frameworks, with applications in both academic and industry settings.
Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .
Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.
Elizabeth A. Gordon serves as Senior Associate Dean for Faculty Affairs and Professor of Accounting at Temple University's Fox School of Business and Management. Her distinguished career focuses on international financial reporting standards, corporate disclosure practices, and accounting policy development, with publications in premier journals including the Journal of Accounting Research and The Accounting Review. She holds editorial leadership roles in the Journal of International Accounting Research and the Journal of International Financial Management and Accounting. Her academic credentials include a Doctorate from Columbia University, an MBA from Yale University, and a B.S. in Accounting with highest distinction from Indiana University. Gordon maintains professional certification as a CPA in Maryland and combines academic expertise with prior industry experience as a PwC auditor and White House Office of Management and Budget intern. Gordon's research examines how international accounting standards influence corporate behavior across diverse regulatory environments, with particular emphasis on earnings management, related party transactions, and cross-border capital market implications. Her recent work analyzes auditing standards for small entities, R&D disclosure effects on innovation, and pedagogical approaches in accounting education, reflecting both scholarly rigor and practical relevance in global financial reporting. As President of the International Association for Accounting Education and Research and former leader of the American Accounting Association's International Accounting Section, Gordon shapes global accounting discourse. Her textbook Intermediate Accounting (3rd edition) and multiple teaching awards highlight her educational impact across undergraduate, graduate, and doctoral programs. Prior academic appointments include faculty positions at the University of Chicago Graduate School of Business, Rutgers Business School, and University of Pennsylvania as a visiting professor, demonstrating extensive contribution to accounting education across leading institutions.
Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.
Jennifer Tang is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), holding dual appointments in the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). She conducts her research under Professor Ali Jadbabaie, focusing on interdisciplinary problems at the intersection of information theory, network science, and social dynamics. Her position is temporary as she actively seeks a permanent academic role through the 2025 job market. Her academic credentials include: Ph.D. in Electrical Engineering and Computer Science from MIT, advised by Professor Yury Polyanskiy Bachelor of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, with independent work supervised by Paul Cuff Dr. Tang's research program centers on theoretical and applied aspects of information theory, including channel capacity, quantization, and data compression. She investigates prediction and estimation in high-dimensional settings, data analytics for complex systems, and mathematical modeling of social dynamics and inference in multi-agent networks. Her work employs tools from statistics, optimization, and network theory to address challenges in communication, decision-making, and societal systems, with particular emphasis on opinion dynamics under social pressure and efficient representation of probability distributions. Analysis of her publication record reveals consistent contributions to information-theoretic limits, social network modeling, and compression techniques. Her works frequently appear in top venues like IEEE Transactions on Information Theory and major conferences (ISIT, CDC, ACC), demonstrating expertise in bridging theoretical foundations with real-world applications in networked systems and societal challenges. Her scientific achievements have been recognized with: Best Student Paper Award at IEEE International Symposium on Information Theory (ISIT) 2022 Best Student Paper Award at IEEE Machine Learning for Signal Processing (MLSP) 2022 Student Competition Winner at the Shannon Centennial Celebration Dr. Tang maintains an active teaching portfolio, having served as instructor for MIT 1.022: Introduction to Network Models (Spring 2025) and teaching assistant for multiple core courses including 6.008 (Introduction to Inference), 6.041/6.431 (Probabilistic Systems Analysis), 6.437 (Inference and Information), and 6.439 (Statistics, Computation and Applications). She also contributed to the MIT Women's Technology Program as a Mathematics Instructor during summer 2017. Her research is embedded within MIT's Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems, and Society (IDSS), two premier interdisciplinary laboratories fostering collaboration on data-driven decision-making, societal challenges, and foundational theory in information and systems.
Fady Alajaji is a Professor of Mathematics and Engineering at Queen's University, with a cross-appointment in the Department of Electrical and Computer Engineering. He holds a B.E. from the American University of Beirut, and M.Sc. and Ph.D. from the University of Maryland, College Park. His research focuses on information theory, coding for communication networks, probability models (e.g., Polya urns, contagion processes), and applications of information theory to machine learning (e.g., generative adversarial networks, data privacy). He has served as Associate Editor for the IEEE Transactions on Information Theory and has received awards for research and teaching. His work spans theoretical foundations (e.g., Shannon limits) and practical coding techniques for wireless systems. Education: B.E. (1988), M.Sc. (1990), Ph.D. (1994) in Electrical Engineering from the University of Maryland. Roles: Professor of Mathematics and Engineering, Cross-appointment in Electrical and Computer Engineering. Research Interests: Information theory, coding for communication networks, stochastic processes (network epidemics, Polya urn models), machine learning applications (information bottleneck, GANs), and data privacy. Recent work includes optimal signaling schemes for sensor networks, privacy-aware estimation, and curing models for contagion networks. Publications: Over 100 journal/conference papers, including foundational work on joint source-channel coding, hybrid digital-analog coding, and theoretical bounds for communication systems. Recent trends focus on information-theoretic machine learning and network science. Awards: Premier's Research Excellence Award (2001), Golden Apple Teaching Award (2015). Grants/Advising: Supervised postdoctoral fellow Jian-Jia Weng. Active in conference organization and editorial roles. Labs/Teams: Member of the Mathematics and Engineering Communications and Information Theory Group at Queen's University.
Yvonne Dittrich is a Professor at the IT University of Copenhagen (ITU), affiliated with the Software Development Group. She holds an adjunct professorship at IIT Mandi, India, and has held roles at institutions in Sweden, Canada, and the U.S. Her research focuses on cooperative and human aspects of software engineering, including Continuous Software Engineering (CSE), use-oriented design, and end-user development (EUD). She has led projects like SAIA-Farm (sustainable irrigation via satellite analytics) and contributed to frameworks like 'Cooperative Method Development.' **Education**: PhD in Computer Science (Hamburg University, 1997), M.Sc. from TU Darmstadt. **Research Interests**: She pioneers methods bridging software engineering with human-centric practices, emphasizing sustainability and participatory design. Her work addresses challenges in global software development, agile methodologies, and software ecosystems. **Awards**: TAT-Förderpreis (1989), Best Paper Award (2018), Distinguished Reviewer recognition (2018). **Grants & Leadership**: Led projects funded by the Danish Innovation Fund, EU, and others. Served on editorial boards for IEEE Transactions on Software Engineering and Journal of Systems and Software. **Labs/Teams**: Collaborates with labs in Denmark, India, and Canada on interdisciplinary projects, including smart irrigation systems and fintech ESG data commons.
Wael Rouatbi is an Associate Professor of Finance at Montpellier Business School's Finance & Accounting department. He obtained his PhD in Management Sciences from University of Paris–Est in 2016 and teaches courses including Corporate Finance, Strategic Finance, Power Platform, and Financial Modeling. His research bridges theoretical finance with practical business applications. Research Interests: Dr. Rouatbi's work spans corporate governance, financial risk analysis, sustainable business practices, and the economic impacts of global crises. Key areas include: Corporate decision-making under market uncertainty Shareholder influence mechanisms Climate and pandemic-related financial volatility Ethical frameworks in investment strategies Publication Focus: His recent articles (2021-2024) demonstrate strong emphasis on how environmental factors and health crises reshape financial markets, with methodologies ranging from econometric modeling to sectoral case studies. Cross-border investment patterns and corporate resilience during disruptions are recurring themes. Awards: 2017 Emerald/EFMD Outstanding Doctoral Research Award in Finance No information is currently available regarding student advising, research grants, or laboratory affiliations.
Wolfgang Nejdl is a Full Professor of Computer Science at Leibniz Universität Hannover since 1995 and the Head of the L3S Research Center since 2001. His research focuses on Web Science, search and information retrieval, semantic web technologies, peer-to-peer infrastructures, databases, technology-enhanced learning, and artificial intelligence. Education: M.Sc. (1984) and Ph.D. (1988) in Computer Science from Vienna University of Technology. Previous Positions: Assistant Professor in Vienna (1988–1992), Associate Professor at RWTH Aachen (1992–1995), and visiting professor/researcher at Xerox PARC, Stanford, University of Illinois at Urbana-Champaign, EPFL Lausanne, and PUC Rio. His research spans foundational and applied Web technologies, including social networks, trust and reputation, Web infrastructure, digital libraries, semantic web, collaborative filtering, and privacy-preserving systems. Recent projects like PHAROS, OKKAM, LiWA, and LivingKnowledge highlight his work in audio-visual search, web entities, web archive management, and diversity bias algorithms. Wolfgang Nejdl published over 230 scientific articles and held leadership roles as General Chair for AH'08 and PC Chair for WWW'09. He co-founded iSearch IT Solutions in 2006 to commercialize digital library and web engineering research from L3S projects. Scientific Awards: Founding member and head of the L3S Research Center The L3S Research Center, with a 2009 budget of €6 million (75% third-party funding), focuses on connecting the Web to real-world entities through research in Web Science, service computing, and security. Funding comes equally from the European Union and national/industry sources.
Dr. Xiang Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (2018), and M.E. and B.E. degrees from Hebei University of Engineering (2011 and 2008). His research focuses on Mobile Edge Computing, IoT, Drone-assisted Networks, Green Computing, and Data Center Optimization. Dr. Sun has been recognized with awards such as the 2018 InterDigital Innovation Award and NJIT Hashimoto Prize. Education: Ph.D., Electrical Engineering, NJIT (2018) M.E., Computer Applications Technology, Hebei University (2011) B.E., Electronic Information, Hebei University (2008) Research Interests: His work emphasizes IoT resource caching, semantic discovery, green communications, and drone-based mobile access networks. He also explores machine learning applications in edge computing and cybersecurity. Publications & Contributions: Dr. Sun has authored over 40 journal/conference papers, including impactful work on cloudlet networks and IoT semantic mashup. His research has been published in IEEE Transactions, Communications Letters, and top-tier conferences like ICC and GLOBECOM. Awards: 2018 InterDigital Innovation Award 2018 NJIT Hashimoto Prize 2017 IEEE Communications Letters Exemplary Reviewer 2016 IEEE ICC Best Paper Award Professional Activities: He serves as a TPC member for IEEE CCNC and MobiEdge, and reviewer for top journals like IEEE Transactions on Cloud Computing and IEEE Internet of Things Journal. He co-chairs the Social Computing and Semantic Data Mining symposium at IEEE ICNC 2019. Labs & Teams: Active in UNM's Electrical & Computer Engineering labs, focusing on mobile edge computing and IoT innovation. Collaborates with industry partners like InterDigital Communications.
Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).
Haemin Park is an Associate Professor at the Naveen Jindal School of Management, University of Texas at Dallas. His research focuses on how external resource acquisition strategies of technology-based ventures influence their development and performance, with a particular emphasis on corporate venture capital (CVC), knowledge governance, and intellectual property rights. He holds a Ph.D. in Technology Entrepreneurship and Strategic Management from the University of Washington, an MBA from Georgetown University, and a B.A. in Computer Sciences, Economics, and Mathematics from the University of Wisconsin. Education: PhD (UW, 2010), MBA (Georgetown, 2000), BA (UW Madison, 1994) Editorial Roles: Area Editor of Journal of Business Venturing ; Editorial Board Member of Strategic Entrepreneurship Journal Research interests include venture capital dynamics, innovation ecosystems, and strategic management of technology firms. His work explores topics like IPO underpricing, corporate venture capital syndicates, and the impact of gender diversity in decision-making groups. Awards: 2017 Journal of Business Venturing Best Reviewer; Finalist for Academy of Management TIM Division Best Dissertation Award. Professional Experience: Prior to academia, he worked as a corporate venture capitalist, business development manager, and management consultant in wireless telecommunications in the U.S. and Asia.
Tushar Athawale is a Research Scientist at Oak Ridge National Laboratory (ORNL) and a Joint Faculty Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His primary research focuses on uncertainty visualization, statistical data analysis, and high-performance computing for large-scale scientific data. He holds a PhD in Computer Science from the University of Florida (2015) and has held roles including Postdoctoral Fellow at the University of Utah's Scientific Computing & Imaging Institute and Application Support Engineer at MathWorks. His academic and professional affiliations include ORNL's Computer Science and Mathematics Division, the IEEE Visualization Conference program chair (2025), and associate editor for IEEE Transactions on Visualization and Computer Graphics. He has organized workshops, tutorials, and served on program committees for major visualization conferences. Key research interests span uncertainty quantification, topological methods, and visualization techniques for biomedical imaging, fusion simulations, and quantum computing. His work emphasizes trustworthy scientific data analysis through advanced visualization frameworks like VTK-m and implicit neural representations. Awards include ORNL's 2024 Special Award and Best Paper Honorable Mention at the IEEE Uncertainty Visualization Workshop 2024. His contributions bridge visualization theory with practical applications in exascale computing and AI-driven decision-making.
