Irregular Big Data Processing Professors and Supervisors

Gilles CaporossiView profile

Professor

HEC MontréalCanada

Gilles Caporossi is a Full Professor in the Department of Decision Sciences at HEC Montréal. He holds a Master’s degree from HEC Montréal and a Ph.D. in engineering mathematics from Polytechnique Montréal. As a member of the Group for Research in Decision Analysis (GERAD), he specializes in complex networks, data mining, algorithmics, combinatorial optimization, and artificial intelligence. He leads the Data Science and Business Analytics specialization in the M.Sc. program at HEC Montréal. His research focuses on privacy-preserving techniques, network optimization, graph theory applications, and machine learning. Notably, he contributed to the 40th-anniversary special issue of the Journal of Global Optimization (2021) and served as Department Head (2019–2021). He has supervised numerous students in Ph.D., M.Sc., and project-based research, including work on federated learning, network analysis, and NLP applications. Caporossi received the 2007 Young Scientist Prize from the International Academy of Mathematical Chemistry. He teaches courses on optimization, big data analytics, and complex networks. His work addresses real-world challenges in telecommunications, energy systems, and marketing analytics, leveraging advanced computational methods and interdisciplinary collaboration.

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Kshitija DeshpandeView profile

Associate Professor

Embry-Riddle Aeronautical UniversityUnited States

Kshitija Deshpande is an Associate Professor of Engineering Physics in the Physical Sciences Department at Embry-Riddle Aeronautical University, Daytona Beach, within the College of Arts and Sciences. She joined ERAU in Fall 2016 after completing her Ph.D. in Electrical Engineering from Virginia Tech, where she specialized in Space Science under Professor C. Robert Clauer and collaborated with Dr. Gary Bust at Johns Hopkins Applied Physics Laboratory. Education: Ph.D. in Electrical Engineering, Virginia Tech (2014) M.S. in Radio Astronomy, Virginia Tech B.Tech. in Electronics and Telecommunication, College of Engineering Pune (COEP), India Her research focuses on space science and engineering applications, particularly in remote sensing, GPS/GNSS technology, space weather effects, ionospheric disturbances, signal processing, high-performance computing, and mathematical modeling . Her work bridges observational data with simulation to understand upper atmospheric and ionospheric phenomena. Her recent publications (2023–2025) highlight investigations into GNSS scintillation, TEC anomalies during geomagnetic storms, ionospheric density structures, auroral physics, and rocket-induced disturbances . These studies employ both observational networks and computational models, contributing to the understanding of space weather impacts on communication and navigation systems. Scientific Contributions and Recognition: Active contributor to space physics literature in journals like Geophysical Research Letters , Journal of Geophysical Research: Space Physics , and Sensors . Collaborates with researchers across institutions including JHUAPL, Virginia Tech, and international stations like KLEF-Guntur. Dr. Deshpande advises students and leads research projects in space science and engineering physics, though specific advisees are not listed. Her work likely involves external funding and participation in national research initiatives such as those under URSI and AGU. She is engaged in developing low-cost GNSS monitoring systems and analyzing large datasets for space weather prediction. She is also involved in experimental campaigns at observatories like Poker Flat, Alaska, and contributes to understanding electron precipitation and auroral scintillation through integrated modeling and data analysis. Her lab or research group appears to focus on space weather monitoring, GNSS remote sensing, and high-latitude ionospheric dynamics .

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Junchen JiangView profile

Assistant Professor

University of ChicagoUnited States

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

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Omair ShafiqView profile

Associate Professor

Carleton UniversityCanada

Omair Shafiq is an Associate Professor at the School of Computer Science, Carleton University. His research focuses on advanced topics in natural language processing (NLP), machine learning, and their applications in knowledge graphs, blockchain, and network security. He leads projects that integrate large language models (LLMs) with structured data to enhance information retrieval and decision-making systems. His work spans areas like explainable AI, adversarial robustness, and real-time traffic prediction. Shafiq is affiliated with Herzberg Laboratories and contributes to interdisciplinary research, including smart contracts, encrypted traffic classification, and IoT security. Education details are not explicitly listed in the provided text. His research interests emphasize practical solutions for challenges in AI ethics, data privacy (e.g., GDPR compliance), and scalable analytics for big data applications. He has developed frameworks like ECSGen/iZen for NLP tasks and CARD-B for encrypted traffic classification. His recent publications (2022–2025) highlight innovations in ensemble learning, adversarial attacks defense, and blockchain-based solutions for secure transactions. Shafiq’s work often bridges theory and practice, addressing real-world problems such as cybersecurity in vehicular networks, stock market prediction using deep learning, and user behavior analysis on social platforms. His contributions include tools like RevDet for event detection in news feeds and HybLoc for indoor localization. Though no specific awards are mentioned, his prolific publication record reflects sustained academic impact.

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Anthony AidooView profile

Professor

Eastern Connecticut State UniversityUnited States

Anthony Aidoo is a Professor of Mathematical Sciences at Eastern Connecticut State University. He holds a Ph.D. in Mathematical Sciences from the University of Vermont, with research interests spanning Mathematical Biology, Epidemiological Modeling, Medical Imaging, and ACh-AChE analysis. His work integrates applied mathematics with real-world applications in biology and medicine. Education: Ph.D., Mathematical Sciences, University of Vermont M.S., Mathematical Sciences, University of Vermont Bachelor's Degree, Kwame Nkrumah University of Science and Technology (Ghana) Research Interests: Mathematical Biology/Physiology Medical Imaging Techniques (e.g., wavelet-based image enhancement) Modeling of diseases like Hepatitis B and campus drinking behavior Collaborations with institutions in Ghana, Canada, Germany, and others Teaching Philosophy: Emphasizes student engagement and the universality of mathematics, particularly through courses like Real Analysis I. Aims to create an enabling environment where students can explore and apply mathematical concepts beyond textbook theories. Professional Contributions: Frequent presenter at international conferences (Bulgaria, Germany, Spain, etc.), with a focus on interdisciplinary research at the intersection of mathematics and life sciences.

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Joana BarrosView profile

Research Fellow

Birkbeck , University of LondonUnited Kingdom

Dr. Joana Barros is a Senior Research Fellow at Birkbeck, University of London's School of Social Sciences. Her expertise spans urban planning, geographic information science, and spatial analysis, with a focus on socio-spatial inequalities in Global South cities. She holds a PhD in Planning Studies from the University of London (2004), a Master's in Urban Planning from Federal University of Rio Grande do Sul (1999), and a BSc-equivalent in Architecture from Federal University of Pelotas (1998). Her research examines urban segregation, transport accessibility, informal settlements, and urban growth through geospatial analytics. Key projects include: RESOLUTION (ESRC/FAPESP): Comparative analysis of London and São Paulo's transport inequalities SIMETRI (ESRC/NWO/NF): Sustainable mobility in megacity regions (London, Randstad, Pearl River Delta) She directs the Geo-Analytics and Modelling Group and holds an honorary position at UCL's Centre for Advanced Spatial Analysis. Recognitions include: Fellow of the Royal Geographical Society Fellow of the Higher Education Academy As primary supervisor, she mentors doctoral researchers studying segregation dynamics and urban accessibility. Her grant portfolio supports international collaborations across UK, Brazil, Netherlands, and China.

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José Carlos Cabaleiro DomínguezView profile

Professor

University of Santiago de CompostelaSpain

Dr. José Carlos Cabaleiro Domínguez is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela's Faculty of Computing, Spain. He has been a member of CiTIUS (Centro singular de investigación en tecnoloxías da información e comunicación) since 2010 and was promoted to Full Professor in 2022 after serving as an Associate Professor since 1994. His academic journey began with a BS and PhD in Physics from the University of Santiago de Compostela in 1989 and 1994 respectively, with initial teaching experience at the University of A Coruña from 1990-1994. His research focuses on high performance computing, particularly in parallel systems architecture, development of parallel algorithms for irregular problems with sparse matrices, performance prediction and improvement of parallel applications, memory hierarchy optimization, and applications for grid and cloud computing. He has developed significant expertise in 3D point cloud processing from remote sensors like LiDAR, with applications in urban infrastructure analysis, powerline detection, and route planning. Analysis of his recent publications reveals a strong emphasis on optimizing resource allocation for big data frameworks, developing deep learning applications for point cloud classification, and creating efficient algorithms for powerline detection in LiDAR surveys. His work bridges theoretical computer science with practical applications in geospatial analysis and infrastructure monitoring. His research has been published in top-tier journals including IEEE Transactions, ISPRS Journal of Photogrammetry and Remote Sensing, and Future Generation Computer Systems, reflecting his significant contributions to the field of high performance computing and its applications. Dr. Cabaleiro actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record with co-authors from various universities and research centers. His work demonstrates a consistent trajectory of advancing parallel computing techniques while applying them to increasingly complex real-world problems involving large-scale geospatial data.

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Rajiv GuptaView profile

Professor

National and Kapodistrian University of AthensGreece

Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.

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蔡瑞煌View profile

Professor

National Chengchi UniversityTaiwan

Professor Cai Ruihuang is a distinguished faculty member in the Department of Information Management at National Chengchi University's College of Business. With over 30 years of academic service, he currently serves as Director of the Computer Center and has held various administrative positions including Deputy Director of Research and Development and Director of the Innovation Incubation Center. His extensive academic career spans teaching and research in information systems, neural networks, and digital innovation. PhD in Industrial Engineering and Operations Research, University of California, Berkeley MS in Industrial Engineering, University of California, Berkeley BS in Nuclear Engineering, National Tsing Hua University Professor Cai's research focuses on technological services, service innovation, digital humanities, neural network systems, and enterprise process analysis. His work bridges traditional business domains with cutting-edge technology applications, particularly in cultural institutions like the National Palace Museum. His research demonstrates a consistent trajectory from foundational neural network theory to practical applications in financial analytics, museum technology, and digital transformation. His extensive publication record shows a progression from theoretical neural network research in the 1990s to applied AI and digital innovation studies in recent years. Notably, he has pioneered applications of AI in cultural heritage institutions and developed innovative approaches to financial fraud detection using neural networks. His work spans computer science, business management, and digital humanities, reflecting an interdisciplinary approach to information systems research. 資深優良教師(30年) - Senior Excellent Teacher Award 特聘教授 - Distinguished Professor 國科會獎勵特殊優秀人才 - MOST Special Outstanding Talent Award 學術研究成果國際化優等研究獎 - International Academic Research Excellence Award 教學特優教師 - Outstanding Teaching Award Professor Cai has successfully led numerous research projects totaling millions in funding from both government agencies like MOST and private sector partners including CTBC Financial Holding and National Palace Museum. His current research focuses on AI applications in finance, digital transformation of cultural institutions, and cybersecurity education initiatives. He leads the Financial AI Cloud Ecosystem research project and serves as principal investigator for multiple cybersecurity training programs funded by Taiwan's Ministry of Education. His work connects academic research with practical applications through the Financial AI Cloud Research Center and various industry collaboration initiatives. Professor Cai's research group actively engages with cultural institutions, financial organizations, and technology companies to develop innovative solutions for real-world challenges in digital transformation.

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