Vinayak R. Borkar is a researcher and software engineer affiliated with the University of California, Irvine, where he completed his PhD in 2016. His work focuses on big data platforms, database systems, and scalable query processing frameworks. PhD in Big Data Processing (UC Irvine, 2016) Contributions to Apache AsterixDB, Hyracks, and Pregelix Industry experience at BEA Systems (2000s) Research interests include database systems , big data management , XQuery optimization , and dataflow engines . His publications analyze scalable similarity queries, memory management, and declarative approaches to machine learning. Recent articles explore Apache AsterixDB , dataflow compilation , and graph analytics . Collaborators include Michael J. Carey and Alexander Behm.
Raj Bhatnagar is a Professor in the Department of Computer Science at the University of Cincinnati, affiliated with the College of Engineering, Architecture and Art. His research focuses on data mining, pattern recognition, AI, and algorithms for bioinformatics. He has secured over 25 grants from federal agencies (NSF, Air Force Research Laboratory), industry (Charles Schwab, Edaptive Computing), and state/private entities, totaling millions in funding. Education: PhD in Computer Science from University of Maryland College Park (1989) Research Interests: His work spans distributed computing, graph theory, machine learning for industrial applications, and cybersecurity. Notable contributions include algorithms for clustering, anomaly detection, and formal concept analysis. He collaborates with Cincinnati Children’s Hospital and the Air Force on projects like predictive analytics for spindle health and verification of microelectronics designs. Recent Grants: Includes a 2023 industry grant for Machine Learning educational seminars (MME I45I), and multiple ongoing Air Force contracts (2018-2022) for ML-based verification test processes. Labs/Teams: Involved with Edaptive Computing’s Data Analytics & Cybersecurity Simulation Center and the Air Force-led STAMP/TAME research initiatives.
Panagiotis Stamatopoulos is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been employed since 1993. He holds a PhD in Computer Science (1988) and a Diploma in Physics (1982) from the University of Athens. His research spans artificial intelligence, constraint programming, natural language processing, machine learning, and optimization. Specific interests include: Hybrid approaches combining constraint programming with operations research Natural language understanding for database access Parallel processing and distributed constraint solving Multi-agent systems and web intelligence applications His publications show consistent focus on constraint satisfaction algorithms, text summarization techniques, educational timetabling systems, and AI applications in diverse domains like sports analytics and robotics. Recent works demonstrate increased attention to NLP evaluation metrics and multimodal learning. He has supervised numerous diploma theses and led projects funded by the European Union (EDS, APPLAUSE, PARACHUTE, PARROT), University of Athens, and Olympic Airways. Stamatopoulos teaches undergraduate courses in Introduction to Programming and Logic Programming, plus postgraduate courses in Advanced Artificial Intelligence. He previously taught Artificial Intelligence, System Programming, and Expert Systems.
Dr. Gheorghi Guzun is an Associate Professor in the Department of Computer Engineering at San José State University (SJSU), where he has served since 2017. He holds a Ph.D. in Electrical and Computer Engineering from the University of Iowa (2016) and a BSc in Electrical Engineering from the Technical University of Moldova (2010). His research focuses on scalable data management systems, machine learning, and hardware-driven software design, with applications in energy-efficient data centers, infrastructure monitoring, and predictive analytics. He is a recipient of the NSF CAREER Award for his work on efficient AI systems. Education background: Ph.D. in Electrical and Computer Engineering, University of Iowa (2016) BSc in Electrical Engineering, Technical University of Moldova (2010) Research interests span data compression, large-scale indexing, and algorithm optimization for machine learning. His projects emphasize scalability and performance in distributed systems, with recent work addressing energy efficiency and sparsity-driven methods for AI. He also develops educational technologies, such as competitive learning platforms to enhance student engagement. Notable achievements include the NSF CAREER Award and contributions to infrastructure monitoring systems, flood event modeling, and predictive university admission yield estimation. His lab's work is documented at https://g-guzun.github.io/ . Grants and advising: His NSF-funded CAREER project explores scalable sparsity-driven methods for AI systems. He collaborates on projects involving distributed data analytics and maintains an active research lab at SJSU.
Ashwathy Ashokan is an Instructor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. With a Master's in Computer Science from UNO and a PhD in Artificial Intelligence and Machine Learning underway, she bridges industry experience (Union Pacific, Microsoft, Wipro) with academia. Her research focuses on algorithmic fairness, recommender systems, and text mining. Research Interests span Text Mining , Information Retrieval , Recommender Systems , Search Engines , Algorithmic Fairness , and Machine Learning . Her recent work in Information Processing & Management (2021) highlights fairness metrics in rating predictions, while earlier publications (2013-2014) explore MapReduce-based topic extraction from timestamped documents. Scientific Awards include the Best Graduate Student for Service Award UNO Advantage Scholarship Special Faculty Development Fellowship Volunteer Activities include involvement with Omaha-based organizations like Girls Who Code, Open Door Mission, and Nebraska AIDS Project. She has served as a Committee Member for UNO's Women in IT Initiative.
Mohamed Y. Eltabakh is an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI), with affiliations in the Data Science and Bioinformatics & Computational Biology programs. He holds a Ph.D. from Purdue University (2010) and has postdoctoral experience at IBM Almaden Research Center (2010-2011). His research focuses on database systems, big data analytics, query optimization, and scientific data management. He has led projects like InsightNotes (annotation management), Redoop (MapReduce optimizations), and CoHadoop (Hadoop data placement). Prof. Eltabakh teaches courses in databases, algorithms, and Hadoop infrastructure at both undergraduate and graduate levels. He has secured NSF funding ($189,952) for large-scale data analytics research and contributed to over 30 peer-reviewed publications in top-tier conferences/journals (e.g., SIGMOD, ICDE, VLDB). His work integrates with WPI’s interdisciplinary programs, including the Healthcare Delivery Institute and Data Science graduate program. Education: Ph.D. Computer Science, Purdue University, 2010 M.S. Computer Science, Purdue University, 2005 M.S. Computer Science, Alexandria University, 2001 B.S. Computer Science, Alexandria University, 1999 Research Interests: Database Management Systems Big Data Analytics & Indexing Scientific Data Curation Hadoop Framework Enhancements Metadata Management Large-Scale Query Optimization Recent Articles Trends: Focusing on scalable indexing solutions for time series data (ChainLink, TARDIS), correlation-driven query optimization in big data environments, and efficient graph processing algorithms (Bermuda). His work bridges theoretical database systems with practical industrial challenges like Teradata’s query execution optimizations and annotation management in relational databases. Awards & Grants: NSF CRI-1305258 Award: Compute Infrastructure for Large-Scale Data Analytics ($189,952, 2013–2015) Advising & Grants: Advised 8 graduate students (MSc/PhD) since 2012. Collaborated with industry partners like Teradata Labs and Teradata Incorporation for applied research projects. Labs & Teams: Member of the Database Systems Research Group (DSRG), Bioinformatics & Computational Biology program (BCB), and WPI’s Data Science program leadership (Associate Director).
Giuseppe Cattaneo is an Associate Professor at the Department of Computer Science , University of Salerno. His research spans multiple domains including Computer Security Distributed Systems Bioinformatics Mobile Communication Security Algorithm Engineering His work has produced significant contributions in cryptographic file systems, secure mobile messaging, and Hadoop-based bioinformatics solutions. Key research trends include Security protocols for mobile environments Scalable genomic data analysis Dynamic graph algorithm optimization Digital forensic countermeasures He has developed frameworks like GRACE for cryptographic visualization and FASTdoop for bioinformatics data processing. Professional activities involve Collaboration with international researchers Development of secure communication protocols Participation in algorithm engineering studies His recent 2021 publications focus on medical data analysis and thyroid cancer outcome prediction.
Márk Asztalos is an Associate Professor at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . He leads research in the Visual Modeling Group, focusing on model-driven engineering, graph rewriting systems, and domain-specific languages. Research Interests: Model transformation verification, text-based modeling, graph pattern matching, and cloud/mobile system modeling. Contact: E-mail: Asztalos.Mark@aut.bme.hu , Office: Q.B226, Department of Automation and Applied Informatics, BME. His publications emphasize model transformation verification (2010-2015), graph rewriting techniques for pattern matching (2015-2017), and domain-specific language design (2014). Recent work (2020) analyzes model integration challenges in model-driven methodologies. Contact details: Address: Budapest 1117, Magyar tudósok krt. 2, Hungary Phone: +36 (1) 463-3702
Neda Maleki is a Senior Lecturer at the Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, starting in September 2024. Her research focuses on Applied IoT, Edge-Cloud Computing, Distributed Systems (Hadoop/Spark), Artificial Intelligence, and Machine Learning for data analysis. PhD in Computer Engineering (2014–2021), Science and Research Branch of Islamic Azad University, Tehran, Iran Master of Science in Computer Engineering (2009–2013), Ghazvin Islamic Azad University Bachelor of Science in Hardware Engineering (2004–2008), Ghazvin Islamic Azad University Her research explores IoT applications in energy forecasting, environmental conservation, and SME digital transformation. Key contributions include frameworks for power-aware Hadoop acceleration, energy-efficient IoT data formats, and predictive models for fuel consumption and city load forecasting. Recent publications highlight collaborations with industry partners in Sweden and international conferences across Qatar, Italy, Denmark, and the Netherlands. She teaches courses in Data Structures, Algorithms, IoT, and programming at the bachelor's and master's levels. 1DV018: Data Structures and Algorithms 1DV501: Introduction to Programming 4DV119: Applied IoT Competence (Master level) Final Thesis supervision
Prof. Dr.-Ing. Markus Fidler is a Professor of Communications Networks at the Institute of Communications Technology , affiliated with the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover since 2009. His academic journey began with a doctoral degree in Computer Engineering from RWTH Aachen University (2004), followed by post-doctoral fellowships at Institute Mittag-Leffler (2004), NTNU Trondheim (2005), and University of Toronto (2006). He led the Emmy Noether Research Group at Technische University of Darmstadt (2007-2008), where he also earned his habilitation (2008). His research interests span Network Calculus , Effective Bandwidths , Available Bandwidth Estimation , and Parallel Systems (e.g., Multi-path Protocols, Synchronization Constraints). He explores Future Internet architectures, Wireless Communication (including Cognitive Radio and Car-2-X systems), and Congestion Control for Cooperative ADAS and Platooning. His work integrates Machine Learning and Stochastic Modeling for network performance analysis. Recent publication trends emphasize Age-of-Information (AoI) in tandem queues, Statistical Bounds for parallel systems, and Granularity Trade-Offs in multi-server environments. He applies Min-plus Algebra to AoI modeling and investigates Hybrid Time-Event Triggered Systems for resource-efficient communication. His projects include ADINeMo (2024) on Deviation-of-Information for sensor sampling and VaMoS 2 (2024) on validated models for MapReduce scaling. Scientific Awards ERC Starting Grant (2012) Advising includes current doctoral students Sami Akin , Brenton Walker , and Mahsa Noroozi , alongside numerous past advisees now holding academic positions globally. He leads DFG-funded projects such as FeelMaTyC (2017-2020) and GRK SocialCars (2014-2023), focusing on IoT, cooperative mobility, and network calculus applications.
Alberto Cano is the Associate Vice President for Research Computing and an Associate Professor in the Department of Computer Science at Virginia Tech. He leads the Advanced Research Computing unit, focusing on high-performance computing, machine learning, and data stream analysis. He holds a Ph.D. (2014) and multiple master's degrees from Spanish universities, including the University of Granada and University of Córdoba. His research spans machine learning applications in cybersecurity, healthcare, and smart cities, with a focus on imbalanced data streams and GPU-accelerated algorithms. Key research areas include concept drift adaptation, distributed computing systems, and scalable machine learning. He has secured over $3 million in grants, including NSF funding for GPU infrastructure and educational initiatives. Cano has authored 100+ peer-reviewed publications and serves as an editor for journals like Information Fusion and Applied Intelligence. He teaches courses on high-performance computing and databases at Virginia Tech. His work addresses challenges in real-time data processing, including fraud detection in 3D printing, health monitoring systems, and anomaly detection in smart transportation. Cano actively contributes to international conferences through tutorials on data stream mining and lifelong learning systems.
Tiffani L. Williams is a Teaching Professor and Director of Onramp Programs in the Department of Computer Science at the University of Illinois at Urbana-Champaign, and serves as a Dean’s Fellow in Inclusion, Belonging, and Engagement in the Grainger College of Engineering. Previously, she held roles as Director of Computer Science Programs and Professor of the Practice at Northeastern University-Charlotte (2017–2020) and was a faculty member at Texas A&M University’s Department of Computer Science and Engineering (2005–2017). Her research focuses on computer science education , computational biology , and inclusive science communication , with a particular emphasis on phylogenetic tree inference , high-performance computing , and diverse educational pathways . She pioneered programs like the iCAN accelerator to broaden participation in computing for non-CS graduates. Her awards include the Denice Denton Emerging Leader ABIE Award , Radcliffe Institute Fellowship , and multiple teaching excellence honors from Texas A&M and Illinois. Her work bridges computational science and equitable education , with notable contributions to phylogenetic algorithms and reproducible research practices. Recent courses include advanced computer science fundamentals and excursions in computing. She leads initiatives to address systemic inequities in STEM through curriculum design and inclusive pedagogy.
Aisling O'Driscoll is a Senior Lecturer at the School of Computer Science and Information Technology (CSIT), University College Cork (UCC), Ireland since 2017. She previously held roles at Cork Institute of Technology (CIT) from 2005 to 2017, including a PhD in Location Management and Hybrid Geo-Routing (2014). Her research focuses on communication protocols for wireless systems, particularly in vehicular networks, IoT, and bioinformatics. She leads the Connected Autonomous Vehicles (CAV) working group in the SFI CONNECT Centre and chairs UCC's Athena Swan committee. Education: PhD (CIT, 2014), MEng (Research, 2006), BSc (2004) Grants: Over €3M in funding from SFI, EU, and industry, including the SFI Blended Autonomous Vehicle (BAV) Spoke and AWS grants Research interests include decentralized network solutions for autonomous systems, parallelized bioinformatics algorithms, and vehicular communication protocols. Awards include the 2021 National Teaching Hero Award and recognition at Áras an Uachtaráin for women in science. Labs/Teams: Leads CAV initiatives in CONNECT, collaborates with Jaguar Land Rover and Teagasc Outreach: Founded IWish Campus Day to promote STEM for girls, and chairs IT@Cork's Tech Talk Committee
Preetam Ghosh, Ph.D. , is a Research Professor in the Department of Computer Science at Virginia Commonwealth University (VCU) , with cross-disciplinary focus in computational biology, machine learning, and network science. His work bridges engineering and biomedical research , particularly in pandemic modeling, multiomics data integration, and bioinformatics tool development. Academic Role: Research Professor, VCU School of Engineering Key Research Areas: Computational Biology, Network Analysis, Pandemic Modeling, Multiomics Data, Bioinformatics Algorithms Recent Research Trends include: Application of machine learning to biomedical data (e.g., drug-target affinity prediction, breast cancer subgroup classification) Development of physics-informed models for pandemic propagation and chemical reaction networks Innovations in network science , such as link prediction and vulnerability analysis for biological and IoT systems Advancing single-cell genomics through consensus algorithms (COFFEE, CHAI, CORTADO) Design of adaptive routing protocols for disaster-resilient IoT networks (ADRIN, ADRIN2.0)
Mouad Lemoudden is a Lecturer in Cybersecurity at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University. He is affiliated with the Blockpass Identity Lab and the Centre for Cybersecurity, IoT and Cyberphysical Systems. PhD in Computer Science (2017) from Mohammed V University, Morocco Postdoctoral Researcher at Inria (2020) Research Interests revolve around data-centric tools in cybersecurity, including cloud security, network security, intrusion detection systems, adversarial behaviors, distributed ledger technology, digital identity, and privacy. His work explores quantum-enhanced cybersecurity analytics and graph-based threat modeling. Notable Publications include studies on post-quantum cryptography, graph theory in cloud security, and quantum machine learning for botnet detection. His research spans cloud logging standards, big data analytics, and identity federation challenges. Scientific Awards include recognition as an Associate Fellow of the Higher Education Academy (AFHEA). Supervision includes PhD projects on quantum machine learning for cybersecurity and metamaterials for sound insulation. Students under his guidance include Madjid Golparvaran Tehrani and collaborative projects with Dr. Nick Pitropakis and Prof. Bill Buchanan.
