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Diomidis SpinellisView profile

Professor

• Software engineering
• IT security
• Computing systems
• +3 more

Diomidis Spinellis is Professor of Software Engineering in the Department of Management Science and Technology at the Athens University of Economics and Business, Greece, where he heads the Business Analytics Laboratory (BALab). He also holds a position as Professor of Software Analytics in the Department of Software Technology at the Delft University of Technology. His academic career spans multiple prestigious institutions and research domains. Spinellis's research interests center on software engineering, IT security, and computing systems. He has made significant contributions through his work on code quality, software tools, program comprehension, and debugging methodologies. His research bridges theoretical foundations with practical applications, evident in his development of widely-used open-source tools and his contributions to operating systems like Apple's macOS and BSD Unix. His scholarly output includes over 300 technical papers with more than 10,500 citations, demonstrating substantial impact across the software engineering community. His publications span diverse topics including software analytics, program comprehension, debugging techniques, and open-source software quality. Among his notable recognitions are two award-winning, widely-translated books: Code Reading and Code Quality: The Open Source Perspective , followed by Effective Debugging: 66 Specific Ways to Debug Software and Systems in 2016. His work has been influential in both academic and industry settings. IEEE Software editorial board member for a decade Editor-in-Chief of IEEE Software for four years Elected member of IEEE Computer Society Board of Governors (2013-2015) Senior member of ACM and IEEE Spinellis actively contributes to the software engineering community through tool development (CScout, UMLGraph, dgsh), conference leadership roles, and mentoring activities. His professional service includes program committee memberships across major software engineering conferences including ICSE, ESEC/FSE, and ASE.

Chet CooperView profile

Professor

• fungal pathogenesis
• fungal morphogenesis
• antimicrobial activities of nanoparticles
• +3 more

Dr. Chet Cooper is a Professor in the Department of Chemical and Biological Sciences at Youngstown State University, where he has served since 1995. His academic career spans over 30 years with previous appointments at the University of Texas Medical Branch, Case Western Reserve University, and New York State Department of Health. Dr. Cooper earned his Ph.D. in Microbiology from the University of Texas at Austin in 1989, following an M.A. in the same field in 1983. He completed his undergraduate studies with a B.S. in Biology from the University of Pittsburgh at Johnstown in 1979. Dr. Cooper's research focuses on fungal pathogenesis and morphogenesis , particularly in Penicillium marneffei , a significant human pathogen. His work examines the molecular mechanisms of fungal development, stress response, and virulence. Additionally, he investigates antimicrobial activities of nanoparticles and wastewater epidemiology , bridging environmental microbiology with public health concerns. His laboratory employs advanced molecular, biochemical, and genomic approaches to understand fungal biology and pathogenesis. Analysis of Dr. Cooper's publications reveals a consistent focus on fungal pathogens, particularly Penicillium marneffei and Wangiella dermatitidis . His research spans molecular genetics, protein expression, stress response mechanisms, and virulence factors. A notable trend is his contribution to knowledgebase development for secretome and subcellular proteome analysis across different organisms. 2015: YSU Student Government John J. Gocala Sr. Service Award 2014: YSU Student Government Association Smith-Murphy Award 2013: Youngstown State University Distinguished Professor Award for Service 2011: YSU Student Government Association Martin T. Manning Award 2009: YSU Student Government Association Smith-Murphy Award 2009: Youngstown State University Distinguished Professor Award for Scholarship 2007: YSU Student Government Association Spirit Award 2007: Elected to Omicron Delta Kappa 2004: YSU Student Government Association Smith-Murphy Award 2004: Youngstown State University Distinguished Professor Award for Scholarship 2004: YSU School of Graduate Studies and Research Dean's Award 1998: National Foundation for Infectious Diseases Young Investigator Award Dr. Cooper has been actively involved in mentoring students through research projects, particularly in his BIOL 3702H honors laboratory section where students conduct original research on fecal bacteria in water sources. His laboratory in Ward Beecher Science Hall Room 3124 serves as a hub for student research activities. He has secured internal university funding to support his research and educational initiatives, particularly those focused on wastewater epidemiology and antimicrobial studies. Dr. Cooper maintains an active research laboratory focused on fungal pathogenesis, with particular emphasis on Penicillium marneffei . His lab employs molecular, genetic, and biochemical approaches to study fungal development, stress response, and virulence mechanisms. The laboratory also conducts research on wastewater epidemiology, with students regularly collecting and analyzing water samples to assess fecal contamination.

Michael E. RoseView profile

Research Fellow

• Economics of Science
• Innovation
• Academic Labor Market
• +4 more

Michael E. Rose is a Senior Research Fellow at the Max Planck Institute for Innovation and Competition, specializing in Innovation and Entrepreneurship Research. He holds a Ph.D. from the University of Cape Town with a dissertation on "Collaboration Networks in Economic Science." His research focuses on the Economics of Science, Innovation, Academic Labor Market, Social Network Analysis, and Machine Learning. Rose earned his Bachelor of Science in Economics and Business Administration from the University of Jena (2009-2012), followed by a Master of Science in Quantitative Economics from Kiel University (2012-2014), where he also participated in the Advanced Studies Program in International Economic Policy Research at the Kiel Institute for the World Economy. He completed his doctoral studies at the University of Cape Town (2015-2018). His academic journey includes research visits to the Deutsche Bundesbank and Georgia Institute of Technology. His research interests center on understanding how social structures and networks influence scientific production and careers. Rose has made significant contributions to bibliometrics, academic collaboration networks, and the application of machine learning to economic analysis. His work on informal collaboration through acknowledgements has revealed patterns of gender differences and reciprocity in academic networks. He has also investigated how advisor connectedness affects student placement outcomes in the economics job market, demonstrating the importance of social networks even in markets with minimal information frictions. Rose has received notable recognition through the University of Cape Town Murray-Jelks Scholarship for International Travel (2017-2018) and the AIFMRM PhD Dissertation Scholarship (2015-2018). His research has been published in prestigious journals including The Review of Economics and Statistics, Journal of European Consumer and Market Law, SoftwareX, and Scientometrics, with numerous forthcoming publications in 2025. He is actively involved in supporting Ukrainian scholars through initiatives like #ScienceForUkraine, which he co-founded following Russia's invasion of Ukraine. The initiative has become a central hub connecting displaced Ukrainian researchers with support offers from institutions worldwide, recording over 2,600 support listings. Rose has also developed several open-source software tools for bibliometric analysis, including pybliometrics for Scopus data, Sprynger for Springer Nature data, and sosia for creating control groups of scientists, significantly enhancing research reproducibility and data integrity in the Science of Science field.

• Big Data
• Artificial Intelligence
• Cloud Computing
• +5 more

Prof. Dr.-Ing. Christoph P. Neumann is an Associate Professor at Amberg-Weiden University of Applied Sciences (OTH) in the Faculty of Electrical Engineering, Media Technology and Computer Science, holding a W2 professorship in Big Data and Cloud Computing for AI since 2021. Previously, he served as a guest lecturer at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) from 2013 to 2020, accumulating over thirteen years of university teaching experience. Education: Neumann earned his doctorate (Dr.-Ing.) from FAU, where his dissertation on distributed data management, system integration, and process management received a top grade and nomination for the GI Dissertation Award. Research Interests: His work focuses on Big Data infrastructure , AI-driven analytics , and cloud-native software architecture , with emphasis on scalable data-centric applications. He aims to establish nationally visible Big Data research integrated with OTH's Faculty EMI, ensuring AI students receive industry-relevant curricula. Key innovations include data lake technologies for BI modernization and graph databases for content management systems. Scientific Awards: Nomination for GI Dissertation Award Two-time recipient of FAU's best course award (Exercises/GOP subject) Ranked among OTH's Top-5 scientists (out of 200+ professors) Best Paper Award (2024, with Patrick Levi) IARIA Fellowship (2025) Best Paper Award (2025, with Philipp Stangl) Advising and Grants: Supervised 25 student theses at FAU and secured approximately 900,000€ in external funding through three cluster projects, creating additional PhD positions. Industry projects included 768,000€ revenue contribution at EXASOL and 2.7 million € budget responsibility at Teambank AG for BI modernization. Labs and Teams: Speaker of the Research Cluster Robotics & Big Data (RBD-OTH) and member of the Innovation and Competence Center Artificial Intelligence (IKKI). Also serves on INDIGO consortium's steering committee and advises four profile schools for computer science (PIZ), with a guest professorship at Hefei University (China) teaching Big Data principles.

Klavs Martin SørensenView profile

Associate Professor

• Process Analytical Technology
• Food Quality and Safety
• Vibrational Spectroscopy
• +7 more

Klavs Martin Sørensen is an Associate Professor at the Department of Food Science, Faculty of Science, University of Copenhagen. His research focuses on applying advanced chemical sensors combined with Process Analytical Technology (PAT) and Deep Learning/AI to optimize food manufacturing processes, ensuring high food quality and safety while minimizing environmental footprint. His educational background includes: Ph.D. in Food Technology, University of Copenhagen (2015) Participant in the DNATF-funded Pasteur-program (project manager education), Harvard University (2012) Engineering Technician, Electronics, Frederiksberg Tekniske Skole, Denmark (1998) With over 20 years of industry experience in developing measurement equipment for the food industry prior to obtaining his PhD, Professor Sørensen brings a unique practical perspective to his academic work. His research centers on vibrational spectroscopy, chemometrics, gas chromatography & mass spectrometry, and sustainable food production technologies. He has developed expertise in applying spectral sensors in online scenarios for process optimization, with special focus on Quality by Design and Process Analytical Technology frameworks. His recent publications demonstrate a strong focus on applying spectroscopic techniques and data analysis methods to food quality assessment across various food matrices including meat, cheese, wine, and protein side streams. His work bridges the gap between theoretical chemometrics and practical industrial food manufacturing applications. His notable awards include: Niels Foss Talent Prize (2019) Course of the year award by Department of Food Science for Rapid Food Analysis Method (2019) Nomination as Faculty teacher of the year by B.Sc. students (2020) Professor Sørensen has led multiple significant research projects including Better utilization of protein side streams (2016-2028), QC-CREW (2018-2020), and SayCheese (2018-2021). He has served as a censor for the BSc/MSc course Quantitative Food Spectroscopy since 2009 and was responsible for the course Rapid Food Quality Analysis Methods starting in 2017. He was a key organizer for the 2017 International Conference On Near-Infrared Spectroscopy (ICNIRS 2017). His technical expertise spans vibrational spectroscopy apparatus design, chemometric tools (PCA, PLS, PLSDA), GC/MS techniques, ultrasonic measurement and imaging, and artificial intelligence modeling for time series data. He also possesses extensive software development skills with over 20 years' experience in C++, C, Python, MATLAB, and various database systems, which he applies to real-time on-line data processing systems for food manufacturing.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +8 more

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.

Andrzej WąsowskiView profile

Professor

• Software Engineering
• Formal Methods
• AI and Robotics
• +8 more

Andrzej Wąsowski is a Professor in the Computer Science Department at IT University of Copenhagen. His research focuses on software engineering, formal methods, AI/robotics, and safety-critical systems. He leads projects like REMARO (Marine Robotics reliability) and INSIGHT (program repair). His work emphasizes model-driven engineering, probabilistic systems, and open-source collaboration. Research Interests Formal Verification of Autonomous Systems Probabilistic Programming and Privacy Analysis Software Product Line Engineering Robotics and Cyber-Physical Systems Recent Work Recent articles explore symbolic execution for probabilistic programs, risk-aware marine robotics planning, and privacy quantification in genetic data. He has contributed over 200 ROS bug findings and developed tools like Clafer for model-based design. Teaching & Community Teaches courses in probabilistic programming, advanced software analysis, and model-driven development. Active in open-source communities, including ROS and Clafer. Supervised 20+ PhD students who now hold academic and industry positions. Labs & Projects Leads the SQUARE Group and VBDB bug database. Coordinates the REMARO EU training network. Previously worked on ROSIN (robot software quality) and EBA (Linux kernel analysis).

Devavrat ShahView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +7 more

Devavrat Shah is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Laboratory for Information and Decision Systems (LIDS). His research focuses on machine learning, causal inference, and algorithmic fairness, with applications in healthcare outcomes, criminal justice reform, and cryptocurrency analysis. He has contributed to groundbreaking work, such as developing a machine-learning algorithm that nearly doubled Bitcoin investments over 50 days using real-time price data analysis. Shah's academic work integrates statistical methods with real-world challenges, including personalized medicine, policy evaluation, and bias detection in algorithms. His recent projects include global clinical studies on lymphoma treatments and frameworks for auditing racial bias in law enforcement systems. His interdisciplinary approach bridges computer science, statistics, and domain-specific applications. Key areas of his research include causal inference frameworks for data-rich environments, algorithmic fairness in decision-making systems, and predictive modeling for healthcare and public policy. His work often combines theoretical rigor with practical impact, addressing societal challenges through data-driven solutions. Shah's contributions to synthetic interventions and causal simulators have advanced trace-driven simulation methodologies, while his collaboration with global consortia like PETAL highlights his commitment to translational research. His students, including Kang Zhang, have contributed to impactful projects in algorithm design and data science.

Divyakant AgrawalView profile

Professor

• Big Data
• Cloud Computing
• Distributed Systems
• +5 more

Divyakant Agrawal is a Distinguished Professor and Chair of the Computer Science Department at the University of California, Santa Barbara (UCSB). He holds a PhD from SUNY Stony Brook and a BS from Birla Institute of Technology and Science. His research focuses on large-scale distributed systems, cloud data management, and database security, with notable contributions to geo-replicated data, privacy-preserving techniques, and social network analysis. Agrawal has led NSF-funded projects on data summarization, hardware acceleration, and wireless sensor networks. He served as VP of Data Solutions at ASK.com and as a visiting senior researcher at NEC Laboratories and the University of Hong Kong. Education: PhD (Computer Science, SUNY Stony Brook), BS (Electrical Engineering, BITS Pilani). Leadership Roles: Chair of UCSB Computer Science Department (1999–2003), Visiting Professorships at National University of Singapore and University of Hong Kong. His research spans distributed algorithms, database concurrency control, and privacy in cloud systems. Key areas include transaction processing, data stream operators, and fraud detection in advertising networks. Agrawal has authored over 300 papers and co-edited journals like the VLDB. He is a Fellow of IEEE, ACM, and AAAS, and has received awards such as the Outstanding Graduate Mentor from UCSB (2011). Professional Contributions: Served on program committees for SIGMOD, VLDB, and IEEE conferences; led editorial roles for Distributed and Parallel Databases and the VLDB Journal. His work on systems like G-Store (cloud data retrieval) and Zephyr (elastic databases) showcases innovations in scalability and autonomy.

Evan Walter Clark Spotte-SmithView profile

Assistant Professor

• Computational Chemistry
• Electrochemistry
• Energy Storage
• +4 more

Evan Walter Clark Spotte-Smith is an Ad Astra Fellow and incoming Assistant Professor of Digital Chemistry at University College Dublin (UCD). He holds a PhD in Materials Science and Engineering from UC Berkeley and previously completed postdoctoral work at Carnegie Mellon University (CMU) under Prof. Rachel Kurchin and Prof. Gabe dos Passos Gomes. His research focuses on computational methods in electrochemistry, energy storage, and the ethical implications of machine learning in chemistry. He leads the CoReACTER initiative, an anti-oppressive research group, and has held fellowships including the Carnegie Bosch Institute Fellowship (2024–present) and the Philomathia Graduate Student Fellowship (2022). Evan’s education includes a B.S. in Materials Science and Engineering from Columbia University (2019), with minors in Sustainable Engineering. He has conducted research at Tesla Motors, Lawrence Berkeley National Laboratory (LBNL), and Columbia University. His teaching experience includes roles as an instructor for courses like 'Algorithms and Data Science for Chemical Engineers' at CMU and 'General Chemistry' at UC Berkeley. His research emphasizes computational simulations, reaction networks, and machine learning to study electrolyte decomposition in batteries and sustainable energy technologies. He has authored over 30 peer-reviewed articles and developed software tools like SimpleDirectedHypergraphs.jl and MPcat . Notable awards include the 2025 Outstanding Reviewer Award from the Institute of Physics and the 2023 Winton-Kavli Research Pitch Competition prize. Funding: Lead PI on grants totaling over $5.5M, including Toyota Research Institute Synthesis (2024–2026) and National Artificial Intelligence Research Resource allocations. Service: Topic Editor for The Journal of Open Source Software , mentor for DisabledInSTEM, and advocate for graduate student welfare at UC Berkeley. Software: Contributions to pymatgen , custodian , and Emmet for materials informatics.

Guo MinyiView profile

Professor

• Database systems
• Cloud computing
• Parallelizing compilers
• +4 more

Minyi Guo is a Chair Professor and Head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China. Previously, he served as Professor and Department Chair at the School of Computer Science and Engineering, University of Aizu, Japan. Dr. Guo received his BSc and ME degrees from Nanjing University, China in 1982 and 1986, and his PhD from University of Tsukuba, Japan in 1998. Dr. Guo's educational background includes: BSc in Computer Science, Nanjing University, China (1982) ME in Computer Science, Nanjing University, China (1986) PhD in Computer Science, University of Tsukuba, Japan (1998) Dr. Guo's research spans multiple areas in computer science, with a primary focus on parallel/distributed computing , compiler optimizations , cloud computing , database systems , and big data . He has published over 400 papers including approximately 150 in major journals and 250 in international conferences, with more than 60 papers in IEEE/ACM transactions and over 100 papers in prestigious conferences. Dr. Guo has also authored 7 books (4 in English, 3 in Chinese) and received 5 best/highlight paper awards from international conferences. Dr. Guo's publication record demonstrates strong contributions across multiple domains of computer systems research. His recent work shows particular emphasis on big data processing, edge computing, graph neural networks, and data center optimization. The publications reveal a consistent trajectory of impactful research in parallel and distributed systems, with increasing focus on AI/ML applications and blockchain technologies in more recent years. Dr. Guo has received numerous prestigious awards and honors: State Technological Invention Award of China (second class award, 2019) Shanghai Technological Invention Award (first class award, 2018) IEEE Technical Committee on Scalable Computing Award for Excellence in Scalable Computing (2018) Ministry of Education Natural Science Award (first class award, 2017) IEEE Fellow (2017) Chief Scientist of National Basic Research Project (973 Program, 2014) Recruitment Program of Global Experts (2010) Excellent Academic Leaders of Shanghai (2010) National Science Fund for Distinguished Young Scholars (2007) As an academic leader, Dr. Guo has served as Department Head for ten years, managing a department with over 100 faculty members and 1000+ students. Under his leadership, the department was promoted to the top tier in China and ranked among the top 40 in the world. He has secured significant research funding, including serving as Chief Scientist of the prestigious 973 Program in 2014 and receiving the National Science Fund for Distinguished Young Scholars in 2007. Dr. Guo has also been selected for the Recruitment Program of Global Experts in China (2010). Dr. Guo actively contributes to the academic community as an associate editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and Journal of Parallel and Distributed Computing. He has served as General/Program Chair for IEEE conferences and delivered keynote speeches at well-established conferences. His research group has developed practical technologies with industry impact, including 28 licensed patents, some of which have been transferred to companies like Alibaba.

Mark GrimesView profile

Professor

• Human Computer Interaction
• Conversational Agents
• Deception Detection
• +3 more

Mark Grimes is a Professor of Practice in the Decision and Information Sciences Department at the University of Houston's Bauer College of Business. His academic background includes a PhD in Management Information Systems from the University of Arizona (2015), an MBA from Belmont University, and a BBA from the University of Mississippi. He has conducted research at the National Center for Border Security and Immigration (BORDERS), a DHS center of excellence, focusing on deception detection, human-computer interaction, and cybersecurity. His research interests center on analyzing human-computer interaction behaviors (e.g., mouse movements, typing patterns) to detect cognitive/emotional states, conversational agent applications in security, and judgment/decision-making processes. He has secured over $220,000 in research funding and presented work to groups like the 111th Military Intelligence Brigade and the International Information Systems Security Certification Consortium. Grimes' publications emphasize conversational AI ethics, chatbot design, and cybersecurity applications. His work bridges technical systems with human behavioral analysis, addressing challenges in deception detection and adversarial thinking. He teaches courses in database management, information systems administration, and quantitative methods.

• Computer Vision
• Remote Sensing
• Machine Learning
• +4 more

Yin Zhang is a Professor at Zhejiang University's College of Computer Science and Technology, specializing in computer vision, remote sensing, and machine learning. With over 300 publications spanning from 1992 to 2025, their research has evolved from foundational work to cutting-edge applications in multimodal analysis and AI-driven imaging systems. Research interests focus on computer vision with particular expertise in object detection, image fusion, and remote sensing applications. Their work bridges theoretical advancements with practical implementations across medical imaging, autonomous systems, and geospatial analysis. Recent publications demonstrate innovative approaches to multimodal data integration, including diffusion models and knowledge distillation techniques for enhanced image understanding. Publication trends show increasing specialization in remote sensing applications since 2019, with significant contributions to vehicle detection, land use classification, and maritime surveillance. The research portfolio demonstrates consistent methodological innovation while addressing real-world challenges in areas ranging from agricultural monitoring to conflict analysis. As a research supervisor, Yin Zhang has mentored numerous students including Zian Ning, Xiaoyu Zhang, and Shiyu Zhao, with collaborative projects frequently appearing in top-tier venues like IEEE Transactions and CVPR. Their work demonstrates strong interdisciplinary connections between computer science, electrical engineering, and domain-specific applications.

Wei ZhangView profile

Assistant Professor

• Linguistics
• Speech Perception
• Speech Prosody
• +4 more

Wei Zhang is an Assistant Professor in the School of Foreign Studies at Nanjing University, China. His research focuses on speech perception, production, and prosody, employing behavioral experiments, corpus studies, and computational modeling. He holds a PhD in Linguistics from McGill University, where his advisors included Meghan Clayards, Morgan Sonderegger, and Michael Wagner. His educational background includes a doctoral program at McGill University specializing in Linguistics. Research interests center on tonal systems (particularly Mandarin and Taiwanese Southern Min), acoustic cue interactions, and the role of F0 and duration in speech processing. He has also explored machine learning applications in natural language processing and speech technologies. Zhang's recent work reveals a focus on cross-linguistic comparisons of prosodic features, computational methods for pitch analysis, and neural network architectures enhancing natural language understanding. His 2024 studies on focus effects and constituency in Mandarin/English prosody demonstrate methodological innovation in phonological interfaces. Earlier work established foundational insights into F0 range dominance in tonal perception. Key contributions include advancements in pitch-range estimation, tonal imitation patterns, and speech corpus methodologies. Despite prolific publication (over 20 peer-reviewed works across 2014-2025), no advising roles or grants are explicitly mentioned. His lab/teams' activities remain unspecified in the provided materials.

Nik BessisView profile

Professor

• Smart Systems
• Autonomous Systems
• Big Data
• +4 more

Professor Nik Bessis is a full Professor of Computer Science at Edge Hill University (UK), serving as founder and former Head of the Department of Computer Science (2015-2021) and Engineering (2018-2021). He leads strategic initiatives including the University’s 2023-30 Research Strategy and the international multi-institutional alliance for global societal challenges. His roles include Senior Advisor for Research Partnerships and Institutional Lead for UKRN/OR4 projects. He directs the interdisciplinary Data Science research centre, generating over £5M in external funding from UKRI, EPSRC, and Innovate UK. Affiliations: Edge Hill University, Data Science STEM Research Centre, IEEE, HEA, BCS Education: BA (Graphic Design, T.E.I. Athens), MA/PhD (De Montfort University) His research focuses on smart systems, IoT, disaster management, and big data analytics. He has published over 330 works, including the influential Big Data and Internet of Things (2014, 150k downloads), with an h-index of 40. Awards include 4 best paper prizes and Fellowships from HEA/BCS. He led the University’s REF2021 submission, achieving a 40-place rise in UK rankings. Projects include the £13M Tech Hub building with advanced labs (including a 4K CAVE and robots). He advises on global initiatives like the NW Sustainability in Computing Group and EUA-CDE Building Bridges peer group.