Vibhuti Gupta, Ph.D., serves as Associate Professor of Computer Science and Data Science at Meharry Medical College, where he joined in 2021 after completing his doctorate at Texas Tech University. His work bridges computational methods with healthcare innovation through cutting-edge research in AI and data science. Dr. Gupta's academic background includes: Ph.D. in Computer Science from Texas Tech University (2019) M.Tech in Computer Science from SRM University B.Tech. in Computer Science from Bundelkhand Institute of Engineering and Technology His research pioneers trustworthy AI applications in medicine, focusing on multimodal health data analysis from wearables and mHealth apps. He develops scalable machine learning techniques for early disease diagnosis while emphasizing explainability, ethics, and fairness in healthcare AI systems. This work addresses critical challenges in processing longitudinal health data streams for actionable clinical insights. Dr. Gupta has secured competitive research funding as Principal Investigator for American Cancer Society (ACS) DCRIDG, NIH AIM-AHEAD, and NSF ExpandAI CAP initiatives, plus Co-Investigator roles in NSF MRI, RCMI Supplement, and NASA MEUREP projects. His mentorship extends to graduate students in computational health sciences. He directs the mHealth Wearables Sensors Lab, which develops computational frameworks to transform raw sensor data into clinically meaningful knowledge while maintaining rigorous ethical standards in AI-driven healthcare solutions.
Thorsten Hennig-Thurau is a Full Professor holding the Chair for Marketing & Media Research at the University of Münster's Marketing Center (MCM). He has served as the spokesperson of MCM since 2019 and is the academic director of both the university's M.B.A. in Marketing program and the MCM's eXperimental Reality Lab (XRLab@MCM), which he initiated in 2020. Prior to joining the University of Münster in 2010, he was a professor at the Bauhaus University of Weimar, and from 2005 to 2014, he also served as a part-time Research Professor of Marketing at City University London's Cass Business School. Professor Hennig-Thurau earned his Habilitation and venia legendi in Business Administration at University of Hanover in 2002, his Doctoral degree (Dr. rer. pol.) at University of Hanover in 1998, and graduated with a diploma in Business Administration (Diplom-Kaufmann) from University of Lüneburg in 1994, where he studied from 1989-1994. He has been recognized as one of the leading German scholars in business administration by various national and international rankings. His research primarily focuses on the impact of digitalization on consumers and businesses, with expertise spanning spatial/immersive marketing, digital marketing, technology & innovation, and entertainment marketing. Professor Hennig-Thurau is renowned for his seminal work on electronic word of mouth (with over 11,000 citations), the "pinball framework" of digital marketing, and the value-creation framework for the virtual-reality metaverse. He is also a leading expert in entertainment science, having co-authored a 900+ pages book that established this paradigm for successfully marketing entertainment products in the digital age. His extensive publication record demonstrates a clear evolution from traditional marketing concepts to cutting-edge digital and immersive technologies. Recent publications focus heavily on metaverse applications, virtual reality consumer behavior, and the transformation of media markets in the digital era, reflecting his ability to anticipate and investigate emerging marketing phenomena. His work consistently bridges academic rigor with practical business applications. Lifetime Award for Published Scholarly Contributions from UCLA JAMS Sheth Foundation Best Paper Award (2015, 2018, 2023) Ranked as the 52nd most influential marketing scientist globally (Stanford Study, 2024) Recognized as one of the Top 2% Most-Cited Scholars Worldwide Initiator and long-time editor of the influential VHB-JOURQUAL journal ranking Professor Hennig-Thurau has secured substantial research funding, including multiple grants from the German Research Foundation (DFG). His current "Wisdom of Virtual Crowds" project (2024-2027) continues his exploration of digital consumer behavior. He has advised numerous doctoral students and has been instrumental in establishing the DFG Research Unit "Social Media Marketing," the first of its kind in marketing research. His leadership extends to serving as president of marketing professors in Germany and as the first European member of the American Marketing Association's Academic Council. He leads the eXperimental Reality Lab (XRLab@MCM), which explores value creation in immersive settings, and has established significant research collaborations including the Social Media Think:Lab with Roland Berger Strategy Consultants. His work bridges academic research with industry applications, particularly in entertainment, media, and digital marketing sectors.
Simon Jouet is Research Fellow at the University of Glasgow's School of Computing Science specializing in software-defined networking and network security. Research focuses on: Programmable data planes using eBPF technology Distributed anomaly detection systems Network function virtualization DDoS mitigation strategies Cloud data center resource management Publications show emphasis on SDN/NFV technologies (70%) and network security (30%), with recent work expanding into operational technology resilience. Developed Glasgow Network Functions framework for virtual network function management. Research addresses challenges in cloud computing infrastructures and critical network protection during disasters. Collaborates with industry partners through European research projects on resilient networking.
Timothy J. Strathmann is a Professor and Associate Department Head in Civil and Environmental Engineering at Colorado School of Mines , where he leads the Strathmann Research Group. His work focuses on applying chemical research tools to develop sustainable environmental and energy technologies , particularly for PFAS remediation , waste valorization , and renewable resource recovery . Education : PhD in Environmental Engineering (Johns Hopkins, 2001), MS (Purdue, 1996), BS (Purdue, 1995) Affiliations : Associate Editor for Environmental Science & Technology , Board of Directors at AEESP, Collaborative Researcher at NREL His research integrates catalytic , hydrothermal , and photochemical processes to solve challenges like groundwater contamination by PFAS, biofuel production from wastewater , and cost-effective environmental treatments . Recent projects emphasize mechanistic studies and life cycle assessments of PFAS destruction technologies. Key trends in his publications include PFAS defluorination , membrane rejection systems , and catalytic hydrothermal conversion of organic waste. His work has been featured in CBS News for innovative "Forever Chemical" destruction methods. Scientific Awards : NSF CAREER Award, AEESP Distinguished Service Awards, Honorary Professorship at Tongji University He mentors PhD and MS students like Camille Amador (2023 graduate), Anderson Ellis (postdoctoral researcher), and Aron Griffin (PhD candidate). The group also collaborates with Aquagga for commercializing HALT technology and maintains partnerships with institutions like NREL and EPFL .
Prof. Dr. Peter Bug is a Professor of Fashion Marketing & Retail Management and Dean of Studies for the B.Sc. International Fashion Retail program at Reutlingen University. He holds a Diplom in Business Cybernetics and a PhD in Business Management from the University of Stuttgart. His career includes roles as a research assistant at the Institute for Textile and Process Engineering Denkendorf, management consultant for adidas France/Central, and independent sales forecasting advisor. He leads the Professorship for Fashion Marketing & Retail Management, overseeing teaching areas like Industry Projects, Fashion Forecasting, and International Retail Studies. His research focuses on the intersection of fashion, film, and digital marketing. Notable projects include analyzing moving images' impact on consumer behavior, fashion diffusion trends, and omnichannel retail strategies. He has authored/co-authored over 30 publications, including the 2020 Springer book Fashion and Film: Moving Images and Consumer Behavior , which explores how cinema, social media, and video content shape fashion consumption. Recent article trends emphasize digital transformation in fashion retail (e.g., AR/VR applications, e-commerce video strategies) and consumer behavior in niche markets (e.g., sneaker resale, luxury rental). His work bridges academic research with industry needs, addressing topics like predictive analytics in sales forecasting and brand film storytelling. Bug collaborates with industry partners on projects like InBiO (bio-based car interiors) and MoTraScha (adaptive seating for disabled youth). He heads the Market Research Lab at Texoversum and supervises numerous student excursions to study global retail practices.
John Murphy is a Full Professor at the School of Computer Science, University College Dublin. He holds academic positions as an IBM Faculty Fellow and Fellow of multiple professional organizations, including the Institution of Engineering and Technology and Engineers Ireland. His research focuses on Performance Engineering, Telecommunication Systems, and Middleware, with a strong emphasis on cloud computing, distributed systems, and network optimization. Education: B.E. (Electronic Engineering) from University College Dublin (1988), M.Sc. (Electrical Engineering) from California Institute of Technology (1990), and Ph.D. (Electronic Engineering) from Dublin City University (1996). Research Interests: His work spans Performance Engineering, Telecommunication Systems, Enterprise Software, Middleware, Mobile Networks, and Queueing Theory. Notable contributions include energy-efficient cloud resource management and network-aware scheduling in data centers. Publications: Over 200 peer-reviewed articles, including recent work on intelligent computing in IoT, energy-efficient VM mapping, and load balancing in wireless networks. His research trends emphasize cloud optimization, distributed systems performance, and smart city infrastructure. Awards: Recognitions include the Real Time Correlation Engine award and prestigious fellowships. He has supervised 24 PhD students and secured over €9.5M in research grants. Teaching: Coordinates modules on Computer Networking, Databases, and Programming. He advocates for foundational learning to foster adaptability in students. Grants: Includes Horizon 2020 projects like NEO-QE and PRTLI grants for SimSci. Collaborates with industry partners for applied research. Labs: Leads the Performance Engineering Laboratory, advancing research in distributed systems and network performance.
Dr. Xiangmin Zhou is a Senior Lecturer at the School of Computing Technologies, RMIT University, located at City Campus Australia. His research focuses on artificial intelligence, data management, distributed computing, and multimedia systems. He actively supervises Masters and PhD students in areas such as fairness-aware task recommendation in spatial crowdsourcing and adaptive multi-participant analytics. His teaching interests include social media analysis, multimedia databases, query optimization, and cloud data management. Dr. Zhou’s research interests span information systems, AI-driven recommendation frameworks, privacy-preserving techniques, and distributed computing solutions. His work emphasizes context-aware systems, ethical AI, and scalable data processing. Recent publications highlight innovations in federated learning, privacy preservation, and social media-based disaster detection. He is open to supervising students interested in his core research areas and collaborative projects. Dr. Zhou’s academic contributions include developing novel algorithms for recommendation systems, social media analysis, and real-time event detection. His projects often bridge theory and practice, addressing challenges in spatial crowdsourcing, video novelty detection, and multi-platform coordination. His work aligns with RMIT’s focus on technology-driven solutions for societal challenges.
Prof. Dr. Heike Trautmann is a leading researcher in statistics and optimization at the University of Twente (2021-2026) and former Professor at WWU Münster (2013-2016). Her work bridges computational statistics, evolutionary optimization, and social media analytics. She has held visiting positions at TU Dortmund, Leiden University, and RWTH Aachen. Current affiliation: University of Twente (Data Science: Statistics and Optimization) Previous roles: WWU Münster (Professor for Information Systems and Statistics), TU Dortmund (Postdoctoral researcher) Research Focus: Multi-criteria optimization, automated algorithm selection, data stream mining, and disinformation detection in social media. Her methodological innovations in exploratory landscape analysis and evolutionary computation have transformed algorithm configuration practices. Developed COSEAL consortium for algorithm selection Co-founder of Benchmarking Network (2019) Principal investigator in projects like PropStop and MODERAT! Academic Contributions: Over 150 publications in top venues like GECCO, PPSN, and Evolutionary Computation journal. Pioneered feature-based landscape analysis tools (flacco, pflacco) and stream clustering frameworks.
Yu-Ping Chin is a Professor at the University of Delaware's Department of Civil and Environmental Engineering within the College of Engineering. Her research focuses on environmental and water resources, particularly investigating pollutant fate, biogeochemical processes, and climate change impacts in Arctic and coastal ecosystems. She holds a Ph.D. and M.S. from the University of Michigan, Ann Arbor, and an A.B. from Columbia College, Columbia University. Key research areas include dissolved organic matter dynamics, redox chemistry, and the environmental fate of contaminants such as pesticides and flame retardants. Her work integrates field studies, electrochemical analysis, and computational modeling to address challenges in water quality, coastal resilience, and ecosystem health. Recent studies highlight stemflow's role in coastal forest solute cycling, wildfire smoke impacts on dissolved organic matter, and the phototransformation of pollutants in Arctic environments. She collaborates on projects analyzing tidal stream alkalinity, microbial soil carbon cycling in wetlands, and the resilience of Arctic systems under climate stress. Her grants include collaborative research on stemflow dynamics and coastal critical zone networks. She leads studies in the ISE Lab and contributes to initiatives like the Antarctic supraglacial stream characterization. No awards or formal advisees are explicitly listed, but her work underscores interdisciplinary environmental science contributions.
Peter Wetz is a researcher affiliated with the Institute of Software Technology and Interactive Systems at TU Wien. His work focuses on semantic data processing, environmental data systems, and interactive data exploration. He has contributed to frameworks like YABench for RDF stream processing and StatSpace for statistical data integration. His research integrates Linked Data, real-time environmental monitoring, and user-friendly data visualization tools. Notable projects include Linked Widgets and Open Mashup Platform, aiming to lower barriers for data exploration. He has authored over 20 publications between 2013–2017, emphasizing interdisciplinary approaches to data management and semantic technologies. Key Areas: Semantic Web, Environmental Data, Stream Processing Tools Developed: YABench, StatSpace, Linked Widgets Platform
Matthias Uflacker is a Professor at the Hasso Plattner Institute (HPI), University of Potsdam, Germany, where he conducts research in database systems, in-memory computing, and data-driven enterprise applications. His work bridges systems research and practical applications in Industry 4.0, education, and bioinformatics. University: University of Potsdam School: Hasso Plattner Institute Department: Department of Digital Engineering Research Focus: In-Memory Databases, Stream Processing, Causal AI, and Database Education His research interests span in-memory database systems , real-time data stream processing , GPU-accelerated analytics , and causal structure learning . He also contributes to computer science education , particularly in agile software engineering and interactive learning tools for K-12. The recent publications reflect a strong trend in enterprise data management , with a focus on benchmarking (e.g., ESPBench), performance optimization (e.g., Kafka, Hyrise), and intelligent decision support using causal models in industrial contexts. His work increasingly integrates AI for monitoring and automation in manufacturing and e-commerce. He has co-authored numerous publications in top-tier venues such as VLDB, ICDE, EDBT, and KDD, often in collaboration with Hasso Plattner and other HPI researchers. Matthias Uflacker leads and contributes to research projects on autonomous database systems , dynamic pricing simulations , and educational technology platforms for programming exercises. His team develops tools for real-time data integration, causal analysis, and classroom software engineering assessment.
Marcello La Rosa is a Professor in the field of Business Process Management at Queensland University of Technology. His work focuses on process mining, workflow systems, and business process modeling. He has contributed extensively to research on process variability, predictive monitoring, and automated discovery techniques for business processes. His research has been published in top-tier journals such as Information Systems , ACM Transactions on Management Information Systems , and IEEE Transactions on Knowledge and Data Engineering . La Rosa's research interests include process model repositories, conformance checking, and the application of machine learning in process analytics. His work bridges theoretical advancements with practical tools such as the Apromore platform, which supports process model management and analysis. He has collaborated internationally on projects involving business process configuration, blockchain integration, and event log analysis. His contributions span over 134 publications and include seminal works on process mining manifesto, configurable process models, and drift detection in business processes. He has also co-edited special issues on BPM workshops and contributed to standards in workflow management systems like YAWL.
Dr. Sarah Goodwin is a Senior Lecturer at Monash University's Department of Human Centred Computing, specializing in geospatial analysis and information visualization. She leads the Embodied Visualisation research group and holds roles as Director of Engagement for Human-Centred Computing and co-Director of the Monash Grid Innovation Hub. Her work focuses on creating visual solutions for complex data, including energy grid analysis, cancer prevalence visualization, and urban data exploration. Dr. Goodwin has over 20 years of experience in academic and professional roles, collaborating globally with institutions like the giCentre (City University London) and RMIT University. Education: She holds a PhD in Geographical Information Science (City University London, 2015), MSc in Geographical Information Systems (City University London, 2007), and BSc (Honours) in Geography (University of Manchester, 2003). Research Interests: Geovisualization, energy data analytics, urban data visualization, HCI, and geostatistical modeling. She has developed tools like Gazealytics and led projects such as the Australian Cancer Atlas and the Australia's Discourse Explorer. Teaching: She teaches Data Exploration and Visualisation (FIT5147) and Research Methods at Monash, impacting over 1000 students annually. She has also taught at RMIT University. Grants & Awards: Recipient of the IEEE InfoVis Best Paper Honorable Mention (2016). Active in conferences like IEEE VIS (General Chair, 2023) and organizes workshops on EnergyVis and CityVis. Labs & Teams: Involved with the Immersive Analytics Lab and collaborates with Monash Energy Institute and Data Futures Institute. Her work aligns with UN SDGs on sustainable cities and energy systems.
John Breslin is a Personal Professor in Electronic Engineering at the University of Galway, leading the TechInnovate/AgInnovate programs. His roles include Director of the Insight (Data Analytics) and VistaMilk (AgTech) research centers. He has authored 350+ publications and co-created the SIOC framework, widely used in web applications. His research spans Data Science, AI, Semantic Web, IoT, and Entrepreneurship. He has won multiple awards, including the Best Irish-Published Book and IIA Net Visionary Awards. His teaching spans 25 years, covering topics like Digital Control Systems and Innovation. He co-founded boards.ie, StreamGlider, and leads initiatives in Galway's innovation ecosystem. Research Interests: Data Science, AI, Social Semantics, IoT, Agricultural Technology, and Innovation. He is ranked top in Engineering and Computer Science citations at the University of Galway. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure. Education: Details not explicitly provided in text. Collaborations include Bosch, Cisco, IBM, and Harvard Medical School. Active in industry partnerships and startups, including the PorterShed Innovation District. Awards: Best Paper Awards, Entrepreneurship Recognition, and Academic Excellence. Supervised nearly 30,000 ECTS across modules, with a focus on graduate and postgraduate tech innovation programs.
Michael Kamp is an Associate Professor for Machine Learning and Artificial Intelligence at TU Dortmund University and a faculty member of the Lamarr Institute for Machine Learning and Artificial Intelligence. He maintains affiliations with the Institute for Artificial Intelligence in Medicine (IKIM) at University Medicine Essen and collaborates with institutions in the UA Ruhr consortium. Formerly led Trustworthy Machine Learning group at IKIM Postdoctoral researcher at CISPA Helmholtz Center (2021) and Monash University (2019-2021) Doctorate from University of Bonn His research focuses on trustworthy machine learning through three pillars: deep learning theory (loss surfaces, flatness, generalization), causal representation learning for explainability, and federated learning for privacy-preserving decentralized systems. Applications span healthcare, autonomous driving, and cybersecurity. Recent publications at AAAI/ICLR/NeurIPS explore: federated optimization algorithms for non-IID data, privacy-preserving causal discovery, and flatness-aware regularization techniques. His work combines theoretical rigor with real-world deployment challenges. NeurIPS 2021 Outstanding Reviewer Award ICLR 2021 Reviewer Award Best Paper at PDFL'20 Workshop NeurIPS 2019 Best Reviewer Award ICML 2019 Reviewer Award He contributes to the editorial board of Springer's Machine Learning journal and serves in the ELLIS society. His group's tools address early disease detection, 3D medical shape analysis, and domain transfer challenges while maintaining ethical constraints.
