Andres Lucero is Associate Professor of Interaction Design at Aalto University's Department of Art and Media, specializing in human-computer interaction for mobile devices and interactive surfaces. His research integrates human-computer interaction, design methodologies, and playful interfaces. He holds a PhD from Eindhoven University of Technology and has industry experience from Nokia, where he led user-centered design projects as Senior Researcher. Research explores tangible interfaces, social wearables, museum experience design, human-AI collaboration, and cross-cultural co-design approaches. Recent projects investigate conversational agents in design processes, persona generation workflows, and freeform interactive devices. Dr. Lucero received the Honourable Mention Award at MUM 2020 for innovative interaction research. His work advances design methods through first-person perspectives, AI integration, and novel interaction paradigms.
Travis Desell is a Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), part of the B. Thomas Golisano College of Computing and Information Sciences. His research focuses on data science and machine learning applied to large-scale datasets using high-performance and distributed computing. He specializes in neuro-evolution, combining evolutionary algorithms with neural networks, particularly through his EXACT and EXAMM algorithms. He leads the D2S2 Lab and has developed the SALSA programming language based on the actor model. Currently funded projects include the National General Aviation Flight Information Database (NGAFID) and an NSF award exploring contextual bandits for decision-making in cyber-physical systems. His work emphasizes practical scientific applications, including stock forecasting, power plant data prediction, and explainable time series models. Education details are not explicitly provided, but his roles and publications indicate advanced academic credentials. Research interests span neuro-evolutionary techniques, recurrent neural networks, and distributed computing frameworks. Key projects include EXAMM for time series forecasting and NGAFID for flight safety analysis. Collaborations involve students and teams at RIT and beyond, with a focus on advancing AI-driven solutions in dynamic environments. Lab affiliations include the D2S2 Lab, where he mentors students and conducts cutting-edge research. Current opportunities exist for PhD students with backgrounds in software engineering and expertise in areas like NLP, web development, and distributed systems.
Felix A. Epp is a Postdoctoral Researcher at Aalto University's Department of Design, with an external position at the University of Helsinki (2024-2026). His work sits at the intersection of human-computer interaction, wearable technology, and design research, focusing on how interactive technologies shape human practices and future societies. Education: Doctor of Science in Technology, Aalto University (2023) Master's degree in Arts and Design, Hochschule Darmstadt (2014) Bachelor's degree in Arts and Design, Hochschule Darmstadt (2011) Epp's research explores embodied technological experiences and technology-mediated social practices, with a particular focus on wearable technologies and their impact on social-cultural practices. His doctoral work investigated how wearables shape clothing practices, and his recent work integrates critical futures studies with practice-based research to incorporate anticipation in technology innovation. He employs generative design research and qualitative fieldwork methods, including research through design in everyday contexts and participatory design approaches. His fingerprint reveals strong engagement with Wearable Technology (100%), Human-Computer Interaction (62%), and Design Research (56%). His publication record shows a clear trajectory toward anticipatory design and futures thinking in HCI, with increasing focus on how technologies can help us engage with uncertain futures. His recent work bridges design research with critical futures studies, creating methods like the Future Ripples Method to activate anticipatory capacities in innovation teams. This evolution reflects a growing recognition of the need for designers to anticipate multiple possible futures rather than designing for a single predetermined outcome. Scientific Awards: CHI 2025 Honorable Mention Award MUM 2020 Honourable Mention Award Epp has been actively involved in significant research projects including 'FutureMethods: Methodology for HCI evaluations of possible futures' (2020-2024) and 'Digital Aura: Crafting a Digital Representation of Self in the Physical World' (2017-2021). His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of his research. He serves on program committees for major conferences including ACM DIS and CHI, demonstrating leadership in the HCI community. His research contributes to Sustainable Development Goals related to technology and society, with particular relevance to responsible consumption and production through his work on smart clothing in circular economies.
Dr. Ali Ahrari is a Lecturer at the School of Systems and Computing, University of New South Wales, Canberra. He holds a Ph.D. in Mechanical Engineering from Michigan State University (2016) and has extensive experience in research and academia, including roles as a Research Fellow and Associate at UNSW-Canberra and the University of Sydney. His research focuses on evolutionary algorithms, multimodal and multi-objective optimization, and surrogate-assisted optimization. Ahrari is a recipient of prestigious awards, including the ARC-DECRA 2023 and multiple international competition wins in optimization (e.g., CEC/GECCO competitions). He leads research groups like the Canberra Evolutionary Optimization (EvOpt) and serves on editorial boards, including Applied Soft Computing. Education: Ph.D. (2016, Michigan State University), M.Sc. and B.Sc. (University of Tehran). Awards: ARC-DECRA, ISCSO, and GECCO/CEC competition wins. Grants: ARC DECRA (2023), NCI Adapter Schemes, UNSW HPC allocations. Supervision: Currently advising 1 PhD student at SEIT, UNSW-Canberra. Engagements: Chair of IEEE Task Force on Multi-modal Optimization, organizer of optimization competitions (GECCO'2024, CEC'2022). His research emphasizes computational optimization, evolutionary computation, and swarm intelligence, with applications in engineering design and dynamic environments. He actively contributes to academic communities through editorial roles and conference organization.
Guo Li is affiliated with the Beijing Institute of Technology, School of Management and Economics. Their research spans computer vision, optimization algorithms, signal processing, and machine learning. Collaborations include work on image super-resolution, sensor networks, and energy systems. Publications are distributed across journals like Comput. Electron. Agric. , IEEE Trans. Circuits Syst. , and Entropy . Research interests focus on computational methods for image processing, algorithm design, and interdisciplinary applications in agriculture and energy. Recent work emphasizes lightweight neural network architectures, sparrow search algorithms, and thermodynamic modeling in materials science. Notable contributions include advancements in citrus fruit detection, fatigue life assessment of superalloys, and load forecasting techniques. Active in international conferences such as CVPR, ICC, and NSDI, with a strong publication record since 1998.
Annie S. Wu is an Associate Professor in the Department of Computer Science at the University of Central Florida (UCF). She directs the Evolutionary Computation Laboratory and holds joint appointments with the Department of Electrical and Computer Engineering and the Institute for Simulation and Training at UCF. Wu earned her Ph.D. in Computer Science and Engineering from the University of Michigan in 1995. Ph.D. in Computer Science and Engineering – University of Michigan Her research spans genetic algorithms , evolutionary computation , complex adaptive systems , multi-agent systems , and machine learning . She has served on editorial boards for Evolutionary Computation and Memetic Computing , and held leadership roles with ACM SIGEVO and the International Society for Genetic and Evolutionary Computation. Notable accolades include: UCF Teaching Incentive Program Award (2019) Excellence in Graduate Teaching Award, UCF College of Engineering and Computer Science (2017) National Research Council Research Associateship Award (1996–1999) Wu’s work bridges theoretical and applied computational methods, with a focus on adaptive systems and collaborative research across engineering disciplines.
Sal Hagen is a postdoctoral researcher at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, working within the Natural Language Processing & Digital Humanities group. His research focuses on the intersection of computational methods and cultural analysis, particularly examining online communities, political discourse, and meme culture. His educational background includes a Research Master's in Media Studies from the University of Amsterdam, for which he received the prestigious 2018 Internet Thesis Prize in the Internet & Social Sciences or Humanities category for his work "Here I Am, Praying to an Egyptian Frog: Exploring Political Fluidity on 4chan/pol/". He also won the Audience Award for his presentation at the ceremony. Hagen's research interests center on understanding the dynamics of online communities, with particular expertise in 4chan and related platforms. His work combines qualitative and quantitative approaches to analyze political discourse, far-right movements, and the evolution of memes across digital spaces. He examines how anonymity, platform architecture, and cultural context shape online interactions and political expression. His publication record demonstrates a consistent focus on tracing political movements through digital footprints, with particular attention to the interplay between platform-specific cultures and broader political trends. Hagen's work often bridges computational social science with cultural studies, creating methodologies that capture both the quantitative patterns and qualitative meanings of online discourse. Scientific Awards: 2018 Internet Thesis Prize (Internet & Social Sciences or Humanities category) Audience Award for presentation at Internet Thesis Prize ceremony Hagen has secured significant research funding, including an NWO PhD grant for humanities research running from 2019-2024. He has been involved in multiple research projects including the CAT4SMR project (2024), OILab (2017-present), and the ERC-funded ODYCCEUS project (2018-2019). As part of OILab (Online Intelligence Lab), Hagen contributes to research on online political subcultures, while his work on the CAT4SMR project focuses on stabilizing and developing tools for social media data collection and analysis. He is also the developer of the 4CAT Capture and Analysis Toolkit, which provides transparent and traceable methods for social media research.
Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.
Dr. Tim Lynar serves as a Senior Lecturer at the University of New South Wales Canberra within the School of Systems & Computing. With a strong background in both academic research and industry practice, he has established himself as a leading figure in cyber security and computer science. His work bridges theoretical research with practical applications, focusing on innovative solutions for complex computing challenges across multiple domains including IoT security, machine learning applications in cyber defense, and high-performance distributed systems. Dr. Lynar's research interests span a wide spectrum of cyber security applications, with particular emphasis on the application of machine learning techniques to security challenges and the innovative use of epidemiological approaches to understand and combat cyber threats. His work in modeling & simulation, statistical & data analysis, network & systems administration, and high-performance distributed computing demonstrates his commitment to developing comprehensive security frameworks that address evolving threats in digital environments. The interdisciplinary nature of his research connects computer science with biological modeling approaches, creating novel methodologies for understanding security vulnerabilities. Analysis of Dr. Lynar's recent publications reveals a strong trend toward applying advanced machine learning techniques to cyber security challenges, particularly in IoT environments. His work increasingly integrates epidemiological models with security frameworks, creating a unique approach to threat detection and mitigation. The research spans practical applications in network security, drone systems, and AI security, demonstrating both theoretical depth and real-world applicability. A notable pattern is the consistent application of cutting-edge deep learning architectures like Vision Transformers and Variational Autoencoders to solve specific security problems across diverse domains. IBM Master Inventor (2016) Multiple IBM Innovation Awards (2011-2018) Client Value Outstanding Technical Achievement Awards (2015-2016) High Value Patent Awards (2014-2016) Best Article Award – International Journal of Information Systems & Social Change (2010) Multiple research scholarships from 2007-2010 Dr. Lynar's extensive patent portfolio demonstrates significant industry impact, with numerous issued US patents spanning diverse applications from energy efficient supercomputing to vehicle collision avoidance and drone-based microbial analysis. His research has attracted substantial industry collaboration, particularly with IBM, where he received multiple prestigious awards including the IBM Master Inventor designation. The practical applications of his work are evident in the wide range of patented technologies addressing real-world security and optimization challenges across multiple industries. Dr. Lynar's work spans multiple research domains simultaneously, with active projects in cyber security, drone systems, AI safety, and maritime traffic analysis. His research methodology consistently combines theoretical modeling with practical implementation, often leveraging simulation environments to test and validate approaches before real-world deployment. The interdisciplinary nature of his work creates connections between traditionally separate fields, enabling innovative solutions to complex problems.
Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.
Liji Shen is Professor of Operations Management and Chairholder at WHU – Otto Beisheim School of Management, Campus Vallendar, Germany. She is affiliated with the Supply Chain Management Group and leads research in scheduling, optimization, and sustainable manufacturing. Her academic journey includes a Ph.D. and Habilitation from Technische Universität Dresden, and she has held visiting scholar positions at institutions including École des Mines de Saint-Étienne and Huazhong University of Science and Technology. Ph.D. (Dr.rer.pol.), summa cum laude, Technische Universität Dresden (2009) Habilitation, Technische Universität Dresden (2015) Master of Business Administration (Dipl.-Kffr.), Technische Universität Dresden (2006) Liji Shen's research focuses on Operations Management , particularly scheduling optimization in manufacturing systems. Her work spans flexible job shops , parallel machine scheduling , energy-efficient production , and sequence-dependent setup times . She applies advanced techniques such as evolutionary algorithms , hybrid metaheuristics , and mathematical programming to solve complex industrial problems. Her recent publications emphasize sustainability through energy-aware scheduling and time-of-use pricing models. The 15 most recent publications highlight a consistent research trajectory in production scheduling , with increasing emphasis on energy efficiency , distributed manufacturing , and real-world constraints like eligibility and delivery times. Her work frequently appears in top journals such as European Journal of Operational Research , IEEE Transactions on Evolutionary Computation , and Computers & Operations Research , often in collaboration with leading researchers like Dauzère-Pérès, Mönch, and Buscher. Scientific Awards: European Journal of Operational Research, Best Paper Award (2021) DFG and TU Dresden, 'Support the Best' Prize for Outstanding Researchers (2013) Dr. Feldbausch-Prize for Best Dissertation, TU Dresden (2010) Scholarship for Young Researchers in Saxony (2006–2009) DAAD Prize for Best Foreign Students (2007) Best Master’s Thesis, German Operations Research Society (2007) Liji Shen has been an active advisor and researcher, leading projects in operations research and industrial optimization. Her editorial role on Operations Research Perspectives underscores her standing in the academic community. She has directed research labs and collaborated internationally, contributing to both theoretical advancements and practical applications in manufacturing and logistics. No specific grants are mentioned, but her sustained publication record and leadership roles indicate strong research support. She leads the Operations Management research group at WHU, focusing on algorithmic solutions for complex scheduling problems. Her team investigates energy-aware production, hybrid flow shops, and distributed systems, aiming to bridge the gap between theoretical models and industrial implementation. The lab collaborates with researchers across Europe and China, fostering a global research network in operations research and supply chain management.
Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
Dr. Ray Drainville is a lecturer at the University of Waterloo, specializing in digital media and visual culture. His research explores the intersection of iconography, social media image analysis, and machine learning applications. With nearly two decades of industry experience in web development and graphic design, he brings practical insights to his teaching on contemporary digital visual culture. PhD in Visual Culture from Manchester School of Art, Manchester Metropolitan University (2018) MA in Information Studies from University of Sheffield (1996) MA in History of Art from Princeton University (1995) BA in History of Art from New College (1992) Dr. Drainville's research interests include digital media, visual culture, iconography, media theory, and hermeneutics. His work increasingly examines linguistic and visual dogwhistles in political contexts, memetic superposition, and algorithmic iconography. He has published in journals such as AI & Society , Studies In Communication Sciences , and Hyperallergic , often combining historical art analysis with contemporary digital platforms. He teaches courses including: GBDA 101 Introduction to Digital Media Design GBDA 203 Introduction to Digital Culture GBDA 228 Digital Imaging of Online Applications GBDA 301 Global Digital Project 1 GBDA 401 Cross-Cultural Digital Business GBDA 402 Capstone Course: Cross-Cultural Digital Business ARTS 290 Theories of Media
Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.
Matthias Baitsch serves as Professor of Construction Informatics and Numerical Methods in the Department of Civil and Environmental Engineering at Bochum University of Applied Sciences, where he concurrently heads the BIM Institute. His academic trajectory includes research assistant and senior engineer roles at Ruhr-University Bochum (2000-2009), academic coordination at the Vietnamese-German University (2009-2012), and an acting professorship at the University of Kassel (2012-2014). His educational foundation comprises: Civil Engineering studies at the University of Dortmund (1991-1997) under the interdisciplinary "Dortmund Model" Doctorate from Ruhr-University Bochum (2003) on geometric imperfection-based optimization of compressive beam structures Professor Baitsch's research integrates computational mechanics with civil engineering practice, specializing in construction informatics, numerical optimization, and high-order finite element methods. His work pioneers distributed optimization frameworks, structural health monitoring for wind energy infrastructure, and BIM-based construction informatics. Key methodological contributions include hp-FEM implementations, parallel optimization algorithms, and mobile structural analysis tools. Analysis of his recent publications reveals three dominant research trajectories: (1) Advanced numerical methods for structural optimization under uncertainty, (2) Health monitoring-driven lifetime prediction for wind turbine systems, and (3) Computational modeling of tunnel environments using viscoacoustic inversion techniques. These threads demonstrate consistent focus on robust numerical implementations and real-world civil engineering applications. As Head of the BIM Institute, he leads institutional efforts in digital construction technologies, fostering industry-academia collaboration on building information modeling standards and applications. His teaching portfolio spans foundational mathematics, numerical methods, and computer science for civil engineering students, emphasizing practical computational skills.
