Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.
Ariel Rubinstein is a prominent Israeli economist and Professor of Economics at Tel Aviv University's School of Economics and the Department of Economics at New York University. Born on April 13, 1951, he has established himself as a leading figure in game theory and economic theory over his decades-long career. His research spans Game Theory, Bounded Rationality, Economic Theory, and Experimental Economics. Rubinstein is particularly renowned for developing the Rubinstein bargaining model, published in 1982, which describes two-person bargaining as an extensive game with perfect information. He also co-authored the highly influential A Course in Game Theory (1994) with Martin J. Osborne, which has been cited over 4,000 times. His recent scholarly output shows continued productivity across multiple subfields of economic theory, with a focus on behavioral aspects of decision-making, implementation theory, and the philosophical foundations of economic modeling. His work often challenges conventional approaches in economics while maintaining rigorous theoretical foundations. Honorary Fellow recognition Author of highly cited textbooks and scholarly articles Creator of educational resources including game theory experiments website Rubinstein maintains an active teaching role at NYU, where he has taught PhD microeconomics courses through 2024. His work extends beyond traditional academic boundaries through his 'Rubinstein's Atlas of Cafes where one can think,' his political commentary, and his engagement with public discourse on economic methodology and social issues. He has created protest materials and written extensively on contemporary political matters, particularly regarding the Israeli-Palestinian conflict. His laboratory focuses on Economic Theory, Bounded Rationality, Game Theory, and Experimental Economics, reflecting his interdisciplinary approach to understanding human decision-making within economic frameworks.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Dr. Johnson Xuesong Shen is an Associate Professor at the School of Civil and Environmental Engineering , University of New South Wales . His work integrates Digital Twins , Building Information Modeling (BIM) , and Construction Automation with a focus on robotics, AI, and LiDAR/UAS technologies. Research Interests: Digital Twins, BIM, Construction Robotics, Emissions Modeling, LiDAR/UAS, Structural Health Monitoring Education: Ph.D. in Construction Engineering and Management, The Hong Kong Polytechnic University His publications span 2025–2005, emphasizing construction automation , environmental impact reduction , and innovative tunneling solutions . Recent work includes IoT-Bayes fusion for real-time safety monitoring and life cycle analysis of construction waste. Scientific Awards: Vice Chancellor's Award for Teaching Excellence, UNSW, 2014 Best PhD Student Paper Award, CONVR, UK, 2013 Postdoctoral Fellowship, University of Alberta, 2011-2013 Best Paper Award, ASCE Construction Research Congress, 2010 Dr. Shen mentors 9 PhD candidates in areas like 3D object detection , fuel consumption modeling , and UAV-based LiDAR . His grants include $5.98M from the Australian Research Council (2022–2027) for resilient infrastructure systems and projects on modular construction and intelligent tunneling .
Secil Ugur Yavuz is a Professor at the Faculty of Design and Arts, Department of Design, at Free University of Bozen-Bolzano. Her academic work spans eco-social design, research through design, and tangible human-technology interactions, with a particular focus on sustainable materials and collective making practices. She teaches courses including Design 3 for the Master in Eco-social Design and doctoral programs in Experimental Research through Design, Art and Technologies. Professor Yavuz's research explores the intersection of analog and digital through tangible human-technology interactions, with significant contributions to sustainable material systems. Her FERAL WOOL project investigates ecological and cultural dimensions of wool, while her work with biomaterials like microbial cellulose and open-source bioreactors addresses circular production systems. She employs embodied and speculative approaches to examine microplastic emissions, children's nature engagement through tools like TREESENSE, and social play with interactive toys. Her recent publications reveal a strong thematic focus on material culture, sustainability, and human-centered design across multiple scales. The research demonstrates consistent exploration of how digital and physical realms interact to create meaningful user experiences while addressing ecological challenges. Key trends include the use of embodied interaction methods, speculative design for cultural critique, and co-creative processes that bridge technological innovation with social and environmental responsibility. Yavuz leads the Design Friction Lab, which investigates the philosophical foundations of design through customizable interfaces and DIY approaches. Her work with doctoral students in Experimental Research through Design emphasizes reshaping technologies through collective making processes. The FERAL WOOL research project represents a significant current initiative exploring sustainable textile futures through both ecological and cultural lenses.
Marco Bernardi is a Professor of Applied Physics, Physics and Materials Science at the California Institute of Technology (Caltech). His research focuses on theoretical and computational materials physics , developing first-principles methods to investigate electron transport, ultrafast dynamics, and light-matter interactions in materials. His work has applications in electronics, optoelectronics, ultrafast spectroscopy, energy technologies, and quantum devices. Education : Ph.D. in Materials Science from MIT (2013), M.S. from University of Rome Tor Vergata (2008), B.S. from University of Rome La Sapienza (2004). Research Interests : Electron-phonon interactions, polarons, spin relaxation and decoherence, nonequilibrium electron dynamics, quantum materials, and software development for materials simulations ( PERTURBO code). Scientific Awards : NSF CAREER Award (2018) AFOSR Young Investigator Award (2017) Psi-K Volker Heine Young Investigator Award (2015) Intel Ph.D. Fellowship (2013) Franco Strazzabosco Award (2020) Teaching : Offers graduate courses at Caltech including Structure and Bonding in Materials (MS 131) , Computational Solid State Physics (APh/MS 256) , and Introduction to Computational Methods (APh/MS 141) . Group Members : Mentors current graduate students and postdocs in developing advanced computational techniques for materials research, with former advisees now in academic and industry positions.
Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
Jindal Shah is a Professor and holds the Anadarko Petroleum Chair in Chemical Engineering at Oklahoma State University, where he also serves as the Graduate Program Director. He is affiliated with the Department of Chemical Engineering within the College of Engineering at Oklahoma State University. Dr. Shah received his educational training from prestigious institutions worldwide. He earned his Ph.D. in Chemical Engineering from the University of Notre Dame in 2005, followed by an M.S. in Environmental Engineering from the University of Cincinnati in 1999, and completed his undergraduate education with a B.Tech. in Chemical Engineering from the Indian Institute of Technology (IIT) Bombay in 1996. Dr. Shah's research focuses on the application of molecular simulation methodologies to understand molecular-level interactions that give rise to macroscopic phenomena. His primary research interests include Monte Carlo and Molecular Dynamics Simulations, Phase Equilibria, Ionic liquids, and Dye-sensitized solar cells. A significant portion of his work centers on designing novel biodegradable ionic liquids with properties suitable for chemical processes, with applications in next-generation batteries and carbon capture. He also investigates molecular-level interactions responsible for device efficiency in dye-sensitized solar cells to rationally design novel dye molecules. Additionally, Dr. Shah employs data science and machine learning techniques to correlate properties of ionic liquids and generate new molecules with desired properties. An analysis of Dr. Shah's recent publications reveals a strong focus on ionic liquids and their applications in energy storage and carbon capture technologies. His work consistently bridges fundamental molecular-level understanding with practical applications, particularly in developing electrolytes for batteries and CO2 capture systems. A notable trend is the integration of machine learning techniques with traditional molecular simulation methods to accelerate materials discovery and optimization. His research demonstrates a progression from fundamental molecular simulations toward applied technologies with significant environmental impact, particularly in climate action (SDG 13) and affordable clean energy (SDG 7). Dr. Shah has secured substantial research funding from multiple prestigious sources including the National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, and industry partners. His funded projects include 'Collaborative Research: Cyber Training-Implementation, Medium, Establishing Sustainable Ecosystem for Computational Molecular Science Training & Education' (NSF), 'Ionic Liquids for Direct Air Capture of CO2 using Electric-Field-Mediated Moisture Gradient Process' (DOE), and 'CAREER: Computation-Enabled Rational Design of Cytochrome P450 for Ionic Liquid Biodegradation' (NSF). These grants support his research in computational molecular science, CO2 capture technologies, and the development of biodegradable ionic liquids. As an educator, Dr. Shah has been actively involved in teaching graduate courses including Principles of Chemical Engineering Thermodynamics, Doctoral Thesis supervision, and specialized courses such as Machine Learning for Chemical Processes and Introduction to Chemical Process Analytics. His teaching philosophy integrates cutting-edge research with educational practice, preparing students for the computational challenges of modern chemical engineering. He has also mentored numerous doctoral students through their dissertation research, contributing to the development of the next generation of chemical engineers and computational scientists.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Tomohiro Nagashima is an Assistant Professor of Technology-Enhanced Learning at Saarland University in Germany. He is also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University, with previous affiliations at Carnegie Mellon University's HCII program and Stanford Graduate School of Education. His work bridges learning sciences and human-computer interaction to co-design technology that supports human learning in STEM domains. Ph.D. in Human-Computer Interaction, Carnegie Mellon University M.S. in Human-Computer Interaction, Carnegie Mellon University M.A. in Learning, Design, and Technology, Stanford Graduate School of Education Nagashima collaborates with over 800 educators and school-aged students across the US, Europe, and East Asia to develop AI-driven learning technologies and instructional strategies. His research emphasizes stakeholder-inclusive design of educational tools and open educational practices, advocating for equity and accessibility in digital learning environments. As an open education advocate, he contributes to Creative Commons Japan and the Open Education Group, focusing on systemic challenges in sharing educational materials. His methodological approach combines participatory design and school-based empirical studies to create practical solutions for modern pedagogical needs.
Paulo Blikstein is an Associate Professor of Communication, Media, and Learning Technologies Design at Teachers College, Columbia University. He holds affiliations with the Mathematics, Science & Technology department and the Communication, Media, and Learning Technologies Design program. His expertise spans curriculum design, digital innovation, science education, and educational technology. Dr. Blikstein earned a Ph.D. in Learning Sciences from Northwestern University (2009), M.Sc. in Media Arts & Sciences from MIT Media Lab (2002), and degrees in Engineering from the University of São Paulo (Brazil). His research focuses on leveraging technology to enhance learning through computational modeling, maker education, and tangible interfaces. He leads the Transformative Learning Technologies Lab and the FabLearn Program, which develop innovative tools like MoDa and PlayData, integrating computational thinking with real-world science experiments. His work emphasizes equitable access to technology-driven education, particularly in the Global South. Recent projects include deploying cloud labs for biology education, analyzing disinformation dynamics via agent-based models, and exploring how social media influences political radicalization. He critiques commercial education technology discourse through a critical pedagogy lens, advocating for culturally responsive, hands-on learning. Blikstein’s research bridges theory and practice, addressing systemic challenges in science education through participatory design with teachers and communities. His labs create sustainable educational technologies, such as DIY liquid handling robots and haptic feedback systems, to democratize STEM access. He also investigates computational identity formation in K-12 students and the role of making in fostering gender equity in STEM.
João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .
Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.
Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.
Patrick Skeba is a Teaching Assistant Professor at the University of Pittsburgh's Department of Computer Science within the School of Computing and Information. He holds a PhD in Computer Science from Lehigh University (2022) and bachelor's degrees in Cognitive Science and Computer Science from Johns Hopkins University (2017). His research focuses on internet privacy, AI ethics, and the responsible use of data. He teaches courses in machine learning and programming. Research Interests: Skeba's work bridges technology and societal impact, emphasizing privacy risks in data systems, algorithmic fairness, and user-centric privacy frameworks. His recent studies explore informational friction in data collection, community-based privacy strategies, and lay-expert disparities in understanding privacy-enhancing technologies (PETs). Publications: His articles analyze privacy dynamics in digital spaces, from pandemic-era discourse on r/privacy to methodological approaches for categorizing technology non-use. His earlier work includes breakthroughs in sleep disorder diagnostics, particularly periodic leg movement (PLM) analysis and telemedicine applications for neurological conditions. Awards: No scientific awards listed. Grants and advising details are currently unspecified. Labs/Teams: No specific lab affiliations mentioned in provided materials. His teaching and research emphasize collaboration across computational and social domains.
