Dr. Kate Sicchio is Associate Professor of Dance and Media Technologies at Virginia Commonwealth University, serving as Graduate Director for Kinetic Imaging. Her interdisciplinary work explores choreography-technology interfaces. Research areas include: Wearable technology for performance Live coding systems for dance Human-robot choreography Real-time video systems Recent publications examine movement notation systems and collaborative live coding platforms, with performances exhibited internationally at venues including London's V&A Museum.
Dr. Amin Reza Rajabzadeh is an Associate Professor at the W Booth School of Engineering Practice and Technology, McMaster University, with affiliate roles in the McMaster School of Biomedical Engineering and Mechanical Engineering. He specializes in biochemical engineering, focusing on biosensors, bioseparation processes, and bioprocess monitoring. His research includes developing biosensors for biological process monitoring and nanotechnology-based cancer therapies. He holds a Professional Engineer license (P.Eng.) and is a member of the Canadian and American Engineering Education Associations. Dr. Rajabzadeh's teaching spans core biochemical engineering courses like Bioreactor Design and Bioprocess Control. He has received the McMaster President’s Award for Teaching and a MacPherson Leadership in Teaching Fellowship. His research clusters span Energy, Environment, Health & Bio-innovation, and Micro-Nano Systems. Recent work includes nanoplatforms for photothermal cancer therapy (ACS Applied Materials & Interfaces, 2021) and innovations in sustainable protein enrichment via tribo-electrostatic separation. Collaborations span biomaterials, environmental engineering, and nanotechnology. Awards: Teaching Excellence Awards, Leadership Fellowships Research Themes: Biosensors, Nanomedicine, Bioseparation Technologies Labs/Teams: Biomedical Engineering Research Group, Nanotechnology Applications Lab
Elena Niculina Dragoi is a Lecturer at the Faculty of Chemical Engineering and Environmental Protection 'Cristofor Simionescu' at Gheorghe Asachi Technical University in Iasi, Romania. Her academic work integrates Artificial Intelligence and Machine Learning tools for solving complex problems in Chemical Engineering and Environmental Protection . With over 30 published papers and six active research projects, her contributions span process optimization, nanomaterials, and sustainable technologies. Teaches Applied Informatics (Years 1 & 4) and Artificial Intelligence at the Faculty of Chemical Engineering Contributes to Programming Engineering at the Faculty of Computer Science, University 'Alexandru Ioan Cuza' Engaged in interdisciplinary courses at the Faculty of Automatic Control and Computer Engineering Research Interests : Elena's work focuses on modelling and optimization (90% emphasis) of chemical processes using AI methodologies, with cross-disciplinary applications in environmental engineering (70%) and chemical engineering (95%). Her recent publications highlight innovations in: 3D-printed nanocomposite adsorbents for pollutant removal Metaheuristic optimization algorithms for industrial processes Hydrogen generation via nanocatalysts Electrochemical biosensors for environmental and health monitoring AI-driven wastewater treatment systems Green chemistry applications in pharmaceutical and dye removal
Dr. Sumanta Das is an Associate Professor and Graduate Director in the Department of Civil and Environmental Engineering at the University of Rhode Island. His research focuses on sustainable infrastructure materials, with particular expertise in cementitious materials, composite structures, and advanced computational modeling techniques. He directs a vibrant research group that bridges experimental mechanics with computational modeling and machine learning approaches to address challenges in infrastructure durability and performance. Dr. Das received his educational training from prestigious institutions: Ph.D. in Materials and Structures from Arizona State University (2015) M.Tech. in Structural Engineering from Indian Institute of Technology, Kanpur (2012) B.E. in Civil Engineering from Jadavpur University (2010) His research interests center around developing sustainable and durable infrastructure materials through innovative design approaches. Dr. Das investigates microstructure-property relationships in cementitious systems, with special focus on materials containing microencapsulated phase change materials for freeze-thaw durability, fiber-reinforced composites, and smart cementitious materials with self-sensing capabilities. His work integrates advanced experimental techniques like nanoindentation with computational modeling approaches including finite element analysis, molecular dynamics simulations, and machine learning algorithms to predict material behavior and optimize performance. Dr. Das's recent publications demonstrate a clear trajectory toward integrating machine learning with traditional materials science approaches. His research group has made significant contributions to understanding the behavior of cementitious composites under extreme conditions, developing multifunctional composites with embedded sensing capabilities, and creating computational frameworks that bridge multiple scales from molecular to structural levels. The work shows increasing sophistication in combining experimental validation with predictive modeling. Dr. Das has successfully secured numerous research grants as PI or Co-PI from diverse funding sources including the Office of Naval Research, Department of Defense, US Department of Transportation, and industry partners like Goetz Composites. His research portfolio spans infrastructure durability, composite materials for marine applications, and smart sensing technologies for structural health monitoring. As an educator and mentor, Dr. Das has supervised multiple doctoral and master's students who have completed theses on topics including: Multiscale simulation and machine learning-assisted performance prediction for cementitious composites Performance-based multiscale tuning of inclusion-modified and 3D printed composites Enhancing freeze-thaw durability of cementitious composites through innovative materials design Underwater explosion response of composite structures Implosion pulse mitigation using additively manufactured filler profiles
Oscar Carl Olof Dahlsten is an Associate Professor in the Department of Physics at City University of Hong Kong. He works in the field of quantum information science with research spanning information thermodynamics, foundations of quantum theory, and quantum computation and machine learning. His academic journey includes training at Imperial College and previous positions at ETH Zurich, NUS Singapore, Oxford University, and SUSTech before joining CityUHK. Dahlsten's research interests focus on the intersection of quantum mechanics and information theory. His work explores how quantum systems process information, the thermodynamic implications of quantum operations, and the application of quantum principles to computational problems. Key areas include quantum causal inference, quantum energy harvesting, black hole information theory, and quantum machine learning algorithms. His fingerprint analysis shows strong contributions to Quantum Theory (100%), Statistical Mechanics (55%), Quantum Dot physics (55%), and Free Energy concepts (40%). Recent publications demonstrate a strong trend toward experimental validation of quantum information concepts, particularly in quantum causal inference and quantum thermodynamics. His work bridges theoretical foundations with practical applications, especially in energy harvesting and quantum computing. The integration of quantum principles with thermodynamic laws appears as a consistent theme across his recent publications. Dahlsten currently serves as Principal Investigator for the GRF project 'Exploiting Quantum Systems for More Efficient Extraction of Energy From Random Sources' starting September 1, 2025. He actively supervises PhD students in quantum information science and is accepting new PhD candidates. His research group focuses on cutting-edge problems at the intersection of quantum information, thermodynamics, and computation.
David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.
Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.
Dr. Mohammad Naraghi is a Professor and Associate Department Head for Academics in the Department of Aerospace Engineering at Texas A&M University. He leads the Nanostuctured Materials Lab , focusing on advanced nanomaterials for aerospace applications. His work integrates material science principles to develop lightweight, high-performance materials for structural, energy storage, and smart textile systems. Education: Ph.D., Aerospace Engineering (2009), University of Illinois at Urbana-Champaign M.S., Civil Engineering (2004), Sharif University of Technology B.S., Civil Engineering (2004), Sharif University of Technology Research Interests: Graphitic carbon nanomaterials, bio-inspired composites, experimental nanomechanics, and polymer nanofiber processing. His lab explores multifunctional materials for aerospace applications, including self-healing polymers, structural batteries, and sustainable carbon fiber recycling. Publications: Dr. Naraghi has authored over 150 peer-reviewed articles, with recent work focusing on carbon nanomaterial synthesis, self-healing vitrimers, and all-electric aircraft sustainability . His studies bridge nanoscale mechanics and macroscale applications, emphasizing scalability and industrial relevance. Awards: Best Paper Award (2009) for nano viscoelastic composites research Roger A. Strehlow Memorial Award (2009) for outstanding research First Place in Sandia MEMS Design Competition (2007) Advising & Grants: Leads NSF-funded projects on sustainable materials and structural energy storage. Advises graduate students in aerospace and materials engineering. Collaborates with Sandia National Labs and industry partners on advanced composite development. Labs & Facilities: Directs the Nanostuctured Materials Lab, equipped with advanced nanomechanical testing systems, electrospinning setups, and characterization tools for nanoscale materials analysis.
Professor Chen Xiaodong is a Distinguished University Professor at Nanyang Technological University (NTU), Singapore, holding primary appointment in the School of Materials Science & Engineering with courtesy appointments in the Lee Kong Chian School of Medicine and School of Chemistry, Chemical Engineering and Biotechnology. He serves as Deputy Director of the Institute for Digital Molecular Analytics and Science (IDMxS) and Director of both the Innovative Centre for Flexible Devices (iFlex) and Max Planck-NTU Joint Lab for Artificial Senses. His research spans mechanomaterials science and engineering, flexible electronics, sense digitalization, cyber-human interfaces and systems, and carbon-negative technology. Professor Chen's work focuses on developing methods for controlling materials architecture at 1-100 nm scale to solve fundamental and applied problems in energy, environment, and healthcare. His group integrates expertise from materials science, chemistry, biology, physics, and engineering to create innovative solutions. His scientific contributions have been recognized through numerous prestigious awards including the Singapore President's Science Award, National Research Foundation Investigatorship and Fellowship, Friedrich Wilhelm Bessel Research Award, Dan Maydan Prize in Nanoscience and Nanotechnology, and election to multiple national academies including Singapore National Academy of Science, Academy of Engineering Singapore, and German National Academy of Sciences Leopoldina. Professor Chen serves as Editor-in-Chief of ACS Nano and sits on editorial boards of numerous prestigious journals including Advanced Materials, Chemical Reviews, and Matter. He has mentored numerous PhD students and research fellows who have gone on to faculty positions at institutions worldwide. His laboratory develops cutting-edge technologies in flexible electronics, bio-inspired materials, and nano-bio interfaces, with strong industry collaborations and translational research focus.
Ricardo Zednik is a Professor at the Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS) in Montreal. Holding degrees from Rice University (BA, BS) and Stanford University (MS, PhD), he specializes in piezoelectric materials, fracture mechanics, and microelectronic systems. His research focuses on sensors, innovative materials, and health technologies. Fields of Interest: Piezoelectricity, Fracture Mechanics, MEMS, Smart Materials, Crystallography With over 36 peer-reviewed publications and extensive supervision of graduate research (including 15+ co-directed theses and projects since 2016), Zednik contributes to applied research in materials science and biomedical engineering. He collaborates with LaCIME and PULÉTS laboratories on cutting-edge projects involving ultrasonic transducers, flexible sensors, and high-temperature material characterization. Current courses include Materials Technology (MEC200) and advanced research topics in Functional and Smart Materials (SYS877). His students explore applications like terahertz quality control, piezoelectric earcanal sensors, and Kirigami techniques for wearable electronics.
David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.
Gayithri Jayathirtha is an Assistant Professor in the College of Education at the University of Illinois at Urbana-Champaign, specializing in Curriculum & Instruction. Her research focuses on integrating social justice into K-12 computing education through electronic textiles and pedagogical frameworks. She holds a Ph.D. and Master's in Learning Sciences from the University of Pennsylvania, along with a B.S. in Computer Science Engineering from Bangalore University, India. Research Interests: Jayathirtha explores how electronic textiles can bridge technical and societal dimensions in computing education. Her work emphasizes critical engagement, equity, and teacher roles in reshaping curricula to include sociopolitical implications of technology. Key areas include computational thinking assessment, collaborative debugging, and justice-centered learning. Publications: Since 2017, she has published extensively in ACM and IEEE conferences, focusing on topics like teacher identity in social justice, student problem-solving with e-textiles, and portfolio assessments for computational thinking. Notable works include redesigning introductory computing programs to address colonialism and bias, as well as frameworks for politicized trust in classrooms. Scientific Contributions: Jayathirtha has co-authored studies on bugs as catalysts for peer collaboration, the invisibility of everyday computing systems, and strategies for equitable K-12 computing education. Her research often involves partnerships with educators to develop and test innovative teaching methods. Professional Impact: Through her publications and presentations at RESPECT, SIGCSE, and ICLS conferences, Jayathirtha advocates for expanding computing education beyond technical skills to include societal implications. Her work provides practical tools for teachers to integrate critical perspectives into curricula.
Andrea Appolloni is an Associate Professor at the Department of Management and Law, University of Rome Tor Vergata. His academic career focuses on Management with emphasis on Sustainable Supply Chain Management , Digital Transformation , and Circular Economy . His research explores the intersection of technological innovation and sustainability, particularly through topics like AI in Logistics , Green Procurement , and Policy Optimization . Publications span both theoretical frameworks and empirical studies in China, Italy, and Malaysia, with a strong focus on environmental impact and organizational performance. Recent work includes digital twin applications for human-AI collaboration, blockchain integration in sustainable supply chains, and analyzing barriers to circular economy adoption. His 15 most recent articles (2025-2022) demonstrate a trend toward combining Artificial Intelligence , Operations Management , and Environmental Governance .
Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .
David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.
