Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Dr. Chengmo Yang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware, with a joint appointment. She received her PhD and MSc in Computer Science from the University of California, San Diego, and a BSc in Microelectronics from Peking University, China. PhD, University of California, San Diego MSc, University of California, San Diego BSc, Peking University, China Her research focuses on enhancing security, reliability, non-volatility, and energy efficiency in embedded systems, cyber-physical systems (CPS), and Internet-of-Things (IoT). She leads the Computer Architecture, Reliability, and Security (CARES) group and has authored over 70 peer-reviewed publications. Recent publications emphasize neural network accelerators, non-volatile memory (NVM) optimization, and security frameworks for embedded systems. Her work explores fault tolerance, wear leveling, and side-channel attack mitigation in NVM-based FPGAs, STT-RAM, and SSDs. National Science Foundation Career Award (2013) University of Delaware Mid-Career Faculty Excellence in Scholarship Award (2020) Best paper award at ICESS (2019) Three best paper nominations at DAC, DATE, and GLSVLSI (2019) Dr. Yang serves as Associate Editor for IEEE TCAD and ACM TODAES. She is the Diversity & Inclusion Representative for IEEE CEDA and has organized numerous conferences including Technical Program Chair for CODES-ISSS 2022. Her lab has hosted current and former students like Fanruo Meng (PhD candidate), Fateme Hosseini (Qualcomm), and Patrick Cronin (Intel), focusing on graduate-level research in hardware security and reliability.
Ronald C. Lasky is a Professor of Engineering at Dartmouth College's Thayer School of Engineering and a Senior Technologist at the Indium Corporation. His academic roles include teaching courses such as ENGM 187: Technology Innovation and Entrepreneurship and ENGS 155: Intermediate Thermodynamics . Dr. Lasky holds a BS in Engineering Physics from Cornell University (1970), an MS in Applied Mathematics from Binghamton University (1974), and a PhD in Materials Science from Cornell University (1986). His research focuses on process optimization, electronic assembly, materials science, and environmental compliance. Notable contributions include work on lead-free solder assembly and Lean Six Sigma methodologies. He received the Member of Technical Distinction Award from the SMTA in 2021. Dr. Lasky actively engages with industry, exemplified by his collaboration with Galanz in China, exploring modern manufacturing practices and infrastructure. His insights on global supply chains and technological advancements highlight his dual academic and professional expertise.
Professor Catherine Greenhill is a faculty member at the School of Mathematics and Statistics, UNSW Sydney , where she serves as Professor and head of the Combinatorics group. Her academic career spans institutions including the University of Queensland, University of Oxford, University of Leeds, University of Melbourne, and Australian National University. D.Phil., University of Oxford (1996) M.Sc. (Research) in Combinatorics (1992) B.Sc. (Hons) in Pure Mathematics (1991) Her research focuses on the intersection of discrete mathematics , theoretical computer science , and probability , particularly in asymptotic combinatorics , probabilistic methods , and analysis of algorithms . Her work includes asymptotic enumeration of combinatorial structures and design of randomized algorithms for graph sampling and counting. Her recent publications (2025–2021) center on random graphs and hypergraphs , with key contributions to switch Markov chains , degree sequence analysis , and chromatic number bounds . These works reflect her expertise in probabilistic combinatorics and algorithmic complexity . Scientific Awards: Fellow of the Australian Academy of Science (2022) Christopher Heyde Medal in Pure Mathematics (2015) June Griffith Fellowship (2013) Hall Medal (2010) Advising and Grants: She has supervised numerous PhD/Masters students and secured multiple ARC Discovery Grants (2019–2021, 2014–2016, 2012–2014). Her grants address topics like hypergraph modeling, random discrete structures, and network analysis in illicit drug trafficking.
Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.
Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.
Prof. Hedan Bai is an Assistant Professor at the Department of Materials, ETH Zürich, specializing in robotics materials, bio-inspired systems, and soft robotics. Their research focuses on developing advanced materials for sensing, energy-efficient systems, and biomedical applications. Notable projects include the SmartSuit architecture for space exploration and self-healing optical sensors for soft robots. Research interests span bioelectronics, stretchable sensors, haptic interfaces, and biomimetic materials. Bai's work integrates material science with robotics to create adaptive, sustainable, and intelligent systems. Key areas include wireless implants for neuromodulation, environmental-responsive textiles, and energy-harvesting devices. Publications highlight innovations in optical waveguides, self-healing materials, and wearable technologies. Their interdisciplinary approach bridges robotics, biomedical engineering, and aerospace applications. No formal awards are listed, but contributions to next-gen materials for robotics are prominent. Advising and grants are not detailed in the text, but Bai's lab focuses on projects like SmartSuit for extravehicular activities and synthetic afferent neural networks. Collaborations likely involve aerospace and biomedical sectors.
Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.
Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.
Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.
Arti Singh is an Assistant Professor in the Department of Agronomy at Iowa State University. Her research focuses on plant breeding, soybean diseases, genomics, and phenomics, with a strong emphasis on integrating artificial intelligence and high-throughput technologies into agricultural systems. She leads projects involving AI-driven disease identification, precision agriculture, and crop improvement strategies. Her expertise includes developing machine learning models for real-time weed and insect classification (e.g., WeedNet and InsectNet), deploying drones and ground robots for crop phenotyping, and leveraging genomic data to map traits like flowering time and disease resistance in legumes. Singh collaborates on initiatives like the AIIRA Institute for Resilient Agriculture and the BioTrove biodiversity dataset. Singh’s work spans plant stress phenotyping, digital twin technologies for plant sciences, and multi-sensor phenotyping for early disease detection. Her research bridges computational methods with traditional agronomy, aiming to enhance crop resilience and sustainability in the face of environmental challenges. Her recent projects include optimizing robotic navigation for precision agriculture, improving soybean yield estimation via video analysis, and dissecting genetic architectures of traits in mungbean and soybean using GWAS and genomic tools. She actively contributes to conferences and publishes in high-impact journals, advancing both foundational and applied aspects of agricultural science.
Michael Carbin is the Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and leads the MIT Programming Systems Group. His research focuses on programming systems that address system uncertainty to enhance performance, energy efficiency, and resilience, particularly in environments involving neural networks , approximate computing , and unreliable hardware . His work spans probabilistic programming , quantum computing , and machine learning systems . Articles highlight contributions in pruning neural networks , quantum data structures , and compiler optimization , reflecting trends in deep learning , formal verification , and language-driven systems . Scientific Awards : MIT Frank E. Perkins Award (2020) Sloan Research Fellowship (2020) Facebook Research Award (2019) NSF CAREER Award (2018) Best Paper Awards at OOPSLA (2013, 2014) He has advised numerous graduate students and postdocs including Eric Atkinson, Cambridge Yang, and Charles Yuan, and served on program committees for conferences like POPL, OOPSLA, and ICLR. His group collaborates with institutions such as MIT CSAIL and explores applications in quantum algorithms and probabilistic inference .
Tal Malkin is a Professor of Computer Science at Columbia University, directing the Cryptography Lab and serving as inaugural chair of the Cybersecurity Center at Columbia's Data Science Institute. She holds a Ph.D. from MIT (2000), joined Columbia after AT&T Labs research experience, and focuses on cryptography, security, complexity theory with applications in secure computation, zero-knowledge proofs, and privacy-preserving systems . Education: B.S. in Math and Computer Science, Bar-Ilan University M.S. in Computer Science, Weizmann Institute of Science Ph.D. in Computer Science, MIT (2000) Research Interests: Malkin's work spans foundational and applied cryptography, including homomorphic encryption, lattice-based protocols, attribute-based encryption, and tamper-resilient systems . She explores intersections with machine learning and information theory , addressing challenges in multi-party computation , public-key encryption , and side-channel resistance . Scientific Contributions: Her publications include breakthroughs in non-malleable codes , secure computation , and privacy-preserving databases . Notable works involve continual leakage resilience , optimally-fair coin tossing , and garbled circuits for efficient cryptographic protocols. Awards & Grants: Recipient of the NSF CAREER award, IBM and Google faculty research awards, and the IACR Fellow designation. Her research is funded by NSF, NSA, DHS, NYSIA, IARPA, and industry partnerships with Google, IBM, Mitsubishi, and NEC. Professional Leadership: Former conference chairs at CRYPTO 2021 , CCS 2017 , and CT-RSA conferences. Active in program committees for over 20 leading cryptography and security events, including FOCS , Eurocrypt , and Real World Crypto . Advising: Supervised numerous Ph.D. students and postdoctoral researchers, including Marshall Ball , Chengyu Lin , and Negev Shekhel Nosatzki . Her lab has mentored graduates like Ghada Almashaqbeh (2019) and Fernando Krell (2016), focusing on decentralized networks , secure learning , and cryptographic primitives .
Halil Ali is a Lecturer in Data Science (Education Focused) at the School of Computing Technologies, RMIT University. His research spans privacy-preserving machine learning, blockchain technologies, and cybersecurity. Key research areas include federated learning , quantum-enhanced AI , secure biometrics , edge unlearning , and privacy in healthcare data . His recent publications focus on resilient AI systems , blockchain applications , and ethical data handling in emerging technologies. His work demonstrates expertise in integrating machine learning with blockchain security across domains like IoT, smart grids, and metaverse healthcare. He contributes to practical frameworks for zero-trust architectures , lightweight consensus protocols , and quantum-classical hybrid models .
Rui Zhang is an Associate Professor in the Department of Computer and Information Sciences at the University of Delaware. He holds a PhD in Electrical Engineering from Arizona State University (2013) and has held academic positions at the University of Hawaii and industry roles at UTStarcom. His research focuses on cybersecurity, wireless networks, and privacy-preserving technologies, with notable work on secure edge computing and mobile authentication systems. Education: PhD, Electrical Engineering, Arizona State University, 2013 M.E., Communication and Information Systems, Huazhong University of Science & Technology, 2005 B.E., Communication Engineering, Huazhong University of Science & Technology, 2001 Research Interests: Rui’s work spans secure wireless systems, mobile crowdsourcing, accessibility technologies for visually impaired users, edge computing, and social network privacy. He has led NSF-funded projects on trustworthy hierarchical edge computing and secure mobile cloud sensing. Grants & Awards: Recipient of the NSF CAREER Award (2017) and multiple NSF grants totaling over $1M. Current focus includes NSF-funded research on edge computing security and privacy-preserving data aggregation. Teaching: Has taught courses in algorithms, network security, and computer networks at both University of Delaware and University of Hawaii. Labs & Teams: Leads research projects in secure edge computing and collaborates with Arizona State University on federally funded initiatives. Active in organizing academic conferences like ISC 2016 and serving on editorial boards of IEEE journals.
