Rajesh Krishna BALAN is a Full-Time Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU) . His research focuses on Human-Machine Collaborative Systems , Pervasive Sensing , and Health & Wellbeing technologies. Based in Singapore, he leverages mobile computing to address urban sustainability and quality-of-life challenges. PhD from Carnegie Mellon University (2006) Specializes in WiFi sensing , VR/AR , and health monitoring Advises PhD students in areas like urban mobility , empathetic design , and cyber-physical systems Beyond academia, BALAN's work bridges ubiquitous computing and public health , with applications in ageing populations , mental health analytics , and smart city optimization . His recent publications highlight cross-disciplinary approaches to sleep analysis , group behavior modeling , and contactless physiological sensing . BALAN actively contributes to educational technology through projects like Technology-Enhanced Learning frameworks. He is also a mentor in collaborative research areas including biomedical informatics and lifestyle monitoring , with a focus on mobile GPU optimization and low-power systems .
Associate Professor Fengling Han is affiliated with RMIT University's School of Computing Technologies in Melbourne, Australia. He holds the rank of Associate Professor since January 2022. His research focuses on complex networks, industrial electronics, AI/machine learning, and network security. Notable contributions include steganography frameworks for healthcare data, sliding mode control for energy systems, and blockchain applications in surveillance and voting systems. His work spans interdisciplinary areas such as renewable energy integration, battery management systems, and privacy-preserving recommendation systems. He has supervised numerous projects, including AI-driven chatbots, medical imaging watermarking, and peer-to-peer energy trading systems. Han's service roles include conference reviewing and committee memberships in international conferences like IEEE and ISMST. Research Interests: His expertise spans electrical engineering, control systems, and AI applications. Key areas include battery management, cybersecurity, and smart manufacturing. Recent projects emphasize Industry 5.0 technologies, blockchain for data integrity, and deep learning for steganalysis. Teaching and Supervision: Teaches network security, data communication, and IT infrastructure. Current supervision includes AI-powered business modeling, medical imaging tampering detection, and renewable energy sharing systems. Over 14 research projects are documented, reflecting his interdisciplinary impact. Awards and Recognition: While specific awards are not listed, his extensive publications (over 150 outputs) and high citation counts (e.g., 119 citations for the Industry 5.0 survey) highlight his scholarly contributions.
Dr Emily Hewson is a Cancer Institute NSW Early Career Fellow and member of the Sydney School of Health Sciences at the University of Sydney's Faculty of Medicine and Health. Her research focuses on advancing real-time radiation therapy techniques, particularly in managing intrafraction motion for prostate and other cancers. She leads projects involving multileaf collimator (MLC) tracking, dose optimization, and deep learning integration in radiation oncology. Research interests include adaptive radiotherapy systems, kilovoltage intrafraction monitoring (KIM), and clinical trial implementation (e.g., TROG 15.01 SPARK trial). Her work emphasizes improving treatment accuracy through real-time dose-guided approaches and multitarget tracking for tumors with complex motion patterns. Developed experimental validations for MRI-linac integration and MLC tracking systems Authored a textbook chapter on Adaptive Radiation Therapy (ART) Recipient of Cancer Institute NSW Early Career Fellowship (2023) Recent grants include an AI platform for targeted radiotherapy (2024) and national critical infrastructure funding for lung cancer applications (2023). Her lab collaborates on real-time dose calculation algorithms and clinical trial implementation across multiple institutions.
Yu Xiao is an Associate Professor at the Department of Information and Communications Engineering, Aalto University, specializing in edge computing, extended reality (XR), wearable computing, and crowdsensing. Their research contributes to the UN Sustainable Development Goals, particularly in education and technology innovation. Active in mobile cloud computing and decentralized systems Principal Investigator in EU-funded projects (EMIL, TUTL) Expert in 5G networks, autonomous systems, and human activity recognition Yu Xiao's work spans interdisciplinary domains, including healthcare (cardiovascular resuscitation devices) and urban mobility (autonomous vehicle interactions). They have received multiple awards, including Best Paper Awards and Nokia Foundation Scholarships. Focus on low-latency communication and multiagent reinforcement learning Developed frameworks like FediLive for decentralized social networks Contributed to 128+ publications and software tools Recent collaborations include institutions like Pontificia Universidad Católica de Chile and participation in IEEE committees. Their research integrates blockchain for secure IoT communication and advanced AR applications.
Professor David Thomas holds the position of Professor in Computer Engineering at the University of Southampton's Electronics and Computer Science Department. His research focuses on the intersection of software and hardware, particularly leveraging FPGAs for novel digital architectures and event-driven computing. He has a notable academic trajectory, having previously served as a Lecturer and Senior Lecturer at Imperial College London before joining Southampton in 2021. Dr. Thomas is actively involved in supervising PhD students and contributes to interdisciplinary research projects funded by the EPSRC, such as the SONNETS initiative exploring scalable event-triggered systems. Education: BSc in Computer Science (Imperial College London), PhD in Digital Architectures (Imperial College London). Postdoctoral roles included Research Associate and Research Fellow at Imperial's Department of Computing. Research Interests: Event-driven computing, FPGA-based systems, high-level synthesis, and high-performance computing. His work emphasizes practical implementations of theoretical models, such as custom processors and application-specific accelerators. Current projects include optimizing random number generation for FPGAs and exploring meta-programming techniques for hardware design. Advising and Grants: Supervises multiple PhD students in areas like neuromorphic computing and algorithm optimization. Active in securing funding for distributed system architectures and FPGA-based solutions. Labs/Teams: Member of the Cyber Physical Systems research group. Collaborates with interdisciplinary teams on projects like POETS (Partially Ordered Event-Triggered Systems) for large-scale parallel computing.
Kurt Maute is a Professor and Palmer Engineering Chair at the University of Colorado Boulder’s College of Engineering and Applied Science (CEAS). He currently serves as Associate Dean for Undergraduate Education. His academic journey includes a PhD in Civil Engineering (University of Stuttgart, 1998) and a Dipl.-Ing. in Aerospace Engineering (University of Stuttgart, 1992). He has held progressively senior roles at CU Boulder, including Associate Dean for Research (2012–2014), Associate Professor (2006–2012), and Assistant Professor (2000–2006). Maute’s research focuses on structural topology optimization, multi-disciplinary optimization, and aeroelastic systems. He has pioneered methods integrating XFEM, level-set techniques, and isogeometric analysis for complex engineering problems. His work spans fluid-structure interaction, hypersonic vehicle design, and additive manufacturing. His notable contributions include advancements in immersed boundary methods, multi-material optimization, and uncertainty quantification. Awards include the NSF Career Award (2004) and Palmer Endowed Chair (2016–present). Maute’s lab (Aerospace Mechanics Research Center, AMREC) addresses challenges in computational mechanics and multi-physics systems. He has advised numerous students and led grants in battery modeling, topology optimization, and aerospace systems. His research bridges theory and application, emphasizing industrial relevance and computational innovation.
Debbie Senesky is an Associate Professor at Stanford University in both the Aeronautics and Astronautics Department and the Electrical Engineering Department, as well as a Senior Fellow at the Precourt Institute for Energy. She serves as the Principal Investigator of the EXtreme Environment Microsystems Laboratory (XLab) and Site Director of nano@stanford. Dr. Senesky received her B.S. in mechanical engineering from the University of Southern California (2001), followed by M.S. (2004) and Ph.D. (2007) degrees in mechanical engineering from the University of California, Berkeley. Prior to joining Stanford, she held positions at GE Sensing (formerly NovaSensor), GE Global Research Center, and Hewlett Packard. Her research focuses on developing nanomaterials and electronic systems capable of operating in extreme environments, including high-temperature conditions for Venus exploration, microgravity synthesis of nanomaterials, and harsh environment electronics. Dr. Senesky's work bridges multiple disciplines, connecting aerospace engineering, electrical engineering, materials science, and space technology to solve challenges in extreme environment applications. Dr. Senesky has made significant contributions to the field of high-temperature electronics, GaN-based sensors, graphene aerogel synthesis in microgravity, and materials for space applications. Her recent publications demonstrate a strong focus on practical applications of these technologies, particularly for space exploration and extreme environment sensing. Presidential Early Career Award for Scientists and Engineers (PECASE), NASA (2025) Emerging Leader Abie Award from AnitaB.org (2018) Early Faculty Career Award from NASA (2012) Gabilan Faculty Fellowship Award (2012) Sloan Ph.D. Fellowship (2004-2006) Dr. Senesky actively advises students at all levels, from undergraduate to postdoctoral researchers, and has established herself as a leader in promoting diversity in STEM through her role as Faculty Advisor for the Stanford Chapter of the National Society of Women Engineers. Her collaborative approach is evident in her numerous interdisciplinary projects and partnerships with NASA, industry, and other research institutions. She directs the EXtreme Environment Microsystems Laboratory (XLab), which focuses on developing technologies for operation in extreme environments including high temperature, radiation, and microgravity conditions. The lab's work has applications for space exploration, particularly for Venus missions, as well as terrestrial applications requiring robust electronics.
Sebastian Kube is an Assistant Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison's College of Engineering, with additional affiliation in Mechanical Engineering. His research accelerates alloy development through autonomous discovery methods combining robotics, data science, and advanced characterization. Dr. Kube's educational background includes: Postdoctoral Researcher (2023), University of California Santa Barbara (Tresa Pollock Lab) PhD (2021), Yale University (Jan Schroers Lab) BS (2016), Giessen University His work focuses on refractory multi-principal element alloys for extreme environments (>1300°C) and metallic liquid structure-property relationships. He develops autonomous platforms to navigate complex parameter spaces, targeting improved glass forming ability and rapid solidification processing through B2 precipitation strategies and novel characterization techniques. Recent publications emphasize refractory high-entropy alloys, BCC-B2 systems, and metallic glasses, integrating experimental and computational approaches to decode phase stability, deformation mechanisms, and glass formation for accelerated materials design. Major recognitions include: 2025 DARPA Young Faculty Award 2024 ARPA-E IGNIITE Early Career Award RCSA Scialog Fellowship for Automating Chemical Laboratories He mentors graduate students through thesis courses (M S & E 790/890/990) and leads the Autonomous Alloy Discovery Lab, which develops robotic systems for high-throughput experimentation. Current projects target next-generation turbine alloys and environmentally sustainable materials for aerospace, energy, and defense applications.
Athanasios Rontogiannis is an Associate Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He holds a PhD in Signal Processing from the National University of Athens (1997) and has held roles including Research Director at the National Observatory of Athens (2017–2021). His research focuses on signal processing, machine learning, and hyperspectral image analysis. Education: MEng (Electrical Engineering, NTUA, 1991), M.A.Sc. (University of Victoria, Canada, 1993), PhD (Signal Processing, National University of Athens, 1997). Research interests include adaptive algorithms, sparse representations, and tensor models. He has served on editorial boards of IEEE Transactions on Signal Processing and EURASIP journals, receiving an honorary distinction in 2020. He is a Senior Member of IEEE and affiliated with EURASIP and the Technical Chamber of Greece. Key contributions span hyperspectral unmixing, Bayesian algorithms, and space data exploitation. His work integrates machine learning for applications in space science and signal processing.
Dr. Saiedeh Razavi is an Associate Professor and the inaugural Chair in Heavy Construction at McMaster University's Department of Civil Engineering, directing the McMaster Institute for Transportation and Logistics (MITL). She holds a multidisciplinary background with degrees in Computer Engineering (B.Sc., Sharif University), Artificial Intelligence (M.Sc., Iran), and Civil Engineering (Ph.D., Waterloo). Her research focuses on smart infrastructure, connected mobility, and construction safety, funded by NSERC and the Ontario Ministry of Transportation. Key areas include transforming construction management through AI, autonomous vehicles, and smart work zones. Education: B.Sc. (Sharif), M.Sc. (Iran), Ph.D. (Waterloo) Research Interests: Smart cities, connected vehicle systems, data fusion, risk analysis, and sustainable logistics Leadership Roles: Director of MITL, Associate Chair (Research), and lead of national/international multidisciplinary projects Her work bridges academia, government, and industry to enhance mobility and safety. Notable grants include NSERC funding for transformative transportation systems. Awards include teaching excellence and innovation in team-based projects. Grants & Projects: NSERC, Ontario Ministry of Transportation, and industry collaborations Labs/Teams: MITL, CPS-based construction safety initiatives, and autonomous vehicle research groups
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Qing (Cindy) Chang is a Professor in the Department of Mechanical Engineering at the University of Virginia. Her research focuses on cyber-physical systems for smart manufacturing, real-time production control, and human-robot collaboration. Prior to academia, she worked at General Motors, earning three Boss Kettering Awards for innovation. She holds an M.S. from the University of Wisconsin-Madison and a Ph.D. in Manufacturing from the University of Michigan. Education: M.S. in Mechanical Engineering, University of Wisconsin - Madison Ph.D. in Manufacturing, University of Michigan – Ann Arbor Research Interests: Cyber-Physical Systems for Smart Manufacturing Real-time Production Control Knowledge-guided Machine Learning-based Control Human-Robot Collaboration in Industrial Settings Intelligent Maintenance and Energy Management Awards: 20 most influential professors in smart manufacturing (2020) NSF CAREER Award (2014) General Motors Boss Kettering Awards (2005, 2006, 2008) GM R&D Charles L. McCuen Special Achievement Awards (2005, 2006, 2008) Leadership & Grants: She serves on the board of NAMRI/SME and holds editorial roles in ASME, IEEE, and SME journals. Her work bridges AI, robotics, and manufacturing systems, with notable grants including the NSF CAREER Award. Labs & Teams: Her Intelligent Systems Lab develops AI-driven solutions for manufacturing efficiency and sustainability, focusing on energy management, predictive analytics, and human-robot collaboration.
Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.
Inseok Hwang is the Paul Stanley Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. He earned his Ph.D. from Stanford University, specializing in multiple-vehicle control systems. His research focuses on hybrid systems, air traffic control, unmanned systems, and cybersecurity of cyber-physical systems. He leads the Flight Dynamics and Control/Hybrid Systems Laboratory and has received numerous awards, including the NSF CAREER Award and AIAA Associate Fellow designation. His work spans theoretical advancements in control theory and practical applications in aerospace systems. He has over 150 peer-reviewed publications and actively collaborates with industry and government agencies like NASA and the FAA. Education: B.S. (Seoul National University, 1992), M.S. (KAIST, 1994), Ph.D. (Stanford, 2004). Professional memberships include AIAA and IEEE. Research Interests: Hybrid systems analysis, air traffic surveillance and control, fault detection and isolation, spacecraft control, and cybersecurity for autonomous systems. His lab develops algorithms for safe and efficient operation of networked systems, including UAS traffic management and resilient control protocols against cyberattacks. Awards: NSF CAREER (2008), AIAA Associate Fellow (2012), University Faculty Scholar (2017), C.T. Sun Award (2019), multiple Seed for Success Awards (2020–2024), and Paul Stanley Professorship (2024). Grants and Collaborations: Active projects funded by NSF, NASA, FAA, and industry partners. Focus areas include resilient navigation, anomaly detection in air traffic systems, and cyberattack mitigation for autonomous vehicles.
Miklos Z. Racz is an Assistant Professor at Northwestern University with a joint appointment in the Department of Computer Science and the Department of Statistics and Data Science. He is affiliated with the IDEAL Institute. Previously, he was an Assistant Professor at Princeton University (ORFE Department) and a postdoc at Microsoft Research. His research focuses on probability, statistics, computer science, and information theory, with emphasis on combinatorial statistics, discrete probability, and applied probability. Key interests include statistical inference on random discrete structures like random graphs, community detection, latent geometry inference, and DNA data storage. He has advised numerous PhD and undergraduate students. Education: PhD in Statistics (UC Berkeley, 2015), MS in Computer Science (UC Berkeley), MS in Mathematics (Budapest University of Technology and Economics). Research interests span random graph theory, network analysis, information cascades, and computational biology. He teaches courses like Mathematical Foundations of Computer Science and Probability for Statistical Inference. His work has been published in top venues like Annals of Applied Probability, NeurIPS, and IEEE journals. Notable contributions include breakthroughs in graph matching algorithms for stochastic block models, community recovery, and DNA synthesis optimization. His research has practical applications in data storage and network science.
