Professor Albert Cheng is a faculty member in the Department of Computer Science at the University of Houston. His research focuses on real-time systems, cyber-physical systems, smart cities, and embedded systems with societal impacts. He has authored over 270 publications and a textbook on real-time systems. Cheng holds roles as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering and ACM Computing Surveys. His research interests span real-time scheduling, machine learning applications, and systems optimization. Recent work includes vehicular traffic modeling for epidemiological risk reduction, quantum computing response time analysis, and satellite mission planning. Awards include Fulbright Specialist, Distinguished ACM membership, and IEEE Senior Member status. Cheng’s articles demonstrate expertise in real-time scheduling algorithms, cyber-physical systems development, and smart city infrastructure. His contributions bridge theoretical computer science with practical implementations in transportation, healthcare, and aerospace domains. Ongoing efforts include fault-tolerant systems, energy-efficient scheduling, and CPS education initiatives. Awards: Fulbright Specialist, ACM Distinguished Member, IEEE Senior Member, Institute of Physics Fellow Labs/Teams: Hewlett Packard Enterprise Data Science Institute (HPE DSI), Research Computing Data Core (RCDC)
Rajesh Karki is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan’s College of Engineering. He holds a B.E., M.Sc., and Ph.D. in related fields. His research focuses on power system reliability, renewable energy integration, and microgrid resilience, with particular emphasis on addressing challenges posed by extreme weather, cyber threats, and decarbonization targets. Dr. Karki’s work spans theoretical modeling, probabilistic analysis, and practical implementation strategies for smart grids, energy storage systems, and distributed generation. His educational background includes advanced degrees in electrical engineering, complemented by professional engineering licensure (P.Eng.). His research has explored diverse topics such as wind energy curtailment mitigation, energy storage optimization, and demand response mechanisms in developing economies like Nepal. He has authored numerous peer-reviewed publications on grid resilience, reliability economics, and cyber-physical system security. Key themes in his work include: (1) quantifying the reliability value of energy storage in active distribution systems, (2) modeling cyber-physical threats to microgrids, and (3) developing frameworks for extreme weather-resilient infrastructure. Despite the volume of his publications (over 50 articles), no specific awards or grants are explicitly listed in the provided materials. His research often intersects technical, economic, and policy dimensions of sustainable energy systems.
Dr. Alvin J. K. Chua is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship. He holds a joint appointment in the Department of Mathematics and a courtesy appointment in the Department of Statistics and Data Science. His research focuses on gravitational-wave astronomy , particularly extreme mass ratio inspirals (EMRIs) as key sources for the LISA mission. He develops computational and statistical methods for modeling GW sources and analyzing detector data, with recent work on machine learning and Bayesian inference techniques. His selected publications highlight advancements in modeling beyond-vacuum-GR effects in EMRIs non-local parameter degeneracies rapid relativistic waveform generation neural networks for GW inference statistical sampling on manifolds These works span gravitational-wave astrophysics , computational relativity , and applied statistics . Scientific recognition includes the NUS Presidential Young Professorship. He collaborates with the LISA Consortium and the North American Nanohertz Observatory for Gravitational Waves (NANOGrav).
Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.
Khalid M. Mosalam is the Taisei Professor of Civil Engineering and Director of the Pacific Earthquake Engineering Research (PEER) Center at the University of California, Berkeley, where he has served in the Department of Civil and Environmental Engineering since 1997. He also chairs the Al Falah Program in Science and Engineering, facilitating academic exchange between UC Berkeley and Middle Eastern/OIC institutions. His educational background includes: Ph.D. in Civil Engineering from Cornell University (1996) M.S. in Structural Engineering from Cairo University (1991) B.S. (Honors) in Civil Engineering from Cairo University (1988) Mosalam's research centers on structural health monitoring using vibration-based and vision-based techniques, with expertise in experimental methods including hybrid simulations. His work spans earthquake engineering, bridge engineering, and the assessment/rehabilitation of critical infrastructure like bridges and electrical substations. He investigates structural behavior of concrete, masonry, steel, and wood systems under extreme events (earthquakes, blasts, wind), integrating machine learning tools for predictive modeling and developing solutions for energy efficiency and sustainable infrastructure resilience. Current projects include Building Efficiency and Sustainability in the Tropics (BEST) and AI applications in vision-based structural health monitoring. His scientific achievements include: ASCE Huber civil engineering research prize (2006) UC-Berkeley chancellor award for public services (2013) EERI outstanding paper award (2015) Mosalam has secured continuous research funding for 26+ years from NSF, Caltrans, FEMA, Intel, US-DOE, NRF-Singapore, and private industries, leading major initiatives like the $5.075M BRACE2 bridge research program (2025). He advises graduate students and directs multiple research entities including the STAIRlab, PEER Center, StEER Network, and SinBerBEST program. His leadership encompasses the PEER Center's large-scale structural testing facilities, the StEER Network for post-disaster response, and international collaborations through SinBerBEST, focusing on experimental validation, computational modeling, and practical implementation of resilient infrastructure systems.
Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.
Kyojin Choo is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Engineering , affiliated with the Mixed-Signal Integrated Circuits Lab (MSIC-LAB). He also holds teaching roles in Microengineering and Electrical and Electronics Engineering at EPFL. B.S. and M.S. in Electrical Engineering from Seoul National University (2007, 2009) Ph.D. in Electrical Engineering from the University of Michigan (2018) His research focuses on charge-domain analog/mixed-signal circuits , low-power sensor interfaces , and compact ADCs for IoT, wearables, and millimeter-scale systems. He has pioneered charge-injection cell techniques for energy-efficient circuits in energy management, sensor front-ends, and communication. His work emphasizes reducing power consumption to nanowatt levels while enabling ultra-compact designs. His recent publications highlight advancements in compact SAR ADCs , low-power MEMS accelerometers , millimeter-scale imaging systems , and ultra-low-power timing generators . His research integrates charge-domain circuit design with sensor interface optimization , energy harvesting , and high-speed link architectures . He holds over 20 US patents and has taught courses in Microengineering and Electrical Engineering at EPFL. His group (MSIC-LAB) addresses challenges in battery-free sensor design, power-constrained system scaling, and commercialization of wearables with unconventional form factors.
Simon Hanslmayr is a Professor in the School of Psychology & Neuroscience at the University of Glasgow. His research investigates neural oscillations' role in attention and memory processes, employing EEG, fMRI, and transcranial stimulation techniques. He focuses on healthy populations and clinical conditions like Schizophrenia and PTSD. His lab develops tools like the Brain Time Toolbox for electrophysiological data analysis. Education: Ph.D. in Cognitive Neuroscience (not explicitly detailed in text) Research interests include understanding how precise neural timing via oscillations underpins cognitive functions. Key areas: hippocampal memory coding, theta phase synchronization in associative memory, and causal effects of rhythmic stimulation on memory plasticity. Recent articles highlight mechanisms linking theta oscillations to memory formation, thalamocortical interactions in perception, and hippocampal-neocortical coupling. His work bridges experimental and computational approaches to model memory dynamics. Grants: Sensory stimulation for memory impairment (BIAL Foundation, 2025-2026) EU-funded studies on neural oscillations and memory (2020-2021) Awards: None explicitly listed, but active grant recipient. Supervised students include Kiera Capstick, Eleonora Marcantoni, and others. Collaborates with researchers worldwide through lab affiliates and visiting scholars. Current work emphasizes scalable neurotechnologies for cognitive enhancement and memory rehabilitation. Labs/Teams: Leads the Memory & Oscillations Lab at the University of Glasgow, collaborating with institutions like the University of Zurich and Maastricht University on neuroimaging and clinical studies.
Eshed Ohn-Bar is an Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. He leads the Human-to-Everything (H2X) Lab, focused on developing intelligent systems for assistive and autonomous technologies. His research bridges machine perception, learning, and human-computer interaction, with applications in autonomous driving and accessibility for visually impaired individuals. Educated at UCLA (BS in Mathematics, 2010; MEd, 2011) and UCSD (PhD in Electrical Engineering, 2017), he holds a Humboldt Fellowship and has received the IEEE ITS Society Best PhD Dissertation Award (2017) and the 2025 BU Early Career Excellence in Research Award. His work emphasizes robust autonomy, real-time assistance, and inclusive design, collaborating with industry partners like Motional and receiving NSF grants (e.g., IIS-2152077). Research interests include autonomous systems, computer vision, and assistive technologies. Recent trends in publications highlight advancements in decision-making frameworks, neural volumetric models, and scalable learning for navigation. His lab’s projects address challenges in accessibility, such as blind motion generation and inclusive autonomous vehicle design. Awards: Humboldt Fellowship, IEEE ITS Best Dissertation, BU Early Career Award Grants: NSF IIS-2152077 Labs/Teams: H2X Lab, collaborating on projects with industry and academic partners
Antonello Monti is a Professor and Director of the Institute for Automation of Complex Power Systems at RWTH Aachen University. His research focuses on modern power systems, including smart grid technologies, hybrid AC-DC grids, and quantum computing applications in energy systems. Recent publications demonstrate innovations in grid resilience, EV charging optimization, quantum-assisted power system planning, and advanced simulation techniques. His team develops open-source tools like JuliaGrid for power system analysis and validates concepts through real-time testing platforms. Research addresses energy transition challenges including renewable integration, grid modernization, cyber-physical security, and next-generation optimization methods combining quantum computing with traditional power engineering approaches.
Michael J. Shelley is the Lilian and George Lyttle Professor of Applied Mathematics and holds joint appointments in Mathematics, Neural Science, and Mechanical Engineering at New York University's Courant Institute of Mathematical Sciences. He also serves as Co-Director of the Applied Mathematics Laboratory and Director of the Center for Computational Biology at the Flatiron Institute. Education: PhD (Applied Mathematics) from the University of Arizona (1985), MS (Applied Mathematics) from the University of Arizona (1984), BA (Mathematics) from the University of Colorado (1981). Research: Focuses on complex phenomena in active matter, biophysics, and complex fluids. Key areas include fluid-structure interactions (e.g., swimming/flying mechanics), cytoskeletal dynamics, and collective behavior in biological systems. Collaborates closely with experimentalists through the Applied Math Lab and Flatiron Institute. Labs & Affiliations: Co-Director, Applied Mathematics Laboratory; Director, Center for Computational Biology (Simons Foundation); affiliated with NYU’s Courant Institute and Department of Mathematics. Notable Work: Models for microtubule-motor assemblies, active suspensions, and fluid-structure interactions. Pioneered computational frameworks for Stokes suspensions and fiber dynamics in viscous fluids.
Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.
Kavan Modi is a Professor at the School of Physics and Astronomy, Monash University. His research focuses on quantum information theory applied to dynamics, metrology, computation, thermodynamics, and relativity. He leads the Monash Quantum Information Science (MonQIS) group and serves as Director of the Centre for Quantum Technology at Transport for NSW (2022–2024). Education: B.Sc. Engineering Physics (Embry-Riddle Aeronautical University, 2001), M.A. Physics (University of Texas at Austin, 2004), Ph.D. Physics (University of Texas at Austin, 2008). Postdoctoral positions included the Centre for Quantum Technologies (Singapore, 2008–2011) and Clarendon Lab, Oxford (2011–2013). Joined Monash in 2014. Research interests center on quantum dynamics, non-Markovian processes, and their applications in quantum computing and information science. Projects include developing error correction codes, quantum algorithms for network analysis, and mitigating correlated noise in quantum systems. He has authored over 111 publications, with recent work emphasizing non-Markovian characterization, quantum process tomography, and topology-based quantum algorithms. Awards and grants include leadership in multiple Australian Research Council projects. Advising/Grants: Primary Chief Investigator in projects like 'Quantum Software Platform' (2023–2026) and 'Mitigating Correlated Noise in Quantum Machines' (2020–2021). Supervises graduate students and collaborates globally on quantum information science. Labs/Teams: MonQIS group focuses on foundational and applied quantum research, integrating theory and experimental collaborations.
Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.
Lars O. Nord is a Professor in the Department of Energy and Process Engineering at NTNU, specializing in thermal energy systems, CO2 capture technologies, and dynamic process modeling. He holds a PhD from NTNU (2010) and a Master's from Virginia Tech (2001). His research focuses on power cycles, turbomachinery optimization, and decarbonization strategies for energy systems. Nord has led projects such as DEXPAND and InnCapPlant, addressing CO2 capture under variable loads and expander efficiency in renewable systems. Current roles: Head of the Thermal Energy research group and teaches courses like Engineering Thermodynamics. Research highlights include thermal energy storage integration, moving bed adsorption processes, and offshore hybrid energy systems. His work spans over 80 publications, emphasizing CO2 capture dynamics, turbine design, and control strategies for flexible power plants. Notable collaborations include SINTEF and Aker Solutions. Nord advises multiple PhD candidates and has mentored alumni now leading roles in industry and academia.
