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Yang YangView profile

Assistant Professor

• Quantum Chemistry
• Electronic Structure Theory
• Nuclear Quantum Effects
• +3 more

Yang Yang is an Assistant Professor of Chemistry at the University of Wisconsin-Madison , focusing on electronic structure theory and nuclear quantum effects in molecular systems. He leads the Yang Group , a theoretical and computational team specializing in the Constrained Nuclear-Electronic Orbital (CNEO) framework for accurate quantum simulations. B.S. in Chemistry and Physics, Peking University (2011) Ph.D., Duke University (2016) Postdoctoral Associate, University of Illinois at Urbana-Champaign and Yale University (2016–2019) His research spans method development in multicomponent quantum theory , excited states theory , and practical quantum chemistry calculations . Recent work includes landmark papers in Journal of Chemical Theory and Computation on CNEO-based reaction rate calculations and quantum computation applications. The Yang Group has secured NSF funding (2024) for CNEO integration into computational software. Publications emphasize nuclear quantum delocalization effects , vibrational spectra modeling , and nonadiabatic dynamics . NSF Grant (2024) for CNEO software integration Awards at 2024 ACS Fall Meeting Current advisees include graduate students Haoran Chen , Lin Han , Zhe Liu , Yiwen Wang , and Yuzhe Zhang . The group also collaborates with postdoctoral researchers like Zehua Chen and Tanner Culpitt .

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +5 more

Lung-Pan Cheng is an Associate Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he leads the Human-Computer Interaction Laboratory. His research focuses on bridging the gap between physical and virtual realities through innovative interface technologies. Cheng's research expertise spans human-computer interaction, augmented/virtual reality, and tactile feedback systems. His work explores how people exist across three types of reality: physical reality governed by physics, imagined realities where things act by wishes, and programmable virtual realities. By examining the mismatches between these realities, Cheng develops novel interfaces, techniques, and devices that extend and interface these realities toward an ultimate unified experience. His recent publications reveal strong trends in haptic feedback systems, procedural generation in VR, and tangible interaction techniques. Cheng's work often involves creating physical props and mechanisms that enhance virtual experiences, with a particular focus on how humans can interact with and manipulate virtual objects through physical interfaces. His research has significant implications for VR training, gaming, and therapeutic applications. Best Paper Award at CHI 2022 for AirRacket: Perceptual Design of Ungrounded, Directional Force Feedback to Improve Virtual Racket Sports Experiences SIC People's Choice Award at UIST 2022 for Garnish into Thin Air Cheng has secured numerous research grants supporting his work in VR and haptic interfaces, with collaborations spanning multiple institutions. His laboratory develops working prototypes that demonstrate practical applications of his theoretical concepts, often resulting in award-winning publications at top HCI conferences. Current projects appear to focus on advanced fabrication techniques for interactive objects, perceptual illusions in VR, and novel haptic feedback mechanisms that don't require traditional grounded hardware. The Human-Computer Interaction Laboratory under Cheng's direction consists of graduate students and researchers working on various aspects of tangible and virtual interfaces. The team frequently publishes in premier venues like CHI, UIST, and IEEE VR, demonstrating consistent productivity and impact in the field. Current research directions include density-varying soft fabrication, polarized light mosaics, and systems for creating infinite virtual spaces within limited physical environments.

• Causal Inference
• Machine Learning
• Econometrics
• +3 more

Michael Knaus is a Junior Professor (Assistant Professor) in the Department of Economics within the Faculty of Economics and Social Sciences at the University of Tübingen, Germany. His office is located at Mohlstraße 36, 4th floor, room 415. He teaches graduate-level courses on causal inference and causal machine learning. Dr. Knaus specializes in the intersection of causal inference and machine learning, with particular expertise in Double Machine Learning methods. His research focuses on developing advanced statistical techniques to estimate treatment effects across various economic contexts including labor markets, finance, education, and health economics. His work bridges theoretical econometrics with practical applications, emphasizing methodological rigor and real-world relevance. His recent publications demonstrate a clear progression toward increasingly sophisticated methods for handling heterogeneous treatment effects and complex causal structures. His research shows strong integration of machine learning algorithms with causal inference frameworks to address challenging policy questions across multiple domains. Double Machine Learning based Program Evaluation under Unconfoundedness (The Econometrics Journal, 2022) Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach (Journal of Human Resources, 2022) How Does Post-Earnings Announcement Sentiment Affect Firms' Dynamics? (Journal of Financial Econometrics, 2024) Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments (2021) Dr. Knaus has made significant methodological contributions through his development of the causalDML R package, which implements Double Machine Learning methods for binary and multiple treatment effect estimation. His work has been published in top econometrics and economics journals and has gained recognition in the research community, with his GitHub repository accumulating 36 stars. He frequently collaborates with Michael Lechner, a leading researcher in causal inference and program evaluation. His teaching includes E464 Causal Inference and E463 Causal Machine Learning, both graduate courses that combine theoretical foundations with practical implementation using R. These courses prepare students for advanced research and data science roles requiring sophisticated causal reasoning skills, emphasizing hands-on application of methods to real-world problems.

Craig HancockView profile

Research Professor

• Geospatial Engineering
• Structural Health Monitoring
• Global Navigation Satellite Systems (GNSS)
• +3 more

Dr. Craig Hancock is a Research Professor in Geospatial Engineering with 15 years of research experience in Surveying and Geodesy. His expertise spans GNSS error mitigation, structural monitoring, and geospatial techniques for digital construction. He has supervised 10 PhD students and published over 80 academic papers. Education: BSc and PhD in Surveying/Geomatics Key Projects: Principal Investigator for projects on GNSS error mitigation, structural health monitoring, and marine economy technology. His research focuses on three core areas: GNSS error categorization and mitigation (particularly ionospheric effects), structural and environmental change monitoring, and geospatial data acquisition for BIM and digital construction. Recent work includes improving 3D modeling accuracy, UAV-based GNSS spoofing detection, and BIM-enabled facility management in healthcare infrastructure. His articles explore topics like sensor optimization, structural dynamics, and geospatial data fusion. Grants include £150k for bridge deformation studies and £9k for ionospheric error analysis. He actively contributes to teaching and enterprise initiatives, integrating geospatial technologies with industry needs.

• Chemical Engineering
• Environmental Engineering
• Inorganic Chemistry
• +3 more

Alex Durkin is a Researcher and Research Associate at the Sargent Centre for Process Systems Engineering within the Department of Chemical Engineering at Imperial College London. His academic background includes both an MEng and PhD from Imperial College London's Department of Chemical Engineering. Alex specializes in applying machine learning and optimization techniques to design autonomous industrial systems, with a focus on sustainable resource recovery and emission reduction in wastewater and industrial processes. Education: MEng, Chemical Engineering, Imperial College London PhD, Chemical Engineering, Imperial College London His research interests span chemical engineering, environmental engineering, and sustainability, particularly in developing methodologies for circular economy frameworks, process system optimization, and resource recovery from organic waste. Alex also explores stochastic and bilevel optimization frameworks for industrial applications, alongside Gaussian processes and surrogate modeling for robust design. His publications highlight trends in wastewater valorization, carbon capture technologies, and closed-loop systems for sustainable resource management. Notable work includes frameworks for food processing waste sustainability and bio-based protein production solutions for food security challenges. Alex is affiliated with the Sargent Centre for Process Systems Engineering, focusing on interdisciplinary approaches to industrial sustainability challenges. His research bridges computational methods with practical engineering solutions to address global environmental and industrial needs.

Edgar J.D. VredenbregtView profile

Associate Professor

• Quantum Computing
• Atomic Physics
• Ultracold Atoms
• +3 more

Edgar J.D. Vredenbregt is an Associate Professor in the Department of Applied Physics at Eindhoven University of Technology (TU/e). His research focuses on quantum technologies, including ultracold atom trapping for quantum computing and novel charged particle sources. He leads projects on Rydberg atom-based quantum computing and ultracold ion beams for nanoscale applications. Education: MSc in Applied Physics (TU/e, 1986), PhD in Atomic and Molecular Physics (TU/e, 1990). Postdoctoral research at SUNY Stony Brook (1991-1993) and NIST (1998). He holds a KNAW fellowship (1995-2000) and has been a project member in KAT-1: Rydberg Atom Quantum Computing and Simulation since 2020. Research interests include ultracold electrons/ions for high-brightness applications, Rydberg atom arrays for quantum gates, and laser-cooled ion beams for nanotechnology. He has developed ultracold electron sources for ultrafast diffraction and focused-ion beam tools for 1 nm-scale silicon wafer modification. Publications span quantum computing, atomic physics, and nanotechnology. He teaches courses like Hybrid Quantum Computing and Physics of Plasma and Radiation. Supervised 69 student works but no names are listed in the provided texts.

• Cyber-physical system security
• Threat modelling
• Critical infrastructure resilience
• +3 more

Dr. Carmen Cheh is a Research Scientist in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She holds a Ph.D. in Computer Science from UIUC and a Bachelor of Computing (Hons) from the National University of Singapore. Her research focuses on cyber-physical system security, threat modeling, and critical infrastructure resilience. She has served as Co-Principal Investigator on two major grants: the 2023-2024 project on threat modeling for government systems and the 2022-2025 initiative on real-time fraud detection in e-commerce platforms. Her work bridges theoretical computer security with practical applications in critical infrastructure protection. Dr. Cheh has contributed to tools like CyberSAGE, which aids in automated security argument evaluation, and frameworks for detecting business logic flaws in software systems. She collaborates extensively with industry and government entities to advance cybersecurity practices in both digital and physical domains. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign Bachelor of Computing (Hons), National University of Singapore Her research interests emphasize real-world security challenges in interconnected systems, including automated threat modeling, vulnerability discovery in business logic, and compliance frameworks for financial services. She has pioneered constraint-based methodologies for identifying security flaws in complex systems and developed novel approaches to insider threat detection using physical access data. Recent work explores the intersection of machine learning and cybersecurity in fraud detection systems. Her publications span top venues like ACM Transactions on Modeling and Computer Simulation, IEEE Secure Development Conference, and the IEEE International Conference on Computer Communications. Collaborations include projects with institutions like Argonne National Laboratory and the National Science Foundation.

Joshua GarciaView profile

Assistant Professor

• Software Engineering
• Software Architecture
• Software Vulnerability Management
• +5 more

Joshua Garcia is an Assistant Professor in the Informatics Department at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. His research focuses on software architecture, automated testing, and cybersecurity, particularly in autonomous systems and mobile applications. He leads projects like DeltaDroid, Doppelgänger Test Generation, and Darcy, which address software vulnerability management, architectural consistency, and safety-critical systems. Key achievements include an NSF CAREER Award (2025), an NSF CRI Grant (2018), and a DARPA competition win (2024). His work is adopted by organizations like Boeing, Google, and NASA. Garcia collaborates internationally, involving institutions in Padova and researchers like Luca, Jessy Ayala, and Philipp. Research Interests: Software architecture evolution, automated exploit generation, autonomous vehicle testing, and accessibility in software development Grants: NSF CAREER ($500K+), NSF CRI ($1M+) Labs/Teams: HexHive Group, Autonomous Systems Testing Lab

Monika NedaView profile

Professor

• Computational Fluid Dynamics
• Turbulence
• Numerical Methods for Partial Differential Equations
• +4 more

Monika Neda is a Professor in the Department of Mathematical Sciences and Associate Dean for Research at the University of Nevada, Las Vegas (UNLV). She holds a Ph.D. in Mathematics from the University of Pittsburgh (2007) and a B.S. in Mechanical Engineering from the Technical Faculty Mihajlo Pupin in Serbia (2001). Her research focuses on theoretical and computational fluid dynamics, turbulence modeling, and numerical methods for partial differential equations, with particular emphasis on Navier-Stokes equations and sensitivity analysis. Her work explores model derivation, numerical stability, finite element error estimates, and computational simulations of fluid flow problems. She also investigates educational strategies to improve STEM students' foundational math skills through game-based and activity-based instruction. Neda is affiliated with UNLV's Center for Applied Math & Statistics and has contributed to interdisciplinary projects involving engineering education and photovoltaic panel efficiency. Recent research trends highlight advancements in stabilization techniques for fluid models (e.g., time relaxation, deconvolution operators) and innovative pedagogical approaches leveraging technology platforms like Canvas. Her publications span computational mathematics, turbulence closure models, and education-focused interventions. Neda's academic leadership includes roles in curriculum development and institutional research, reflecting her dual commitment to scholarship and academic administration.

Alexander RodríguezView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +7 more

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

Ahmet AvsarView profile

Assistant Professor

• Spintronics
• 2D Materials
• Quantum Devices
• +3 more

Dr. Ahmet Avsar is an Assistant Professor and NRF Fellow in the Department of Material Science and Engineering at the National University of Singapore (NUS), appointed since September 2022. Prior to this, he served as an Assistant Professor of Physics at Newcastle University (UK) and held an EPFL Fellowship (co-funded by the European Marie Curie COFUND) at the Swiss Federal Institute of Technology Lausanne (EPFL, Switzerland) from 2016 to 2020. He completed his PhD in Physics at NUS. His research focuses on exploiting quantum degrees of freedom (spin, pseudospin, valley) in atomically thin materials for energy-efficient information technologies. This includes fabricating mesoscopic devices with 2D heterostructures and studying their electronic, magnetic, and optical properties at low temperatures. His work emphasizes sustainable computing through spin, magnon, and superconducting quasiparticle-based information processing. Key research areas include spintronics in graphene and 2D materials, van der Waals magnetism, excitonic devices, and low-temperature characterization. His recent projects explore anisotropic spin transport in black phosphorus, valleytronics, and logic-in-memory systems using atomically thin semiconductors. Dr. Avsar has been recognized as an NRF Fellow and EPFL Fellow. His research is supported by the National Research Foundation (NRF) of Singapore. He has authored or co-authored numerous high-impact publications in journals like Nature Materials , Nature Nanotechnology , and Review of Modern Physics . His academic trajectory includes postdoctoral research at EPFL and prior roles in the UK, reflecting a trajectory of excellence in quantum materials and nanotechnology research.

Jasmin JahicView profile

Researcher

• Concurrent Computing
• Software Engineering
• Embedded Systems
• +3 more

Jasmin Jahic is a Researcher at the Computer Architecture Group of the University of Cambridge, working under Timothy M. Jones. She holds a PhD in 'Supervised Testing of Embedded Concurrent Software' from the University of Kaiserslautern (2020) and has extensive experience as a researcher and project manager at the Fraunhofer Institute for Experimental Software Engineering. Her research focuses on concurrency in embedded systems, software engineering, AI integration, and low-power systems. Education: PhD in Computer Science from the University of Kaiserslautern (2020). Prior roles include Project Manager at Fraunhofer IESE and Coordinator of the European Master Program in Software Engineering. Research Interests: Concurrent computing, embedded systems architecture, AI applications in software engineering, and low-power system design. She explores concurrency bugs, synchronization mechanisms, and software architecture evolution in the context of Industry 4.0 and autonomous systems. Professional Activities: Co-Organizer of SAMOS workshops (2018–2021), Reviewer for IEEE/ACM conferences, and contributor to European Strategic Research Agendas for embedded systems. Teaches courses on software architectures for embedded systems and supervises numerous graduate students. Key Contributions: Frameworks like BOSMI for multithreaded software testing, FERA for concurrency bug detection, and research on AI adoption in traditional embedded systems. Active in HiPEAC conferences and industry partnerships.

Ferdinand Schmidt-KalerView profile

Professor

• Quantum Computing
• Quantum Communication
• Quantum Simulation
• +6 more

Ferdinand Schmidt-Kaler is a Professor and leads the Cold Ions and Experimental Quantum Information Processing group at the Institute of Physics, Johannes Gutenberg Universität Mainz . His research focuses on leveraging quantum effects in trapped ions for applications in quantum computing, communication, simulation, and enhanced measurement techniques. Projects: Quantum computer, Trapped ion quantum thermodynamics, Rydberg states, Cold Thorium ions, Quantum cooperativity, Vortex light fields Collaborations: TUM (Technical University of Munich), Universities of Hannover and Siegen, PTB, eleqtron, qudora, Infineon, neQxt GmbH His team explores experimental quantum information processing using trapped ions, with recent advancements in 3D segmented Paul traps and EU-Chips Act funded initiatives. Current grants include an ERC Synergy Grant (2024) and BMBF funding for the 2025 Quantum Repeater Network. Research Areas: Quantum computing, Trapped ions, Quantum cooperativity, Vortex light fields, Thorium-based quantum systems Former advisees include PhD graduates like Dr. Thomas Feldker , Dr. Niels Kurz , and Dr. Stefan Ulm . The group actively participates in seminars and events through the Quantum Mainz network.

Markus PiroView profile

Associate Professor

• Nuclear Engineering
• Energy Systems
• Thermodynamics
• +3 more

Dr. Markus Piro is an Associate Professor in the Department of Engineering Physics at McMaster University, specializing in Nuclear Engineering and Energy Systems. He teaches ENG PHYS 3D04, focusing on fission/fusion energy systems, reactor design, and radiation interactions. His research emphasizes thermodynamic modeling of nuclear fuels, computational fluid dynamics (CFD), and severe accident analysis in reactors like CANDU and molten salt systems. Key projects include phase equilibrium studies of advanced fuels, corrosion mechanisms, and coupling CFD with thermodynamic simulations for reactor safety. He leads the Nuclear Fuels And Materials Group, developing tools like Thermochimica and collaborating on fuel design, cladding interactions, and accident mitigation strategies. Recent work includes investigations into Nd-C/Ce-C TRISO coatings, molten salt reactor chemistry, and FeCrAl cladding behavior under accident conditions. Dr. Piro’s computational expertise spans reactor hydraulics, thermal-hydraulic modeling, and material compatibility studies. He actively contributes to international initiatives like the TAF-ID database and engages in experimental validation of corium behavior. Current activities include accepting graduate students and advancing multiphysics simulation frameworks for next-gen reactors.

• Educational Psychology
• Sport Psychology
• Mental Health
• +3 more

John William Mahoney serves as Academic Lead for ACU Thrive & HELTA at the Centre for Education and Innovation, Australian Catholic University (ACU), focusing on educational innovation and psychological research across academic and sporting contexts. His research spans educational psychology, sport psychology, and adolescent development, with emphasis on teacher-student dynamics, mental toughness development, psychological interventions for depression, and positive youth development in refugee/migrant populations. Methodologically, he employs systematic reviews, intervention studies, and psychometric validation. Analysis of his 14 publications (2011-2024) reveals consistent application of self-determination theory across educational and sports settings. Key trends include systematic reviews on exercise for depression (2024), mental toughness interventions in youth athletes (2014-2016), and work-integrated learning outcomes (2017), demonstrating interdisciplinary collaboration and real-world impact. Affiliated with ACU's Centre for Education and Innovation, Mahoney contributes to HELTA and ACU Thrive initiatives aimed at enhancing student employability, well-being, and educational outcomes through evidence-based psychological frameworks.