David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University, where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research bridges human perception, extended reality (AR/VR), and computational interaction techniques, focusing on developing systems that dynamically adapt interface elements based on environmental context, user cognition, and task requirements. He completed his PhD at TU Berlin under Prof. Marc Alexa and held a postdoctoral position at ETH Zurich's Advanced Interactive Technologies lab. His work has been published extensively at top venues including ACM CHI, UIST, and IEEE VR, with research themes spanning gaze tracking, spatial audio optimization, haptic feedback, and multimodal notification systems. Media outlets like MIT Technology Review and Fast Company Design have featured his innovations. Dr. Lindlbauer has received prestigious grants from Meta, NSF, and ETH Zurich, and serves on program committees for CHI, UIST, and ISMAR. He has been recognized with Best Paper awards at ISS 2023 and CHI 2016, and his lab develops tools like MineXR for personalized XR interfaces and RealityReplay for temporal change visualization in mixed reality environments.
Dr. Jurgen Becque is an Associate Professor in Structural Engineering at the University of Cambridge's Department of Engineering. He specializes in cold-formed steel structures, stainless steel structural behavior, and stability analysis, with a focus on local-overall buckling interaction and innovative design methodologies. His work bridges experimental investigations with computational modeling and machine learning applications. Research Interests: Cold-formed steel structural systems Stainless steel column stability Local and overall buckling interaction Mechanics-based design optimization Machine learning for structural behavior prediction Recent publications demonstrate expertise in cross-sectional stability, connection mechanics, and composite systems like UHPC-confined stainless steel columns. His work addresses both monotonic and cyclic loading scenarios, contributing to Eurocode 3 design standards.
Carlos G. Levi is a Research Professor in the Department of Materials at the University of California, Santa Barbara (UCSB), part of the College of Engineering. He holds the additional title of Professor Emeritus. His research focuses on microstructure evolution in inorganic materials, particularly in the design of advanced coatings, composites, and alloys for extreme environments. He has contributed significantly to thermal barrier coatings, ceramic matrix composites, and high-temperature alloys for aerospace and energy applications. Education: Ph.D. in Metallurgical Engineering, University of Illinois at Urbana-Champaign (1981) M.Sc. in Metallurgical Engineering, University of Illinois at Urbana-Champaign (1977) Degree in Chemical Engineering, Universidad Autonoma de Nuevo Leon, Mexico (1972) Research Interests: Levi’s work addresses challenges in structural materials, including advanced thermal/environmental barrier coatings resistant to molten silicates, ceramic matrix composites, hypersonic materials, and high-temperature alloys such as multi-principal element alloys. His studies explore degradation mechanisms, phase stability under extreme conditions, and novel synthesis techniques like vapor-mediated melt infiltration. Awards and Honors: 2014 TMS Morris Cohen Award 2012 Fellow of the American Ceramic Society 2008 NIMS Award (shared) for breakthroughs in materials science for energy/environment 2004 DLR Wissenschaftspreis (collaborative paper) 2002 Alexander von Humboldt Research Prize Advising and Grants: No formal student advisees are listed, though his research group likely includes graduate students/postdocs. Grant details are not specified in the provided texts. Labs/Teams: Levi’s research is conducted within UCSB’s Materials Department, leveraging advanced characterization tools (e.g., TriBeam tomography) and computational modeling to study material behavior under extreme conditions.
John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.
Dr. Muhammad Gulzari is an Assistant Professor at the School of Civil Engineering, University College Dublin (UCD). Previously, he held positions as Lecturer/Assistant Professor at the University of Galway (2023–2024), Adjunct Assistant Professor at Trinity College Dublin (2022–2023), and a Research Fellow at Trinity College Dublin (2021–2023). He earned his Ph.D. in Civil Structural Engineering from City University of Hong Kong (2021) and a B.Sc. in Civil Engineering from the University of Engineering and Technology Lahore (2017). Education: Bachelor of Engineering, Civil Engineering, University of Engineering and Technology Lahore Ph.D., Civil Structural Engineering, City University of Hong Kong Research Interests: His work focuses on structured materials and dynamics, including finite element modeling, phononic crystals, acoustic and mechanical metamaterials, vibration and noise control, and applications in structural health monitoring. He leads the Structured Materials and Dynamics Lab at UCD, exploring nonlinear and nonreciprocal metamaterials. Teaching and Awards: He has taught courses in Fluid Mechanics, Thermodynamics, and Combustion Engineering. Notable recognitions include the Seal of Excellence Award from the EU-Horizon MSCA Postdoctoral Fellowship (2021) and nominations for teaching excellence awards at Trinity College Dublin and University of Galway. Grants: TimberFlow: Enhancing Efficiency in Mass Timber Construction (Enterprise Ireland, 2025) Indoor Acoustic Quality via Acoustic Metamaterials (Enterprise Ireland, 2025) RUBBERPAVE: ELT Integration in Pavement Construction (Enterprise Ireland, 2024–2026) Deep Learning for Metamaterial Design (Irish Research Council, 2021–2023) Labs and Collaborations: His research group collaborates with industry partners like Amplitude Acoustics and G-frame Structures Ltd. Key projects include developing metamaterials for noise/vibration control and sustainable construction materials.
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.
Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.
Rahul Sarkar is a Postdoctoral Fellow at the University of California, Berkeley, affiliated with the Department of Mathematics . He was previously a Ph.D. student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, graduating in 2022 under the advisement of Biondo Biondi and András Vasy. Research Interests : Quantum information theory, inverse problems, machine learning, microlocal analysis, and numerical methods for PDEs. Scientific Contributions : Developed novel quantum computing algorithms and numerical schemes for geophysical imaging, with applications in seismic tomography and quantum signal processing. Teaching : Taught courses at Stanford including Introduction to Quantum Computing and 3D Seismic Imaging , with roles as instructor and course assistant. Awards : Schlumberger Innovation Fellowship (2019-2020). His work bridges mathematical analysis and quantum computation , with a focus on solving real-world problems through interdisciplinary approaches. He has collaborated with institutions like IBM and Schlumberger to apply quantum algorithms to geoscience and financial optimization.
Giuseppe Carlo Marano is a Full Professor at the Department of Structural, Building and Geotechnical Engineering at Politecnico di Torino. He is also a component of the SISCON Interdepartmental Center for Infrastructure Safety. With expertise in civil and structural engineering, his work focuses on machine learning applications, seismic risk reduction, and sustainable structural optimization. Education Graduated cum laude in Structural Engineering from Polytechnic University of Bari PhD in Structural Engineering from University of Florence (2000) Research Interests Marano's research spans structural optimization, seismic engineering, and machine learning applications in civil infrastructure. He develops advanced computational models for: Seismic retrofitting of existing structures Optimization of steel and masonry structures Recycled materials in concrete production AI-driven structural health monitoring Multiobjective design methodologies Publication Trends His recent work emphasizes: Machine learning for concrete mix design and damage assessment Optimization of gridshells and arch structures Seismic isolation systems and vibration control Sustainable construction practices with recycled materials Multiobjective genetic algorithms for structural design Scientific Recognitions National Scientific Qualification - First Band (2013, MIUR Italy) Certificate of Appreciation for Outstanding Lecture (2012, China) Academic Contributions As an educator, he teaches: Consolidamento Strutturale (Structural Consolidation) Dinamica delle Vibrazioni Random (Random Vibration Dynamics) Progettazione Generativa (Generative Design) He also leads Challenge@PoliTo initiatives and contributes to national infrastructure safety regulations. Research Projects ADAPT4CE - Adaptive Digital Systems for Circular Economy (2025-2028) AI-ENVISERS - AI for Seismic Retrofit Environmental Impact (2023-2025) ADDOPTML - Additive Manufacturing Optimization (2021-2025)
Evangelos Katsanos is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), where he contributes to research and education in structural engineering and safety. He is affiliated with the Structures and Safety research group and actively supervises PhD students. His work spans advanced computational methods for structural monitoring and risk assessment. Research Interests: His expertise lies in structural dynamics, modal analysis, state estimation, and structural health monitoring of civil and offshore infrastructure. He applies physics-informed models and data-driven techniques to assess structural response under extreme loading conditions such as earthquakes, storms, and wave impacts. His research integrates finite element modeling with Kalman filtering methods for enhanced system identification and damage detection. The recent publications highlight a strong trend toward physics-informed and data-driven structural health assessment, particularly for offshore and wind energy infrastructure. Topics include joint input-state estimation, slamming loads on offshore jackets, and damage identification using Kalman filters. These works emphasize robust modeling under uncertainty and real-world applicability in extreme environments. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Evangelos Katsanos is the main supervisor of PhD student Al-Hagri, A., and co-supervisor or collaborator on several research projects. He is Principal Investigator (PI) or co-PI on multiple funded research initiatives, including projects on physics-informed structural health assessment of offshore infrastructures, residual bearing capacity of damaged concrete beams, and quality assurance for construction 3D printers. These projects reflect his leadership in interdisciplinary and applied research with societal impact. Labs and Teams: He is part of the research environment at DTU Construct, specifically within the Structures and Safety group, which focuses on resilience, risk assessment, and advanced monitoring of civil and mechanical systems. His collaborations extend to national and international partners in offshore and wind energy engineering.
Paul D. Asimow is the Eleanor and John R. McMillan Professor of Geology and Geochemistry at the California Institute of Technology (Caltech), part of the Division of Geological and Planetary Sciences. He holds a B.A. from Harvard University (1991), an M.S. (1993), and a Ph.D. (1997) from Caltech. His career progression includes roles as Assistant Professor (1999–2005), Associate Professor (2005–2010), and Professor (2010–present), with the McMillan Professorship since 2016. Education: A.B. in Geology, Harvard University, 1991 M.S. in Geology, Caltech, 1993 Ph.D. in Geology, Caltech, 1997 Research Interests: Focuses on computational, experimental, and observational approaches to igneous petrology and mineral physics. Key areas include adiabatic mantle melting, water's role in mantle dynamics, high-pressure mineral physics, and processes at mid-ocean ridges. His research utilizes advanced facilities like the Lindhurst Laboratory of Experimental Geophysics and the alphaMELTS software package for thermodynamic modeling. Articles Overview: Recent work spans planetary crust formation, Martian petrogenesis, and high-pressure mineral behavior. Themes include experimental techniques, computational modeling, and cosmochemical studies of meteorites. Awards and Honors: James B. Macelwane Medal (AGU) Frank Wigglesworth Clarke Medal (Geochemical Society) Richard P. Feynman Prize for Teaching Excellence (Caltech) Fellow of the American Geophysical Union Fellow of the Mineralogical Society of America Grants and Labs: Received NSF funding for developing an interactive phase equilibria curriculum. Leads the Lindhurst Laboratory, focusing on shock-wave experiments and high-pressure mineral physics. Collaborates on software tools like alphaMELTS and MAGMASOURCE. Labs and Teams: Active in the Caltech Shock Wave Laboratory, advancing experimental methods for planetary material studies. Engages in interdisciplinary projects on Mars geology and terrestrial planet formation.
Professor David C. Dunand is a faculty member in the Department of Materials Science and Engineering at Northwestern University , where he leads the Dunand Research Group . His work focuses on mechanical metallurgy of advanced metallic materials, including alloys, composites, and foams, with applications in energy-efficient transportation and biomaterials. He also investigates additive manufacturing techniques like laser powder-bed fusion and 3D ink extrusion. Research Interests: Physical and mechanical metallurgy of multiphase metals Additive manufacturing (ink extrusion, selective laser melting) Green/sustainable metal production In situ X-ray tomography for microstructure analysis Metallic foams and scaffolds Thermoelectric materials Recent Publications show expertise in redox cycling stability, precipitation strengthening, and hierarchical microstructures, with applications in batteries, shape-memory alloys, and high-entropy systems. Awards: TMS Fellow (2012) Structural Materials Division Distinguished Scientist/Engineering Award (2008) Fellow, ASM International (2007) Department Teacher of the Year (1998) He has held leadership roles including Co-Director of the Initiative for Sustainability and Energy at Northwestern (2008-2015) and Visiting Professor at École Polytechnique Fédérale de Lausanne (2000). The group operates a SISMA MYSINT 100 laser powder bed fusion machine and collaborates extensively.
Howard Elman is a Professor in the Department of Computer Science at the University of Maryland, with affiliations to the Institute for Advanced Computer Studies (UMIACS) and as an Affiliate Professor in the Department of Mathematics. His research spans numerical analysis, computational fluid dynamics, and uncertainty quantification, focusing on iterative solvers for partial differential equations. Education: PhD in Computer Science, Yale University (1982); BA in Mathematics, Columbia University (1975); Stuyvesant High School (1971) Elman's research integrates Scientific Computing with Numerical Linear Algebra , Computational Fluid Dynamics , and Uncertainty Quantification . His work addresses Stochastic Galerkin Methods , Reduced-Order Modeling , and Low-Rank Approximations for PDEs with random data. Recent publications emphasize Surrogate Models and Deep Learning in Bayesian inverse problems. His scientific awards include SIAM Fellowship (2009) and roles as Associate Editor for journals like Mathematics of Computation and SIAM Journal on Scientific Computing . He served as SIAM Editor-in-Chief (1998-2004) and Vice President for Publications. Contact: helman@umd.edu | Office: 4210 Iribe Center | Courses: AMSC/CMSC 460 Computational Methods
Finlay Maguire is an Assistant Professor jointly appointed in the Faculty of Computer Science and the Department of Community Health & Epidemiology at Dalhousie University. He leads the Maguire Lab, which develops data-driven methods to address health and social crises through genomic epidemiology and interdisciplinary health data science. He is also affiliated with the Shared Hospital Laboratory, Sunnybrook Research Institute, and multiple national and international public health consortia including PHA4GE, CanCOGeN, and IRIDA. PhD: University College London / Natural History Museum (2016) MA: University of Oxford (2011) Donald Hill Family Fellowship, Dalhousie University (2021) Dr. Maguire's research focuses on two main areas: genomic epidemiology of infectious diseases and interdisciplinary health data science collaborations . His work in genomic epidemiology includes developing bioinformatics and machine learning tools to study antimicrobial resistance (AMR) and SARS-CoV-2 dynamics, often in collaboration with public health agencies. His broader health data science work addresses issues such as online radicalization, healthcare access for refugees, and autism-related language use, combining computational methods with social science. His recent publications (2023–2025) reflect a strong trend in pathogen genomics , AMR , zoonotic spillover , and computational social science . He has published on novel coronaviruses in bats, SARS-CoV-2 animal models, invasive Group A Streptococcus, and sociological analyses of incel communities. Much of this work involves tool development (e.g., ArgNorm, Pathoplexus) and data standardization (e.g., PHA4GE metadata standards). Finalist, 2024 Discovery Awards (Emerging Professional) 2023 President’s Research Excellence Award for an Emerging Investigator, Dalhousie Finalist, 2023 Discovery Awards (Emerging Professional) Funding from CIHR, NSERC, Genome Canada, SSHRC, BMGF Dr. Maguire actively mentors graduate students and postdocs, including PhD candidates in Computer Science and MSc students in Community Health & Epidemiology. He has secured major training grants such as the CIHR Health Research Training Platform and the Canadian One Health Training Program for Emerging Zoonoses. He also contributes to capacity-building initiatives like MicroResearch in Ghana and Kenya. The Maguire Lab is embedded in a rich network of collaborations, including the CARD database, Public Health Agency of Canada, Canadian Food Inspection Agency, and Sunnybrook Health Sciences Centre. The lab emphasizes open science, reproducible research, and interdisciplinary training, as seen in the development of open-source tools and participation in international consortia.
