Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.
Outi Tammisola is a Professor at KTH Royal Institute of Technology, specializing in Fluid Mechanics within the Department of Mechanics. Her research focuses on non-Newtonian fluids, viscoelasticity, multiphase flows, and microfluidics. She is actively involved in teaching and course coordination for mechanics and fluid dynamics courses, including Mechanics I, Mechanics II, and Wave Motion and Hydrodynamic Stability. Her work bridges numerical methods, experimental validation, and industrial applications, with a strong emphasis on understanding complex fluid behaviors in both theoretical and applied contexts. Dr. Tammisola’s research interests span topics such as elastoviscoplastic fluids, particle migration in microchannels, and interfacial phenomena in viscoelastic systems. Her recent publications address challenges in multiphase flow dynamics, including droplet coalescence, bubble migration, and the rheology of non-Newtonian fluids. She contributes to advancements in numerical methods, such as phase-field modeling and immersed boundary techniques, to simulate complex fluid-structure interactions. Her articles highlight trends in understanding fluid behavior under extreme conditions, such as high aspect ratio microchannels and porous media. Collaborations with experimentalists and computational scientists ensure her work remains both innovative and grounded in real-world applications. While no scientific awards are explicitly mentioned, her extensive publication record reflects significant contributions to the field of fluid mechanics. Outi Tammisola is also engaged in academic leadership, overseeing degree projects and contributing to the development of educational programs at KTH. Her research group likely explores cutting-edge topics in microfluidics and viscoelastic turbulence, though specific lab or team affiliations are not detailed in the provided texts.
Hans Bihs is a Professor in the Department of Civil and Environmental Engineering, Faculty of Engineering. His research focuses on computational fluid dynamics (CFD), wave hydrodynamics, and wave-structure interaction using the open-source framework REEF3D. Key Research Areas: CFD simulations, wave modeling, floating body dynamics, ocean wave energy, aquaculture hydrodynamics, sediment transport, and high-performance computing. Projects: ERC Consolidator Grant PARTRES (2023-2028), EEA Grants Portugal SurfWave (2023), NFR KPN IPIRIS (2021-2025), EEA Baltic SolidShore (2021-2024), NTNU's MAPLE (2022-2025), and DigiCoast (2021-2024). Email: hans.bihs@ntnu.no His recent publications (2025-2020) analyze fluid-structure interaction, ship-induced waves, floating offshore wind turbines, submerged vegetation, and coastal structures using advanced CFD techniques. Topics include wave hydrodynamics, turbulence, and numerical modeling for marine and aquaculture systems.
Amy Vaughan Van Hecke serves as Assistant Chair and Professor in Marquette University's Department of Psychology, leading research on autism spectrum disorder (ASD) and social development across the lifespan using neuroimaging and psychophysiological methods. She directs community initiatives to improve autism services access for underserved Milwaukee populations through the Next Step Clinic. Her academic foundation includes: B.A. in Psychology from Smith College Ph.D. in Developmental Psychology from the University of Miami Dr. Van Hecke's research centers on brain activity, heart rate regulation, and social behavior in individuals with and without ASD, utilizing high-density EEG and MRI to examine neural responses to interventions like PEERS ® . Current projects investigate pandemic impacts on social interaction in autistic adults and neural mechanisms of social isolation remediation. Her work bridges laboratory neuroscience with community-based clinical applications. Publication analysis reveals consistent focus on intervention efficacy (particularly PEERS ® ), neural plasticity measurement, and comorbid conditions across developmental stages. Recent work emphasizes gender-specific outcomes, family impacts, and pandemic-related mental health, demonstrating methodological diversity from EEG asymmetry to community-based participatory research. Her distinguished scientific recognition includes: Kirschstein National Research Service Award (NRSA) from the National Institute of Mental Health Dr. Van Hecke mentors students through her Marquette Autism Project lab and the Next Step Clinic training program while securing major grants from Marquette University, Johnson Controls Foundation, and the Greater Milwaukee Funders’ Collaborative. She teaches undergraduate/graduate courses in developmental psychology and statistics. Advising: Clinical psychology graduate mentorship (excluding 2025 intake); undergraduate research supervision Grants: $500k+ secured for Next Step Clinic serving underserved Milwaukee children She co-directs the Marquette Interdisciplinary Autism Initiative and the Next Step Clinic, which employs a Family Navigation model in Milwaukee's Metcalfe Park neighborhood to provide autism screening, diagnosis, and therapy for children aged 15 months-10 years facing systemic barriers to care.
Professor Michael Manhart is affiliated with the Technical University of Munich (TUM) as an Extraordinary Professor in the Department of Hydromechanics . His research focuses on fluid mechanics, turbulent flow dynamics, and computational fluid dynamics (CFD) simulations, particularly in porous media and environmental fluid mechanics. Education: Not explicitly stated in the provided text. His recent publications investigate turbulent flow over random sphere packs, scalar transport at porous-turbulent interfaces, acoustic resonances in HVAC systems, and nonlinear oscillatory flow modeling. He employs advanced numerical techniques like direct numerical simulations (DNS) and large-eddy simulations (LES) to study flow structures, energy budgets, and particle transport mechanisms. Professor Manhart collaborates with researchers such as Yoshiyuki Sakai, Simon Wenczowski, and Daniel Quosdorf. His work addresses applications in environmental engineering, hydraulic modeling, and industrial fluid dynamics, with a strong emphasis on validating computational models against experimental data (e.g., PIV measurements). He leads the Professorship for Hydromechanics at TUM, conducting high-fidelity simulations and experimental studies on topics like wall shear stress estimation, sediment erosion around cylinders, and turbulence decomposition in complex flows.
Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.
Dr. Andrew Bassett serves as Head of the Cellular and Gene Editing Research group at the Wellcome Sanger Institute, where he develops cutting-edge genome engineering techniques using human pluripotent stem cells to investigate neurodegenerative diseases including Alzheimer's and Parkinson's. His work focuses on scaling genetic screening approaches and improving CRISPR specificity for modeling complex disease mechanisms. His academic training includes: PhD at the MRC Laboratory of Molecular Biology (MRC-LMB) with Andrew Travers on chromatin remodelling in heterochromatin formation Postdoctoral research with David Baulcombe at the University of Cambridge studying small RNA roles in chromatin modification Additional postdoctoral work with Chris Ponting at the MRC Functional Genomics Unit (MRC-FGU) in Oxford, where he pioneered CRISPR applications in Drosophila Bassett's research program centers on developing advanced genome engineering methodologies for precise modulation of gene expression networks during development and neurodegeneration. His group specializes in creating complex editing events (SNPs, paired knockouts, enhancer perturbations) within iPSC-derived models, with particular emphasis on epigenetic regulation and transcriptional control. Current projects integrate single-cell 'omics and phenotypic assays to decode genetic causes of neurodegenerative disorders through the OpenTargets consortium. Analysis of his 15 most recent publications reveals dominant trends in CRISPR technology development (35%), neurodegenerative disease modeling (30%), and single-cell functional genomics (25%). His work consistently bridges methodological innovation with disease mechanism studies, increasingly incorporating multi-omics approaches and expanding into cancer immunology and infectious disease applications since 2022. As group leader, Bassett mentors postdoctoral researchers and PhD students while securing major funding for genome engineering initiatives. His team operates within the Sanger Institute's Cellular Operations division and maintains critical partnerships with the OpenTargets consortium for therapeutic target validation. The laboratory specializes in high-throughput screening platforms using iPSC-derived neural and microglial models, with recent methodological advances including scSNV-seq and ONE-STEP tagging systems that significantly enhance precision genome editing capabilities.
Roozbeh Mottaghi is a Senior AI Research Scientist Manager at Meta's Fundamental AI Research (FAIR) division and an Affiliate Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His career spans roles as Research Manager at the Allen Institute for AI and Postdoctoral Researcher at Stanford University. He holds a Ph.D. in Computer Science from UCLA, advised by Alan Yuille, and advanced degrees from Simon Fraser University, Georgia Tech, and a B.Sc. from Sharif University of Technology. Ph.D., Computer Science, UCLA (2016) M.Sc., Computer Science, Simon Fraser University M.Sc., Electrical Engineering, Georgia Institute of Technology B.Sc., Computer Engineering, Sharif University of Technology His research focuses on Embodied AI, Robotics, Computer Vision, and Multimodal Learning, with key contributions to human-robot collaboration benchmarks (PARTNR), 3D scene understanding, and adaptive navigation systems. Recent work explores zero-shot manipulation via video tracking (Track2Act) and lifelong navigation benchmarks (GOAT-Bench). Publications span 3D vision, reinforcement learning, and visual reasoning, including NeurIPS 2022 Outstanding Paper Award for "Ask4Help" and leadership in organizing challenges at CVPR and ICCV workshops. He advises students and interns who have transitioned to leading institutions like AI2, Stanford, and Brown University.
Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.
Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.
Reda Mastouri is an Adjunct Professor in the Department of Data Sciences within the College of Computer and Information Sciences at Saint Peter’s University. He combines academic roles with 12 years of industry experience as a Lead Cyber Security Engineer and Product Consultant, collaborating with Fortune 200 and 500 companies. His teaching includes courses such as DS-520 Data Analysis, DS-530 Big Data, and CS-332 Advanced Computing. Ph.D., AI & Data Sciences, Saint Peter’s University M.Eng., Telecommunication and Network Engineering, ENSA-M Cadi Ayyad University M.S., Data Sciences, Saint Peter’s University B.S., Computer Sciences, New Jersey Institute of Technology B.A., Applied Mathematics, Rutgers University His scholarly work focuses on AI-driven algorithms for truth demystification and cluster computing applications in high-fidelity image/video forgery detection within cybersecurity. Additional expertise spans DevSecOps, enterprise architecture, and software economics, with a dedication to innovation in business strategy and technology integration. Dr. Mastouri’s research trends emphasize heterogeneous ad hoc networks, collaborative honeypot architectures, and blockchain-based security models for IoT. His work addresses distributed attack detection, false positive/negative reduction, and protocol optimization, aligning with his specialization in cybersecurity and artificial intelligence. Certified Splunk Super User Palo Alto Networks Certified Cybersecurity Associate (PCCSA) CyberArk Certified Trustee Certified Scrum Professional SFPC Certified 10-Hr OSHA Training for the Construction Industry Certified Project Management Essentials Certified (PMEC)™ Lean Six Sigma Yellow Belt (ICYB) CPR & AED Certified AWS Certified Developer Associate Scrum Foundation Professional Certificate NSE 1 Network Security Associate NSE2 Fortinet's Network Security Expert
Marco Paggi is a Full Professor of Structural Mechanics at the IMT School for Advanced Studies Lucca, Italy, since 2017. He previously held academic roles at Politecnico di Torino (Assistant Professor, 2007-2013) and has been an Alexander von Humboldt Fellow at Leibniz University Hannover. His research focuses on fracture mechanics, contact mechanics, and computational methods applied to renewable energy systems, composite materials, and multi-scale modeling. Key Themes: Fracture propagation, contact interfaces, phase field modeling, photovoltaic durability, and material heterogeneity. Awards: Stanford Top 2% Scientists (2020-2024) Research.com Top Scientists (2022-2024) European Structural Integrity Society Young Scientist Award (2010) Publications: His work spans tribology, computational fracture mechanics, and material degradation, with recent emphasis on phase field modeling for quasi-brittle materials and photovoltaic systems. He has pioneered methods for multi-scale and multi-physics analysis of structural systems. Mentorship: Supervised 17 PhD graduates and 14 postdocs, including award-winning researchers like Pietro Lenarda and Zeng Liu.
Karrie A. Shogren is the Ross and Marianna Beach Distinguished Professor at the University of Kansas. Her work focuses on self-determination for individuals with intellectual and developmental disabilities, emphasizing educational outcomes, policy, and inclusive practices. She holds a Ph.D. in Disability and Education from KU, an M.A. in Psychology from the University of Dayton, and a B.S. in Psychology from The Ohio State University (summa cum laude). Her research spans self-determination interventions, special education policy, and the intersection of disability and citizenship. Key areas include improving IEP goal attainment, evaluating technology-delivered interventions, and co-designing inclusive environments with disability communities. She advocates for supported decision-making alternatives to guardianship and has published extensively on measurement tools for self-determination. Dr. Shogren’s awards include the distinguished professorship, recognizing her contributions to advancing equity in education for marginalized groups. She collaborates internationally, adapting interventions across cultural contexts while prioritizing anti-racism and anti-ableism in transition services. Her work intersects with museum accessibility, telehealth mindfulness programs for adolescents with disabilities, and policy reforms to reduce educational segregation. She emphasizes practitioner partnerships, developing teacher coaching models to implement self-determination practices in inclusive classrooms.
Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.
H. Jane Bae is an Assistant Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Her research focuses on turbulence modeling, particularly developing high-fidelity computational methods to simulate high-Reynolds-number flows for applications in aircraft design, wind farms, and atmospheric predictions. She integrates machine learning, information theory, and numerical techniques to enhance turbulence modeling efficiency. Education: B.S. in Aerospace Engineering from Caltech (2011), Ph.D. in Mechanical Engineering from Stanford University (2018). She joined Caltech in 2021. Research interests include near-wall turbulence dynamics, resolvent analysis, sparse identification of nonlinear dynamics, and reinforcement learning for wall models in LES. Her work addresses computational cost reduction and model accuracy in complex flow simulations. Awards: 2023 Outstanding Referee Award from Physical Review. Teaching includes courses on fluid mechanics (Ae/APh/CE/ME 101 abc) and turbulence (Ae 239 ab). Her lab combines turbulence theory, high-performance computing, and data-driven methods to study unsteady flows over complex surfaces. Notable contributions include machine learning-based wall models and resolvent analysis frameworks for non-stationary flows.
