Dr. Manuj Yadav is a Post Doctoral Researcher at the Chair for Hearing Technology and Acoustics , RWTH Aachen University, Germany. His work focuses on acoustic virtual reality , room acoustics , and psychoacoustics with applications in workplace environments, auditorium design, and vocal performance studies. Research Themes : Auditory distraction in open-plan offices, autophonic perception, speech intelligibility metrics, and retroreflective architectural design Projects : Involved in the Priority Program SPP2236 - AUDICTIVE (Auditory Cognition in Interactive Virtual Environments) Recent publications analyze indoor environmental quality in hybrid workspaces, biophilic sound interventions for stress recovery, and acoustic retroreflection in modern and historical architecture. His experimental studies employ auralization techniques and virtual reality to simulate real-world acoustic environments for empirical testing. Key Collaborations : RWTH Aachen University’s Institute of Hearing Technology and Acoustics (IHTA), University of New South Wales Technical Expertise : Acoustic measurement protocols, ISO 3382-3 compliance, binaural simulation systems
George Atia is an Associate Professor at the Department of Electrical and Computer Engineering, University of Central Florida, directing the Data Science and Machine Learning Lab (DSML). Previously, he was a postdoc at the Coordinated Science Laboratory (CSL) at UIUC and earned his Ph.D. from Boston University, where he was affiliated with the Information Systems & Sciences Lab (ISS) and Center for Information & Systems Engineering (CISE). His research spans big data analytics, sparsity-based learning, controlled sensing, and verifiable planning , with applications in machine learning, cyberphysical systems security, and optical/neural signal processing. His work emphasizes robust algorithms for high-dimensional data, adversarial attacks in machine learning, and inverse problems in optical imaging. Recent projects include tensor completion for visual data recovery and multi-agent reinforcement learning with robustness guarantees. He has secured major funding from NSF, DOE, and ONR, including the NSF CAREER Award. Notable scientific contributions include Robust Tensor Completion for Visual Data Game-Theoretic Frameworks for Cloud Security Adversarial Sample Synthesis in Hierarchical Classifiers Steady-State Policy Synthesis in MDPs His teaching includes graduate courses in random processes and detection theory.
Bernhard Gleich is a faculty member at the Technical University of Munich (TUM), affiliated with the Munich Institute of Biomedical Engineering (MIBE) and the Chair of Biomedical Physics. His academic rank is Lecturer, and he contributes to teaching courses including Biostatistics and Signal Theory. His research focuses on advanced biomedical imaging and instrumentation, with key interests in X-ray tensor tomography, grating interferometry applications, low-field NMR device development, and high-frequency transcranial magnetic stimulation. His work bridges medical diagnostics and engineering innovation. Gleich's recent publications (2019–2024) demonstrate a strong focus on optimizing X-ray imaging techniques, particularly dark-field radiography and computed tomography. His research consistently addresses clinical applications such as fracture detection, pulmonary disease diagnosis, and COVID-19 imaging, with innovations in beamline instrumentation and dose reduction. He maintains active collaborations at the Munich Compact Light Source (MuCLS) and contributes to developing next-generation imaging platforms. No awards or student advisorship details were identified in available materials.
Paul Robinette serves as Associate Professor and Associate Chair for Master of Science Programs in the Electrical and Computer Engineering Department at the Francis College of Engineering, University of Massachusetts Lowell. He maintains significant affiliations with the Printed Electronics Research Collaborative (PERC) and the Raytheon UMass Lowell Research Institute (RURI), where his robotics research integrates printed electronics for field-deployable systems. His office resides in Ball Hall's third floor, with primary contact at Paul_Robinette@uml.edu. Dr. Robinette earned his Ph.D. from the Georgia Institute of Technology, establishing his foundation in robotics and human-systems engineering. This academic background directly informs his experimental approach to human-robot interaction challenges. His research centers on trust dynamics in human-robot teams, particularly examining how moral violations versus performance failures impact trust retention. He investigates drone appearance effects on trust calibration, multi-robot coordination during search and rescue, and trust recovery mechanisms after system malfunctions. His experimental work spans marine environments using platforms like Robowhaler, subterranean scenarios, and emergency evacuation simulations—consistently bridging theoretical AI frameworks with real-world human factors. Analysis of his 2023-2025 publications reveals three dominant trends: 1) Quantitative validation of trust metrics across simulation and reality, 2) Development of relational network architectures for resilient multi-agent teams, and 3) Ethical frameworks for moral trust violations in autonomous systems. His work uniquely integrates reinforcement learning with psychological models to create beneficent AI systems that respect human trust boundaries. As Associate Chair for Master's Programs, Dr. Robinette oversees graduate curriculum development and student mentorship within electrical and computer engineering. His research leadership through PERC and RURI facilitates cross-disciplinary collaboration between robotics, printed electronics, and defense applications—though specific grant details remain unspecified in available materials. He directs experimental robotics teams developing platforms such as Robowhaler for marine autonomy and dataset collection. His lab maintains the publicly available Aquaticus dataset for human-robot teaming research and contributes to standardization efforts like the DECISIVE Test Methods Handbook for subterranean robotics evaluation. Current projects focus on real-world trust validation in navigation deviation scenarios and moral trust repair protocols.
Shinya Sadachika is a researcher affiliated with Tokyo Institute of Science , focusing on robotics, mechanical engineering, and materials science. His work centers on synthetic fiber rope mechanics, motion control systems, and sensing technologies. Research Highlights: Twisted string actuators, non-circular pulley fixation, Brillouin optical reflectometry, and semi-autonomous mobile systems Key Collaborations: Gen Endo, Hiroyuki Nabae, and other robotics experts His research combines material durability testing with advanced optical sensing and mechanical design innovations. Patents highlight practical applications in industrial systems. Scientific Awards: Not explicitly mentioned Grants: Not explicitly mentioned Lab affiliations and student mentorship details remain unspecified, though he contributes to international conferences and collaborative projects.
Dr. Debbie Dailey serves as Interim Dean and Associate Professor in the College of Education at the University of Central Arkansas, providing academic leadership across administrative and instructional domains while maintaining active research in specialized educational fields. Educational Background: Ed.D. in Education Administration and Supervision from University of Arkansas at Little Rock Gifted and Talented Education Certificate from Harding University M.S.E. in Biology from Arkansas State University B.S.E. in Biology, General Science (Co-Emphasis: Chemistry) from Arkansas State University Her research centers on the critical intersection of STEM and gifted education, with particular expertise in engineering design processes for K-8 advanced learners. She pioneers curriculum development for integrating engineering practices into elementary science education while addressing gender disparities in STEM fields. Her work extends to teacher preparation models, digital-age professional development, and classroom assessment strategies specifically tailored for gifted populations. Analysis of her recent publications reveals three dominant trends: (1) Development of spatial reasoning and engineering habits of mind in young learners, (2) Impact of systemic disruptions (like school closures) on gifted programming, and (3) Strategic alignment of gifted education standards with broader educational frameworks including ISTE technology standards. Her scholarship consistently bridges theoretical research with practical classroom applications. No scientific awards were documented in available sources. As Interim Dean, Dr. Dailey oversees all college operations including major federal initiatives like GEAR UP and Upward Bound grant programs. Her leadership extends to statewide educational partnerships through the Arkansas Leadership Academy and Public School Resource Center, where she influences policy development and resource allocation for K-12 education across Arkansas. She maintains active involvement in the Arkansas Research Center and Bears EmpowerED Initiative, focusing on innovative teacher preparation models and STEM talent development pipelines that connect university resources with K-12 classroom implementation.
Janice Evans is a Professor of Biological Sciences and the Associate Dean for College Initiatives and Graduate Education at Purdue University's College of Science. Her research focuses on molecular mechanisms governing mammalian development, particularly sperm-egg interactions and meiotic progression. She also explores fluid dynamics applications in biomedical and energy systems. Research Interests : Molecular Biology, Developmental Biology, Fluid Dynamics, Biomechanics, and Bio-inspired Engineering. Her interdisciplinary work bridges biological processes with engineering solutions, such as cough simulators for respiratory studies and mangrove-inspired energy harvesting systems. Recent studies include wind turbine performance optimization and coastal protection strategies using bio-inspired designs. Key Contributions : Pioneered novel cough simulators, analyzed tracheal flow dynamics with cartilaginous rings, and investigated low-level jet impacts on wind energy systems. Her work on surface coatings reduces aerodynamic noise and vibration in engineering systems. Labs/Teams : Leads collaborative teams in biological and fluid dynamics research at Purdue's College of Science laboratories, focusing on cross-disciplinary projects.
Mikhail Slipchenko is a Research Professor of Mechanical Engineering at Purdue University, affiliated with the Maurice J. Zucrow Laboratories and the College of Engineering. His research focuses on advanced laser diagnostics for high-speed flows, combustion, and plasma physics. He specializes in developing cutting-edge optical techniques such as burst-mode laser systems, coherent anti-Stokes Raman scattering (CARS), and high-repetition-rate planar laser-induced fluorescence (PLIF). These tools enable precise measurements of temperature, velocity, and chemical composition in extreme environments like hypersonic wind tunnels, rotating detonation engines, and shock tubes. Affiliations: Purdue University, College of Engineering, Mechanical Engineering Department, Maurice J. Zucrow Laboratories Research interests include laser-based flow visualization, plasma diagnostics, and the development of ultra-fast diagnostic systems for transient phenomena. His work addresses challenges in aerospace propulsion, combustion optimization, and fundamental fluid dynamics. Recent studies involve kHz-rate OH-PLIF imaging in rotating detonation combustors, 100-kHz CARS thermometry in shock tubes, and microwave scattering for plasma characterization. Publications highlight innovations in laser sources, such as narrowband KTP optical parametric oscillators and burst-mode systems for extended diagnostics. His methodologies have been applied to analyze reactant refill dynamics in rotating detonation engines, hypersonic boundary-layer instabilities, and multiphase blast fields. No scientific awards are explicitly listed, but his contributions to high-speed diagnostics are widely recognized in the field. Slipchenko collaborates on grants related to laser technology and combustion science, advancing both academic and industrial applications. He is actively involved in mentoring students and contributes to the Maurice J. Zucrow Laboratories' mission to solve critical problems in propulsion and fluid dynamics.
Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.
Sean Kearney is a Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), effective October 2024. He holds a Ph.D. and M.S. in Mechanical Engineering from UIUC (1999 and 1995, respectively) and a B.S. in Mechanical Engineering from Clarkson University (1992). Prior to his academic role, he served as a Distinguished Member of Technical Staff at Sandia National Laboratories (1999–2024) and Technical Director at Spectral Energies, LLC (2017–2018). His research focuses on laser-based diagnostics, incompressible and compressible fluid mechanics, hypersonics, and reacting flows. Key projects include developing advanced CARS (Coherent Anti-Stokes Raman Scattering) techniques for temperature/pressure measurements in compressible flows, laser velocimetry for aerodynamics, and combustion diagnostics in high-enthalpy environments. Recent work includes studies on free-piston shock tunnels, nitrogen thermometry in plasma torches, and deflagration dynamics in multi-component fuels. He has contributed to over 12 peer-reviewed articles in journals like Annual Review of Fluid Mechanics, Optics Letters, and Shock Waves. His teaching includes AE 312 (Compressible Flow).
Carson Smith is a Professor in the Department of Kinesiology at the University of Maryland, School of Public Health. He directs the Exercise for Brain Health Laboratory and Cognitive Motor Neuroscience Laboratories, focusing on exercise's impact on brain health in older adults, particularly Alzheimer's disease and age-related cognitive decline. His research uses MRI, neuropsychological testing, and biomarker analyses. Education: PhD in Kinesiology from the University of Georgia (2000). His work integrates multi-modal neuroimaging with behavioral assessments to explore how physical activity mitigates cognitive aging risks in at-risk populations. Key areas include APOE-ε4 genotype effects, cerebellar-cognitive interactions, and sleep-cardio fitness interactions with hippocampal structure. Research highlights include demonstrating exercise-induced neuroplasticity in older adults and identifying biomarkers predicting cognitive decline. His 2023-2025 publications emphasize acute exercise effects on neural connectivity, cerebrovascular health, and genetic vulnerability moderation. He has advised no listed students but leads multi-disciplinary teams in federal grants exploring exercise interventions for dementia prevention. Lab activities focus on translational research bridging exercise physiology and neurocognitive health.
Isabella Verdinelli is a dual-affiliated professor serving as Professor in Residence at Carnegie Mellon University's Department of Statistics (Dietrich College of Humanities and Social Sciences) and as Full Professor at Sapienza University of Rome's Department of Statistical Sciences. She splits her academic year between Pittsburgh (fall) and Rome (spring), maintaining active research collaborations at both institutions. Her education includes a Master's degree from University College London and a PhD from Carnegie Mellon University. Her career spans postdoctoral work, assistant/associate positions, and professorship roles since her student days in Rome. Verdinelli's research focuses on: Nonparametric and high-dimensional methods for uncovering latent structures in complex datasets Bayesian experimental design with applications in medicine and engineering Manifold/filament estimation and minimax convergence theory Monte Carlo Markov Chains and hypothesis testing using Bayes factors Multiple testing procedures (FDR control) Her publications demonstrate sustained focus on Bayesian methodologies, nonparametric inference, and optimization techniques. Recent work (2007-2010) emphasizes high-dimensional data structures and theoretical statistics, while earlier contributions center on experimental design and Bayesian model selection.
Professor J. Kim Vandiver is a faculty member at the Massachusetts Institute of Technology (MIT) in the Department of Mechanical Engineering within the School of Engineering . He serves as the Director of the Edgerton Center and the Dean for Undergraduate Research , focusing on flow-induced vibration of marine structures , damping in vibration control , and design for the developing world . Education: B.S., Harvey Mudd College of Science and Technology (1968) S.M., Massachusetts Institute of Technology (1969) Ph.D., Massachusetts Institute of Technology & Woods Hole Oceanographic Institution (1975) Vandiver's research spans vortex-induced vibrations (VIV) , hydrodynamic damping , and structural dynamics in offshore environments. His work includes experimental studies on marine cables and risers, mathematical modeling for wave-force predictions, and patent development for vibration suppression systems. His scientific contributions emphasize VIV in spatially varying currents, fatigue life estimation, and structural reliability. Awards include the ASME A. Lubinski Best Paper Award , MacVicar Professorship , and Offshore Technology Conference Distinguished Achievement Award . Vandiver also led key initiatives at MIT, including the Office of Experiential Learning and curriculum reform for undergraduate programs.
Aaron Steinfeld is a Research Professor at Carnegie Mellon University 's Robotics Institute with a courtesy appointment in the Human-Computer Interaction Institute . His work bridges human-robot interaction and advanced transportation , focusing on inclusive design for users with disabilities. PhD in Industrial and Operations Engineering (1999) and MSE/BSE in the same field (1994/1993) from University of Michigan Postdoctoral work in Transportation Human Factors at UC Berkeley's California PATH program (2000) Research spans four key areas: Assistive Robotics : Developing inclusive technologies for visually impaired and disabled users Transportation Innovation : Redesigning transit systems through crowdsourcing and citizen science Human-Robot Group Dynamics : Studying social navigation and multi-robot coordination Trust & Communication : Creating frameworks for robot self-assessment and user trust alignment Recent publications emphasize social navigation benchmarks , pedestrian behavior modeling , and adaptive transit interfaces . Current projects include accessible delivery robots (NIDILRR 90IFDV0042) and FHWA-funded work on autonomous vehicle accessibility. Scientific Recognition NSF AI Institute (AI-CARING) co-PI ONR MURI grant recipient DARPA and multiple NSF grant collaborations Best Paper awards at CHI 2019 and IROS 2020 Advising both PhD and Master's students in human-robot interaction, his lab maintains partnerships with Georgia Tech, UMass Lowell, and Disney Research. He co-edited the book Accessible Public Transportation .
Linda H. Moya is a Professor in the Social and Decision Sciences department at Carnegie Mellon University, with affiliations to the Heinz College and the Center for the Neural Basis of Cognition (CNBC). She holds a Ph.D. in Psychology/Cognitive Neuroscience and a Master of Philosophy in Public Policy and Management from Carnegie Mellon University, along with a Master of Science in Industrial Engineering from Stanford University and a Bachelor of Science and Engineering in Electrical Engineering and Computer Science from Princeton University. Ph.D. in Psychology/Cognitive Neuroscience, Carnegie Mellon University M.Phil. in Public Policy and Management, Carnegie Mellon University M.S. in Industrial Engineering, Stanford University B.S. in Electrical Engineering and Computer Science, Princeton University Linda's research focuses on the neuroscience of decision making, particularly how the social and emotional brain influences decision processes. She employs behavioral experiments, psychophysiology, and neuroimaging methods like MEG, EEG, and structural MRI to test hypotheses derived from interdisciplinary neuroscience theories. Her dissertation, The Microgenesis of Object-based vis-a-vis Space-based Visual Attention , explores the temporal and spatial dynamics of visual attention mechanisms via electromagnetic field recordings. Linda serves as Program Director for the Summer Undergraduate Program in Computational Neuroscience (CNBC) and has taught courses such as 88120 Reason, Passion and Cognition , 94800 Negotiation , and 88342 The Neuroscience of Decision Making , among others. She integrates advanced statistical learning techniques into her research to explore both predicted and emergent patterns in data.
