جست‌وجوی استاد و پژوهشگر

Jennifer Millsمشاهده پروفایل

استاد

• Body Image
• Eating Disorders
• Social Media Impact
• +۴ مورد دیگر

Dr. Jennifer Mills is a Professor and Director of Clinical Training in the Clinical Psychology graduate program at York University's Faculty of Health, leading the EASE Lab. Her research focuses on body image, eating disorders, and the impact of social media on women's psychological well-being. She holds degrees from the University of Toronto (PhD 2001, MA 1996, BSc 1995). Key research areas include social media's effects on body dissatisfaction, orthorexia nervosa, and interventions for disordered eating. She has published extensively on topics such as self-compassion interventions, Instagram imagery impacts, and pandemic-related behavioral changes. Her educational background includes a strong foundation in psychology and clinical research, with a focus on translational work between theory and practice. Current projects explore the psychological mechanisms behind body image concerns, including the role of perfectionism and emotional intelligence in mitigating eating disorder symptoms. She collaborates widely, as evidenced by multi-author publications and interdisciplinary research efforts. Labs/Teams: Director of the EASE Lab (Eating, Affect, and Self-Evaluation) at York University. The lab's work integrates behavioral experiments, longitudinal studies, and community-based interventions to address body image and eating disorder prevention.

Siyu Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Digital Humans
• +۵ مورد دیگر

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

Sohmyung Haمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Neural Engineering
• Wearable Sensors
• +۳ مورد دیگر

Sohmyung Ha is an Associate Professor of Electrical Engineering and Bioengineering at NYU Abu Dhabi and holds a Global Network position at NYU Tandon School of Engineering. He leads the Integrated BioElectronics Laboratory, focusing on advancing silicon integrated technologies for biomedical applications such as implantable devices and wearable sensors. His expertise spans biomedical circuits, neural interfaces, and wireless power systems. Education: MS (2004, KAIST), PhD (2016, UC San Diego) with a Best Thesis Award. Prior industry experience includes analog circuit design at Samsung Electronics (2006-2010). Academic affiliations include NYU Abu Dhabi, NYU Tandon, and global collaborations. Research interests include high-performance biomedical sensors, neural prosthetics, and energy-efficient bioelectronic systems. Notable achievements include a Best Paper Award (ISCAS 2024) and innovations in impedance spectroscopy and neural interface ICs. Current projects emphasize closed-loop neural interfaces, subcutaneous glucose monitoring, and retinal prostheses. His lab develops miniaturized, power-autonomous systems for healthcare applications.

Karen Livescuمشاهده پروفایل

استاد

• Speech Processing
• Language Processing
• Machine Learning
• +۵ مورد دیگر

Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.

• Materials Engineering
• Medical Biotechnology
• Mechanical Engineering
• +۵ مورد دیگر

Michael Gasik is a Professor at the Department of Chemical and Metallurgical Engineering, School of Chemical Engineering, Aalto University. With over 30 years of experience in technology transfer, he has led initiatives across the EU, Japan, and the Middle East, including BC-Net (EC DG Enterprise). He serves as an expert for European bodies like EC/REA, COST, and ERC since 1993. His research spans biomaterials ceramic materials metals processing sustainable manufacturing additive manufacturing green energy systems and focuses on interdisciplinary applications in biomedical and environmental engineering. Recent research trends include advanced biomaterials for medical implants (e.g., PEO-coated magnesium alloys) multi-material 3D printing for industrial components green hydrogen production via HyS cycle optimization sustainable polymer composites photocatalytic nanomaterials blockchain-enabled recycling for electric vehicles Scientific accolades include 2023 EORS Ambassador for Finland 2015 National State Prize of Ukraine 2013 M.M. Dobrohotov Award 1997 JSPS Scholarship 1995 Best Doctorate Thesis Award 2007 Certificate of Appreciation for Functional Graded Materials He has secured funding through 10 EU projects, 2 IEA projects, and over 15 national projects, contributing to >300 publications and patents. His expertise extends to technology transfer leadership and peer review for journals and funding agencies in multiple countries.

Hyosang Leeمشاهده پروفایل

استادیار

• Tactile Sensing
• Robotics
• Soft Robotics
• +۴ مورد دیگر

Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.

Dominika Ignasiakمشاهده پروفایل

پژوهشگر

• Biomechanics
• Musculoskeletal Modeling
• Spinal Surgery
• +۳ مورد دیگر

Dr. Dominika Ignasiak is a Researcher affiliated with the Institute of Biomechanics at ETH Zürich. Her work focuses on spinal biomechanics, musculoskeletal modeling, and computational analysis of spinal pathologies. She contributes to understanding the biomechanical implications of surgical interventions, spinal deformities, and age-related changes in spinal alignment and loading. Her research integrates clinical data with advanced musculoskeletal modeling techniques, particularly in predicting postoperative outcomes and assessing spinal load distributions under dynamic conditions. Key areas include spinal stenosis, idiopathic scoliosis, and the biomechanics of spinal fusion surgery. Dr. Ignasiak collaborates on translational studies bridging computational simulations with clinical applications. Her publications emphasize the role of personalized models in optimizing surgical strategies and understanding degenerative spinal conditions. While no formal awards are listed, her contributions to spinal biomechanics research are evident through her active publication record in high-impact journals. Dr. Ignasiak is based at ETH Zürich’s Institute of Biomechanics, where she engages in cutting-edge research and contributes to both academic and clinical advancements in orthopedic biomechanics.

Sharon Rozovskyمشاهده پروفایل

استاد

• Oxidative Stress
• Protein Quality Control
• ERAD Pathway
• +۵ مورد دیگر

Sharon Rozovsky is a Professor in the Department of Chemistry and Biochemistry at the University of Delaware's College of Arts & Sciences, where she leads research on oxidative stress response mechanisms and protein quality control pathways. Her work bridges biochemistry, chemical biology, and structural biology with direct implications for understanding neurodegenerative diseases and viral pathogenesis. Her academic foundation includes a B.S. from Tel Aviv University (1994) and a Ph.D. from Columbia University (2000), establishing her expertise in protein dynamics and redox biochemistry. These credentials underpin her innovative approaches to studying cellular stress responses. Rozovsky's research program centers on selenoproteins—proteins containing the rare amino acid selenocysteine—and their critical roles in endoplasmic reticulum (ER) stress resolution. She investigates how membrane-bound selenoproteins like Selenoprotein S and K regulate the ER-associated degradation (ERAD) pathway, with recent work revealing their surprising autoproteolytic activity and involvement in SARS-CoV-2 replication. Her lab pioneers chemical tools including expressed protein ligation and advanced 77Se NMR spectroscopy to characterize these systems at molecular resolution. Analysis of her 2021-2025 publications shows dominant themes in selenoprotein structure-function relationships, ER stress mechanisms, and viral interactions, alongside methodological innovations in cryo-EM grid technology and NMR. This body of work demonstrates consistent focus on redox biochemistry with expanding applications in virology and structural biology. No major scientific awards or fellowships were explicitly documented in the available materials, though her research impact is evident through high-impact publications and methodological contributions. She directs the active Rozovsky Research Group, mentoring graduate students and postdoctoral researchers in biochemical and biophysical techniques. Her laboratory operations are supported by competitive funding including an NSF CAREER award (2011) focused on selenoprotein reactivity, reflecting sustained recognition of her innovative research program.

Themistoklis Sapsisمشاهده پروفایل

استاد

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +۳ مورد دیگر

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

Graham Sanderمشاهده پروفایل

استاد

• Soil Erosion
• Hydrology
• Environmental Science
• +۳ مورد دیگر

Professor Graham Sander is a Professor of Hydrology at Loughborough University, affiliated with the School of Civil and Building Engineering. His research focuses on mathematical modeling of soil erosion, unsaturated soil flow, contaminant transport dynamics, and nonlinear diffusion-convection equations. He leads the NERC-funded project on multi-dimensional soil erosion and chemical transport, collaborating with Lancaster University’s Department of Environmental Science. His academic qualifications include a BSc (Hons) and PhD. Recent research emphasizes integrating particle size-selective models to predict sediment and contaminant delivery to water bodies, supported by lab and field experiments. He has also pioneered pseudospectral methods for infiltration modeling and explored flood risk management through nature-based solutions like leaky barriers. Key contributions include advancing understanding of rainfall-driven erosion dynamics, including splash effects and rock fragment coverage impacts. His work spans experimental hydrology, numerical simulation, and interdisciplinary approaches to environmental challenges. He co-edits hydrology journals and advocates for rigorous scientific communication in the field. Grants: NERC-funded soil erosion project, Australian Research Council project on unsaturated soil flow. Labs/Teams: Collaborator with Lancaster University’s Environmental Science Department.

• Energy Systems Modelling
• Hydrogen Energy Systems
• Bioenergy Systems
• +۵ مورد دیگر

Paul Dodds is Professor of Energy Systems at University College London's Bartlett School of Environment, Energy & Resources, where he holds joint appointments at the UCL Energy Institute and the Institute for Sustainable Resources. He serves as the Faculty Graduate Tutor for the Bartlett Faculty of the Built Environment, overseeing all doctoral research programs. His academic progression at UCL has been steady, moving from Research Associate (2011-2014) to Senior Research Associate (2014-2015), Lecturer (2015-2016), Senior Lecturer (2016-2018), Associate Professor (2018-2020), and finally to Professor. His educational background includes a PhD from the University of Leeds (2010) focused on climate change and agriculture in Senegal, where he developed a new crop model for adaptation research and created detailed meteorological datasets for West Africa. He also holds a Master of Natural Science (Honours) from the University of Nottingham (2000). Dodds specializes in energy systems modelling with particular expertise in hydrogen and bioenergy systems, and the importance of energy storage. His research examines the interactions between society and the environment, with a focus on energy and food systems. He has developed the UK TIMES energy systems model, which has replaced the UK MARKAL model and is now co-developed with the UK Department of Business, Energy and Industrial Strategy (BEIS). This model has provided underpinning evidence for the UK's Clean Growth Strategy and Net Zero Strategy. His methodological contributions include formalizing a theoretical approach to analyzing the evolution of energy system models using 'model archaeology'. Analysis of his recent publications reveals a strong focus on hydrogen energy systems, with multiple papers examining hydrogen trade pathways, integration methods, and environmental impacts. His work increasingly addresses the geopolitical dimensions of energy transition, as seen in studies about Russian gas pivots to Asia and global energy scenarios. He maintains expertise in energy system modeling techniques while expanding into practical applications for policy development, particularly regarding the UK's net-zero transition. Dodds has supervised 15 PhD students at UCL, with eight under his primary supervision. His professional activities include serving as the UK Alternate Delegate to IEA Hydrogen since 2017, acting as a PhD External Examiner at the University of Edinburgh, and participating in the EPSRC Peer Review College. He has contributed to multiple government initiatives, including the UKERC Future of the Gas Networks workshop and representing the UK Government at IEA ETSAP meetings. He teaches an undergraduate module on 'Energy and Environmental Systems Modelling' and guest lectures on several MSc courses. His research group focuses on energy system modeling, with particular emphasis on the UK TIMES model development and application. His work often involves collaboration with government bodies, particularly BEIS, and he has coordinated significant projects like seven reports on overshoot pathways for the UK Government.

• Medical Imaging
• Molecular Imaging
• Neuroscience
• +۳ مورد دیگر

Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.

Na Young Kimمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Information Processing
• Nanotechnology
• +۵ مورد دیگر

Na Young Kim is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo with affiliations at the Institute for Quantum Computing (IQC) and Waterloo Institute for Nanotechnology. She holds cross-appointments in the Departments of Physics and Astronomy and Chemistry. Her research focuses on developing large-scale quantum processors using novel materials and advanced technologies, including semiconductor quantum processors and multi-functional nanoscale devices. Dr. Kim leads the Quantum Innovation (QuIN) laboratory, pioneering projects in planar architecture design for quantum devices integrating electrical, optical, thermal, and mechanical functionalities. Prior to academia, she worked at Apple Inc. on small display technologies. She earned a BS in Physics from Seoul National University and a PhD in Applied Physics from Stanford University, where she specialized in mesoscopic transport in nanostructures. Her postdoctoral work expanded into quantum optics and nanophotonics through collaborations with international researchers. Current teaching includes courses on quantum mechanics, quantum computing algorithms, quantum information processing devices, and photonic systems. She actively supervises graduate students in quantum technology development and is accepting new applications. Research activities span quantum artificial intelligence, quantum security protocols, and nanotechnology applications. Her work bridges theoretical frameworks with experimental implementations in solid-state quantum systems.

Randall D. Beerمشاهده پروفایل

استاد

• Artificial Life
• Neuroscience
• Computational Biology
• +۵ مورد دیگر

Randall D. Beer is a Provost Professor at Indiana University with affiliations across multiple departments and centers, including the Cognitive Science Program , Program in Neuroscience , School of Informatics, Computing, and Engineering , and the Center for Complex Networks and Systems Research . His research focuses on understanding how organisms function as integrated wholes, emphasizing the interplay between brains, bodies, and environments. He develops computational models of neuromechanical systems, biologically-inspired robotics, and dynamical systems approaches to cognition. Education: While formal educational details are not explicitly listed, Beer's academic trajectory is reflected in his extensive publications and roles in interdisciplinary research programs. Research Interests: Beer investigates: - Embodied cognition and enaction frameworks - Neurodynamics and central pattern generators - Evolution of behavior in artificial agents - Metabolic and developmental systems biology - Dynamical systems theory - Computational modeling of C. elegans locomotion Software Contributions: Beer has developed tools like Dynamica (for dynamical systems analysis), CTRNN (neural network simulation), and Evolutionary Agents (robotics control frameworks). These tools are widely used in computational neuroscience and robotics research. Advising and Teams: He supervises a large group of graduate students and postdocs, contributing to projects such as neuromechanical modeling and evolutionary robotics. His work is supported through grants focusing on embodied cognition and systems biology. Labs and Collaborations: Active in the Center for Complex Networks and Systems Research and collaborates with interdisciplinary teams exploring topics like autopoiesis, viability theory, and robotic embodiment.

Raouf Boutabaمشاهده پروفایل

استاد

• Network Management
• Systems and Services
• Resource Management in Wired/Wireless Networks
• +۹ مورد دیگر

Raouf Boutaba is a Professor at the University of Waterloo , serving as Director of the David R. Cheriton School of Computer Science since July 2020. He holds prestigious fellowships including FRSC , FIEEE , FIEC , and FCAE . 2024: Inaugural Rogers Chair in Network Automation 2024: Ontario Research Fund–Research Excellence (ORF–RE) $2M grant for next-gen mobile networks 2021: University Professor title, University of Waterloo Research Interests span network automation, resource management in wired/wireless networks, network function virtualization (NFV), software-defined networking (SDN), cloud computing, blockchain, future Internet architecture, and cybersecurity. His work focuses on zero-touch networks, 5G/B5G slicing, and AI-driven orchestration. Scientific Contributions include 15+ recent publications on topics like reinforcement learning for RAN slicing, encrypted traffic classification, quantum network optimization, and self-driving infrastructure. His projects 5G LEAP and 5G ELITE explore network isolation and Open RAN principles. 2024: IFIP/IEEE CNOM Test of Time Paper Award 2024: Graduate Supervision Excellence Award 2021: Kenneth C. Sevcik Outstanding Student Paper Award (advisor) Teaching includes co-developing the NSERC CREATE Network Softwarization program, offering courses like Network Softwarization: Principles and Foundations (Winter 2024) and Technologies and Enablers since 2018. He emphasizes hands-on training in SDN, NFV, Open RAN, and 5G. Students and Collaborations : Supervised PhD students such as Shihabur R. Chowdhury (2021), Nashid Shahriar (2020), and undergrad Leni Aniva (2022 Gov. Gen. Silver Medal ). His team includes researchers working on 5G, blockchain, and AI-driven network management. Professional Leadership : Organized Rogers TEP Workshops (2024-2025), delivered keynotes at IEEE Globecom, ColCom, and BalkanCom, and served on expert panels for AI orchestration and 5G cybersecurity at major symposia.