جست‌وجوی استاد و پژوهشگر

• Soft Robotics
• Microfluidics
• Nanofabrication
• +۳ مورد دیگر

Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .

• Semiconductor Process Technology
• Heterogeneous Integration
• Nanoelectronics
• +۴ مورد دیگر

Per-Erik Hellström is a Professor at KTH Royal Institute of Technology, affiliated with the Department of Electronics and Embedded Systems. His research focuses on semiconductor process technology, particularly the heterogeneous integration of materials like SiGe, Ge, high-κ dielectrics, and metal gates with Si CMOS to advance integrated circuits. He leads KTH's FDSOI CMOS process and circuit technology, emphasizing sequential 3D integration for future CMOS developments. Additionally, he manages the Si and SiC process line at Electrum Laboratory, overseeing tool maintenance, process control, and upgrades. Researcher ID: ORCID Location: Kistagangen 16 Email: pereh@kth.se His work involves developing nanometer-sized transistors through double patterning techniques and studying material integration for enhanced device performance. He teaches courses in electrical circuits, semiconductor devices, and nanotechnology at both Bachelor's and Master's levels, including Electrical Engineering (IF1330) , Embedded Electronics (IE1206) , and Introduction to Integrated Circuits (IL2241) . He also supervises degree projects and exams. Scientific achievements include the 2020 G03 Best Paper Award for gate stack research. His recent publications highlight advancements in Type-II superlattices, 3D integration, and high-temperature sensors. Key collaborators include PhD students working on nanotechnology and process engineering.

• Computer Vision
• Robotics
• Computer Graphics
• +۵ مورد دیگر

Ioannis Stamos is a Professor of Computer Science at Hunter College, City University of New York (CUNY), within the School of Arts and Sciences. His research focuses on Computer Vision, Robotics, Computer Graphics, and 3D Visualization, with emphasis on 3D modeling using range and image data. He earned his Ph.D. in Computer Science from Columbia University (2001), followed by an M.S. and M.Phil. from Columbia's Computer Science Department, and a Diploma of Engineering from the University of Patras, Greece. Dr. Stamos has received prestigious awards including the NSF CAREER Award (2003) and Google Research Awards (2014, 2017). His work integrates 2D images and 3D range data for urban scene modeling, sensor fusion, and real-time object detection. Notable contributions include advancements in 6DoF pose estimation, LiDAR-based curb detection, and Kronecker product models for repeated patterns in urban imagery. He leads the Computer Vision & Robotics Lab and teaches graduate courses in 3D Computer Vision and Photorealistic Modeling. His research is supported by NSF grants, including MRI awards for mobile robotics and large-scale 3D modeling. He serves as Area Editor for the Journal of Computer Vision and Image Understanding and has co-chaired conferences like 3DV 2013. His lab collaborates on projects involving procedural modeling of urban environments and online classification of 3D point clouds.

Stephen Adamoمشاهده پروفایل

استادیار

• Visual Perception
• Attention
• Medical Image Perception
• +۳ مورد دیگر

Stephen Adamo is an Assistant Professor in the Department of Psychology at the University of Arizona and directs the Attention Detection And Medical Observation (ADAMO) Lab. He holds a joint appointment in Radiology and Imaging Sciences and is a member of the UA Cancer Center. Education: B.S. in Psychology from the University of Arizona, M.A./Ph.D. in Psychology and Neuroscience from Duke University. His research focuses on visual perception, attention, and medical image perception, utilizing behavioral studies, eye-tracking, and EEG to explore topics like satisfaction of search in virtual mammograms, the impact of AI on diagnostic imaging, and differences between 2D and 3D visual search. He investigates attentional limitations such as "subsequent search misses" and "self-induced attentional blink" in radiology and cognitive science contexts. His recent publications examine the intersection of AI, 3D imaging, and cognitive errors in diagnostic workflows, emphasizing human-AI collaboration in visual search tasks. Scientific Awards: NIH National Cancer Institute Grant 5K99CA267163-02. He collaborates with radiologists and cognitive scientists, integrating interdisciplinary methods to improve medical diagnostics through human factors research.

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +۴ مورد دیگر

Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.

Eric Mörthمشاهده پروفایل

پژوهشگر

• Medical Visualization
• Medical Imaging
• Biomedical Engineering
• +۶ مورد دیگر

Eric Mörth is a PhD Scientist in Multimodal Medical Visualization at the University of Bergen's Department of Informatics, collaborating with the Mohm Medical Imaging and Visualization Center (MMIV). His research focuses on innovative medical data visualization techniques, such as MuSIC and ICEVis, which enhance clinical decision-making. Currently on a research stay at Harvard University's VCG Group, he holds a Master's in Medical Informatics from the Medical University of Vienna and a degree in Biomedical Engineering from the Technical University of Vienna. His awards include the Best Paper Honorable Mention (VCBM2022) and Best Short Paper (VINCI2022). Mörth's work spans cancer imaging, scrollytelling narratives, and interactive visualization tools, supported by grants from Trond Mohn Stiftelse. He advises through Team Smit and has contributed to projects like RadEx and ParaGlyder, advancing medical data exploration and communication.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +۳ مورد دیگر

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

Saibal Mukhopadhyayمشاهده پروفایل

استاد

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +۵ مورد دیگر

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

• Machine Learning
• Computer Vision
• Human Behavior Analysis
• +۵ مورد دیگر

Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.

Szymon Płotkaمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Szymon Płotka is a Researcher in the Medical Imaging and Robotics department at the University of Amsterdam's Informatics Institute. His work focuses on advancing prenatal care through deep learning, particularly in fetal ultrasound analysis and AI-driven medical solutions. He holds a PhD in Computer Science from the University of Amsterdam (2024), with a thesis on enhancing prenatal care via machine learning. Research Interests : Integration of deep learning techniques for medical image analysis Development of AI tools for diagnostic accuracy and clinical workflow optimization Multimodal data fusion in healthcare Real-time surgical imaging applications Recent work emphasizes fetal biometry measurements, endoscopic synthetic datasets, and real-time placental vessel segmentation. His research bridges cutting-edge AI with clinical practice, aiming to improve accessibility and efficiency in medical imaging. Key Contributions : Advances in fetal ultrasound video analysis matching human expert accuracy Pioneering synthetic endoscopic dataset generation with diffusion models Development of BabyNet++ for birth weight prediction No formal students listed, but his projects likely involve collaborations with academic teams. Active in organizing and participating in medical imaging challenges (e.g., FeTA, FetReg).

Satwik Patnaikمشاهده پروفایل

استادیار

• VLSI Design
• Hardware Security
• Computer-Aided Design (CAD)
• +۳ مورد دیگر

Satwik Patnaik is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. His research focuses on strengthening hardware security through CAD tools, leveraging machine learning, and exploring emerging technologies. He holds a Ph.D. in Electrical Engineering from New York University (2020) and was a postdoctoral researcher at Texas A&M University (2020–2021). Education: Ph.D., Electrical Engineering, Tandon School of Engineering, New York University (2020) Postdoctoral Research, Texas A&M University (2020–2021) Research Interests: VLSI design, hardware security (e.g., logic locking, side-channel attacks), computer-aided design (CAD), and applications of machine learning in securing global semiconductor supply chains. His work emphasizes 3D integration, emerging devices (e.g., ferroelectric tunnel junctions), and reinforcement learning for adversarial testing. Key Contributions: Developed tools like SecureX, VIGILANT, and Titan. Published over 48 peer-reviewed articles in top venues (e.g., ICCAD, IEEE Transactions). Co-organized global competitions like HeLLO-CTF 2021 and AI vs. Humans 2022. Served as security track chair for ACM CADAthlon (2022) and technical program committee member for ICCAD, ASP-DAC, etc. Awards: ACM/SIGDA Bronze Medal (2018) Best Paper Award at ARC 2017 Third Place at ARC 2021 Expertise: Integrates hardware design, security, and AI to address vulnerabilities in semiconductor supply chains. Active in standardizing secure CAD flows and educating the next generation of hardware security researchers.

Stefano Invernizziمشاهده پروفایل

دانشیار

• Civil and Structural Engineering
• Mechanics of Solids and Structures
• Fracture Mechanics
• +۱۲ مورد دیگر

Stefano Invernizzi is an Associate Professor at the Department of Structural, Geotechnical and Building Engineering (DISEG) of Politecnico di Torino. His expertise spans civil and structural engineering, focusing on fracture mechanics, finite element analysis, and numerical modeling of historical masonry and concrete structures. Teaches Statics and structural analysis courses at the Faculty of Architecture. Supervised numerous PhD and degree theses on masonry, concrete, and computational mechanics. Research emphasizes scale effects on material strength, fractal properties of structural materials, contact mechanics, and seismic vulnerability of historical buildings. He co-developed the sequentially linear saw-tooth softening model for robust analysis of reinforced concrete structures. Collaborated with Prof. Jan Rots on computational mechanics and contributed to projects like TOMORROW (additive manufacturing for building walls) and DURA_LAM (nano-materials for timber durability). His work aligns with SDGs 9 (Industry Innovation) and 11 (Sustainable Cities). He leads the Laboratorio di Meccanica della Frattura and has authored over 30 publications in journals like Engineering Fracture Mechanics and International Journal of Fracture . His research integrates statistical theories of strength with nonlinear numerical simulations for disordered materials.

• Wine Science
• Viticulture
• Food Chemistry
• +۶ مورد دیگر

Dr. Joanna Gambetta serves as a Lecturer in Food Science at the University of Newcastle , affiliated with the School of Environmental and Life Sciences . With academic qualifications from Montpellier SupAgro (MSc in Oenology and Viticulture) and the University of Adelaide (PhD in Wine Science), her research focuses on grape and wine quality assessment , biotic/abiotic stress impacts on aroma composition , and practical solutions for vineyard challenges in Australian contexts. Education : MSc in Oenology and Viticulture, Montpellier SupAgro PhD in Wine Science, University of Adelaide Research Interests span three primary domains: Antifungal microbial volatile organic compounds (VOCs) for biocontrol applications, particularly against Botrytis cinerea and Alternaria alternata through novel analytical approaches Climate change adaptation strategies in viticulture, including modeling vine phenology and mitigating sunburn damage via secondary metabolite analysis Food chemistry innovations in plant-based protein gels and dairy alternatives, integrating fermentation and enzymatic crosslinking Publication Trends reveal a multidisciplinary approach combining analytical chemistry (SPME-GC-MS, LC-MS/MS), chemometrics , and industrial collaboration . Her work bridges agricultural science with food sensory analysis , while recent studies tackle plant-based cheese development using pea protein emulsions.

• Computational Mechanics
• Micromechanics
• Homogenization
• +۳ مورد دیگر

Prof. Dr. Matti Schneider serves as Professor of Engineering Mathematics and Head of the Institute of Engineering Mathematics within the Faculty of Civil Engineering at the University of Duisburg-Essen. His academic leadership spans computational mechanics research and teaching core mathematics courses for civil engineering students. His educational background includes: Diploma in Applied Mathematics with distinction from TU Bergakademie Freiberg (2009) PhD (Dr. rer. nat.) from Leipzig University (2013) on "The Leray-Serre spectral sequence in Morse homology on Hilbert manifolds and in Floer homology on cotangent bundles" Professor Schneider's research focuses on advancing computational methods for solid mechanics through FFT-based homogenization techniques, microstructure modeling, and multi-scale material analysis. His work bridges applied mathematics and engineering to solve complex problems in heterogeneous material systems, with particular emphasis on numerical stability, boundary condition implementation, and efficient solver development for industrial applications. His methodologies enable accurate prediction of material behavior across scales from microscopic structures to macroscopic components. Analysis of his 15 most recent publications reveals dominant trends in FFT-based computational homogenization, with significant contributions to thermal problems, porous media, and fiber-reinforced composites. He pioneers the integration of machine learning (particularly deep material networks) with traditional numerical methods to model complex material behaviors like shear-thinning suspensions and 3D-printed materials. His work consistently addresses computational challenges in boundary condition implementation and convergence for stochastic microstructures. Professor Schneider leads the Institute of Engineering Mathematics and directs research within the ERC-funded BeyondRVE project, which focuses on extending representative volume element concepts for advanced material modeling. His collaborative network includes major German research institutions like Fraunhofer ITWM and international partners in materials science.

Eric R. Fossumمشاهده پروفایل

استاد

• Solid-State Image Sensors
• CMOS Active Pixel Sensors
• Quanta Image Sensors
• +۴ مورد دیگر

Eric R. Fossum is the John H. Krehbiel Sr. Professor for Emerging Technologies at the Thayer School of Engineering at Dartmouth College. He serves as Vice Provost for Entrepreneurship and Technology Transfer and Director of Dartmouth's PhD Innovation Program. As one of the world's leading experts in solid-state image sensors, he invented the CMOS active pixel sensor technology that revolutionized digital imaging in smartphones, medical devices, and automotive systems. His work has earned him numerous accolades, including the National Medal of Technology and Innovation (2025) and the Queen Elizabeth Prize (2017). His research interests focus on: Solid-state image sensors (CCDs, CMOS active pixel sensors, Quanta Image Sensors) Advanced imaging systems and on-chip processing New applications for image sensors in medicine, security, and space Dr. Fossum's recent publications demonstrate significant advancements in: Photon-counting sensors for low-light applications High-speed imaging for microscopy and radiography Backside-illuminated and sub-diffraction-limit pixel designs Quantum random number generation using sensor technology Infrared spectral extension of CMOS sensors His scientific awards include: National Medal of Technology and Innovation (2025) Queen Elizabeth Prize for Engineering (2017) IEEE Andrew S. Grove Award (2009) Induction into National Inventors Hall of Fame (2011) Emmy Award for Technology & Engineering (2021) Doctor of Science, Honoris Causa from Trinity College (2014) As an entrepreneurial leader, Dr. Fossum has: Co-founded Gigajot Technology with former PhD students Previously led Photobit and Siimpel Corporations Active participant in technology transfer initiatives at Dartmouth Founder and Past President of the International Image Sensor Society