جست‌وجوی استاد و پژوهشگر

Joshua San Miguelمشاهده پروفایل

دانشیار

• Approximate computing
• Processor microarchitecture
• Intermittent computing
• +۴ مورد دیگر

Associate Professor Joshua San Miguel leads research in computer architecture and systems at the University of Wisconsin-Madison, with an affiliate role in Computer Sciences. His work focuses on energy-efficient computing for IoT devices, microarchitecture innovations, and networks-on-chip. He holds a PhD (2017) and BASc (2012) from the University of Toronto. Education: PhD in Electrical & Computer Engineering, University of Toronto (2017) BASc in Engineering Science (ECE), University of Toronto (2012) Research Interests: Approximate computing for energy harvesting systems Branch prediction and value prediction in processors Cache architectures and networks-on-chip for many-core processors Intermittent computing resilience His recent work emphasizes value-level parallelism (Carat/uSystolic), RTL simulation acceleration (TaroRTL), and personalized neural network inference (CAP’NN). His research has been recognized with the NSF CAREER Award (2021) and multiple IEEE Micro Top Picks. Grants & Advising: Active in supervising advanced independent studies and master’s/dissertation research. Extensive grant funding includes the NSF CAREER Award and the Grainger Faculty Scholarship. Labs & Teams: Leads research groups focused on approximate computing and energy-efficient architectures within the Electrical & Computer Engineering department.

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +۱۲ مورد دیگر

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

• Robotics
• Control Systems
• Rehabilitation Robotics
• +۴ مورد دیگر

Timothy W. Bretl is a Professor and Associate Head of the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign. He holds affiliations with multiple institutions including the Coordinated Science Laboratory and Neuroscience Program. His research focuses on robotics, control systems, rehabilitation robotics, and brain-machine interfaces. Bretl has a PhD in Aeronautics and Astronautics from Stanford University (2005) and dual bachelor's degrees from Swarthmore College (1999). Education : Ph.D., Aeronautics and Astronautics, Stanford University, 2005 M.S., Aeronautics and Astronautics, Stanford University, 2000 B.A., Mathematics, Swarthmore College, 1999 B.S., Engineering, Swarthmore College, 1999 Research Interests : Engineering education, rehabilitation robotics, brain-machine interfaces, optimal control, robotic manipulation, motion planning, and theoretical foundations of robotics. His work spans applications in prosthetics, neural systems, and autonomous systems. Awards & Honors : NSF CAREER Award (2010) Best Manipulation Paper (ICRA 2012) Campus Award for Excellence in Undergraduate Teaching (2018) Rose Education Innovation Fellow (2017) Teaching & Grants : Bretl has been recognized for teaching excellence, including multiple awards for innovation and instruction. His research has been supported by grants focusing on robotics, control systems, and medical applications. Labs & Teams : Affiliated with the Coordinated Science Laboratory and Beckman Institute, leading projects in robotics and automation. His group collaborates across disciplines, including neuroscience and biomedical engineering.

Laura Barnesمشاهده پروفایل

استاد

• Wireless Health
• Human Machine Interface
• Biomedical Data Sciences
• +۳ مورد دیگر

Laura Barnes is a Professor in the Department of Systems and Information Engineering at the University of Virginia, and Associate Director of the Link Lab. She directs the Sensing Systems for Health Lab, focusing on technology-enabled health solutions. Her academic background includes a B.S. in Computer Science from Texas Tech University (2003), and M.S. (2007) and Ph.D. (2008) in Computer Science from the University of South Florida. Research interests span wireless health , human-machine interfaces , biomedical data sciences , and machine learning applications . Current projects include wearable sensor systems for mental health monitoring, clinician-patient communication frameworks (CommSense), and mobile interventions for anxiety reduction. Funding sources include NIH, National Institute of Aerospace, and the U.S. Army. Recent publications emphasize real-time monitoring , digital phenotyping , and context-aware interventions . Key themes include leveraging large language models for health analysis, developing scalable mobile sensing frameworks, and addressing health equity through technology. Labs/Teams : Sensing Systems for Health Lab, Link Lab Grants : NIH-funded projects on medication adherence and sleep disturbance analysis

Karim Oweissمشاهده پروفایل

استاد

• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• +۵ مورد دیگر

Karim Oweiss is a Pre-eminent Professor at the University of Florida, with joint appointments in the Department of Biomedical Engineering (Herbert Wertheim College of Engineering), Electrical and Computer Engineering, and Neuroscience (McKnight Brain Institute). He holds a Ph.D. in Electrical Engineering and Computer Science from the University of Michigan (2002). His research focuses on neural mechanisms of sensorimotor integration and the development of clinically viable brain-machine interfaces (BMIs) to restore damaged neurological function. His work spans computational neuroscience, neural decoding, optogenetics, and advanced neurotechnology, with a strong emphasis on closed-loop systems and neural plasticity. 2025 : Chemogenetic stimulation of phrenic motor output and diaphragm activity 2024 : Chemogenetic phrenic motoneuron activation enables increased tidal volume 2023 : Compressive sensing of functional connectivity maps from patterned optogenetic stimulation Oweiss has received the NSF Excellence in Neural Engineering Award (2001) and is a Senior Member of the IEEE. He has published extensively on topics including neural decoding, compressive sensing, and multiscale neural interfacing. As editor of Statistical Signal Processing for Neuroscience and Neurotechnology (2010), he has contributed significantly to the field's methodological foundations. His lab develops tools like NeuroQuest for large-scale neural data analysis and implantable neuroprocessors for wireless BMI applications.

Zahraa Abdallahمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Data Science
• Time Series Analysis
• +۶ مورد دیگر

Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)

Sanjoy Nandiمشاهده پروفایل

پژوهشگر ارشد

• Neuromorphic Computing
• Memristor Technology
• Electronic Materials Science
• +۳ مورد دیگر

Sanjoy Nandi is a Postdoctoral Fellow in the Department of Electronic Materials Engineering at the Australian National University (ANU), specializing in neuromorphic computing and memristor technology. He holds a PhD in Physics from the University of Chittagong (2017). His research focuses on designing novel memristive devices for neuromorphic systems, with particular emphasis on threshold switching, material properties of NbOx and V₃O₅ films, and integrating memristors with CMOS circuits. Nandi has published over 47 peer-reviewed articles, with an h-index of 14 and 161 citations. His work spans memristor-based spiking neural networks, thermosensitive dynamics, and optical tunable oscillators, contributing to advancements in neuromorphic hardware and solid-state electronics. Education: PhD in Physics (University of Chittagong, 2017). Research supervision is registered through ANU's Department of Electronic Materials Engineering. Research interests include memristor engineering, neuromorphic architectures, and nanoelectronics. Key projects involve exploring negative differential resistance in vanadium oxides and developing ternary logic systems using memristor-CMOS hybrids. His contributions also address thermal effects in cross-point devices and material interdiffusion impacts on switching characteristics. Labs/Teams: Collaborates within ANU's Electronic Materials Engineering research groups, focusing on interdisciplinary projects at the intersection of materials science and circuit design. Active in global collaborations through co-authored publications with institutions in Spain, Japan, and the UK.

• Advanced Machining
• Engineering Design and Optimization
• Bio-Engineering
• +۶ مورد دیگر

Robert Heinemann is a Senior Lecturer in the Department of Mechanical and Aerospace Engineering at the University of Manchester, affiliated with the School of MACE. His work focuses on advanced machining, tool condition monitoring, and sustainable manufacturing processes. He holds a PhD from the University of Manchester Institute of Science and Technology (2004) and has extensive research experience in drilling technology, carbon-based coatings, and environmental benign machining. Education: Diplom Ingenieur (Dipl.-Ing. FH) in Mechanical Engineering, University of Paderborn, Germany (1999) MSc in Electronic Engineering and Engineering Management, University of Paderborn/Bolton University (2001) PhD in Mechanical Engineering, University of Manchester Institute of Science and Technology (2004) Research interests include: Drilling and reaming technology for minimally invasive surgery Development of diamond-like carbon coatings for cutting tools Process and tool optimization for aerospace and biomedical applications Environmental sustainability in manufacturing design His research outputs emphasize adaptive drilling strategies, deep learning applications in process monitoring, and sustainable manufacturing practices aligned with UN SDGs. He leads the Laser Processing Research Centre (LPRC), focusing on laser-based machining innovations. Scientific achievements include a Leverhulme Trust Early Career Fellowship (2010) and contributions to over 40 peer-reviewed articles. He advises 9 postgraduate research students and collaborates on multi-disciplinary projects addressing industrial challenges in composites and precision engineering.

Rafeef Garbiمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۷ مورد دیگر

Dr. Rafeef Garbi is a Professor at the Department of Electrical and Computer Engineering, University of British Columbia, and the Founder/Director of the Biomedical Signal and Image Computing Laboratory (BiSICL). Her multidisciplinary research integrates artificial intelligence, computer vision, and medical imaging for clinical applications in pediatric orthopedics, oncology, and neurology. PhD (Chalmers University, Sweden), MSc (with distinction), Technical Licentiate Research Focus: Specializing in Medical Image Computing and Visual Computing , her lab develops AI-driven solutions for: Automated segmentation and analysis of multi-dimensional biomedical data Clinically-translatable biomarkers for disease assessment Computer-aided intervention systems in surgical contexts Scientific Leadership: UBC Killam Faculty Research Fellow Peter Wall Institute for Advanced Studies Early Career Scholar Senior IEEE Member & Founding IEEE EMBS Vancouver Section Member Key Collaborations: Active in the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society and CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. Her team bridges engineering, medicine, and computational biology through translational research.

Anand Krishnanمشاهده پروفایل

استادیار

• Nerve-tumor interface
• Nerve regeneration
• Cancer biology
• +۵ مورد دیگر

Dr. Anand Krishnan serves as Assistant Professor in the Department of Anatomy, Physiology and Pharmacology at the University of Saskatchewan's College of Medicine, where he leads research on nerve-tumor interfaces and neural macrophage biology. His work bridges neuroscience and oncology to identify therapeutic targets for cancer and neurodegenerative disorders. Education: Postdoctoral Fellow, Division of Neurology, University of Alberta, Canada Postdoctoral Fellow, Clinical Neurosciences, University of Calgary, Canada PhD in Biotechnology (Cancer Biology), Rajiv Gandhi Centre for Biotechnology, India Master of Pharmacy (Pharmacology), Manipal University, India Pharma R&D Experience: Orchid Chemicals and Pharmaceuticals Limited; Alembic Pharmaceuticals Limited Dr. Krishnan's research program investigates two core areas: molecular mapping of tumor-nerve interfaces to uncover novel therapeutic targets, and defining the roles of self-renewing resident macrophages in nervous system homeostasis and disease. His lab employs proteomics, murine models, and in vitro neuronal cultures to explore how tumors and nerves mutually support growth through neural invasion and autonomic signaling. Analysis of his 2020-2024 publications reveals consistent focus on neuro-oncology mechanisms, particularly peripheral nerve regeneration pathways (Myc interactome, growth-primed DRG), tumor-nerve crosstalk (sympathetic signaling in prostate cancer), and neurodegenerative targets (MANF). Key methodologies include comparative proteomics, molecular mapping, and drug repurposing screens. Research Funding: Natural Sciences and Engineering Research Council (NSERC) Cancer Research Society (CRS) CIHR Institute of Cancer Research Breast Cancer Society of Canada (BCSC) Prostate Cancer Fight Foundation (PCFF) & Ride for Dad Saskatchewan Health Research Foundation (SHRF) College of Medicine Research Award (CoMRAD) CoMBRIDGE Fund Start-up Research Fund Based at Saskatoon City Hospital within the Cameco MS Neuroscience Research Centre, the Krishnan Lab trains students supported by NSERC USRA, Dean's project fellowships, OVDR Biomedical research fellowships, and Mitacs awards. The lab's strategic location adjacent to the Meewasin Valley provides collaborative opportunities for translational neuroscience research focused on nerve regeneration and cancer therapeutics.

Patrick Kluthمشاهده پروفایل

استاد

• Ion Beam Modification of Materials
• Solid-State Nanopores
• Synchrotron Radiation Techniques
• +۴ مورد دیگر

Patrick Kluth is a Professor at the Research School of Physics, Australian National University, leading a research group focused on swift heavy ion-modified materials and nanopore technology. His work bridges materials science, physics, and biomedical applications. Education : Dipl. Phys. from Düsseldorf, Germany; PhD in Physics from RWTH Aachen, Germany (2002, summa cum laude). Research Interests center on: Ion track technology for solid-state nanopore fabrication Advanced materials characterization (SAXS, X-ray absorption spectroscopy) Defect engineering in semiconductors and superconductors Nano-fabrication and semiconductor processing methods Bio-sensor development and ion separation technologies Recent Research Trends show a focus on: Developing affordable microcontroller-assisted nanopore fabrication platforms Enhancing flux pinning in superconductors via ion irradiation Engineering nanomaterials for space applications (carbon-fibre composites) Exploring radiation effects on perovskite solar cells and graphene-enhanced composites Combining machine learning with nanopore sensing for biomarker detection Scientific Awards : Feodor-Lynen Fellowship Borcherts-Medal for PhD excellence Three ARC Fellowships (Postdoctoral, Research, Future) Leadership Roles : Head of Department (2018-2020), Associate Director HDR (2020-2023). His projects include collaborations on Alzheimer's detection sensors and carbon-fibre additive manufacturing for space applications.

• Multimodal User Interfaces
• Affective Computing
• Social Robotics
• +۵ مورد دیگر

Prof. Elisabeth André is a Full Professor of Computer Science at Augsburg University , leading the Chair for Human-Centered Artificial Intelligence. She holds academic roles including Managing Director of the Institute of Computer Science (2004–2006) and serves on numerous national/international committees such as the Bavarian Artificial Intelligence Council and DFG panels. Her research focuses on multimodal interfaces , affective computing , and social robotics , with emphasis on human-centered design principles and ethical AI. Education: 1988: Diploma in Computer Science (Saarland University) 1995: Dr. rer. Nat. (Saarland University) Research Interests: Multimodal analysis (physiological/gaze/speech/gesture), tangible interfaces, technology-enhanced learning, and AI ethics. She has pioneered projects like CALLAS (affective multimedia systems) and CUBE-G (cultural adaptation for agents). Awards: 2021: Gottfried Wilhelm Leibniz Prize (Germany’s highest research honor) 2013: ECCAI Fellow 2010: Member of Leopoldina and Academia Europaea Grants & Leadership: Led EU projects (e.g., METABO, E-Circus), DFG initiatives, and co-chaired major conferences (ICMI, AAMAS). Supervised over 20+ PhD students and 11 ongoing doctoral candidates. Labs/Teams: Leads the Human-Centered AI team at Augsburg, collaborating on projects like FORSocialRobots and TherapAI. Active in interdisciplinary efforts for ethical AI frameworks and educational robotics.

• Environmental Chemistry
• Mass Spectrometry
• Non-Targeted Analysis
• +۵ مورد دیگر

Dr. Saer Samanipour is a Visiting Professor at the Van 't Hoff Institute for Molecular Sciences, part of the Faculty of Science at the University of Amsterdam. His research focuses on advanced analytical techniques for environmental and biomedical applications, with a strong emphasis on non-targeted analysis, mass spectrometry, and machine learning integration. He leads efforts in developing open-source tools like GcDUO and jHRMSToolBox to enhance data interpretation in complex chemical datasets. Key areas include environmental contaminant detection, chemical exposure assessment via wastewater-based epidemiology, and proteomic analysis of snake venoms. His work bridges computational methods with experimental chemistry to address global challenges in environmental health and toxicology. Primary affiliation: Van 't Hoff Institute for Molecular Sciences Research themes: Non-targeted LC-HRMS workflows, machine learning applications in analytical chemistry, PFAS analysis, and exposome research Software contributions: GcDUO (GC×GC-MS), jHRMSToolBox (HRMS data processing) His publications highlight innovations in data-driven approaches for compound prioritization, toxicity prediction, and method optimization. Recent work explores chemical space exploration and chemometric strategies for complex mixture analysis, with applications to environmental monitoring and forensic science.

• Deep Learning
• Medical Image Analysis
• Biomedical Engineering
• +۲ مورد دیگر

Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.

Albert Treytlمشاهده پروفایل

پژوهشگر

• Communication Technologies
• Security
• Distributed Systems
• +۴ مورد دیگر

Albert Treytl is a Senior Researcher at the Danube University Krems , serving as Head of the Center for Distributed Systems and Sensor Networks and Deputy Head of the Department for Integrated Sensor Systems. He holds a Master’s degree in Electrical Engineering from the Vienna University of Technology (2001) and has over two decades of experience in communication technologies and security. Current roles: Head of Center, Deputy Head of Department Research focus: Security for embedded systems, IoT, digital twins, AI in energy optimization Involvement: IEEE, CEN TC 247 WG4, IEEE1588 standardization His work addresses distributed data management, smart grid security, and model predictive control strategies for energy efficiency in buildings and traffic systems. He leads multiple national and international projects funded by FFG and EU programs. Recent research projects include KI4HVACS (AI-driven HVAC optimization), Factories4Renewables (industrial renewable energy integration), and Dataskop (sensor-based data economy). He has authored over 100 peer-reviewed publications and serves as co-lecturer at the Vienna University of Technology.