جست‌وجوی استاد و پژوهشگر

Anirudh Sivaramanمشاهده پروفایل

استادیار

• Computer Networks
• Network Programmability
• Distributed Systems
• +۳ مورد دیگر

Anirudh Sivaraman is an Assistant Professor of Computer Science at New York University's Courant Institute, specializing in computer networks and programmability. He holds a PhD from MIT (2017) and a BTech from IIT Madras (2010). His research focuses on enabling ubiquitous network programmability through hardware-software systems, including compilers, runtimes, and applications like distributed consensus and financial exchanges. Education: Ph.D. in Computer Science, MIT (2012–2017) S.M. in Computer Science, MIT (2010–2012) B.Tech in CSE, IIT Madras (2006–2010) Research Interests: Network programmability (e.g., P4, Barefoot Networks) High-speed packet processing and compiler optimization Distributed systems and consensus protocols Cybersecurity in programmable networks Cloud computing and microservice autoscaling Notable Articles Trends: Focus on programmable networks (P4, NIC design) Hardware-software co-design for performance Security and isolation in multi-tenant environments Compiler-based solutions for bug detection Awards: ACM SIGCOMM Doctoral Dissertation (2017), SIGCOMM Best Paper (2017), and multiple teaching awards. Advising & Grants: PI on grants totaling $900K+ (Google, Amazon, NSF). Advises 8+ PhD/Master’s students. Serves on committees for SIGCOMM, NSDI, and HotNets. Labs/Teams: Technical advisor at Clockwork. Co-organizes workshops on programmable networks and P4 standards.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

Ming Xiaoمشاهده پروفایل

دانشیار

• Wireless Communications
• Network Coding
• Machine Learning
• +۱۰ مورد دیگر

Ming Xiao is an Associate Professor in the Division of Information Science and Engineering at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS). He is affiliated with the Digital Futures Faculty and leads research in wireless communications, machine learning, and network coding. His work focuses on 6G networks, distributed learning, and secure communications. Affiliations: KTH EECS, Digital Futures, Swedish Research Council, EU Horizon Europe projects Roles: Editor for IEEE Transactions on Wireless Communications, TPC Co-Chair for VTC Fall Research Interests: Dr. Xiao's expertise spans wireless communication systems (e.g., mmWave, NOMA), network coding, machine learning applications in communications, and energy-efficient distributed systems. He has pioneered work on intelligent reconfigurable surfaces (RIS), federated learning in edge computing, and integrated sensing-communications (ISAC). Projects: Ongoing EU-funded projects include ASCENT (autonomous vehicular networks) and COVER (unmanned aerial vehicles for emergency response). Past projects include 6G channel coding and intelligent energy management in smart communities. Grants: Over 12 active grants from VR, EU Horizon Europe, FORMAS, STINT, and VINNOVA Awards: IEEE Vehicular Technologies Society Best Paper Award (2023), World Top 2% Researcher (2020–2023), Highly Cited Researcher in Computer Science/Engineering. Labs/Teams: Leads the KTH Digital Futures initiative, collaborates with RISE Research Institutes, and manages a team of 12 PhD students/postdocs focusing on 6G, distributed ML, and secure IoT.

• Machine Learning
• Medical Device Design
• Statistical Pattern Recognition
• +۶ مورد دیگر

Adjunct Professor Andrew Bradley is affiliated with Queensland University of Technology (QUT), where he serves as the Associate Dean (Research) for the Science and Engineering Faculty. He holds positions in the Faculty of Engineering and School of Electrical Engineering & Robotics at QUT. Bradley has a PhD from the University of Queensland and extensive industry experience, including roles as Chief Technology Officer at Ausonex Pty Ltd and Principal Biomedical Engineer at Maxwell MRI. His expertise spans biomedical and electrical engineering, computing science, and communications technology. His research focuses on computer-aided diagnosis (CAD) systems in medical imaging, machine learning, medical device design, and statistical pattern recognition. He has contributed to projects involving deep brain stimulation, artificial hearts, neonatal hearing screening, and cancer diagnosis imaging. His work bridges academia and industry, emphasizing commercially oriented solutions while maintaining a strong publication record. Bradley has published over 140 peer-reviewed papers and holds 20 patents across multiple regions. His research has garnered over 8,400 citations with an h-index of 35. He has secured $6M+ in competitive grants and supported startups raising $4M in venture capital. His awards include the ARC Future Fellowship and Fellow of Engineers Australia. He has advised 21 PhD and 4 Research Masters graduates, with three current advisory roles. Bradley’s contributions span interdisciplinary collaborations and innovation in labs focusing on medical imaging, AI, and biomedical engineering applications.

Jaime Llorcaمشاهده پروفایل

استاد پژوهشی

• Network Algorithms
• Network Optimization
• Distributed Control
• +۵ مورد دیگر

Jaime Llorca is a Research Professor at the New York University (NYU) Tandon School of Engineering's Electrical and Computer Engineering Department. He holds affiliations with NYU WIRELESS, a research center focused on 6G and beyond. His work bridges network algorithms, optimization, and distributed control with applications in edge computing, cloud networks, and content delivery. Prior roles include Senior Research Scientist at Nokia Bell Labs and positions at Alcatel-Lucent Bell Labs and the University of Maryland's Center for Networking of Infrastructure Sensors. Education: M.S. and Ph.D. in Electrical and Computer Engineering from the University of Maryland, College Park (MD). Research Focus: Llorca pioneers advancements in network-coded caching, distributed cloud control, and edge-cloud integration. Notable contributions include 'cache-aided coded multicasting' and 'cloud-network flow' frameworks. His work addresses latency constraints, resource allocation, and service orchestration in next-generation networks. Key Achievements: Over 100 peer-reviewed publications and 20 patents. Awarded the 2007 IEEE Sensors Best Paper Award, 2016 IEEE ICC Best Paper Award, and the 2015 Jimmy H.C. Lin Award for Innovation. Co-developed foundational technologies for content delivery and distributed cloud networks. Advising & Grants: Advises on distributed computing networks and edge-cloud systems. His research is supported by grants focused on 6G, network optimization, and IoT integration. Collaborates closely with NYU WIRELESS on projects like the Vineyard Digital Twin initiative leveraging UAV imaging. Labs & Teams: Active member of NYU WIRELESS, advancing 6G, mmWave/terahertz networks, and cross-layer network programmability. Leads teams exploring morphable networks and end-to-end service orchestration.

• Computer-aided drug design (CADD)
• Molecular dynamics simulations
• Structural biology
• +۳ مورد دیگر

Associate Professor Jacob D. Durrant leads the Durrant Lab at the University of Pittsburgh’s Department of Biological Sciences, affiliated with the Dietrich School of Arts and Sciences. His research focuses on computational biology, particularly computer-aided drug design (CADD), molecular dynamics simulations, and structural biology. The lab develops novel algorithms and software tools like AutoGrow, NNScore, and MolModa, emphasizing open-source platforms for drug discovery. Key projects include identifying small-molecule ligands for cancer, neurological, and infectious disease targets, as well as advancing molecular visualization through Blender and virtual reality (ProteinVR). Education includes a PhD in Chemistry, though specific details are not provided. Current graduate students include May Ahmed (machine learning in drug discovery), Nacef Guessaymi (amyloid fibril dynamics), and Joshua Mercado (LARP1 structural biology). Undergraduates and postdocs contribute to projects ranging from virtual screening to GPU-accelerated simulations. Research emphasizes ligand identification via ensemble docking, molecular visualization techniques, and the application of AI/machine learning to drug design. Recent work includes studies on SARS-CoV-2 treatments (RECOVERY trial), TEM-1 β-lactamase inhibition, and allosteric drug targets for hexokinase 2. Lab tools like FPocketWeb and PCAViz enable collaborative, browser-based molecular analysis. Advisees and collaborations extend across computational biology programs at Pitt and CMU. The lab prioritizes translational research, bridging in silico simulations with experimental validation for clinical applications. Future directions include AI-driven ligand optimization, VR-enhanced molecular exploration, and open-source software development for global research accessibility.

Wong Kok Sheikمشاهده پروفایل

دانشیار

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +۳ مورد دیگر

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

Ali Adjorluمشاهده پروفایل

استادیار

• Virtual Reality (VR)
• Autism Spectrum Disorder (ASD)
• Educational Technology
• +۴ مورد دیگر

Ali Adjorlu is an Assistant Professor at Aalborg University's Department of Architecture, Design and Media Technology. His research focuses on innovative applications of Virtual Reality (VR) to support marginalized populations, particularly children with Autism Spectrum Disorder (ASD), hearing impairments, and social anxiety. He leads the Multisensory Experience Laboratory and collaborates on interdisciplinary projects such as the GAMESS initiative, which develops game-based learning tools for social skills training in ASD education. His work bridges technology, education, and healthcare, emphasizing accessibility and user-centric design. Key collaborations include: Co-supervision of the Haptic Feedback for Hearing Impaired PhD project (2022–2025) Leadership in the GAMESS project (2022–2024), addressing ASD social skills through gamified VR Partnerships with Cirkus Summarum and Danish National Children’s Choir for inclusive VR applications Research interests include: VR interventions for ASD life skill training (e.g., public transportation, communication) Music therapy in immersive environments Hearing conservation and auditory training for cochlear implant users Accessibility frameworks for neurodiverse and disabled populations His publications highlight VR's potential in education, healthcare, and social inclusion, with notable contributions to conferences like IEEE VR and SMC. He actively engages with public media to disseminate research outcomes, appearing in articles about VR therapy for autism and hearing impairment interventions. Adjorlu’s work also explores: VR-based empathy training for parents of hearing-impaired children Collaborative music interfaces for ALS patients Remote UX evaluation methodologies for assistive technologies His research has led to practical tools like Earnormous (a hearing health VR game) and DecibelDefender (concert safety training), demonstrating real-world impact. He emphasizes iterative design processes involving end-users to ensure interventions are both effective and accessible.

Grzegorz Korcylمشاهده پروفایل

استادیار

• Field Programmable Gate Arrays
• Data Processing on Programmable Logic Devices
• Real-Time Distributed Processing Systems
• +۳ مورد دیگر

Grzegorz Korcyl, PhD, is an Assistant Professor at the Department of Information Technologies, Jagiellonian University in Kraków. He holds a PhD in Applied Computer Science from AGH University of Science and Technology (2015) and an MSc in Applied Computer Science from Jagiellonian University (2010). His research focuses on Field Programmable Gate Arrays (FPGAs), real-time distributed systems, and data acquisition technologies. He co-authored a chapter in the book *Modern Techniques for Computer-Aided Melanoma Diagnosis* (ISBN:978-953-307-571-6), contributing to medical imaging and diagnostic systems. He is involved in academic initiatives such as the Juna Network Academy (Juniper Networks Academic Alliance) and the Students Computer Laboratory, promoting networking education and software development. His work intersects computer engineering with healthcare applications.

Claire Priceمشاهده پروفایل

مدرس ارشد

• Cytochrome P450 Enzymes
• Antifungal Resistance
• Alkanes and Fatty Acid Metabolism
• +۵ مورد دیگر

Claire Price is a Senior Lecturer in Biomedical Sciences at Swansea University, associated with the School of Medicine within the Joint Faculty of Medicine, Health and Life Science. She holds a BSc in Psychology from Cardiff University and a Doctorate from Swansea University School of Medicine, focusing on Candida CYP52-mediated metabolism of alkanes and fatty acids. Her research expertise includes cytochrome P450 enzymes, antifungal resistance mechanisms, and GC-MS analytical techniques. Her work spans over two decades, with recent publications (2025) emphasizing computational tools for biochemical education (enzyme kinetics simulators, 3D-printed labware generators) and clinical diagnostics training. She also investigates environmental toxicology effects of cannabinoids and novel antifungal strategies. Teaching responsibilities include modules like Microbiology Fundamentals (PM-004), Clinical Chemistry Interpretation, and Public Engagement in Science (PM-256). Dr Price is actively involved in STEM outreach, co-founding Merthyr Tudful's inaugural Science Festival and developing inclusive educational tools such as tactile X-ray models. Her lab focuses on microbial enzyme systems with implications for biotechnology and healthcare innovations.

Zongyuan Geمشاهده پروفایل

دانشیار

• Medical Artificial Intelligence
• Computer-aided Diagnosis
• Biomedical Engineering
• +۷ مورد دیگر

Associate Professor Zongyuan Ge is an interdisciplinary researcher in Medical Artificial Intelligence, Computer-aided Diagnosis, and Biomedical Engineering at Monash University, holding tenure in both the Faculty of IT and the Faculty of Engineering. He leads the Monash Medical AI group, supervising over 30+ PhD students and 5 Research Fellows. His research focuses on leveraging AI for medical imaging, genomics, and clinical decision-making, addressing diseases like diabetic retinopathy, cardiovascular conditions, and neurodegenerative disorders. Ge has attracted over $35M in funding from NHMRC, MRFF, and industry partners like NVIDIA and Airdoc, with a h-index of 40 and 10,000+ citations. Awards: Wu Wenjun Award (2019, 2022), NVIDIA AI Fellowship, APRS Early Career Award (2021) Entrepreneurship: Founded Eyetelligence (venture-backed $25M) and serves as Chief Scientist at Airdoc, whose products have served millions globally. Community Engagement: NVIDIA DLI instructor, program committee member for AAAI/IJCAI, and editorial roles in journals like IEEE Transactions on Medical Imaging. Ge’s work bridges academia and industry, contributing to AI-driven healthcare solutions such as eye disease screening systems and the HELIX healthcare data platform. His research emphasizes ethical AI frameworks and interdisciplinary collaboration.

Mark V. Pezzoمشاهده پروفایل

دانشیار

• Judgmental Biases
• Cognitive Psychology
• Social Psychology
• +۷ مورد دیگر

Mark V. Pezzo serves as an Associate Professor at the University of South Florida, conducting research at the intersection of cognitive and social psychology with a specialized focus on judgmental biases in medical contexts. His work examines how motivational factors (self-presentation) and cognitive processes (sensemaking) interact in phenomena including hindsight bias, planning fallacy, and algorithm aversion. Education: Ph.D. in Experimental Psychology (Social/Cognition), Ohio University M.S. in Experimental Psychology (Social/Cognition), Ohio University Dr. Pezzo's research program centers on judgmental error mechanisms across diverse contexts, from rumor propagation to clinical diagnostics. His methodology emphasizes experimental paradigms revealing how cognitive shortcuts lead to systematic misjudgments, particularly in high-stakes medical environments where computerized diagnostic aids are increasingly prevalent. Recent work investigates physician resistance to AI tools and patient preferences for algorithmic versus human decision-makers. Analysis of his 2013-2021 publications reveals a clear evolution toward applied medical psychology, with 80% of recent work (2020-2021) focused on human-AI interaction in healthcare. Key thematic threads include algorithm aversion (three publications), diagnostic aid utilization (two publications), and medical judgment biases (all five publications), demonstrating consistent application of core cognitive principles to contemporary healthcare challenges. Scientific Awards: No awards listed in source material Advising and Grants: The provided documentation contains no references to student advisees, research grants, or funded projects. Teaching responsibilities include 13 courses spanning Advanced Cognitive Psychology, Medical Judgments, and Computer Programming in Psychology.

• Human-Robot Interaction
• Explainable Robotics
• Assistive Robotics
• +۵ مورد دیگر

Dr. Maryam Banitalebi Dehkordi is a Senior Lecturer in Robotics and AI at the University of Hertfordshire, UK. She holds a PhD in Perceptual Robotics from Scuola Superiore Sant'Anna (Italy) and a master's in Mechatronics from University Technology Malaysia. Her career spans academia and industry, with roles at institutions like Technical University of Munich and NavVis GmbH, as well as industry projects such as the Innovate UK-funded AgriRobot. Her research focuses on Human-Robot Interaction (HRI) , Explainable Robotics , Assistive Robotics , and autonomous systems . Key projects include DOC (navigation aid for visually impaired individuals) and AgriRobot (precision agricultural robotics). She has expertise in activity recognition via smartphone sensors, navigation systems, and social robotics interfaces. Publications (2012–2025) emphasize explainable AI in robotics, feature selection for activity recognition, and social behavior interpretation. Her work bridges theoretical robotics with real-world applications in healthcare, agriculture, and accessibility. No awards are explicitly listed, but her extensive collaboration network (Italy, Germany, UK) reflects interdisciplinary impact. She advises on robotics projects and has contributed to both academic journals and industry collaborations.

He Liمشاهده پروفایل

استادیار

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +۳ مورد دیگر

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.

• Physics Education Research
• Teaching Methodologies in STEM
• Conceptual Understanding in Physics
• +۳ مورد دیگر

Claudio Fazio is a Full Professor in the Department of Physics and Chemistry at the University of Palermo. His academic focus lies in physics education research, particularly in innovative teaching methodologies, active learning strategies, and overcoming student misconceptions in physics. He actively engages in designing inquiry-based learning experiences and leveraging technology (e.g., smartphones, AR/VR, simulations) for effective STEM education. Research interests include conceptual understanding in mechanics, thermodynamics, and relativity, with a strong emphasis on pedagogical tools like cluster analysis to evaluate student reasoning patterns. He has pioneered low-cost experimental setups and open-inquiry approaches to enhance learning outcomes, especially in engineering and teacher training programs. Professor Fazio’s work addresses challenges in remote education, pandemic-era pedagogy, and integrating contemporary technologies (e.g., Moodle, MATLAB) into curricula. His contributions span K-12 teacher preparation through university-level educational innovation, emphasizing interdisciplinary collaboration and evidence-based practices.