جست‌وجوی استاد و پژوهشگر

Aritra Duttaمشاهده پروفایل

استادیار

• Machine Learning
• Federated Learning
• Optimization
• +۷ مورد دیگر

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

• Artificial Intelligence and Data Science
• Data Management & Mining
• Machine Learning & Intelligence
• +۳ مورد دیگر

ZHENG Baihua serves as Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS), concurrently holding leadership roles as Associate Dean for SCIS Post-Graduate Research Programmes and Director of the Master of Science in Computing programme. Currently on leave but maintaining full-time faculty status, his academic career spans over two decades with foundational training from Hong Kong University of Science and Technology. Professor Zheng's research program integrates artificial intelligence, data science, and urban computing to solve critical challenges in mobility and sustainability. His expertise centers on trajectory data management, social network analysis, and spatio-temporal modeling, with significant contributions to trajectory compression algorithms, influence minimization in social networks, and real-time traffic prediction systems. His work bridges theoretical database innovations with practical applications in smart city infrastructure and public health interventions. Analysis of recent publications (2024-2025) reveals a dominant focus on physics-informed trajectory processing, GPU-accelerated indexing for high-dimensional data, and transformer-based models for urban mobility prediction. Key trends include the fusion of graph neural networks with spatio-temporal dynamics, novel approaches to contact tracing through timeline graphs, and differentiable search techniques for structured data discovery. These works consistently target real-world deployment in transportation systems and epidemic control. No scientific awards are documented in available institutional records. Information regarding student supervision, research grants, laboratory facilities, or collaborative teams remains unspecified in current public profiles.

George Slotaمشاهده پروفایل

دانشیار

• Graph Mining
• Network Analysis
• Big Data Analytics
• +۵ مورد دیگر

George Slota serves as an Associate Professor in the Computer Science Department within the School of Science at Rensselaer Polytechnic Institute (RPI). Previously, he held positions as a Computer Scientist at Sandia National Labs New Mexico and as a Computer Engineer at the Naval Undersea Warfare Center. His research spans graph and network mining, big data analytics, machine learning, bioinformatics , and their intersections with parallel computing, scientific computing, and high performance computing . His work focuses on scalable algorithms for trillion-edge graphs, graph partitioning, community detection, and GPU-accelerated graph processing. His recent publications reveal strong emphasis on graph coarsening and spectral methods distributed graph algorithms for biconnectivity and coloring random graph generation and validation GPU acceleration for graph neural networks His research has been recognized with a prestigious NSF CAREER award supporting his project "Scalable Graph Processing to a Quadrillion Edges and Beyond." Award: National Science Foundation Faculty Early Career Development (CAREER) Award His work demonstrates consistent funding and collaboration with major national laboratories including Sandia National Labs and involvement in ExaGraph projects targeting exascale computing challenges. He maintains an active research group focused on extreme-scale graph analytics.

Hany Elgalaمشاهده پروفایل

استادیار

• Telecommunications
• Digital Signal Processing
• Embedded Systems
• +۶ مورد دیگر

Dr. Hany Elgala is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York, and Director of the Signals and Networks (SINE) Lab. Previously, he served as a Research Professor at Boston University, co-leading the Multimedia Communications Lab and acting as Communications Testbed leader at the NSF Smart Lighting ERC. His research focuses on telecommunications, digital signal processing, and embedded systems, with specialization in visible light communications (VLC), LiFi networks, IoT security, and AI-driven wireless solutions. He has coordinated industrial projects with Airbus and EADS on optical wireless networks in aircraft cabins. Elgala's work emphasizes heterogeneous wireless networks, backscatter communication, and security in IoT. He has authored/co-authored ~50 publications/patents, contributing to fields like spectral efficiency, energy optimization, and network coexistence. His leadership in the SINE Lab drives innovations in 6G networks, UAV-based systems, and hybrid LiFi-WiFi architectures. His research portfolio includes breakthroughs in VLC system design, machine learning applications for physical layer optimization, and jamming-resistant IoT networks. Recent efforts focus on indoor flying networks and low-complexity VLC frameworks leveraging multi-task learning and autoencoder-based modulation.

Kevin Englehartمشاهده پروفایل

استاد

• Biomedical Engineering
• Prosthetics and Orthotics
• Myoelectric Control Systems
• +۴ مورد دیگر

Kevin Englehart is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick and currently serves as the Associate Dean of Graduate Studies. He holds a PhD and is a Professional Engineer (PEng). His primary affiliation is with the Institute of Biomedical Engineering, where he conducts groundbreaking research on advanced prosthetic control systems. Englehart's work focuses on improving the usability and adaptability of myoelectric prostheses through machine learning, signal processing, and biomechanical modeling. He previously served as Director of the Institute of Biomedical Engineering, demonstrating leadership in fostering interdisciplinary research. His research interests include electromyography (EMG) signal analysis, human-machine interfaces, and the application of artificial intelligence in healthcare technologies. Englehart's publications span over two decades, with a strong emphasis on real-time control systems, noise reduction techniques, and user-centric design principles for assistive devices. His academic contributions extend to the development of novel training paradigms for prosthetic users and the mitigation of technical challenges such as electrode shift and signal instability. Englehart collaborates extensively with clinical and engineering teams to translate theoretical advancements into practical solutions for individuals with physical disabilities. His office is located at R.N. Scott Hall 219 in Fredericton, New Brunswick.

Chad Dutyمشاهده پروفایل

استاد

• Additive Manufacturing
• Polymer Composites
• Material Extrusion
• +۷ مورد دیگر

Chad Duty is a Professor in the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee (2021-present), previously serving as Associate Professor (2015-2021). His research focuses on Additive Manufacturing (AM), particularly polymer and composite structures, with emphasis on Big Area Additive Manufacturing (BAAM). He leads initiatives in large-scale AM tooling, material development, and process optimization. Duty has held roles at Oak Ridge National Laboratory (ORNL), including Group Leader of Deposition Science & Technology (2013-2015) and Senior Research Scientist at the Manufacturing Demonstration Facility (2011-2015). He earned a PhD from Georgia Tech (2001) and BSME from Virginia Tech (1997). Research interests include anisotropic mechanical behavior, melt flow characterization, and recycling of composite materials. Notable contributions include the development of BAAM for multi-meter structures and novel coating materials. His work bridges academia and industry, with applications in aerospace, automotive, and renewable energy sectors. Awards include the ORNL Director's Award (2014), R&D 100 Awards (2009, 2011), and the Eugene P. Wigner Fellowship (2004). His lab focuses on advancing AM technologies through material innovation and process scalability, collaborating with institutions like ORNL and industry partners.

Paul Savaمشاهده پروفایل

استاد

• Seismic Imaging
• Planetary Geophysics
• Wavefield Tomography
• +۵ مورد دیگر

Paul Sava is a Professor and C.H. Green Chair of Exploration Geophysics at the Colorado School of Mines, serving as Department Head of Geophysics since 2019. He holds a PhD in Geophysics from Stanford University (2005), an MSc from Stanford (1998), and an Engineer Geophysicist degree from the University of Bucharest (1995). His research focuses on seismic and radar wavefield imaging, planetary geophysics, and infrastructure monitoring. Notable contributions include advancements in wave-equation migration and planetary radar tomography. Education: PhD in Geophysics, Stanford University, 2005 MSc in Geophysics, Stanford University, 1998 Engineer Geophysicist, University of Bucharest, 1995 Research interests emphasize wavefield-based methods for subsurface imaging, planetary resource exploration, and autonomous data acquisition. Recent work explores seismic imaging of asteroids and Mars, leveraging orbital radar systems. His publications span seismic inversion, microseismic monitoring, and elastic wavefield tomography. Awards include the Reginald Fessenden Award (2007), T.K. Young Leadership Award (2020), and multiple Top 30 presentations at SEG Annual Meetings. He leads the Center for Wave Phenomena and actively collaborates with NASA and industry consortia on grants totaling over $6 million. Advising and grants involve mentoring over 30 students and postdocs, with grants focused on planetary radar imaging, seismic FWI, and Mars subsurface analysis. Key projects include the Planetary Orbital Radar Processing and Simulation System (PORPASS) and autonomous seismic exploration systems. Labs/Teams: Directly leads the Center for Wave Phenomena and the Seismic Imaging Team at CWP, advancing open-source tools like Madagascar for reproducible geophysical research.

Laura Balzanoمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Signal Processing
• +۷ مورد دیگر

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Alex K. Jonesمشاهده پروفایل

استاد

• Quantum Computing
• Computer Architecture
• Nanoscale Magnetic Memory Systems
• +۵ مورد دیگر

Dr. Alex K. Jones is a Professor in the Department of Physics & Astronomy at the University of Pittsburgh, with a secondary appointment. His research focuses on quantum computing architectures, sustainable computing, and emerging memory technologies. He leads projects funded by NSF, DARPA, NSA, ARO, and industry, emphasizing energy-efficient computing systems and fault-tolerant designs. Dr. Jones' work spans quantum system co-design (e.g., superconducting qubit architectures), nanoscale magnetic memories (racetrack/STT-MRAM), and carbon lifecycle modeling for computing systems. He has pioneered concepts like GreenChip sustainability tools and CORUSCANT processing-in-memory frameworks. His IEEE Fellowship and Best Paper Award (IGSC 2018) highlight his impactful contributions. Key Research Areas: Quantum Computing, Non-Volatile Memories, Sustainable Computing, Fault Tolerance Funding Agencies: NSF, DARPA, NSA, ARO, Industry Collaborations Major Projects: Quantum circuit decomposition, racetrack memory applications, carbon modeling for datacenters His 200+ publications include seminal works on fault tolerance in domain-wall memories, energy-efficient architectures, and cross-disciplinary innovations at the intersection of physics and computer engineering. Current efforts explore carbon depreciation models and real-time accelerator customization for edge computing.

Blesson Vargheseمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Dr. Blesson Varghese is an Associate Professor and Reader in the School of Computer Science at the University of St Andrews, leading the Edge Computing Hub funded by Rakuten, Japan. He holds an honorary faculty position at Queen's University Belfast. His research focuses on distributed systems, edge computing, and federated learning, addressing challenges in scalability, resource efficiency, and real-time applications. He has received prestigious awards, including the 2021 IEEE Rising Star Award and a Royal Society Industry Fellowship. Teaching includes CS5052: Data-Intensive Systems, emphasizing large-scale distributed systems design. He advises PhD students in edge computing and collaborative machine learning. Notable publications include DNNShifter (2024), EcoFed (2024), and NeuroFlux (2024), advancing edge computing and federated learning techniques. Awards include the IEEE Rising Star Award, Best Paper Award (IEEE Edge Conference 2022), and Finalist at GSMA Mobile World Congress 2019. His work bridges academia and industry, with collaborations across continents and contributions to open-source projects like FedAdapt and DNNShifter.

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.

Masoumeh Ebrahimiمشاهده پروفایل

دانشیار

• Computer Architecture
• Machine Learning
• Embedded Systems
• +۶ مورد دیگر

Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.

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.

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +۴ مورد دیگر

Andreas Kaltenbrunner is a Part-Time Lecturer at the Universitat Pompeu Fabra (UPF) in Barcelona, specializing in Data Driven Social Analytics and Information Retrieval. He serves as Director of Research on AI and Data Science at IN3 - UOC R&I and is an Affiliate Researcher at ISI Turin, Italy. His research focuses on computational social science, user behavior analysis, and applications of AI/ML in social media and network dynamics. Key interests include large language models (LLMs), personality assessment simulations, and digital epidemiology. His work bridges theoretical frameworks with practical applications, such as predicting urban mobility patterns and evaluating AI systems’ performance in political contexts. He collaborates with institutions like UNICEF and the ISI Foundation, contributing to projects on data-driven decision-making and social impact. His interdisciplinary approach integrates network science, computational methods, and socio-cultural analysis. Recent publications highlight innovations in LLM validation, cross-lingual Wikipedia reliability modeling, and climate-related forecasting. While no explicit awards are listed, his prolific output reflects sustained contributions to computational social science and AI ethics.

James Caverleeمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Information Retrieval
• +۷ مورد دیگر

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.