جست‌وجوی استاد و پژوهشگر

• Information Retrieval
• Data Classification
• Anomaly Detection
• +۴ مورد دیگر

Sri Devi Ravana is a Professor in the Department of Information Systems at the University of Malaya's Faculty of Computer Science and Information Technology in Kuala Lumpur, Malaysia. She holds a PhD from the University of Melbourne (2011) where her doctoral research focused on experimental evaluation of information retrieval systems. Her research spans several key domains: Information Retrieval : Specializing in evaluation methodologies, relevance judgment, and system performance metrics Machine Learning Applications : Including data classification, anomaly detection, and sentiment analysis Network Systems : Particularly IoT networks and vehicular communication systems Applied Computing : Addressing real-world challenges in healthcare, agriculture, and social media Her recent publications demonstrate a consistent focus on enhancing machine learning algorithms for classification tasks, improving evaluation methodologies for information systems, and developing practical applications for IoT and network technologies. She maintains active collaborations across academia and has supervised numerous graduate students in computer science research.

• Machine Learning
• Biomedical Engineering
• Artificial Intelligence
• +۳ مورد دیگر

Yrd. Doç. Dr. Ghazaal Sheikhi is an Assistant Professor in the Department of Artificial Intelligence Engineering at Final University, within the Faculty of Engineering. Her research focuses on machine learning applications in healthcare, biomedical engineering, and natural language processing. She holds a Ph.D. in Computer Engineering from Eastern Mediterranean University (2020), an M.Sc. in Biomedical Engineering from Amirkabir University of Technology (2007), and a B.Sc. in Biomedical Engineering from the University of Isfahan (2003). Education: Doctoral Degree in Computer Engineering, Eastern Mediterranean University, North Cyprus (2020) Master's Degree in Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (2007) Bachelor's Degree in Biomedical Engineering, University of Isfahan, Isfahan, Iran (2003) Her research interests span Machine Learning, Biomedical Engineering, and Natural Language Processing. She specializes in applying deep learning techniques to medical image analysis, developing explainable AI models for healthcare diagnostics, and enhancing fact-checking systems using NLP. Her work in biomedical data analysis focuses on feature selection methods and predictive modeling for diseases like diabetes. Additionally, she explores speech processing techniques for language-specific challenges, such as Farsi syllable segmentation using signal processing and fuzzy logic approaches. Recent contributions include breast tumor segmentation (2025), NLP-based claim detection (2023), and novel feature selection methods (2021). Earlier work addressed speech signal analysis (2011–2013) and diabetes cost analysis (2016). Scientific Awards: No awards explicitly mentioned in the provided text. Advising & Grants: Information on students or grants is not provided in the text. Administrative tasks are listed but no details are given. Labs/Teams: No specific lab affiliations or teams mentioned.

K. Selçuk Candanمشاهده پروفایل

استاد

• Data Science
• Database Systems
• Machine Learning
• +۵ مورد دیگر

K. Selçuk Candan is a Professor of Computer Science and Engineering at Arizona State University (ASU) and the Director of the Center for Assured and Scalable Data Engineering (CASCADE). He holds affiliations with the Global Institute of Sustainability and the Center for Cybersecurity and Digital Forensics. His academic roles include interim co-director of the School of Computing and Augmented Intelligence (2022) and coordinator of the Big Data Systems concentration. He earned his Ph.D. from the University of Maryland and his B.S. from Bilkent University. Education: Ph.D. Computer Science (University of Maryland, 1997), B.S. Computer Engineering (Bilkent University, 1993) Affiliations: CASCADE Director, Global Futures Scientist, NSF APPEX Center His research focuses on data management, analysis of heterogeneous data, and applications in healthcare, energy, and cybersecurity. He has authored over 250 peer-reviewed publications and secured $34M in grants. Key projects include pandemic modeling, building energy optimization, and cybersecurity for smart infrastructure. Recipient of the 2023 Daniel Jankowski Legacy Award and ACM SIGMOD Contributions Award, he emphasizes interdisciplinary collaboration and mentorship. He has advised 16 doctoral students and 35+ graduate students, contributing to teaching excellence awards. His work bridges academia and industry, with grants from NSF, DOE, and DoD. Current efforts include causal learning frameworks, spatio-temporal modeling, and cyber-infrastructure for pandemic response.

K. Selcuk Candanمشاهده پروفایل

استاد

• Data Management
• Multimedia Databases
• Distributed Systems
• +۵ مورد دیگر

K. Selcuk Candan is a Professor of Computer Science and Engineering at Arizona State University (ASU) and Director of the Center for Assured and Scalable Data Engineering (CASCADE). He holds affiliations with multiple institutes, including the Global Futures Scientists and Scholars and the Center for Cybersecurity and Trusted Foundations. Candan has been at ASU since 1997, following his PhD from the University of Maryland. His research focuses on managing and analyzing non-traditional data types like multimedia, web, and scientific data. Key interests include scalable data processing, sensor data integration, and accessibility technologies for visually impaired individuals. He has led numerous grants from NSF, DoD, and others, resulting in over 250 peer-reviewed publications and 9 patents. Notable contributions include the DataStorm framework for coupled simulations and the OASIS system for accessible digital content. Candan has served as program chair for top conferences like SIGMOD and MM, and is an ACM Distinguished Scientist. His awards include the SIGMOD Contributions Award and Dan Jankowski Legacy Award. He has advised numerous students and led interdisciplinary projects in areas like pandemic modeling (APPEX Center) and building automation security. His work bridges theory and application, emphasizing real-world impact in healthcare, urban systems, and educational accessibility.

Ladan Tahvildariمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +۵ مورد دیگر

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.

Mahdi Soltanolkotabiمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

• Statistical Machine Learning
• Exploratory Data Analysis
• Data Science
• +۴ مورد دیگر

Jaakko Peltonen is a Professor of Statistics and Data Analysis at Tampere University, Faculty of Information Technology and Communication Sciences, Department of Computing Sciences. He leads the Statistical Machine Learning and Exploratory Data Analysis (SMiLE) research group and is affiliated with the Tampere Research Center in Information and Systems and the Academy of Finland Center of Excellence in Game Culture Studies. His research focuses on statistical machine learning, exploratory data analysis, and their applications in diverse domains including healthcare, social media analytics, gaming, and political discourse analysis. Notable projects include developing algorithms for transparent AI systems (e.g., TraQuLA, Flow of Trust framework) and analyzing online communities through platforms like Twitch and Steam. Recent work emphasizes ethical AI integration in education and healthcare, predictive modeling for emergency department management, and game studies involving player behavior analysis. Peltonen collaborates across disciplines, leveraging text mining, graph theory, and visualization techniques to address complex societal challenges. Key contributions span methodological advancements (e.g., constrained non-negative matrix factorization) and interdisciplinary applications in policy analysis, digital humanities, and health informatics. His work bridges technical innovation with real-world societal impact through collaborative research initiatives.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Petri Myllymäki is a Director at Helsinki Institute for Information Technology (HIIT) , Computer Science Adjunct Professors , and Finnish Center for Artificial Intelligence (FCAI) within Aalto University's School of Science, Department of Computer Science . His research focuses on Bayesian networks, information discovery, and interactive intent modeling . Active in ACM Transactions on Information Systems and SIGIR Conference publications Recipient of 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up and 2017 Kone Foundation Rajapinta award Collaborated with institutions like Stockholm University and researchers including Giulio Jacucci and Samuel Kaski His 15 most recent publications (2025-2015) span decision-making under conflicting objectives, machine learning applications in social science, Bayesian network structure learning algorithms , and cognitive modeling for exploratory search . Key technical subfields include hashing techniques, two-dimensional representation, computational efficiency, and user experience optimization . Scientific awards : 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up 2017 Kone Foundation Rajapinta award As an information discovery expert , he has contributed to open-access research through Interactive Visualization and Scinet frameworks, impacting Sustainable Development Goals related to digital innovation and knowledge dissemination.

• Distributed Machine Learning
• Edge Computing
• Federated Learning
• +۲ مورد دیگر

Christos Anagnostopoulos is Reader (Associate Professor) in Distributed Computing & Data Engineering Systems at the University of Glasgow, where he directs the MSc Information Technology and MSc Software Development programmes. His research focuses on distributed computing, federated learning, and edge intelligence for large-scale systems. Research interests span: Distributed ML/AI for edge environments Federated learning optimization Proactive data management in pervasive systems Edge-centric predictive analytics Funded projects include EU Horizon grants TERRA, COIN-3D, ELLIE, and TRACE, focusing on intelligent platforms for climate services, 3D VLSI reliability, cultural heritage technologies, and logistics optimization. Additional support comes from EPSRC, Royal Academy of Engineering, and industry partners including BMW and NXP. Recent publications (2024-2012) demonstrate innovations in edge-based task allocation, federated learning efficiency, distributed query processing, and mobile edge offloading. Article trends show consistent development of resource-aware distributed intelligence algorithms. Serves as Editor-in-Chief of Open Computer Science and Associate Editor of IEEE Access, and General Chair of IEEE ICDCS 2025. Leads the Knowledge and Data Engineering Systems research group.

Tobias Schreckمشاهده پروفایل

استاد

• Visual Data Analysis
• 3D Object Retrieval
• Visual Analytics
• +۵ مورد دیگر

Tobias Schreck is a Professor at the Institute of Visual Computing (formerly Computer Graphics and Knowledge Visualization) at Graz University of Technology, affiliated with the Faculty for Computer Science and Biomedical Engineering. His research focuses on Visual Data Analysis, 3D Object Retrieval, and Immersive Analytics, with applications in engineering and industrial contexts. He holds a Dr.rer.nat. from the University of Konstanz and has held academic positions including Assistant Professor at the University of Konstanz and Head of the Visual Search and Analysis Group at TU Darmstadt. Education: Dr.rer.nat., University of Konstanz (2006) M.Sc. in Information Engineering, University of Konstanz (2002) Dipl.-Volkswirt (M.Sc. Economics), University of Konstanz (1999) His research interests include visual analytics for high-dimensional and spatial-temporal data, digital libraries, and collaborative analysis tools. He leads funded projects like HEREDITARY (EU Horizon Europe) and VR4CPPS (FFG), and has served as a program chair for IEEE VAST and EuroVis. His work emphasizes user interaction, eye-tracking integration, and immersive visualization techniques. He supervises a research team including postdocs, PhD students, and student assistants, and collaborates on projects like CrossSAVE-CH and the Joint PhD Programme with Nanyang Technological University. His contributions span publications in IEEE Transactions, EuroVis, and ACM conferences, addressing challenges in data exploration, anomaly detection, and visualization design.

Robert Beinertمشاهده پروفایل

مدرس ارشد

• Applied Mathematics
• Inverse Problems
• Optimal Transport
• +۳ مورد دیگر

Robert Beinert is a Senior Lecturer (Privatdozent) in Applied Mathematics at the Technische Universität Berlin, affiliated with the Department of Applied Mathematics within the Faculty of Mathematics and Natural Sciences. He holds a Dr. rer. nat. (Ph.D.) from Georg-August-Universität Göttingen (2015) and completed his Habilitation at Technische Universität Berlin in 2025. His research focuses on inverse problems, optimal transport, phase retrieval, and mathematical imaging, with applications in signal processing and data analysis. He has held academic positions including Research Associate roles at TU Berlin (2020–2025), Karl-Franzens-Universität Graz (2016–2020), and Georg-August-Universität Göttingen (2016). His work bridges theoretical foundations and practical applications, with contributions to regularization techniques, optimal transport theory, and algorithmic development. Key research interests include phase retrieval uniqueness analysis, Gromov-Wasserstein transport, and denoising methodologies for manifold-valued data. His recent publications emphasize advancements in optimal transport frameworks, regularization strategies, and applications in image processing and machine learning.

Michael Quellmalzمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Inverse Problems
• Tomography
• +۴ مورد دیگر

Dr. Michael Quellmalz is a Researcher in Applied Mathematics at the Technical University of Berlin (TU Berlin), affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on inverse problems, tomography, optimal transport, and Fourier analysis with applications in medical imaging and computational geometry. He actively contributes to the SFB Tomography Across the Scales collaborative research center, advancing reconstruction techniques in diffraction tomography and motion detection. He teaches a variety of courses including Analysis II for Engineering, Harmonic Analysis I, and Numerical Mathematics, emphasizing both theoretical foundations and their practical implementations. His work has been recognized through awards such as the Universitätspreise 2020 and a Digital Fellowship for innovative educational contributions in adaptive feedback systems. Key publications include advancements in sliced optimal transport on spheres, motion detection algorithms, and Fourier-based reconstruction methods. Dr. Quellmalz's research group develops MATLAB toolboxes for applications like FourierODT and NFFT-Sinkhorn, reflecting his commitment to bridging mathematical theory with computational tools. His academic journey includes a PhD from TU Chemnitz (2019) and extensive participation in international conferences, workshops, and collaborative projects across Europe.

Stephen Wrightمشاهده پروفایل

استاد

• Computational Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Stephen Wright is a Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, serving as Department Chair from 2023-2025. He previously held roles at Argonne National Laboratory (1990-2001) and the University of Chicago (2000-2001). His research focuses on computational optimization, with applications in data science, machine learning, and engineering. He co-authored seminal books such as Numerical Optimization (with J. Nocedal) and Optimization for Data Analysis (with B. Recht). Wright leads the Wisconsin Institute for Discovery's research initiatives and has developed widely-used optimization software like PCx and SpaRSA . Key awards include the 2024 George B. Dantzig Prize, 2020 Khachiyan Prize, and SIAM Fellow status since 2011. He has served as editor-in-chief of the SIAM Journal on Optimization and Mathematical Programming, Series B . His teaching includes courses on nonlinear optimization (CS726) and introductory optimization (CS524). Wright’s work bridges theory and practice, emphasizing scalable algorithms and interdisciplinary applications.

• Computer Vision
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.

Monika Dörflerمشاهده پروفایل

استاد

• Applied Harmonic Analysis
• Audio Signal Processing
• Machine Learning
• +۳ مورد دیگر

Monika Dörfler is a Professor in the Department of Mathematics at the University of Vienna. Her research focuses on applied harmonic analysis, audio signal processing, and the mathematics of machine learning. She specializes in time-frequency analysis, nonstationary Gabor frames, and deep learning applications in audio processing. Dörfler has contributed significantly to methods like the invertible constant-Q transform and has explored the intersection of convolutional neural networks with signal representation theory. Affiliations: Department of Mathematics, University of Vienna Roles: Professor, Privatdozent, Principal Investigator (PI) in grants including WWTF Hertha Firnberg and SALSA projects Teaching: Courses such as Applied Analysis and Bachelor seminar Her research interests emphasize both theoretical foundations and practical applications of signal processing techniques. Notable contributions include work on Gabor frames in deep scattering networks and orthogonal projections for dimension reduction in learning problems. Dörfler has received awards for her papers, including Best Paper Awards at ISMIR19 and DAFx-11, and has organized international conferences like SAMPTA19 and DAFx2020. Awards: Best Paper Gold (DAFx-11), ISMIR19 Award, WWTF Grant Leadership Grants: Co-PI in aMOBY (Acoustic Monitoring of Biodiversity), PI in Audio-Miner and Hertha Firnberg grant Her work addresses challenges in audio denoising, music information retrieval, and algorithmic biases in recommendation systems. Ongoing projects include "Filter them all! Gabor theory for image quality assessment" submitted to FWF in 2023.