Find Academic Supervisors

• Computer Adaptive Testing
• Database Management
• Software Engineering
• +7 more

Dr. Svetlana Andjelic is a prominent researcher at Singidunum University, with a career spanning over two decades in educational technology, information systems, and machine learning applications. She holds a PhD in Management of Information Systems from "Union" University (2010), a Master's in Electronic Business from University of Belgrade (2007), and a specialist degree in Electronic Business (2005). Her work focuses on computer adaptive testing, database applications, and digital transformation in education. Education PhD in Management of Information Systems (2010), Faculty of Engineering Management, "Union" University Master's in Electronic Business (2007), Faculty of Organizational Sciences, University of Belgrade Specialist in Electronic Business (2005), Faculty of Organizational Sciences, University of Belgrade BSc in Information Systems (2001), Faculty of Organizational Sciences, University of Belgrade Her research explores: computer adaptive testing for knowledge assessment, database management systems, software engineering methodologies, cloud computing security, and educational technology implementation during crises. She has co-authored multiple books including "Application of databases" (2022) and "WPF and ASP.NET Framework" (2016). Recent publications demonstrate her evolving focus from educational assessment tools to modern security challenges and renewable energy forecasting. She collaborates extensively across Serbian institutions on topics ranging from rural tourism digitalization to banking sector innovations.

Sze Chai KwokView profile

Associate Professor

• Cognitive Neuroscience
• Memory Research
• Metacognition
• +4 more

Sze Chai Kwok is an Associate Professor of Cognitive Neuroscience at Duke Kunshan University and a Faculty Network Member of the Duke Institute for Brain Sciences. He holds a Ph.D. from the University of Oxford (2008) and maintains an active research program in cognitive neuroscience with a focus on memory mechanisms, metacognition, and neuropsychiatric applications. His research explores neural basis of temporal memory, metacognitive introspection, and episodic recall using multimodal approaches including primate neurophysiology, human neuroimaging (fMRI/EEG), and computational modeling. Key interests include: Neural replay mechanisms in memory consolidation Prefrontal and parietal contributions to metacognition Schizophrenia-related memory deficits Cross-species cognitive architectures Publication analysis reveals consistent focus on: 1) neural correlates of memory confidence/vividness, 2) primate-to-human translational models, 3) clinical applications in schizophrenia, and 4) innovative neuroimaging methodologies across 50+ publications. Awards and honors include: Jiangsu 333 Talent Program (2024) Kunshan Shortage Talent Program (2023) Jiangsu Qinglan Award (2021) Elected member of Memory Disorders Research Society (2021) Young IBRO Regions Connecting Award (2020) Shanghai Pujiang Talent Award (2016) Collaborates globally on primate neuroimaging initiatives including PRIMatE Data Exchange (PRIME-DE) consortium. Contributes to open neuroscience resources through Neurovault and international databases.

• Information Retrieval
• Data Science
• Quantum-inspired Information Retrieval
• +2 more

Massimo Melucci serves as Associate Professor of Computer Science at the University of Padua's Department of Information Engineering, maintaining an active research and teaching profile with office location in Building DEI/G, Room 312, Padova, Italy. His instructional responsibilities include undergraduate Information Retrieval and Databases courses, alongside graduate-level Computing Methods for Data Science instruction, complemented by freely available lecture notes on information retrieval fundamentals. His research program centers on two pioneering domains: Quantum-inspired Information Retrieval , where he develops mathematical frameworks applying quantum probability to model contextual relevance and interference effects in search systems, and Algorithmic Fairness , investigating trade-offs between ranking effectiveness and equitable outcomes. These threads converge in his exploration of fairness-effectiveness optimization through eigensystems and variation modeling, while his interdisciplinary work extends into diving medicine neurophysiology , analyzing EEG-based brain connectivity disruptions in professional divers under hyperbaric conditions. Recent publication analysis (2020-2025) reveals three dominant trajectories: quantum-inspired neural architectures for multimodal sentiment analysis, rigorous fairness-effectiveness quantification in ranking algorithms, and biomedical applications of signal processing in extreme environments. He actively fosters academic collaboration through initiatives like the BIRDS workshop series and CIRCLE conference, which bridge information science, retrieval, and data science communities. His work demonstrates consistent innovation in applying non-classical probability models to information access challenges while maintaining strong connections to real-world applications in both search technology and human safety.

• Solar Physics
• Observational Astrophysics
• Magnetic Reconnection
• +6 more

Luc Rouppe van der Voort is a Professor and Head of the Department of Theoretical Astrophysics at the University of Oslo since 2025. He serves as Principal Investigator for the Rosseland Centre for Solar Physics (RoCS) under the Norwegian Centre of Excellence (SFF) program until 2027. His academic career at the department spans from Postdoctoral Fellow (2003-2007) to Researcher (2007-2008), Associate Professor (2009-2013), and Professor (2013-2025). He earned his PhD in Astrophysics from Stockholm University (2003) after a PhD fellowship at the Institute for Solar Physics of the Royal Academy of Sciences, and holds an MSc in Astrophysics from Utrecht University (1997). His research focuses on Solar Physics , particularly high-resolution observations of the solar chromosphere, photosphere, and transition region. Key areas include magnetic reconnection (e.g., Ellerman bombs, UV bursts), spicules/downflows , sunspot dynamics , and coronal heating mechanisms . He utilizes data from ground-based telescopes like the Swedish Solar Telescope (SST) on La Palma and space missions such as Hinode , IRIS , Solar Orbiter , and Solar Dynamics Observatory . Recent publications (2023-2025) highlight his work on neural field applications to spectropolarimetric inversions, spectral resolution effects in solar diagnostics, and magnetic topology of quiet-Sun Ellerman bombs. Collaborative studies with teams across Europe and Japan emphasize multi-wavelength analysis and 3D simulations to unravel energy transport and atmospheric coupling. As Deputy Head (2016-2024) and council member of the Nordic Optical Telescope (2015-present), he contributes to leadership and observational infrastructure in solar physics. His work bridges observational datasets with computational models, advancing understanding of solar atmospheric dynamics and heating processes.

Daniel HashimotoView profile

Assistant Professor

• Computer Vision
• Artificial Intelligence
• Surgical Education
• +4 more

Daniel Hashimoto is an Assistant Professor in the Department of Surgery at the Perelman School of Medicine, University of Pennsylvania. He directs the Penn Computer Assisted Surgery and Outcomes (PCASO) Laboratory, a multidisciplinary research group bridging surgery, computer science, and robotics. He also holds appointments in the School of Engineering and Applied Science, focusing on General Robotics, Automation, Sensing, and Perception. His research centers on applying artificial intelligence (AI), computer vision, and robotics to enhance surgical decision-making and education. Key areas include intraoperative data analysis, digital health literacy, and automated feedback systems for surgical technique improvement in clinical and simulated environments. Recent publications highlight his work on AI-driven surgical video analysis, federated learning for medical data, and ethical considerations in robotic surgical training. The PCASO Lab collaborates on validating AI tools for procedures like cholecystectomy and endoscopic myotomy while addressing data quality and privacy frameworks. Scientific Awards: Linda Pechenik Montague Investigator Award (Perelman School of Medicine) Daniel mentors student researchers such as Vivek Singh, Aarush Sahni, and So Hee Ahn, who contribute to projects on robotic procedures and surgical education. The PCASO Lab emphasizes interdisciplinary collaboration between clinicians, engineers, and data scientists.

• Signal Processing
• Speech and Language Processing
• Machine Learning
• +3 more

Prof. Dr. Ümit Güz serves as a full-time Professor in the Department of Electrical and Electronics Engineering at Işık University's Faculty of Engineering and Natural Sciences. With over two decades of academic experience, his expertise spans biomedical signal processing, speech technology, and machine learning applications. Education: Post-Doc (2006-2007): SRI International, Menlo Park, CA - Speech Technology and Research Laboratory Post-Doc (2007-2008): UC Berkeley, International Computer Science Institute PhD (1997-2002): Istanbul University, Electronics Engineering MSc (1995-1997): Istanbul University, Electronics Engineering BSc (1990-1994): Istanbul University, Electrical-Electronics Engineering Associate Degree (1988-1990): Yıldız Technical University, Computer Programming His research centers on multimodal biometric systems using ECG and speech signals, medical image compression techniques, and Turkish language processing. Key innovations include SYMPES speech modeling framework and quadtree-based energy pattern blocks for biomedical image compression. His work bridges theoretical signal processing with practical healthcare and security applications. Publication trends reveal consistent focus on biometric identification (fingertip ECG/speech fusion), efficient compression algorithms for medical data (ECG/EEG/images), and semi-supervised learning for Turkish sentence segmentation. Recent work emphasizes multi-view learning strategies and deep neural network integration in compression systems. Scientific Awards: J. William Fulbright International Fellow (Postdoc Scholar Program, USA) Overseas Postdoctoral Research Award (TÜBİTAK BİDEB, Turkey) Prof. Güz has supervised 9 master's theses and 2 PhD dissertations on biometric recognition, speech compression, and medical image processing. His research is supported by TÜBİTAK grants including Project No 107E182 (Turkish spoken language processing) and Project No 10A301 (ECG-based biometric recognition). He actively contributes to IEEE and IEICE as a longstanding member.

• Neuroinformatics
• Biomedical Engineering
• Cognitive Neuroscience
• +2 more

Bernadetta Bartosik serves as an Assistant Professor in the Department of Neuroinformatics and Biomedical Engineering at Maria Curie-Skłodowska University (UMCS) within the Faculty of Mathematics, Physics and Computer Science. Her academic affiliation places her at the intersection of computer science, neuroscience, and biomedical applications, with her office located in room 511 of the Institute of Computer Science. Dr. Bartosik's research interests span neuroinformatics and biomedical engineering with particular focus on EEG analysis, brain-computer interfaces, and cognitive processes. Her work investigates cortical activity patterns related to social cognition, trust assessment, attractiveness judgment, and cognitive impairments such as post-COVID brain fog. She employs advanced methodologies including ERP analysis, machine learning algorithms, and neural network approaches to decode brain activity and understand human cognitive processes. Analysis of her recent publications (2019-2023) reveals a strong research trajectory in cognitive neuroscience with applications to real-world problems. Her work demonstrates expertise in both theoretical computational approaches and clinical applications, particularly in understanding how brain activity correlates with social judgments and cognitive impairments. The research shows consistent methodological rigor with emphasis on EEG signal processing, classification algorithms, and neural decoding techniques. Dr. Bartosik maintains active academic engagement through regular consultations for students, currently scheduled for the 2024/2025 summer semester with stationary sessions on Mondays from 10:00-12:00 and individual Teams appointments. Her scholarly contributions are tracked through ORCID (0000-0002-7228-452X) and various academic databases including Google Scholar, Scopus, and Web of Science, with documented metrics including an h-index of 2 (Web of Science), Total Impact Factor of 6.673, and a Total ministerial score of 260.

Lovro ŠubeljView profile

Associate Professor

• Network Science
• Machine Learning with Graphs
• Data Analysis
• +12 more

Lovro Šubelj is an Associate Professor at the University of Ljubljana, Faculty of Computer and Information Science , specializing in Network Science and Machine Learning with Graphs . His work bridges theoretical and applied network analysis, with contributions to community detection, graph convexity, and network simplification. Research focuses on graph algorithms for network abstraction Teaching: MSc/PhD courses on Network Science, BSc programming courses Collaborates with institutions like Leiden University and Imperial College London Research Interests: His primary work explores structural and dynamic properties of complex networks, including community detection via label propagation, convex skeletons for network simplification, and intermediacy metrics for citation analysis. Secondary interests span data mining, stream mining, and statistical network modeling. Publication Trends: Recent work emphasizes scalable network algorithms (2023-2025), graph embeddings (2021), and geometric network properties like convexity (2018-2019). Earlier projects (2012-2017) addressed citation network analysis, social network fraud detection, and database consistency evaluation. Teaching & Outreach: Provides educational materials on YouTube, including courses on network science and programming. Maintains open-access data/code repositories through KONECT, ICON, and GitHub.

• Explainable AI (XAI)
• time series data analysis
• exploratory data analysis
• +6 more

Naveed Khan serves as Lecturer in Computer Science at Ulster University's School of Computing, with prior research roles at Queen's University Belfast (Data Analytics Research Fellow) and Ulster University (Research Associate at BTIIC and Post-Doctoral Researcher at NIBEC). He co-leads a £3.3M PwC-funded project on synthetic data generation and contributes to EPSRC research evaluation. His academic credentials include: BSc (Hons) in Computer Science from Hazara University MSc in Computer Science (Network Security) from King Saud University PhD in Change detection in physical human activities from Ulster University Dr. Khan's research centers on Explainable AI (XAI) development, time series analysis for dynamic environments, and medical image processing using deep learning. His work integrates data engineering with IoT systems to address concept drift and activity recognition challenges, particularly in healthcare and security applications. Recent publications (2023-2025) reveal cross-domain impact across deepfake detection, smart grid cybersecurity, medical IoT security, and cardiac imaging analysis. His work consistently bridges theoretical AI advancements with practical implementations in energy systems, healthcare diagnostics, and business process optimization. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) As co-investigator on the £3.3M Advanced Research and Engineering Centre project, he directs synthetic data generation research while serving on the EPSRC Peer Review College. No doctoral students are publicly documented under his supervision. His lab affiliations span Ulster University's British Telecom Ireland Innovation Centre (BTIIC) and Nanotechnology and Integrated Bioengineering Centre (NIBEC), enabling interdisciplinary work in telecommunications and healthcare technology.

Gleb TikhonovView profile

Researcher

• Ecology
• Evolutionary Biology
• Statistical Modelling
• +3 more

Gleb Tikhonov is a postdoctoral researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . He also holds a postdoctoral position at Aalto University in the Department of Computer Science . His work bridges statistical ecology, machine learning, and computational biology. Research Interests : Development of joint species distribution modelling (JSDM) for ecological communities Integration of statistical and machine learning methods High-performance computing implementations Phenological and dispersal pattern analysis Publications demonstrate expertise in applying advanced statistical frameworks to ecological questions, including fungal dispersal mechanisms, microbiome variation, and climate change impacts. His LIFEPLAN project (funded by the European Commission Joint Research Centre) focuses on planetary biodiversity synthesis using big data. Education : PhD in Biological and Environmental Sciences, University of Helsinki (2018) MS in Applied Mathematics, Lomonosov Moscow State University (2014)

• Biomechanics/Bioengineering
• Biophysics
• Cardiovascular
• +16 more

Evren U Azeloglu is a Professor of Medicine in the Division of Nephrology at the Icahn School of Medicine at Mount Sinai, with a secondary appointment in the Department of Pharmacological Sciences. He is affiliated with the Center for Engineering and Precision Medicine and the Black Family Stem Cell Institute. B.E., Stony Brook University M.S., Stony Brook University Ph.D., Columbia University His research focuses on biomechanics , systems biology , and precision medicine , utilizing multiomics and computational modeling to study kidney and cardiovascular diseases. He develops microfabricated platforms for tissue engineering and mechanobiology research. Recent publications emphasize multiomics approaches to kidney injury, machine learning in transplant assessment, and mechanistic modeling of drug toxicity. Key themes include cell adhesion , cytoskeletal dynamics , and biomechanical regulatory networks . NephCure Kidney International ASN Foundation Kidney Research Award (2015) Howard Hughes Medical Institute Fellowship (2010) Stony Wold Herbert Fellowship (2005) Yuen-huo Hung & Chao-chin Huang Award (2004) Dr. Azeloglu's Systems Bioengineering Lab receives extramural grants and collaborates with Flowpoint Medical Inc. , where he serves as Board Member , Equity Owner , and Founder .

• Neuroscience
• Neural Circuits
• Behavioral Neuroscience
• +5 more

Gwyneth Card is an Associate Professor of Neuroscience at the Mortimer B. Zuckerman Mind Brain Behavior Institute of Columbia University. Her research focuses on the neural mechanisms governing visually guided behavioral choices in Drosophila , particularly escape behaviors, using advanced genetic, electrophysiological, and functional imaging techniques.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

Emine Sümeyra Turalı EmreView profile

Assistant Professor

• Chiral nanoparticles
• Antibacterial nanoparticles
• Extracellular vesicle capturing
• +5 more

Dr. Emine Sümeyra Turalı-Emre is an Assistant Professor at the Institute of Biomedical Engineering, Bogazici University, where she joined in 2024. She combines expertise in nanotechnology, biomedical engineering, and data science to solve critical healthcare challenges. Previously, she was a Postdoctoral Research Fellow in Chemical Engineering at the University of Michigan (2021-2024) and completed her PhD in Biomedical Engineering from the same institution in 2021. Her educational background includes: PhD in Biomedical Engineering, University of Michigan, 2021 MSc in Biomedical Engineering, University of Michigan, 2015 BSc in Molecular Biology and Genetics, Istanbul University, 2008 Dr. Turalı-Emre's research focuses on engineering inorganic nanoparticles for applications such as drug and gene delivery, antibacterial and anticancer treatments, bone regeneration, and extracellular vesicle capturing for diagnostics. Her work spans chiral nanoparticles, antibacterial nanoparticles, extracellular vesicle capturing, AI-driven nanoparticle-protein interactions, drug and gene delivery systems, cancer therapies, and translational nanotechnology. She approaches biomedical challenges through multidisciplinary collaborations that bridge engineering, life sciences, and computer science. Her recent publications in leading journals like Advanced Materials, Matter, and PNAS demonstrate strong trends in chiral nanomaterials for diagnostics and therapeutics, antibacterial applications against biofilms and antibiotic-resistant bacteria, and the integration of artificial intelligence with nanotechnology. Her work shows a clear progression from fundamental nanoparticle synthesis to translational applications in healthcare. Scientific awards and recognitions include: BioInterfaces Research Community Innovator Award, 2024 Full Member, Sigma Xi, The Scientific Research Honor Society, 2024 Women in Science and Engineering, Cinda Sue Davis STEM Equity Leadership Award (Nominee), 2024 Women in Science and Engineering, Willie Hobbs Moore Achievement Award (Nominee), 2024 Selected Participant, AI in Science & Engineering Summer Academy, 2023 And several other prestigious awards and recognitions Dr. Turalı-Emre is actively involved in mentoring students and fostering interdisciplinary collaboration. She currently leads a TÜBİTAK-BİLGEM project developing an AI-driven database to optimize antibacterial nanoparticles for diagnostics and treatments, in collaboration with Dr. Betül Özateş from the Institute of Data Science and Artificial Intelligence. She has also contributed to Coulter Translational Research Partnership projects focused on extracellular vesicle capture, optimizing RNA isolation from biofilms, and developing antibacterial surfaces using chiral nanoparticles. Her laboratory provides a vibrant environment for undergraduate, graduate, and non-traditional students from various fields, including engineering, life sciences, and computer science, to conduct impactful research and contribute to transformative innovations in healthcare.

• Data Integration
• Machine Learning
• Schema Matching
• +5 more

C. Koutras is a researcher at the Data-Intensive Systems group within the School of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His work focuses on data integration, schema matching, and machine learning applications in modern data systems. Research Areas: Data Integration, Schema Matching, Machine Learning, Data Lakes, Graph Neural Networks, Biomedical Data Systems Collaborations: Active collaborations with researchers including R. Hai, A. Katsifodimos, and M. Jarke. Recent work includes developing tools like Amalur and Valentine , which address challenges in data lake integration and scalable schema matching. His research leverages large language models and graph-based techniques for biomedical and distributed data environments. Despite significant contributions to data integration and machine learning, no explicit scientific awards or part-time status are documented in the provided materials. His 2024 dissertation at TU Delft highlights expertise in modern data challenges.