Find Academic Supervisors

Matthew MalensekView profile

Associate Professor

• Big Data
• Distributed Systems
• Cloud Computing
• +4 more

Matthew Malensek is an Associate Professor in the Department of Computer Science at the University of San Francisco and Academic Director of the MSCS, MSCS Bridge, and MSCS 4+1 programs. He holds a PhD in Computer Science from Colorado State University. Education: PhD in Computer Science (Colorado State University) His research focuses on systems approaches to data science, including scalable analytics, geospatial data management, fog computing, and cloud-edge integration. Notable projects include Galileo (distributed geospatial storage), Minerva (cloud resource management), and Agami (visual analytics for data streams). He co-authored An Introduction to Parallel Programming , a textbook used at USF. Recent work explores computer science identity development among undergraduates and community-engaged learning initiatives addressing tech inequities. His awards include Best Paper recognitions at IEEE/ACM Symposia in 2014 and 2012. Teaching responsibilities include Operating Systems (CS 326) and the Senior Team Project (CS 490). He advises capstone projects in areas like distributed systems, cloud computing, and machine learning. Research labs and collaborations involve fog/cloud hybrid systems, high-throughput data streams, and geospatial analytics frameworks. He actively contributes to open-source projects like FogOS, emphasizing collaborative development and real-world applicability.

Frank IorfinoView profile

Academic

• youth mental health
• early intervention
• digital health
• +7 more

Dr. Frank Iorfino is a Senior Research Fellow at the Brain and Mind Centre within the Faculty of Medicine and Health at the University of Sydney. He serves as an NHMRC Emerging Leadership Fellow with additional affiliations at the Sydney Southeast Asia Centre. His work focuses on developing innovative approaches to youth mental healthcare through digital technologies and multidisciplinary partnerships with health services, data scientists, and industry stakeholders. Dr. Iorfino holds a BSc (Psych), MBMSc, and PhD. His educational background has prepared him for his current research focus on youth mental health and digital health innovation. His research interests center on youth mental health and early intervention, with particular expertise in how digital health technologies can transform the assessment and management of emerging anxiety, mood, and psychotic disorders. He has made significant contributions to understanding illness trajectories, progression, suicidality, and functional outcomes among youth mental health service populations. His work bridges clinical practice with technological innovation to create more effective, personalized care pathways for young people. Analysis of Dr. Iorfino's recent publications reveals a strong focus on developing and implementing digital mental health solutions, particularly for youth populations. His research spans clinical staging frameworks, predictive modeling for functional impairment, implementation science for digital tools in mental healthcare settings, and economic modeling of mental health interventions. A consistent theme across his work is the application of data-driven approaches to improve service delivery and clinical outcomes for young people experiencing mental health challenges. NHMRC Emerging Leadership Fellow Dr. Iorfino leads a digital mental health program that has established multidisciplinary partnerships across health services, academia, and industry. His current research includes a project with William titled 'Towards Personalised Youth Mental Health Assessment: Leveraging Technology and AI to Capture Multidimensional Complexity.' He has secured significant grant funding including NHMRC Clinical Trials and Cohort Studies grants and MRFF-funded projects focused on AI applications in mental health and personalized care models. As a key member of the Brain and Mind Centre, Dr. Iorfino contributes to a collaborative research environment that integrates clinical practice with cutting-edge research. His work with the Sydney Southeast Asia Centre extends his impact beyond Australia, fostering international collaborations in youth mental health research and service innovation. His recent Substack publication 'Minds & Machines' demonstrates his commitment to translating research into practical insights for mental health system transformation.

Ramazan SariView profile

Professor

• Energy Economics
• Climate Policy
• Renewable Energy
• +3 more

Ramazan Sari is a Professor at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His work focuses on energy economics, climate policy, and socio-economic impacts of renewable energy technologies. He leads interdisciplinary research initiatives addressing sustainable development goals, particularly in energy poverty reduction and climate resilience. Key research areas include renewable energy systems, socio-economic impact assessments of solar and wind technologies, and policy design for just energy transitions. His work integrates quantitative analyses with policy evaluation, emphasizing real-world applications in Europe and beyond. Recent projects include large-scale collaborations on nature-based solutions for urban resilience and interdisciplinary energy systems modeling. Dr. Sari has published extensively on climate change mitigation strategies, energy market dynamics, and socio-economic dimensions of energy policies. His research has been cited in policy frameworks and international reports, reflecting its practical relevance. He actively engages in policy advisory roles and international conferences on energy transition topics. Notable projects include the LILY initiative on nature-based health solutions, PV4Plants for agrivoltaic systems, and the PEN@Hydropower network for sustainable hydropower development. These projects highlight his commitment to bridging academic research with actionable solutions for sustainable development challenges.

Jonas Schou Neergaard-NielsenView profile

Associate Professor

• Quantum Computing
• Quantum Optics
• Squeezed Light
• +2 more

Jonas Schou Neergaard-Nielsen is an Associate Professor in the Department of Physics at the Technical University of Denmark (DTU). His research focuses on quantum physics and information technology, with expertise in quantum optics, continuous-variable quantum information processing, and quantum sensing. Key research areas include: Generation and application of squeezed light states Quantum metrology and sensing enhancements Integrated photonic quantum systems Quantum computation with continuous variables Recent experimental work demonstrates advances in thin-film lithium niobate quantum light sources, long-distance quantum communication, and measurement-device-independent protocols. His publications frequently explore quantum state engineering, optical quantum technologies, and quantum-enhanced measurement techniques. Dr. Neergaard-Nielsen serves as Principal Investigator at DTU's Center for Quantum Technologies and Center for Macroscopic Quantum States.

Atau TanakaView profile

Professor

• Embodied Interaction
• Human-Computer Interaction (HCI)
• Musical Performance Technology
• +3 more

Prof Atau Tanaka is a Professor of Media Computing at Goldsmiths, University of London. His research focuses on embodied musical interaction, integrating physiological sensing, machine learning, and participatory design methodologies. He leads the EAVI (Embodied AudioVisual Interaction) group, exploring gestural interfaces, bio-interfaces, and audiovisual systems. Tanaka has held roles at institutions worldwide, including Sony CSL and Apple, and his work spans academic research and artistic performance. His educational background includes a Doctor of Musical Arts from Stanford University and degrees from Harvard and Peabody Conservatory. Research Interests: Embodied interaction, gestural computing, EMG-based musical interfaces, HCI, machine learning for live performance, and interdisciplinary art-science collaborations. Awards include recognition from Ars Electronica and Fraunhofer Institute. Publications highlight his work on telematic performance, EMG-based instruments, and audiovisual user interfaces. He explores topics like latency in networked performance and the entanglement of body signals with artistic expression. Tanaka’s projects include installations like Sonic Womb and collaborations with institutions such as the ZKM and SFMOMA.

Jacob ShelleyView profile

Professor

• Mass Spectrometry
• Chemical Instrumentation
• Analytical Chemistry
• +4 more

Jacob Shelley is the Alan Paul Schulz Career Development Professor of Chemistry at Rensselaer Polytechnic Institute (RPI), leading the Shelley Research Lab. He holds a B.S. from Northern Arizona University (2005), a Ph.D. from Indiana University (2011), and completed postdoctoral work at Purdue University and the University of Münster (Germany via Alexander von Humboldt Fellowship). His research focuses on advancing mass spectrometry instrumentation for rapid, in situ analysis in complex matrices, emphasizing ambient ionization techniques and multi-dimensional analytical capabilities. Education: B.S. in Chemistry, Northern Arizona University, 2005 Ph.D. in Chemistry, Indiana University, 2011 Postdoctoral Researcher, Purdue University, 2011–2012 Alexander von Humboldt Postdoctoral Fellow, University of Münster, 2012–2014 Research Interests: Prof. Shelley’s work bridges analytical chemistry and instrumentation development, targeting innovations in plasma-based ionization sources, acoustic field manipulation of ions, and multidimensional mass spectrometry. Applications span astrobiology, environmental analysis, drug development, and homeland security. His lab emphasizes rapid, non-destructive molecular analysis at atmospheric pressure. Key Contributions: Authored 35+ journal articles, 3 patents, and 30+ invited presentations. Notable projects include resonant acoustic ion gates, glow discharge systems, and VUV Raman pathogen detection. His work aligns with RPI’s interdisciplinary initiatives, including the Rensselaer Astrobiology Research and Education Center. Awards: Recipient of the prestigious Alexander von Humboldt Postdoctoral Fellowship (2012–2014).

Shervin ErfaniView profile

Professor

• Computer & Network Security
• Data Networking
• Communication Network Management
• +3 more

Dr. Shervin Erfani is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor, Faculty of Engineering. His research focuses on computer and network security, data networking, communication network management, and multidimensional digital filter realization. He holds a position in 3048 CEI and can be reached at erfani@uwindsor.ca. His research interests span a wide range of topics, including IoT security, signal processing in linear time-varying systems, and fractional calculus applications. Recent work emphasizes bifrequency analysis, iterated Laplace transforms, and fractional differential equations. His contributions bridge theoretical advancements with practical implementations in security frameworks and network protocols. Publications highlight trends in secure communication architectures, such as Bitcoin security models and IoT applications, alongside foundational work in signal processing and circuit theory. Collaborations address challenges in network management, spectrum sensing, and system stability.

Johann GamperView profile

Professor

• Temporal Databases
• Time Series Analysis
• Query Processing
• +3 more

Johann Gamper is a Professor and Head of the Database Systems Group at the Free University of Bozen-Bolzano. He previously served as Vice-Rector for Research (2018-2024) and Vice-Dean for Studies (2014-2018). Education: Information not provided in text. Research Focus: His core research investigates data-intensive systems with emphasis on temporal data management, time series analysis, and efficient query processing. His team combines fundamental research with system development for applications in governance, tourism, healthcare, and agriculture. Current projects include temporal RDBMS extensions, time series motif mining, and multidimensional range count approximation. Publication Trends: Recent work addresses temporal query optimization, time series classification, and generative approaches for e-commerce data. His research demonstrates consistent innovation in temporal data handling and database performance optimization. Teaching: Currently teaches Database Management Systems and advises PhD students in temporal database research.

Michael ShekelyanView profile

Lecturer

• Data Management
• Database Systems
• Differential Privacy
• +5 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

• Online Learning
• Educational Technology
• Distance Learning
• +3 more

Mike Howarth is an academic affiliated with Middlesex University's Education department. His primary role is as an Hourly Academic, focusing on educational technology and pedagogical innovation. His research explores hybrid learning environments, user experience (UX) design in healthcare simulations, and the adaptation of historical educational broadcast techniques for modern online teaching. He has contributed to conferences such as 'The BBC at 100 Symposium' and published works on post-Covid learning strategies, webinar production, and immersive web video engagement. Education background includes a PhD from Middlesex University (2003) on graphical user interfaces for children. His work emphasizes practical applications of technology in education, bridging historical media practices with current digital tools. Notable outputs include analyses of BBC School Radio's methods and frameworks like '45° Learning' for pandemic-era teaching. Research interests span online learning design, adaptive teaching strategies, and the integration of UX principles in medical education. He collaborates frequently with colleagues like A. Basiel and J. Hager on projects addressing student engagement and employability through innovative pedagogical tools.

• Quality education in the early years
• Children’s rights
• Children's subjective wellbeing
• +3 more

Dr. Taiwo Frances Gbadegesin is a Lecturer in Initial Teacher Education at Queen Margaret University (QMU), affiliated with the Psychology, Sociology and Education Division. She is also an Associate Member of the Centre for Applied Social Sciences. Her academic background includes a bachelor's degree in Economic Education from Obafemi Awolowo University (Nigeria), a master's degree from Obafemi Awolowo University, and a PhD from the University of Leeds (UK). She held a postdoctoral fellowship at the University of the Free State in South Africa and previously served as academic staff at Ekiti State University, Nigeria before joining QMU in 2022. Education: Bachelor's in Economic Education (Obafemi Awolowo University) Master's degree (Obafemi Awolowo University) PhD in Education (University of Leeds) Research Interests: Quality education in early childhood Children’s rights and wellbeing Refugee/migrant children’s integration Comparative and international education Gender and inclusive education practices Dr. Gbadegesin employs interdisciplinary and cross-national approaches, with a focus on qualitative and mixed-method research. Her work often addresses systemic challenges in education and housing policy, particularly in developing nations. She teaches modules on early childhood education, global health, and comparative education, serving as a module coordinator for topics such as Early Childhood Development and Learning in Context and Global Health and International Education . Her research highlights the intersection of socio-cultural factors and policy interventions, with notable contributions to understanding child welfare in post-pandemic contexts, urban housing impacts on child health, and educational equity in Nigeria and South Africa.

Daniel WeiskopfView profile

Professor

• Scientific Visualization
• Eye Tracking
• Human-Computer Interaction
• +5 more

Prof. Daniel Weiskopf is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute of Parallel and Distributed Systems. His research focuses on visualization techniques, eye tracking, and human-computer interaction, with applications in virtual/augmented reality (VR/AR), data analysis, and uncertainty modeling. He has contributed to advancements in scientific visualization, including ML-driven flow visualization, energy-efficient rendering, and gaze-aware interfaces. His work emphasizes empirical methodologies, such as eye-tracking studies for evaluating visualization literacy and collaborative learning in AR environments. Research interests include: Scientific Visualization and Uncertainty Representation Eye Tracking Methodologies and Human Factors VR/AR Systems and Immersive Analytics Machine Learning Applications in Visualization Recent publications highlight trends in optimizing visualization performance, improving data interpretation through gaze-aware systems, and integrating advanced visualization into gaming and engineering contexts. His work bridges computational methods with user-centric design principles. He leads efforts in the Institute of Parallel and Distributed Systems , collaborating on tools like Gazealytics for exploratory gaze analysis and mint for VR visualization integration. No awards or grants are explicitly listed in the provided information.

Michael BerryView profile

Professor

• Data Science
• Machine Learning
• Text Mining
• +4 more

Michael Berry is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. He holds a PhD in Computer Science from the University of Illinois at Urbana-Champaign, an MS in Applied Mathematics from North Carolina State University, and a BS in Mathematics from the University of Georgia. His research focuses on data science, machine learning, text mining, nonnegative matrix factorization, parallel computing, and their applications in biomedical and environmental domains. Notable contributions include work on tensor decomposition for big data analysis, algorithms for text mining, and computational tools like PolyLens and CodeAssessor. Berry's publications emphasize interdisciplinary applications, such as using nonnegative tensor factorization for biomedical literature analysis and developing GPU-accelerated methods for traffic flow analysis. His work spans conferences like the International Conference on Soft Computing in Data Science (SCDS) and journals in computational science. He has contributed to software tools like FutureLens for text visualization and SHEPPACK for interpolation algorithms. His research also addresses environmental challenges via parallel ecosystem modeling and spatial control problems. No scientific awards or grants are explicitly mentioned in the provided text.

Kevin McDonnellView profile

Teaching Professor

• Computer Science Education
• Visualization
• Visual Analytics
• +2 more

Kevin McDonnell is the CS Undergraduate Program Director and a Teaching Professor in the Department of Computer Science at Stony Brook University. He is affiliated with the College of Engineering and Applied Sciences and focuses on Computer Science Education, Visualization, Visual Analytics, Geometric Modeling, and Computer Graphics. He has received numerous awards, including the 2024 Provost's Outstanding Lecturer Award and multiple Excellence in Teaching Awards. His teaching portfolio includes courses such as CSE 101, CSE 114, CSE 215, CSE 220, CSE 564, and ISE 218. His research emphasizes data visualization, visual analytics frameworks, and computational methods in environmental and biomedical contexts. Recent work includes studies on peptide identification algorithms, microbial community dynamics, and geo-spatial data exploration. Awards: SUNY Chancellor's Award (2021), NSF GAANN Fellowship (1998), and honor society memberships (Phi Beta Kappa, Golden Key). Advising & Grants: No formal student advisees listed, but contributes extensively to curriculum development and accessibility initiatives. Labs/Teams: Engaged with interdisciplinary projects in visualization and computer graphics, though specific lab affiliations are not detailed.

• Biomedical Engineering
• Medical Image Processing
• Health Information Systems
• +3 more

George Matsopoulos is Professor in Biomedical Engineering at the National Technical University of Athens, heading the Biomedical Engineering Laboratory. His research focuses on medical image processing, diagnostic support systems, and health information technologies. With over 300 publications, he develops computational methods for medical data analysis and leads the BIOMIG research group specializing in medical informatics and neuroimaging applications. Primary research areas include multidimensional medical data fusion, digital biosignal processing, telemedicine systems, and visualization techniques. His laboratory utilizes advanced computational tools including ITK, VTK, and CUDA to address clinical challenges in medical imaging and diagnostics.