Find Academic Supervisors

Chris MisaView profile

Researcher

• Network Traffic Monitoring
• Programmable Switch Hardware
• Network Security
• +5 more

Chris Misa is a post-doctoral researcher at the University of Oregon's Department of Computer Science, affiliated with the Oregon Networking Research Group and Oregon Cybersecurity Center of Excellence. His work bridges programmable network hardware, virtualization, and security research. PhD in Computer Science (University of Oregon, mentor: Rejaie & Durairajan) Key research: hardware-accelerated network telemetry, DDoS defense via ZAPDOS, cloud networking Publications in top venues: USENIX NSDI, IEEE Security & Privacy, IEEE Transactions on Networking His research focuses on optimizing network measurement systems by combining programmable switch hardware, machine learning, and innovative algorithmic approaches. Recent projects like ZAPDOS demonstrate his emphasis on addressing high-volume DDoS threats through prefix-level traffic analysis and dynamic resource allocation. Current work spans runtime telemetry, multi-cloud management, and optical network security solutions. Scientific awards include: Ripple Faculty Fellowship Ripple Graduate Fellowship NSF grant CNS 1850297 (as team member) As a postdoc, he contributes to sponsored research collaborations with Broadcom and leads academic advising for junior researchers. His projects integrate network testbeds, open-source tools (BONSAI), and interdisciplinary approaches to network science.

Anastasios GounarisView profile

Professor

• Distributed Databases
• Autonomous data processing
• Big Data Management
• +7 more

Anastasios Gounaris is a Professor at the Department of Informatics, Aristotle University of Thessaloniki, where he has been a faculty member since April 2008. He previously served as a Visiting Lecturer at the University of Cyprus (2007-2008) and held research positions at the University of Manchester and CERTH. His academic journey includes a PhD from the University of Manchester (2005), an MPhil from UMIST (2002), and a degree in Electrical and Computer Engineering from Aristotle University of Thessaloniki (1999). His research spans Distributed Databases, Autonomous Data Processing, Big Data Management, Workflow Optimization, and Data Mining . He has made significant contributions to large-scale data management systems, massive parallelism techniques, and business process analytics. His work bridges theoretical computer science with practical industrial applications, particularly in predictive maintenance and edge computing environments. His recent publications (2024-2025) demonstrate a strong focus on process mining, anomaly detection, predictive maintenance systems, and edge analytics . These works address critical challenges in handling evolving data streams, optimizing task allocation in resource-constrained environments, and developing parameter-free algorithms for real-world applications. His research shows consistent progression from foundational database systems work to cutting-edge applications in industrial IoT and healthcare analytics. Dr. Gounaris has successfully supervised numerous PhD students to completion, including Athanasios Naskos (2017), Georgia Kougka (2017), Christos Bellas (2022), Theodoros Toliopoulos (2022), Anna-Valentini Michailidou (2023), Ioannis Mavroudopoulos (2024), and Konstantinos Varvoutas (2025). His research has been supported by multiple national and European projects including DataflowOpt, PRECognition, NavGreen, CUREX, Rainbow, Lifechamps, and Trineflex. He is an active member of Datalab (formerly Delab) and has contributed to industry applications through collaborations with companies such as Comidor, Gnomon, Istognosis, Atlantis, AFS, Follow-Apps, Sboing, and Upcom. His work demonstrates a strong commitment to transferring research results to real-world applications while maintaining academic rigor.

• Artificial Intelligence
• Computer Graphics
• Virtual Reality
• +3 more

Carlos Cglez-Morcillo is an Associate Professor at the Higher School of Computer Science, University of Castilla-La Mancha (Spain), with an active career in academia since 2002. He holds a European PhD in Computer Science (2007) and a degree in Computer Science (2002) with an Extraordinary End-of-Degree Award. Currently, he serves as Academic Director of the Vice-Rectorate of Digital University and Director of the G9 Shared Digital Campus, while also being a founding member of the AIR Research Group and Creative Director of the university spin-off Furious Koalas. PhD in Computer Science (2007) BSc in Computer Science (2002) Technical Engineer in Computer Systems (2000) Research interests include Artificial Intelligence, Computer Graphics, and their applications in rehabilitation technology and educational innovation. His work spans mixed reality systems for stroke recovery, gamification in learning environments, and 3D rendering optimization. He has led 9 research projects with over €550,000 in funding and contributed to over 30 technology transfer initiatives. Recent publications focus on VR/AR applications for neurological rehabilitation, e-commerce metaverse environments, and AI-driven educational tools. His 15 most recent articles reflect interdisciplinary work bridging computer science with healthcare and business innovation. Scientific recognition includes being the first Spaniard to obtain the Blender Foundation Certified Trainer (2009) and leading award-winning projects like ICEIS 2025 Best Student Paper. He actively participates in conferences and media outreach, demonstrating commitment to public engagement through platforms like Naukas and CMM Radio.

• Computer Science Education
• Educational Technology
• Machine Learning
• +3 more

Leila Shafti serves as a Teaching Fellow in the Department of Computer & Information Sciences within the Faculty of Science at the University of Strathclyde. Her primary responsibilities encompass teaching undergraduate computer science and computer engineering courses, along with GADA work-based learning IT programs. She holds advanced degrees from Imperial College London and Universidad Autónoma de Madrid. Education: Master of Science, Imperial College London Doctor of Engineering, Universidad Autónoma de Madrid Dr. Shafti's research centers on computer science education innovation, focusing on making complex technical concepts accessible through novel pedagogical approaches. Her expertise spans educational technology, ambient intelligence systems, and machine learning methodologies, with particular emphasis on feature construction techniques and evolutionary algorithms for educational applications. This interdisciplinary work bridges computer science fundamentals with practical teaching enhancement. Her publication history reveals a trajectory from ambient intelligence research (2009-2013) toward contemporary computer science education scholarship. Early work examined evolutionary feature extraction for behavioral pattern recognition in smart environments, while recent efforts prioritize educational data mining and learning analytics within higher education contexts. Scientific Awards: Senior Fellowship of the Higher Education Academy (2024) Teaching Excellence Award - Team Award (2022) Teaching Excellence Award - Team Award (longlisted, 2021) Dr. Shafti actively contributes to teaching innovation initiatives at Strathclyde, though specific grant details and student advising records remain undisclosed in available materials. Her recognition through multiple teaching awards underscores significant impact on pedagogical practices within technical education. She participates in the Computer Science Education Research group, collaborating on projects that integrate data-driven approaches with curriculum development to enhance student engagement in computational disciplines.

C. Garcia SanchezView profile

Researcher

• Urban Data Science
• Computational Fluid Dynamics
• Building Energy Modeling
• +4 more

C. Garcia Sanchez is a researcher at Delft University of Technology within the Architecture and the Built Environment school, specializing in Urban Data Science. Their work bridges computational fluid dynamics, urban microclimate modeling, and building energy systems through advanced data-driven approaches. Education includes a PhD in Physics from von Karman Institute for Fluid Dynamics and Antwerp University (2017), a Research Master in Fluid Dynamics from von Karman Institute (2012), and an Aerospace Engineering degree from Universitat Politécnica de Valencia (2011). Postdoctoral research was conducted at Carnegie Institution for Science. Research focuses on urban data science with emphasis on High-fidelity computational fluid dynamics for urban environments Synthetic data generation for turbulence modeling Integration of building energy models with urban canopy systems Automated 3D building reconstruction for microscale simulations Recent publications demonstrate expertise in creating efficient simulation workflows that address urban ventilation, climate resilience, and energy efficiency challenges. Awarded the Outstanding Paper Award for 3D Processing & Visualization (2021) for contributions to urban simulation techniques. Research outputs include 19 publications, 3 datasets, and media coverage of digital twin applications for heatwave response. Active in supervising research projects and developing computational tools for urban science, with recent work featured in Computers and Fluids, SoftwareX, and Building and Environment. Current projects involve creating high-resolution urban models that integrate meteorological data with human behavior simulations during extreme weather events.

• Process Mining
• Process Modelling and Simulation
• Distributed Computing
• +5 more

Claudio di Ciccio is an Associate Professor at the Department of Information and Computing Science within the Faculty of Science at Utrecht University, Netherlands. Previously, he worked with the Department of Computer Science of Sapienza University of Rome (Italy) and the Institute for Information Business of the Vienna University of Economics and Business (WU Vienna), Austria. He received his PhD in Computer Science and Engineering in 2013 from Sapienza University. His research interests span across Process Mining, Formal Methods, Automated Reasoning, and Blockchain & Distributed Ledger Technologies. He has developed expertise in Process Modelling and Simulation, Distributed Computing, Logic, and Artificial Intelligence. His work bridges theoretical formal methods with practical applications in business process management and decentralized systems. His recent publications focus on advancing Process Mining techniques while addressing critical challenges in data privacy and security, particularly in decentralized environments. His research explores the intersection of visual analytics and process mining, developing innovative approaches for multi-faceted process information analysis through time and space. He has made significant contributions to formal methods for process specification verification and constraint satisfaction. Member of the Steering Committee of the IEEE Task Force on Process Mining General Chair of the Conference on Process Mining (ICPM) in 2023 PC chair of ICPM in 2021 PC chair of the Conference on Business Process Management (BPM) in 2022 PC chair of the Blockchain Forum at BPM in 2019 and 2024 Di Ciccio has been actively involved in numerous significant research projects related to process mining, blockchain applications, and formal methods. His work demonstrates a consistent focus on developing practical tools and frameworks that address real-world challenges in business process management while maintaining strong theoretical foundations. His research group appears to focus on the intersection of process science with emerging technologies, particularly in secure and decentralized environments.

Errin W. FulpView profile

Professor

• Computer Security
• Network Security
• Cyber Threat Intelligence
• +5 more

Dr. Errin W. Fulp is a Professor in the Department of Computer Science at Wake Forest University's College of Arts and Sciences. He earned his PhD in Computer and Electrical Engineering from North Carolina State University in 1999. His professional experience includes roles as a Research Scientist at Pacific Northwest National Laboratory (PNNL) and Cisco Systems, and he currently serves as Scholar in Residence at Centripetal Networks. Dr. Fulp's research focuses on intelligence-driven approaches to computer security and the development of resilient, dynamically managed computer networks. His work leverages Cyber Threat Intelligence (CTI) to enhance situational awareness and inform proactive defense strategies. He investigates nature-inspired design principles such as genetic algorithms for optimizing defense strategies, ant colony optimization for dynamic resource allocation, and AI agency for autonomous decision-making in threat detection and mitigation. Analysis of Dr. Fulp's recent publications reveals a strong focus on evolutionary approaches to security challenges, particularly in moving target defense, network segmentation, and threat intelligence analysis. His work consistently applies bio-inspired computing techniques to address sophisticated cyber threats, with increasing emphasis on temporal aspects of threat intelligence and ransomware analysis in his most recent research. Wake Forest Reid-Doyle Prize URECA Mentorship Award for Undergraduate Research DOE CAREER Award IEEE ACSOS Award IEEE MILCOM Best Paper Award Dr. Fulp has mentored numerous undergraduate and graduate students, guiding research in computer security and network defense. His work has been supported by major funding agencies including the Department of Energy (DOE), National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Cisco Systems, and Centripetal Networks. He has co-founded a startup specializing in computer security and holds multiple patents related to firewall technology and network security. Dr. Fulp leads the Network Security Group at Wake Forest University, which investigates techniques to address next-generation security threats, from distributed firewall designs to ant-based intrusion detection systems. His research team explores approaches for identifying domain shadowing and other DNS attacks, with current projects focusing on intelligence-driven security leveraging diverse intelligence sources.

Muhammad Ikhlaas GurribView profile

Professor

• Event analysis
• Macroeconomic analysis and financial markets
• Portfolio management

Dr. Muhammad Ikhlaas Gurrib is a Professor at the School of Management of the Canadian University Dubai (CUD), where he serves as Program Coordinator for Accounting & Finance. He holds a PhD in Economics & Finance from Curtin University of Technology, Australia, and a Master of Finance and Master of Professional Accounting from Victoria State University, Australia. His academic career includes prior roles as Assistant Professor at Prince Sultan University, Saudi Arabia, and teaching positions at Curtin Business School, Australia. Dr. Gurrib’s research focuses on event analysis , macroeconomic analysis and financial markets , and portfolio management . His recent work explores financial forecasting with machine learning, energy stock price dynamics during crises, and technical analysis tools like the Ichimoku cloud and Relative Strength Index (RSI) in cryptocurrency and energy markets. His publications span journals, conferences, and book chapters addressing GCC economic integration, financial technologies, and regulatory impacts on global markets. Scientific honors include the Best Researcher Award at CUD (2013-2014, 2015-2016) and the Curtin University Post Graduate Scholarship Award (2006-2007). He is a Senior Associate (SA Fin) of FINSIA, a member of the American Finance Association, and an affiliate of GARP. Dr. Gurrib contributes as a reviewer for journals like Studies in Economics & Finance and serves on the editorial board of Risk Governance and Control: Financial Markets & Institutions.

Nishtha SrivastavaView profile

Research Fellow

• Seismology
• Artificial Intelligence
• Machine Learning
• +5 more

Nishtha Srivastava is a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) under the Theoretical Sciences school. Her work bridges Seismology and Artificial Intelligence, focusing on enhancing Earthquake Early Warning Systems (EEWs) and seismic event detection through AI/ML models. Her research explores seismic stress release patterns, deep learning architectures for earthquake magnitude estimation (e.g., CREIME, PolarCAP), and automated volcanic event analysis. Key projects include the BMBF-funded Seismology and Artificial Intelligence (SAI) initiative, which develops tools like AWESAM for real-time volcano monitoring. She collaborates with PhD students (Jonas Köhler, Abel Daniel Zaragoza Alonzo) and postdocs (Wei Li, Claudia Quinteros) at FIAS, contributing to advancements in seismic phase picking, P-wave detection, and industrial AI applications. Her work emphasizes rapid warning systems in high-risk regions like Southern California and Indonesia.

• Semantic technologies
• Ontology engineering
• Knowledge graphs
• +3 more

Vadym Yermolayev serves as a Professor at the Department of Computer Science and Information Technology within the Faculty of Applied Sciences at Ukrainian Catholic University (UCU). He leads UCU's PhD program in Intelligent Systems and holds an Honorary Professorship at Kherson State University. His academic work focuses on semantic technologies, ontology engineering, and knowledge graph construction, with active participation in international research projects and organizations like ACM and ELLIS. Professor of Semantic Technologies Head of PhD Program in Intelligent Systems Honorary Professor at Kherson State University Member of ACM and ELLIS Research Interests span semantic technologies, ontology engineering, and knowledge representation, with applications in education, industrial analytics, and anti-corruption systems. His work integrates machine learning with formal knowledge modeling and develops frameworks for knowledge ecosystem dynamics. Scientific Contributions include leading research groups at Zaporizhia National University and collaborating on European Commission-funded projects. He has published extensively in ICTERI conference proceedings and developed methodologies for terminology saturation analysis and ontology alignment. Honorary Professor at Kherson State University Member of ACM (Association for Computing Machinery) Member of ELLIS (European Laboratory for Learning and Intelligent Systems) Professional Involvement includes external expert roles for European Commission programs (FP6, FP7, H2020) and industrial consulting with Cadence Design Systems GmbH.

Tatiana PodladchikovaView profile

Associate Professor

• Solar Physics
• Space Weather
• Coronal Mass Ejections
• +5 more

Dr. Tatiana Podladchikova serves as Associate Professor, Director of the Center for Digital Engineering, and Director of the Engineering Systems Master's Program at Skolkovo Institute of Science and Technology (Skoltech). Her work bridges fundamental solar physics research with operational space weather services to protect critical infrastructure including satellites, aviation systems, and power grids from solar-induced geomagnetic disturbances. Her research centers on solar activity dynamics—particularly solar cycles, eruptive flares, coronal mass ejections (CMEs), and high-speed solar wind streams—and their terrestrial impacts. She develops predictive models for space weather phenomena using advanced data analysis techniques, with recent work integrating deep learning frameworks for solar observation translation and physics-informed neural networks for magnetic field extrapolation. Her research directly addresses infrastructure vulnerabilities during extreme space weather events through operational forecasting systems. Dr. Podladchikova's publications demonstrate a clear evolution toward AI-enhanced space weather prediction, combining multi-point solar observations (including Solar Orbiter mission data), coronal dimming analysis for early CME detection, and innovative applications like the DIRECD method for real-time propagation tracking. Her work spans fundamental solar physics, engineering applications for satellite systems, and environmental monitoring techniques. Her scientific recognition includes: International Alexander Chizhevsky Medal for Space Weather and Space Climate (2015, Belgium) Skoltech Best Professor Award (2018-2020) Outstanding Achievements in Education and Research (2022-2023) As an educator, she directs Skoltech's Engineering Systems Master's Program, leads the Space Weather Laboratory, and teaches Experimental Data Analysis and Data Analysis for Space Weather courses. Her leadership extends to public outreach initiatives including science communication events like 'Mysteries of the Sun: Space Weather of the 21st Century' lectures. She heads the Skoltech Space Weather Laboratory where her team develops operational forecasting tools, analyzes multi-spacecraft solar observations, and advances CubeSat technologies including the Skoltech-F mission. Current projects focus on real-time geomagnetic storm prediction systems and AI-driven translation of historical solar datasets to maintain observational continuity.

Bingcong LiView profile

Researcher

• Machine Learning
• Optimization
• Signal Processing
• +3 more

Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.

Thomas Vikhamar SchulerView profile

Professor

• Mass balance of glaciers
• Glacier hydrology
• Glacier dynamics
• +5 more

Thomas Vikhamar Schuler is a Professor at the Section for Geography and Hydrology (GeoHyd) within the Department of Geosciences at the University of Oslo, Norway. He also holds an Adjunct Professor position at the Arctic Geophysics program of UNIS since 2017. His research focuses on glacier mass balance, hydrology, and dynamics, with strong emphasis on cryosphere modeling and climate change impacts. Education: PhD in Glaciology (2002) from University of Oslo, with a thesis on subglacial water drainage in alpine glaciers. Research Themes: Cryospheric modeling, glacier hydrology, climate-glacier interactions, permafrost dynamics, and machine learning applications in glaciology. Teaching: Courses include GEO9440 – Cryosphere Modelling , GEO4432 – Surface Energy Balance in Cold Environments , GEO4420 – Glaciology , and GEO2300 – Physical Processes in Geoscience . His recent publications (2025-2019) span topics like Antarctic supraglacial lakes, Svalbard glacier mass balance, subglacial friction dynamics, and machine learning frameworks for glacial classification. Articles frequently involve interdisciplinary approaches combining glaciology, hydrology, and climate science, often using field measurements and computational models. Scientific Awards: ERC Starting Grant 'DYNAMICE' (2020) Projects: Leads initiatives like COLOSSAL, EMERALD, ESCYMO, MAMMAMIA, JOSTICE, and SatPerm Collaborations: Active in EarthFlows, Climate Cryosphere, LATICE, and Perma-Nordnet research groups

• Scientific Computing
• Machine Learning
• High-Energy Physics
• +3 more

Prof. Dr. Ralf Keidel is a faculty member at the Chair for Scientific Computing , Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU), since 2022. His academic career spans roles at Worms University of Applied Sciences , where he has served as Professor of Computer Science since 1993, Scientific Director of the Center for Technology and Transfer (ZTT) since 2000, and Head of the Technology Transfer Office since 1996. He is a key figure in international collaborations, including the ALICE Collaboration Board at CERN (2003–2023), Bergen pCT Collaboration (since 2018), and the MODE Collaboration (since 2022). His research merges Computer Science with High-Energy Physics , focusing on machine learning , secure heterogeneous distributed systems , and optimization of detectors in particle physics . Projects like SIVERT (2020–2024) aim to enhance Proton Computed Tomography (pCT) for clinical radiation therapy using AI-driven visualization and real-time reconstruction. He also contributes to serious games for financial education and mobile applications for musical training. Key publications highlight his work on differentiable programming for detector optimization, deep reinforcement learning in particle tracking, and convolutional neural networks for radiation topology analysis. His projects often involve interdisciplinary teams from institutions like the University of Bergen , DKFZ Heidelberg , and Western Norway University of Applied Sciences .

Anastasios TsiamisView profile

Lecturer

• Statistical Learning and Control
• Data-Driven Control
• Online Learning
• +3 more

Dr. Anastasios Tsiamis is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, working in the Automatic Control Laboratory (Professur Control and Computation). His research focuses on the intersection of control theory and machine learning, specifically investigating how system theoretic properties affect the statistical difficulty of learning in system identification, online estimation, and control. Dr. Tsiamis received his Diploma (MEng, five-year degree) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). He completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania under Professor George Pappas, following graduate research with Professor Petros Maragos and undergraduate work with Professor Kostas J. Kyriakopoulos at NTUA. His primary research areas include Statistical Learning and Control, Data-Driven Control, Online Learning, Risk-Aware Control, and Security and Privacy in Networked Control Systems. Dr. Tsiamis has made significant contributions to understanding the fundamental statistical limits of learning in control systems, particularly focusing on sample complexity. His work on risk-aware optimization develops algorithms that safeguard against catastrophic events while maintaining good average performance, and his security research addresses eavesdropping attacks in remote estimation and motion planning. Analysis of Dr. Tsiamis's recent publications reveals a strong focus on data-driven approaches to control theory with emphasis on distributionally robust methods, risk-aware optimization, and finite sample guarantees. His work bridges theoretical foundations with practical applications across system identification, online learning, and adaptive control, providing rigorous non-asymptotic guarantees for learning-based control algorithms. Dr. Tsiamis has received several notable research recognitions: Best student paper award at IEEE 61th Conference on Decision and Control (2022) Spotlight Presentation at 41st International Conference on Machine Learning (2024) Finalist for best student paper award at American Control Conference (2019) Finalist for young author prize at IFAC World Congress (2017) Oral presentation at 2nd L4DC Conference (2020) Dr. Tsiamis teaches Linear System Theory (227-0225-00L) at ETH Zürich and collaborates extensively with Professor John Lygeros, Professor Manfred Morari, and researchers from the University of Pennsylvania. His publication record demonstrates strong collaborative research across multiple institutions while advancing theoretical foundations of learning-based control. As an active member of the Automatic Control Laboratory at ETH Zürich, Dr. Tsiamis contributes to advancing control systems science through rigorous mathematical analysis and innovative algorithmic development, with applications spanning robotics, energy systems, and networked control.