Find Academic Supervisors

Jakob HuberView profile

Researcher

• Operations Research
• Machine Learning
• Supply Chain Management
• +3 more

Dr. Jakob Huber is a Researcher at the University of Mannheim's School of Business Informatics and Mathematics, affiliated with the Data and Web Science Group. His research focuses on operations research, machine learning, and data-driven decision-making in supply chain management and inventory systems. He also explores probabilistic reasoning with ontologies and semantic web technologies. Huber's work addresses challenges such as perishable goods inventory optimization, demand forecasting incorporating calendar effects, and real-time decision support systems. His contributions bridge theoretical advancements in machine learning and practical applications in retail and logistics. Selected research highlights include developing cluster-based hierarchical demand forecasting models and infrastructure for probabilistic semantic web reasoning. His publications span journals like the International Journal of Production Economics and the Semantic Web. He holds a PhD from the University of Mannheim, and his expertise spans data science, artificial intelligence, and combinatorial optimization for data integration.

Marta AriasView profile

Associate Professor

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +3 more

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

Matthew CampbellView profile

Professor

• Design Automation
• Computational Synthesis
• Additive Manufacturing
• +2 more

Matthew Campbell is a Professor at Oregon State University's School of Mechanical, Industrial, and Manufacturing Engineering. An ASME Fellow and NSF CAREER awardee, his research automates engineering design through computational synthesis, graph theory, and AI. Recent work (2023-2024) develops algorithms for additive manufacturing optimization and machine learning-based 3D modeling. He created open-source tools like GraphSynth and Planar Mechanism Kinematic Simulator. Teaching covers machine design and optimization courses.

• Economic Model Predictive Control
• Stability of Nonlinear Systems
• Chemical Reaction Networks Theory
• +5 more

David Angeli is a Professor of Nonlinear Network Dynamics in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. Affiliations: Control and Power Research Group Energy Futures Lab His research focuses on Economic Model Predictive Control, Stability of Nonlinear Systems, Chemical Reaction Networks Theory, Systems Biology, and Control of Smart Grids. He develops theoretical frameworks for networked systems with applications in energy infrastructure and biological processes, emphasizing robustness and scalability in distributed settings. Recent publications (2023-2025) highlight work on distributed control algorithms, Lyapunov-based stability certification, and small-gain theorems for interconnected systems. His research bridges control theory, game theory, and machine learning to solve problems in cyber-physical systems, particularly in resilient consensus protocols and economic optimization for heterogeneous networks. As a core member of the Control and Power Research Group and Energy Futures Lab, he contributes to sustainable energy solutions and advanced control methodologies for complex networked systems.

Bruno CorreiaView profile

Associate Professor

• Protein engineering
• Computational design
• Immunoengineering
• +7 more

Bruno Correia is an Associate Professor at the Laboratory of Protein Design & Immunoengineering (LPDI) within the Institute of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His work bridges computational protein design with experimental validation, focusing on immunoengineering applications for vaccines and cancer immunotherapy. PhD in Computational Biology (Universidade Nova de Lisboa, 2010) BSc in Chemistry (Universidade de Coimbra, 2004) Correia's research spans protein engineering , synthetic biology , and computational design , with key contributions in: Developing MaSIF for protein surface interaction analysis Designing chemically-controlled protein switches for CAR T-cell therapy Creating de novo functional proteins for vaccine development His publications highlight vaccinology , structural bioinformatics , and machine learning applications in protein science. Awards include the Protein Society Young Investigator Award (2021) and ERC Starting Grant (2016) . Scientific contributions: Protein surface fingerprinting for interaction prediction Rational immunogen design for HIV and hepatitis C Deep learning frameworks for protein structure generation

Oded YacobiView profile

Associate Professor

• Representation Theory
• Algebraic Groups
• Categorification
• +3 more

Associate Professor Oded Yacobi is affiliated with the University of Sydney's Faculty of Science. His research focuses on advanced algebraic structures, including representation theory, categorification, and geometric methods in quantum groups. He holds a Ph.D. from the University of California, San Diego (2009), followed by postdoctoral roles at the University of Toronto and Tel Aviv University. Yacobi is actively involved in grants exploring dualities in representation theory and braid group dynamics. His work bridges algebraic geometry and combinatorics, with recent articles addressing Weyl groups, braid group actions, and affine Grassmannians. He leads a vibrant research program with collaborations spanning North America and Australia. Education: Ph.D. in Mathematics, University of California, San Diego (2009) Postdoctoral Fellowships: University of Toronto, Tel Aviv University Research Interests: Representation theory of algebraic groups and quantum groups Categorification techniques and their applications Geometric methods in Lie theory and combinatorics Braid groups and their algebraic structures Key Grants: 2023: ARC DP Grant (Braid groups via representation theory and machine learning) 2018: ARC DP Grant (New Dualities in Modular Representation Theory) Professional Activities: Maintains an active research group and collaborates internationally. His work has been published in top journals like Mathematische Zeitschrift and Advances in Mathematics. He is also engaged in teaching advanced algebra courses at the University of Sydney.

Tim FrenchView profile

Associate Professor

• Logic
• Artificial Intelligence
• Knowledge Representation and Reasoning
• +5 more

Tim French is an Associate Professor in the Department of Computer Science and Software Engineering at the University of Western Australia's School of Physics, Maths and Computing. He is affiliated with the UWA Oceans Institute and serves as Regional Contest Director for the South Pacific Programming Contest and Programme Chair for the Australasian Conference on Artificial Intelligence 2022. His research focuses on logic, artificial intelligence, knowledge representation, and reasoning about uncertainty in multi-agent systems, probabilistic reasoning in games, and industrial applications like automated planning and machine learning for complex processes. French holds a PhD in Computer Science (2007) and BSc in Computer and Mathematical Sciences (1999), both from UWA. His expertise spans algorithms, automated reasoning, formal methods in software, and temporal logic verification systems. He has led or contributed to 6 major research projects including the ARC Research Hub for Transforming Energy Infrastructure and the ARC Training Centre for Transforming Maintenance through Data Science. His research outputs include 118 publications covering topics like aleatoric logic for probabilistic reasoning, semantic knowledge extraction from industrial maintenance systems, and deep learning applications in wastewater treatment. He has developed novel methods for knowledge graph construction, state estimation in complex systems, and user interface design informed by work characteristics models. Key grants: 6 active/finished projects totaling $M+ funding Leadership roles: Programming contest director, conference chair Interdisciplinary focus: Combines formal logic with industrial automation challenges French's work contributes to UN Sustainable Development Goals through education and innovation in sustainable industrial processes and environmental systems modeling. His team has developed practical solutions for maintenance procedure digitization, wastewater plant optimization, and robust agent-based systems for uncertain environments.

Aishik MandalView profile

Researcher

• Dialogue Systems
• Multimodality
• Privacy
• +2 more

Aishik Mandal is an ELLIS PhD student at UKP Lab, Technical University of Darmstadt, supervised by Prof. Iryna Gurevych and co-supervised by Prof. Aurélien Bellet. He completed his Master's in Artificial Intelligence, Machine Learning and Applications and Bachelor's in Electronics and Electrical Communication Engineering at IIT Kharagpur, where he received the Silver Medal for best academic performance in his department. His research focuses on Dialogue Systems, Multimodality, and Privacy , with significant contributions to mental health AI applications, depression detection, and culturally diverse multilingual visual question answering. His work combines advanced machine learning techniques with practical applications for healthcare and human-computer interaction. Mandal's research output shows a clear progression from foundational work in dialogue understanding and representation learning to more specialized applications in mental health and privacy-aware AI systems. His recent publications demonstrate expertise in multimodal fusion techniques, ordinal classification for depression severity prediction, and developing culturally sensitive AI benchmarks. Silver Medal from Electronics and Electrical Communication Engineering department at IIT Kharagpur DAAD WISE scholarship for internship at TU Munich Charpak Lab scholarship for internship at INRIA Paris GKF International Internship scholarship for INRIA Paris Mandal has been actively involved in research collaborations across multiple institutions, including IIT Kharagpur's Centre of Excellence in AI, Complex Network Research Group, Technical University of Munich, and INRIA Paris. His academic journey shows a strong foundation in both theoretical computer science and practical AI applications, with increasing specialization in multimodal dialogue systems and privacy-conscious AI development.

• Community Detection
• Graph Clustering
• Graph Representation Learning
• +4 more

Susanna Pozzoli is a doctoral student at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. She also serves as the vice chairperson of the EECS PhD Council, representing doctoral students at the school level. Education: Bachelor of Science in Engineering of Computing Systems from Politecnico di Milano (2017) EIT Digital Master's Programme in Data Science (2017–2019), completed first year at Politecnico di Milano and second year at KTH in Stockholm, Sweden Research Interests: Susanna’s work focuses on advancing graph-based machine learning techniques, including community detection, graph clustering, role discovery, and unsupervised learning. She explores how graph representation learning can enhance performance evaluation in complex networks, with applications ranging from social network analysis to biomedical data science. Publications: Her recent work highlights innovations in graph embedding techniques and diffusion-driven role recognition, alongside interdisciplinary research in domain-agnostic feature selection for breast cancer analysis. These studies reflect her expertise in bridging theoretical computer science with practical data-driven solutions. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Teams: While no specific lab affiliations are listed, her research is embedded within the collaborative environment of KTH’s SCS division.

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +4 more

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

Konrad Paul KordingView profile

Professor

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +6 more

Konrad Paul Kording is the Nathan Francis Mossell University Professor at the University of Pennsylvania's School of Engineering and Applied Science, holding primary appointments in Bioengineering and Neuroscience, and a secondary appointment in Computer and Information Science. His research focuses on computational neuroscience, machine learning, and their applications to understanding neural systems and human behavior. He leads a research group located in Room 404 Richards and maintains an active research website and personal academic page. His work explores topics such as neural decoding, causal inference, and the alignment of artificial neural networks with biological systems. He has pioneered methods for analyzing neural activity in macaques, developing large-scale facial analysis resources like PrimateFace. His interdisciplinary approach integrates computer vision, medical informatics, and robotics to address challenges in neurology and developmental disorders. Recent research highlights include advancing techniques for early prediction of cerebral palsy using motion tracking and automated segmentation of synchrotron-scanned fossils. His contributions to causal discovery methodologies and neural network interpretability have been influential in both neuroscience and machine learning communities.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

• Artificial Intelligence
• Educational Technology
• Mobile Applications
• +3 more

Eva García López is a Professor in the Department of Computer Science at the University of Alcalá. She holds a PhD from the same institution, completing her thesis on improving the usability of interfaces for teaching objects in m-learning in 2013. Her research focuses on usability engineering, artificial intelligence, and educational technology, with emphases on mobile applications, accessibility, and gamification. She is affiliated with the INTELIA research group (Interaction Technologies and Artificial Intelligence Lab) and previously contributed to TIFyC (Information Technologies for Training and Knowledge) and PMI (Intelligent Mobile Platforms). Eva’s work spans developing adaptive learning systems, enhancing digital content accessibility, and leveraging large language models for educational tools. Her projects include MOOC development, social gamification experiments, and usability evaluations of mobile instant messaging apps. She has also explored the impact of olfactory stimuli on user performance and the application of NLP in automated question generation. Her contributions include guidelines for accessible digital content creation and frameworks for personalized learning paths in web engineering curricula. Her research integrates interdisciplinary approaches, combining machine learning, human-computer interaction, and educational theory to address challenges in digital learning ecosystems. Notable contributions include frameworks for curriculum development, methodologies for usability evaluation, and tools for automated code vulnerability repair.

Stephen KobourovView profile

Professor

• Algorithm Design
• Computational Geometry
• Graph Drawing
• +3 more

Prof. Stephen Kobourov is a Professor in the TUM School of Computation, Information and Technology at Technische Universität München (TUM). Previously, he held positions at the University of Arizona from 2000 to 2024, progressing from Assistant Professor to Full Professor, and served as Deputy Director of the Data Science Institute. His expertise lies in algorithm design, computational geometry, and graph visualization, with over 250 publications and significant funding from NSF, ONR, and USDA grants. Education: BS in Computer Science and Mathematics from Dartmouth College (1995), PhD in Computer Science from Johns Hopkins University (2000). Research focuses on efficient algorithms for graph drawing, visualization techniques for networks, and geometric algorithms. Notable contributions include work on stress metrics in graph layouts, hypergraph representations, and non-Euclidean visualization frameworks. He has co-chaired committees for ALENEX, IEEE PacificVis, and GD, and serves as an editor for JGAA, CGTA, and IEEE TVCG. Awards include the Fulbright Distinguished Chair (2015–2016), Humboldt Research Fellowship (2011–2014), and NSF Career Award (2006–2011). His recent work explores multi-layer network visualization, AI-driven graph analysis (e.g., MLLMs perception), and graph embeddings in non-Euclidean spaces. Advising and grants: Extensive grants from NSF and other agencies support his research. He has advised numerous students and collaborates internationally, contributing to labs like the Data Science Institute and initiatives such as the Institutional Knowledge Map (KMAP). Visualization projects include MetroSets (metro map-style set visualization), Wooly Graphs (knitting pattern frameworks), and tools for interactive network analysis. His work bridges theoretical algorithms with practical applications in science and technology.

Badis HammiView profile

Assistant Professor

• Cybersecurity
• Internet of Things (IoT)
• Blockchain Technology
• +5 more

Badis Hammi is an Assistant Professor at Telecom SudParis, specializing in cybersecurity and network security. His research focuses on IoT security, blockchain applications, botnet detection, and vehicular networks. He has authored numerous peer-reviewed articles on topics such as malware analysis, phishing detection frameworks, and decentralized security solutions in smart homes and healthcare sectors. Education PhD in Cryptography and Security (2015), Université de Technologie de Troyes Research Interests Cybersecurity frameworks for IoT and blockchain systems Malware detection methodologies PKI security in cooperative intelligent transportation systems (C-ITS) Phishing prevention using graph neural networks Botnet activity analysis in cloud environments Recent Article Trends His recent work emphasizes blockchain's role in mitigating supply chain fraud, improving smart home security, and securing vehicular communications. He also explores machine learning approaches for detecting Sybil attacks and network anomalies in distributed systems. Grants & Advising Active in collaborative projects with industry partners on vehicular network security Advises on EU-funded initiatives for blockchain-based authentication systems Labs/Teams Affiliated with the SAMOVAR research lab at Telecom SudParis, focusing on network and system security.