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Masuma ShahidView profile

Assistant Professor

• LGBTQ+ Rights
• Artificial Intelligence and Law
• Queer Data
• +12 more

Masuma Shahid is an Assistant Professor and Lecturer at the Erasmus School of Law, specializing in International and European Union Law, with a dedicated research focus on LGBTQ+ rights, AI and human rights, and privacy law. She is affiliated with the International and European Union Law (IEUR) department and actively contributes to academic and public discourse on equality and digital governance. Assistant Professor, Erasmus School of Law Lecturer, International and European Union Law (IEUR) Co-Coordinator, LGBTQI+ Working Group, Berkeley Center on Comparative Equality and Anti-Discrimination Law Member, Diversity Platform, Erasmus School of Law Her research centers on the intersection of LGBTQ+ rights and artificial intelligence, queer data, equal marriage rights, European internal market law, competition law, and international human rights law. She employs critical legal theory to examine how legal systems address marginalization and digital discrimination. Her recent scholarly work includes a doctoral thesis on equal marriage rights litigation in major courts, publications on AI’s impact on LGBTQ+ communities, and authoritative textbooks on EU law. The articles reflect a strong trend in using comparative legal analysis to advocate for inclusive legal frameworks, particularly in digital and human rights contexts. She has been recognized through invitations to speak at academic forums and contribute to public debates on LGBTQ+ rights and AI ethics. Her media contributions highlight her role as a public intellectual in advancing legal inclusivity. Invited speaker on Queer Legal Theory and AI governance Expert commentator on LGBTQ+ rights in Dutch media Contributor to discussions on constitutional reforms for LGBTQIA+ and disabled persons Masuma Shahid advises on legal research and contributes to collaborative academic initiatives, particularly through her role at the Berkeley Center. While no formal grants are listed, her extensive publication and speaking record indicate active research funding and institutional support. She is involved in shaping policy discourse through academic and public engagement. She is a key figure in the LGBTQI+ Working Group at the Berkeley Center on Comparative Equality and Anti-Discrimination Law, contributing to an international network focused on anti-discrimination law and equality. Her work bridges academia, policy, and social justice advocacy.

Francesca GrisoniView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +8 more

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

• Airport Operations
• Transportation Simulation
• Air Traffic Management
• +4 more

Miguel Mujica Mota is a Senior Lecturer at the Faculty of Technology, National Autonomous University of Mexico (UNAM), and a member of the Centre of Applied Research Technology. His research focuses on airport operations, multimodal transport systems, and simulation modeling. He has expertise in analyzing capacity challenges in multi-airport systems, particularly in Mexico City, and developing decision support systems for airport security and resource allocation. His work integrates sustainability and efficiency, addressing topics like environmental reporting in airlines and post-pandemic airport recovery strategies. Research Contributions: Dr. Mujica Mota has published extensively on airport capacity optimization, multimodal transport integration, and simulation-based methodologies. Key projects include the X-TEAM D2D initiative for door-to-door travel and the IMHOTEP project for smart passenger flow management. His work often involves collaboration with institutions like Schiphol Airport and the H2020 EU framework. Research Interests: Airport terminal design, air traffic management, simulation modeling, multimodal logistics, and sustainable aviation. Awards: A-BOOST Research Fund (2020) Beste paper award EMM2018 X-TEAM D2D Project Recognition (2020) Activities: Organized conferences like the 2023 EUROSIM Simulation Seminar and served on committees for events such as the 2024 Multilog Conference. Grants & Projects: Involved in EU-funded initiatives like H2020, focusing on multimodal integration and sustainable transport solutions. His research also explores climate change impacts on infrastructure and simulation-based validation approaches.

Konrad KollnigView profile

Assistant Professor

• Digital Privacy
• AI Ethics
• Regulatory Technology (RegTech)
• +4 more

Konrad Kollnig is an Assistant Professor at Maastricht University’s Faculty of Law, specializing in the intersection of law and technology. He leads the RegTech4AI project, which combines legal and technical methods to address challenges in the AI and digital platforms sector. His academic background includes a PhD and MSc from the University of Oxford and a BSc from RWTH Aachen, with his PhD thesis winning the prestigious Stefano Rodotà Award 2024. Research focuses on market power analysis in digital platforms, ethical AI governance, and privacy-preserving technologies. He developed the TrackerControl app (200,000+ downloads) to expose app tracking practices. His work has influenced EU, OECD, US FTC, and other regulatory bodies, and been featured in Forbes, Wired, and New Scientist. Key achievements include winning the United Nations Privacy Competition 2022 and the Best Student Privacy Paper Award 2022. He holds a five-year RegTech4AI project grant (€2.1M) funding six researchers. Talks and collaborations span institutions like Georgetown University, CNIL, and the Council of Europe. His interdisciplinary approach bridges computer science, law, and policy to address systemic risks in digital ecosystems.

Vedran DunjkoView profile

Professor

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +7 more

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

Antonia Krefeld-SchwalbView profile

Assistant Professor

• Cognitive Science
• Consumer Behavior
• Sustainable Marketing
• +5 more

Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.

• Data Collaboratives
• Digital Government
• Open Data
• +5 more

Iryna Susha serves as an Assistant Professor in the Innovation Studies group within the Copernicus Institute of Sustainable Development at Utrecht University's Faculty of Geosciences. Her academic journey includes obtaining a PhD from Orebro University in Sweden, establishing her expertise in digital governance and data collaboration frameworks. Dr. Susha's research focuses on how organizations across different sectors can collaborate and leverage data to address 21st century societal challenges while contributing to sustainability goals. Her work spans digital government, open data initiatives, and data collaboratives for public good, with particular attention to cross-sector partnerships, data governance models, and the practical implementation challenges of data sharing initiatives. She has developed significant expertise in analyzing how data ecosystems can be structured to support societal benefits while navigating complex regulatory and organizational landscapes. Her publication record shows a clear evolution from early work on e-participation and open government data toward more sophisticated frameworks for data collaboratives and digital innovation for sustainability. The research demonstrates consistent engagement with practical implementation challenges while maintaining strong theoretical grounding in governance and organizational theory. Prizes & Grants (1) Dr. Susha actively supervises PhD candidates, currently guiding Dwayne Ansah's research on data donations for social good and Olafur Thorarensen's work on algorithmic decision systems in healthcare. She leads the "Digital Innovation" course where students develop critical understanding of digital innovations' societal and economic impacts. Additionally, she co-leads the Special Interest Group on Digitalization & AI for Sustainability (DAISY), which serves as a hub for interdisciplinary research on sustainable digital transformation. Her research connects with several major projects including the DATA ALLY project and Data Purchasing project, demonstrating practical applications of her theoretical work in real-world contexts. The DAISY group she co-leads provides an important forum for advancing research on sustainable digital innovation across disciplinary boundaries.

Maarten RenkemaView profile

Assistant Professor

• Artificial Intelligence
• Human Resource Management
• Digitalization
• +5 more

Maarten Renkema is an Assistant Professor at the University of Twente, specializing in Artificial Intelligence (AI) and its intersection with Human Resource Management (HRM) , Healthcare , and Organizational Innovation . His research explores how AI transforms work design, employee autonomy, and HRM practices in formalized contexts. Key Research Themes: Algorithmic HRM and Digitalization Human-AI Collaboration in Healthcare Self-Managing Teams and Organizational Structures AI Literacy in Education Scientific Awards: Best Dissertation Award (2019) from the Dutch HRM Network Research Trends: His recent work focuses on AI adoption in HRM, ethical implications of algorithmic management, and redefining professional roles in AI-integrated environments. Articles from 2024-2025 highlight practical applications of AI in healthcare workflows, employee-driven innovation, and the evolving responsibilities of HR professionals. Collaborations: Active in cross-sector partnerships, including projects on AI literacy frameworks and talent management in sports. Regularly contributes to professional discourse through invited talks and media commentary on AI's societal impact.

Matthias SchlottbomView profile

Associate Professor

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +4 more

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

Nathan van de WouwView profile

Professor

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +6 more

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

Farhad MerchantView profile

Assistant Professor

• Hardware Security
• Neuromorphic Computing
• Algorithm-Architecture Co-design
• +3 more

Dr. Farhad Merchant is an Assistant Professor of Innovative Computer Architecture at the Bernoulli Institute, University of Groningen, since July 2024. Previously, he served as a Lecturer (Assistant Professor) at Newcastle University (2022–2024) and held research roles at Bosch Research, NTU, and RWTH Aachen University. His research focuses on emerging technology-based computing and hardware-oriented security, including neuromorphic architectures, in-memory computing, and secure hardware design. Education: PhD in Electronics Engineering from the Indian Institute of Science, Bangalore, with a DAAD-funded visit to RWTH Aachen University. He also holds industry experience from Bosch Research. Research Interests : - Hardware Security - Neuromorphic Computing - Algorithm-Architecture Co-design - Reconfigurable Computing - Computer Arithmetic Projects : - Coordinator for the REACT project (2025–2029): Focuses on self-aware neuromorphic architectures. - Principal Investigator for Privacy-Preserving Computer Architectures (CogniGron, 2025–2029). - Completed BioNanoLock project (DFG-funded, focusing on bio-nanoelectronic security). Awards : - Best Paper Awards at ISQED 2022, NEWCAS 2023, and VLSI-DAT 2024. - Minerva Fellowship (Technion, Israel), HiPEAC Technology Transfer Award (2019). He co-founded the SeHAS workshop (since 2019) and serves on editorial and program committees for major conferences like DAC, ISLPED, and VLSI-SoC.

• Computational Neuroscience
• Machine Learning
• Neural Networks
• +5 more

Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).

Dongsheng YangView profile

Assistant Professor

• Power Electronics
• Renewable Energy Systems
• Power Conversion
• +3 more

Dongsheng Yang is an Assistant Professor with the Electrical Energy Systems Group at the Department of Electrical Engineering of Eindhoven University of Technology (TU/e). He has been working at TU/e since 2019, focusing on power electronics and renewable energy integration, and previously served as Assistant Professor at Aalborg University's Department of Energy Technology (2018-2019). Dr. Yang received his B.S., M.S., and Ph.D. degrees in electrical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2008, 2011, and 2016, respectively. His academic journey progressed from postdoctoral researcher at Aalborg University (2016) to faculty positions at both institutions. Dr. Yang's research focuses on the modeling, analysis, control, and design of power electronics dominated power systems , with the goal of safely accommodating high-penetrations of renewable energy sources and energy-efficient end-uses. His work spans several critical areas in modern power systems: Power electronics dominated grid stability and control Renewable energy integration and grid synchronization EV fast-charging infrastructure development Hydrogen production systems Medium-frequency transformer design and modeling AI applications in power electronics Analysis of Dr. Yang's recent publications reveals a strategic research trajectory toward developing advanced control strategies for power converters, improving modeling techniques through AI approaches, and addressing practical implementation challenges for renewable energy systems. His work spans both theoretical developments and practical applications, with increasing emphasis on neural network frameworks for magnetic modeling, safety boundaries for EV charging architectures, and enhanced fault ride-through capabilities for grid-connected systems. This progression demonstrates his commitment to solving real-world engineering challenges in the transition to renewable energy. Dr. Yang has received professional recognition including: Senior Member of IEEE Corresponding Member of CIGRE Working Group C4.56 Topic chair, technical committee member, and reviewer for top-level conferences and journals in power electronics Dr. Yang actively supervises doctoral candidates and postdoctoral researchers, including Xiao Yang (working on AI for power electronics), Saizhao Yang (postdoc), and L.A. Vlaar. He serves as project manager for multiple significant research initiatives totaling over €5 million in funding: REDCON (2023-2028) - Reconfigurable power electronics testbench Flexible Offshore Wind Hydrogen Power Plant Module (2022-2026) Sectorplan-DCES-Y.D.inv.: Reconfigurable power electronics testbench (2021-2029) E2GO-RDC Cost-reduction of EV fast-charging station (2021-2026) CW620863 System impact analysis for large scale renewable hydrogen production (2022-2023) Dr. Yang leads research within the Electrical Energy Systems group at TU/e's High Tech Systems Center, focusing on power conversion technologies. His work connects with multiple research teams across Europe through collaborative projects focused on renewable energy integration, EV infrastructure, and hydrogen production systems. He also teaches the course 'Dynamic control of power conversion in renewable energy systems' and contributes to the UN Sustainable Development Goals related to affordable and clean energy.

Iris F.A. VisView profile

Professor

• Operations Management
• Supply Chain Design
• Sustainable Logistics
• +5 more

Prof. Iris F.A. Vis is a Professor of Industrial Engineering at the University of Groningen's Faculty of Economics and Business. She specializes in logistics and operations management, focusing on optimizing processes through quantitative and qualitative methods. Her work intersects logistics with sectors like healthcare, education, and energy. She leads major projects such as SMiLES (sustainable mobility-logistics integration) and designs logistics solutions for personalized learning systems in schools. She has advised over a dozen PhD students and collaborates with industry partners globally. Awards include Fellowship in the Netherlands Academy of Engineering. Education: M.Sc. Mathematics (Leiden University), PhD in Operations Management (Erasmus University Rotterdam) Roles: Captain of Science for Topsector Logistics, Member of multiple national advisory boards Research interests span sustainable transportation networks, port optimization, healthcare logistics, and educational logistics. Key projects include LNG supply chain design, offshore wind farm maintenance planning, and synchromodal transport networks. Over 45 peer-reviewed publications and 18 media engagements highlight her impactful contributions. Teaching includes courses on supply chain network design, technology-enabled innovation, and operations management at all academic levels. She advises on industrial partnerships and digital transformation initiatives in the Northern Netherlands region.

• Hybrid Systems
• Networked Control Systems
• Event-Triggered Control
• +7 more

Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.