Alex Levering is an Assistant Professor in socio-cultural landscape interactions at the Faculty of Science, Vrije Universiteit Amsterdam. He is affiliated with the Institute for Environmental Studies (IVM) and specializes in remote sensing, geo-information sciences, and computer vision applications. His work focuses on landscape semantics, AI interpretability, and combining visual/non-visual data for predictive models. Education : PhD in Remote Sensing (2024), Wageningen University MSc in Geo-Information Sciences (2019), Wageningen University BSc in Water Management (2016), Hogeschool Zeeland Research Interests : Levering explores how computer vision and GeoAI can quantify landscape quality preferences, model urban liveability from aerial/satellite imagery, and improve AI model interpretability. He also investigates small dataset challenges and integrates social media data for environmental monitoring. Current Projects : Leading roles in projects like 'Guiding Human Settlements Towards Sustainable Development' (2019–2024), focusing on urban densification and environmental impact assessments. Collaborations with institutions like the Hong Kong University of Science and Technology (Guangzhou) as an advisor (2025–2027). His recent publications emphasize multimodal learning, urban liveability prediction, and explainable AI for socioeconomic analyses. No scientific awards are explicitly listed in the provided texts.
Ileana Buhan is an Assistant Professor at Radboud University Nijmegen's Digital Security Group and a member of the CESCA Lab. Her research focuses on hardware security, particularly advancing tools for secure hardware design and mitigating side-channel vulnerabilities. She previously held roles at Riscure (2011–2020) as a security evaluation manager and product manager, and at Philips Research (2008–2010) as a senior scientist. She earned her Ph.D. in Cryptography with Noisy Data from the University of Twente in 2008, recognized with the 2008 EBF European Biometrics Research Industry Award. Her work emphasizes practical security evaluation methods, automated leakage modeling (e.g., ABBY tool), and hardware-software co-design for resistance against side-channel attacks. She actively contributes to conferences like CHES, FDTC, and CARDIS, often in program committee roles. Recent invited talks include topics such as AI-driven vulnerability prediction, architecture-level simulators for root cause analysis, and automated tools for cryptographic implementation security. Her research spans RISC-V processors, microarchitecture analysis, and the intersection of machine learning with hardware security. Notable contributions include frameworks for leakage detection, fault simulation, and explainable side-channel analysis. She balances academic rigor with industry relevance, aiming to bridge gaps between theoretical security and real-world implementation challenges.
Dirk Fahland is an Associate Professor in Process Analytics on Multi-Dimensional Event Data at the Analytics for Information Systems group, Eindhoven University of Technology (TU/e), School of Mathematics and Computer Science. He combines formal methods with data-driven approaches to analyze complex distributed systems through event data. His current research focuses on process mining, data engineering, and multi-dimensional analysis of business processes. Academic Background: PhD from Humboldt-Universität zu Berlin and TU/e under Profs Wolfgang Reisig and Wil van der Aalst Post-Doc at TU/e on EU-funded ACSI project Research stays at Weizmann Institute, HPI Potsdam, and National University of Singapore Appointments: Assistant Professor (2013), Tenure (2016), Associate Professor (2019) Research Interests: Dirk's work centers on analyzing complex systems through event data by developing techniques for large-scale preprocessing, model synthesis, and multi-angle behavioral analysis. He explores object-centric process mining, anomaly detection, and knowledge graph applications for auditing. His research emphasizes balancing model accuracy with simplicity and integrating domain knowledge for explainable process analysis. Scientific Awards: Best Paper Award BIS 2011 Best Paper Award BPM 2011 Best Paper Award ICPM 2020 Best Paper Award ICPM 2021 Best Reviewer Award ICPM 2019 Educational Contributions: He manages the "Data Science in Engineering" Master's program, leads the "Data Challenge" course series at JADS, and teaches advanced process mining courses. He also contributes to BPMN visualization and performance monitoring education.
Vivek Bhardwaj is an Assistant Professor at the Institute of Biodynamics and Biocomplexity, Department of Biology, Utrecht University. He leads the Quantitative Biology and Data Integration research group, focusing on combining high-throughput genomics and machine learning to understand and manipulate cell fate decisions. Research Interests: His work lies at the intersection of bioinformatics, genomics, and developmental biology. The lab investigates how epigenetic landscapes and transcription factors guide cell identity during animal development. Using single-cell and single-molecule genomics technologies, they generate large-scale datasets to build statistical and machine learning models that explain cellular decision-making processes. Research Approach: The lab employs a dual strategy: (1) extracting biological insights from multi-omics data of developing cells to model cell identity and function, and (2) developing open-source bioinformatics tools and workflows for analyzing single-cell (epi)genomics data. Their long-term goal is to enable in-vivo reprogramming of stem cells for applications in regenerative medicine, healthy aging, and cancer therapy. Publications Trend: Recent research, such as the 2025 preprint on zebrafish embryos, demonstrates a strong focus on single-cell multiomics, integrating histone modification and gene expression data to dissect developmental mechanisms. The work emphasizes quantitative modeling, data integration, and tool development for the broader biological community. No scientific awards listed in the provided text. Advising and Grants: While specific students and grant details are not mentioned, the lab actively hosts interns and new members, indicating a commitment to training and mentorship. The development of multiple open-source software tools suggests involvement in collaborative and computationally driven research projects, likely supported by external funding. Labs and Teams: The Bhardwaj Lab is an inter-divisional group within Utrecht University. They maintain a strong open-science ethos, with tools like sincei, scChICflow, and snakepit publicly available on GitHub. The lab also shares protocols, datasets, and news about open positions, reflecting an integrated and transparent research environment.
Arlene John is an Assistant Professor at the Biomedical Signals and Systems (BSS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science at the University of Twente. She holds a Ph.D. in Electrical and Electronic Engineering from University College Dublin (2022), with prior academic experience including a bachelor’s degree in Electrical and Electronics Engineering from the National Institute of Technology, Calicut (2017) and research internships at the Indian Institute of Science (2016) and Beijing University of Technology (2019). Bachelor: Electrical and Electronics Engineering, National Institute of Technology, Calicut (2017) Ph.D.: School of Electrical and Electronic Engineering, University College Dublin (2022) Her research focuses on biomedical signal processing, machine learning, explainable AI, and multisensor data fusion for wearable health monitoring devices. She has industry experience as a Project Manager at Bosch India Ltd. and a Machine Learning Mathematics Engineer at ASML Netherlands B.V., bridging technical sales, engineering strategy, and computational modeling. Recent publications emphasize language testing frameworks, CEFR alignment, and psychometric modeling for vocabulary and grammar assessment. These works span interdisciplinary themes in education, linguistics, and standardized evaluation systems.
Cynthia Liem is an Associate Professor at the Multimedia Computing Group, part of the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). Her work uniquely bridges computer science and music, leveraging her dual expertise in both domains. Education: BSc and MSc in Media and Knowledge Engineering (Computer Science), TU Delft (2007, 2009); PhD, TU Delft (2015); BMus and MMus in Classical Piano Performance, Royal Conservatoire, The Hague (2009, 2011). Her research centers on human-centered multimedia analysis, with two core themes. First, she investigates how to algorithmically surface information in large multimodal archives that users would not discover through conventional search or recommendation systems, aiming to broaden user perspectives and help uncover underutilized digital content. Second, she focuses on validation and validity in data science, particularly in contexts where human responses are implicit and hard to measure, drawing inspiration from psychometric validity in the social sciences. Her work emphasizes real-world applicability, especially in music and cultural heritage domains. The two recent publications highlight her engagement with cutting-edge AI challenges: one explores algorithmic recourse dynamics, while the other examines evolutionary algorithms in adversarial example generation, indicating strong interests in robustness, explainability, and human-aligned AI systems. Notable scientific recognitions include: Google Anita Borg Scholarship (2008) Google European Doctoral Fellowship (2010) Top-5 nominee, New Scientist Science Talent Prize (2016) Cynthia Liem has been involved in several significant research projects, including the H2020 TROMPA project on crowd-powered enrichment of public-domain music, an NWO-KIEM project on musician well-being, an NWO-Veni project on perspective-broadening in recommender systems, and an ERASMUS+ initiative on big data in psychological assessment. She collaborates with key institutions such as the National Library of The Netherlands and CDR/Muziekweb. Beyond academia, she remains active as a performing pianist, notably in the Magma Duo, which won first prize at the A Feast of Duos competition and was part of the Dutch Classical Talent Tour, leading to a national concert tour. There is no indication of lab or research team leadership beyond project involvement, though her role as an Associate Professor suggests supervision of students and researchers. Future work appears directed toward socially responsible AI, improved validation frameworks, and interdisciplinary applications of music and data science.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Malvina Nissim is a Professor of Computational Linguistics and Society at the University of Groningen's Faculty of Arts, affiliated with the Center for Language and Cognition Groningen (CLCG). Her research focuses on NLP applications to societal challenges, ethics in AI, and science communication. She is actively involved in initiatives like the Sectorplan's Humane AI theme and the ACL Ethics Committee. Education: Not explicitly listed in provided texts, but her career path suggests doctoral training in linguistics/comp. ling. Research interests include multilingual NLP, bias mitigation in AI, ancient language processing (e.g., AGALMA project), and responsible tech communication. She has co-authored a popular introductory book on Computational Linguistics (in Italian). Publications span topics like dialogue systems, model uncertainty in ed tech, and cross-lingual NLP. Key awards include the 2016 UG Lecturer of the Year. Awards: 2016 Lecturer of the Year, multiple grants (e.g., CLICK-NL 800K for PAGINA project with Dagblad van het Noorden).
Prof. Floris Bex holds a dual role as Professor (Regulating Socio-Technical Change) at Tilburg Law School and Associate Professor of Artificial Intelligence at Utrecht University. He also serves as a Scientific Advisor AI for the Dutch National Police (Politie - Landelijke Eenheid) since 2016. His work bridges law, technology, and decision-making, focusing on AI’s societal impacts. Affiliations: Tilburg Law School (TILT institute), Utrecht University Key roles: AI ethics, legal decision-support systems, socio-technical regulation Research focuses on AI’s role in legal contexts, including algorithmic transparency, judicial decision-making, and regulatory frameworks for emerging technologies. His work spans transdisciplinary projects addressing AI ethics, data governance, and the intersection of law & technology. Active in 2 major projects: Regulating Socio-Technical Change (2019–2023) and Digital Legal Studies (2019–2025), exploring EU competition law, data protection, and AI policy. Awards: None explicitly listed in provided texts Grants: Involved in EU-funded research initiatives Led the TILT research group, collaborating globally on AI in legal systems. His lab work involves designing explainable AI systems for law enforcement and judicial processes.
Çiçek Güven is an Assistant Professor at the Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University. With a mathematical background in discrete algebra and geometry, she holds a PhD from Eindhoven University of Technology (2012) and has academic affiliations spanning academia and industry. Education: PhD in Mathematics, Eindhoven University of Technology Master's in Mathematics, Koç University Bachelor's in Mathematics, Koç University Her research focuses on network analysis , learning on graph-structured data , and explainable AI . She investigates how graph topology and spectral properties influence machine learning outcomes, with applications to brain networks, electrical grids, and social systems. She emphasizes socially impactful AI , contributing to projects like Child Growth Monitor (malnutrition detection) and Ilustre (Caribbean energy transition). Recent publications highlight her work in graph neural networks , higher-order network analysis , and ethical data practices . She serves as Lab Manager for the ICAI Ilustre Lab and sits on the Scientific Advisory Board for Informatics at the Lorentz Center. Scientific Awards and Grants are not explicitly mentioned in the provided texts. However, her contributions to AI ethics, interdisciplinary research, and open datasets like ARAN demonstrate significant scholarly impact.
Jannis Kurtz is an Assistant Professor in Mathematical Methods and Applications in Data Science at the University of Amsterdam 's Faculty of Economics and Business, Department of Business Analytics. His research focuses on robust optimization, bilevel optimization, explainable optimization, integer optimization, machine learning, and operations research. Robust Optimization Bilevel Optimization Explainable Optimization Integer Optimization Machine Learning Operations Research Recent publications include advancements in counterfactual explanations for linear optimization, neural bilevel optimization frameworks, and data-driven robust combinatorial optimization. He organizes the Robust Optimization Webinar series and actively participates in international conferences like INFORMS, ICSP, and LION. Contact: j.kurtz@uva.nl
Pradeep Murukannaiah is an Associate Professor in the Interactive Intelligence group at the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft). He co-directs the Hippo Lab, a Delft AI lab focused on AI for fair, efficient, and interpretable analysis of climate policies. He also holds leadership roles as Use Cases Coordinator and Diversity Co-Chair in the Hybrid Intelligence center, and serves as Master Coordinator for the MSc in Data Science and Artificial Intelligence Technology (DSAIT). Dr. Murukannaiah received his PhD in Computer Science from North Carolina State University in 2016. Prior to joining TU Delft, he served as an Assistant Professor at Rochester Institute of Technology (2017-2019), completed an internship at Google, and worked as a Software Engineer at Alcatel-Lucent. His research centers on engineering socially intelligent agents through three interconnected thrusts: Natural Language Processing (focusing on argument mining, value alignment, and claim analysis), Multi-Agent Systems (exploring negotiation, social choice, and multi-objective reinforcement learning), and Hybrid Intelligence (developing frameworks for human-AI synergy in decision making). Cross-cutting these areas are his investigations into values (representing what matters to stakeholders) and norms (representing expectations between stakeholders), which together form sociotechnical systems where humans interact, make decisions, and stay accountable to each other while AI agents augment human intelligence. His recent publications demonstrate a strong focus on value-sensitive AI systems, with work spanning moral frame preservation in news summarization, multi-objective reinforcement learning for climate policy analysis, and mechanisms for responsible autonomy. His research consistently bridges theoretical advances in AI with practical applications in societal decision making, particularly around climate change and democratic processes. Dr. Murukannaiah has successfully mentored numerous graduate students, including current PhD candidates Zuzanna Osika (working on explainable multi-objective decision support), Shubhalaxmi Mukherjee (focusing on fact checking using LLMs), and several recent PhD graduates whose work has contributed to the fields of opinion diversity through hybrid intelligence and context-specific value inference. As co-director of the Hippo Lab and through his leadership roles in the Hybrid Intelligence center, he actively shapes research directions that emphasize fairness, interpretability, and human-centered AI approaches for addressing complex societal challenges.
Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Alberto De Luca is an Assistant Professor at the Image Sciences Institute, Division Imaging & Oncology, University Medical Center Utrecht. He holds a BSc in Biomedical Engineering (2011), MSc in Bioengineering (2013), and PhD (2017) from the University of Padova, Italy, where his thesis focused on non-Gaussian diffusion in the brain and skeletal muscle. His research specializes in diffusion MRI methodologies for neurological diseases and cancer, with key interests including: Fiber-specific quantification in grey/white matter Multi-center MRI data harmonization Physiological parameter estimation via inverse methods Response prediction modeling He leads projects in cerebral small vessel disease and pediatric oncology, utilizing advanced 7T MRI and AI techniques. Recent publications (2025) demonstrate strong focus on: AI-driven lesion-symptom mapping in vascular cognitive disorders Diffusion MRI harmonization across research sites Advanced tractography validation frameworks Pediatric cancer imaging biomarkers He contributes to the Translational Neuroimaging Group and International Society for Tractography, with external collaborations including Erasmus MC (Frontotemporal dementia research) and Hogeschool Utrecht (guest lecturing on brain networks).
