Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her work focuses on programming languages, with specific emphasis on program analysis, verification, and synthesis. B.S., M.S., and Ph.D. from Stanford University Her research aims to enhance software reliability, security, and development efficiency through advanced synthesis techniques. Recent work spans: Neurosymbolic and semantic synthesis Blockchain and smart contract optimization Type systems for safety She has received prestigious accolades including: Sloan Fellowship NSF CAREER award Contributions to the field include leadership roles in conference organization and mentoring initiatives. Her lab actively explores applications in databases, security, and concurrent programming.
Mira Mezini is a Professor of Computer Science at Technical University of Darmstadt (TU Darmstadt), Germany. She leads the Software Technology Lab and is co-spokesperson for the Excellence Cluster Reasonable Artificial Intelligence . Mezini serves on the board of the National Research Center for Applied Cybersecurity ATHENE and co-directs hessian.AI, the Hessian Center for Artificial Intelligence. Her academic leadership includes past roles as Dean of Computer Science (2013-2014) and Vice President of TU Darmstadt (2014-2019). PhD in Computer Science, University of Siegen Assistant Professor, Northeastern University (USA) Visiting Professorships: Lancaster University (UK), Università della Svizzera Italiana (Switzerland) Research Interests : Mezini develops programming systems for reliable distributed software and AI, focusing on large-scale module concepts, adaptability, and extensibility. She creates intelligent software development environments leveraging web-based resources to automate programming rules and patterns. Her work spans automated API misuse detection , session types , and foundational code models . Scientific Contributions : Mezini has (co)authored over 200 peer-reviewed publications, including 15 recent representative works on: Modular multi-language analysis (2024) Secure distributed programming models (2023-2024) Effect systems for concurrency (2023) Reactive programming frameworks (2018-2024) Formal verification of cryptographic APIs (2018) Core languages for cloud computing (2016) ACM Fellow ERC Advanced Grant (2012) AITO Dahl-Nygaard Senior Prize (2025) Horst Görtz IT Security Award (2014, second prize) IBM Eclipse Innovation Awards (2005, 2006) Google Research Award (2017) Leadership : Mezini has chaired major conferences (OOPSLA, ECOOP, ICSE) and served on panels including the European Research Council Consolidator Grant, ACM SIGPLAN Executive Committee, and DFG Senate. She mentors through the SIGPLAN long-term program and contributes to DEI initiatives at conferences like SPLASH. Labs & Collaborations : Leads the Software Technology Lab at TU Darmstadt. Collaborates with institutions including EPFL (session types), ETH Zurich (language design), and Carnegie Mellon University (code analysis). Her teams focus on decentralized systems, API security, and programming language foundations.
Akash Lal is a Partner Researcher at Microsoft Research India, focusing on programming languages, concurrency, verification, and AI applications in software engineering. His work bridges formal methods with practical tools like Coyote and Corral for concurrent system reliability. PhD from University of Wisconsin-Madison (2009), advised by Thomas Reps Key research areas: LLM-driven memory safety (2024-2025) Concurrency testing frameworks (Coyote, P#) Smart contracts verification (Celestial) CodeQL-based resource leak detection His 15 most recent publications span 2021-2025, emphasizing LLM integration for verification, concurrency analysis, and systems research. Notable trends include industrial-strength concurrency testing (TACAS 2023), ML-driven documentation (ASE 2023), and Rust/ML pipeline safety (ICSE 2025). Major awards: CAV Award (2023) for context-bounded analysis EASST Best Paper (2023) for Coyote ACM Distinguished Paper (OOPSLA 2021) ACM Distinguished Artifact (OOPSLA 2020) Best Paper (FMCAD 2020) Advisees: 18 researchers including Ankush Das (CMU), Samvid Dharanikota (CMU), and Nausheen Mohammed (Leuven)
Viola Priesemann is a Professor of Physics at the University of Göttingen and heads the Max Planck Research Group on the Theory of Neural Systems at the Max Planck Institute for Dynamics and Self-Organization in Göttingen. She also holds affiliations with the Cluster of Excellence 'Multiscale Bioimaging', the Campus Institute for Data Science, and the Max Planck-University of Toronto Centre for Neural Science and Technology. Her research focuses on understanding the fundamental principles of collective information processing in both neural and social networks. She employs approaches from statistical physics and information theory to investigate self-organization and learning processes in living networks. Her work spans several interconnected areas: neural criticality and subsampling effects in network analysis, information processing in neural circuits, and the spread of information (and misinformation) in social networks. During the COVID-19 pandemic, she applied her expertise in spreading dynamics to study viral transmission and the effectiveness of interventions, becoming a member of the German government's expert panel on the pandemic. Her recent publications reveal a strong focus on understanding how neural networks maintain optimal information processing through controlled criticality, how subsampling affects our ability to infer network properties, and the parallels between information spread in neural and social networks. Her work bridges theoretical physics, computational neuroscience, and epidemiology, demonstrating how principles from statistical physics can illuminate collective phenomena across vastly different scales. Medal for Scientific Journalism of the German Physical Society (DPG) Communitas Award of the Max Planck Society Member of the Junge Akademie Dannie-Heineman Award of the Academy of Science and Humanities, Göttingen Science award of Lower-Saxony - Wissenschaftspreis Niedersachsen Young Scientist Award for Socio- and Econophysics from the German Physical Society Professor Priesemann actively mentors numerous PhD students and postdocs in her research group, fostering interdisciplinary collaboration between physics, neuroscience, and data science. Her lab investigates topics ranging from neural plasticity and learning to epidemic modeling and information spread in social networks. She has secured significant research funding, including a German-Israel Foundation Young Investigator Grant and projects within the Cluster of Excellence 'Multiscale Bioimaging' and the Max Planck-University of Toronto Centre. Her research group maintains strong connections with experimentalists and theorists across multiple institutions, creating a vibrant environment for studying the principles of living adaptive networks. She has initiated several interdisciplinary research projects, including a major initiative on curiosity and hierarchical information processing across species.
Johannes Kruse is a Senior Research Fellow at the Max Planck Institute for Research on Collective Goods , where he focuses on Competition Law , Procedural Law , Judicial Decision-Making , Computational Legal Studies , and Behavioural Law and Economics . His work bridges legal theory with empirical and computational approaches. He earned his Dr. iur. (Doctor in Law) from the University of Münster in 2023 and passed the Second State Exam in Law in 2022 after a Legal Clerkship (2020–2022) . He studied Law and Economics at the University of Münster, University of Turin, and FernUniversität in Hagen. Kruse’s research integrates computational methods with legal analysis , particularly in competition law and procedural law. His recent work includes AI-driven legal commentary, empirical studies on judicial behavior, and neuroscientific perspectives on fundamental rights. His publications emphasize quantitative legal analysis , AI applications , and evidence-based legal frameworks . Key trends include computational antitrust , economic evidence in court , and neurojurisprudence . Scientific recognition includes the WuW Award „Future of Competition” (2024) for his computational analysis of Article 102 TFEU. He contributes to legal education through empirical case studies and digital legal tools , with a focus on interdisciplinary collaboration.
Olaf Wolkenhauer is a Full Professor and Chair in Systems Biology & Bioinformatics at the Faculty of Computer Science & Electrical Engineering, University of Rostock, a position he has held since 2003. Additionally, he serves as Guest Professor and Interim Head of Section III at the Leibniz Institute for Food Systems Biology, Technical University of Munich, until 2025. He maintains adjunct professorships at Chhattisgarh Swami Vivekanand Technical University (India) and Case Western Reserve University (USA). His research integrates data science and mathematical modeling to decipher emergent properties in cellular systems, combining data-driven and model-driven approaches. Utilizing machine learning, statistics, systems theory, and stochastic processes, he investigates how interactions between cellular components produce system-level functions that constrain part behaviors. His work bridges computational sciences with life sciences applications in biomedical research, pharmaceutical development, and clinical decision support. Recent publications reveal dominant themes in cancer systems biology (melanoma, metastasis), network analysis of high-throughput data, drug repositioning, and computational methods for imbalanced datasets. Key interdisciplinary applications include SARS-CoV-2 virus-host interaction mapping, inflammation resolution mechanisms, and E2F1 transcription factor networks in therapeutic resistance. Scientific recognition includes: SPIE Pioneer Award (2009) IBM Computing Prize (1994) Wolkenhauer leads collaborative projects with biomedical researchers, pharmaceutical companies, and IT firms, developing machine learning workflows for wet-lab data analysis, molecular network modulation studies, and clinical decision support systems. His teaching includes graduate courses in BioSystems Modelling, Life Sciences Simulation, and Python-based Data Science. He directs the Research Group Network Regulation & Modeling / Machine Learning at the Leibniz Institute, contributing to major initiatives like the COVID-19 Disease Map and Atlas of Inflammation Resolution, fostering international networks in systems medicine and computational biology.
Dr. Jingyuan Ren is a Postdoctoral Researcher at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany, where she works in the Minerva Research Group Neural Codes of Intelligence Lab under PI Dr. Stephanie Theves since 2025. Her research focuses on the neural mechanisms underlying cognitive processes including conceptual knowledge representation, relational reasoning, navigation memory, and creative thinking. Dr. Ren has an extensive educational background with dual PhD trajectories: Ph.D. in Cognitive Neuroscience (2021-2025) from the Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Netherlands, supervised by Dr. Martin Dresler, Dr. Boris Konrad, and Dr. Isabella Wagner Ph.D. and M.Ed. in Psychology (2014-2021) from the Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China, supervised by Dr. Jing Luo Her research interests span multiple domains of cognitive neuroscience with particular focus on: Neural coding of conceptual knowledge and relational reasoning Navigation memory and method of loci mnemonic strategy Brain mechanisms underlying creative thinking and novel concept formation Memory processes and their interaction with creative cognition Dr. Ren employs various neuroimaging techniques to investigate how the brain forms and manipulates conceptual knowledge, with implications for understanding both normal cognitive functioning and potential applications in cognitive enhancement. Dr. Ren's publication record demonstrates significant contributions to the field of cognitive neuroscience, particularly in understanding the neural basis of creativity and memory. Her work shows consistent focus on the intersection of memory systems and creative cognition, with particular emphasis on how mnemonic strategies can enhance cognitive performance. Her research bridges experimental psychology, cognitive neuroscience, and practical applications for cognitive enhancement. Dr. Ren has collaborated extensively with researchers across multiple international institutions, reflecting the collaborative nature of contemporary cognitive neuroscience research.
Iván Ruiz-Rube is a prolific researcher with a focus on Software Engineering , Educational Technology , and Human-Computer Interaction . His work spans augmented reality , chatbots , and domain-specific languages (DSLs) , aiming to democratize technology for non-experts and educators. Key Contributions : Tools for end-user development, frameworks for conversational agents, and methodologies for immersive educational VR/AR scenarios. Collaborations : Juan Manuel Dodero, José Miguel Mota, Antonio Balderas, and Rubén Baena-Pérez. Research Themes : • Enhancing educational experiences through mobile apps and VR/AR. • Innovations in DSLs and model-driven development . • Applying linked data and privacy-preserving techniques to web and software engineering. • Leveraging block-based programming for IoT education . Article Trends : His recent work emphasizes chatbots and end-user development (2024–2025), while earlier studies focus on DSLs and augmented reality in education (2018–2020). Biomedical applications (2021) and linked data (2010–2017) also appear frequently.
Prof. Philipp Wiedemann serves as a Professor at Mannheim University of Applied Sciences (Technische Hochschule Mannheim) within the Faculty of Biotechnology. His research program centers on advanced monitoring techniques for biotechnological processes, with particular expertise in developing and implementing in situ microscopy systems across diverse applications. His research spans multiple domains of biotechnology: Development and application of in situ microscopes for cell density and viability assessment Image analysis using Artificial Intelligence for bioprocess characterization Cell stress detection via mass spectrometry Glucose monitoring systems for animal cell cultures Applications in pharmaceutical production, wastewater treatment, blood banking, and bioethanol manufacturing Wiedemann's recent publications (2021-2025) reveal a strong interdisciplinary approach, integrating bioprocess engineering with medical diagnostics and environmental science. His work demonstrates consistent innovation in monitoring technologies, with increasing incorporation of AI and machine learning techniques for real-time process control. The publications show applications ranging from pharmaceutical manufacturing to wastewater treatment plants, highlighting the versatility of his monitoring approaches. He teaches several specialized courses: Applied Cell Biology (covering animal cell culture techniques) Quality Assurance in the Pharmaceutical Industry (GMP requirements and compliance) Advanced Animal Cell Technology (process development and practical applications) Prof. Wiedemann maintains active collaborations with industry partners including KVE Rhein-Neckar and Bilfinger Industrial Services Salzburg, as well as research institutions. His work on in situ microscopy has been implemented commercially for wastewater treatment monitoring (ismanalytics.com), demonstrating successful translation of academic research to industrial applications.
Prof. Dr. Nataša Živić is a Professor of Information and Coding Theory and Real-Time Image Processing at the Faculty of Digital Transformation , HTWK Leipzig . Her work bridges theoretical foundations in coding, cryptography, and signal processing with practical applications in IoT, smart grids, automotive systems, and blockchain technologies. Academic background: Dr.-Ing. habil. and Dr.-Ing. in Electrical Engineering, University of Siegen; Magister and Dipl.-Ing. from University of Belgrade. Professional journey: Founder of Secutanta GmbH; Senior Technical Security Engineer at Volkswagen (Cariad); academic roles at University of Siegen. Her research focuses on secure and robust communication , including noise-tolerant data authentication, joint channel coding and cryptography, and real-time image processing. She explores probabilistic AI algorithms and distributed ledger technologies for industrial applications in mobility and energy systems. The 15 most recent publications highlight her leadership in applying blockchain and DLT to automotive and IoT contexts, advancing secure communication in smart metering and charging infrastructure, and integrating machine learning for environmental and financial prediction. Her work spans IEEE, MDPI, and Springer venues, reflecting strong academic impact. Scientific Awards: Best Paper and Innovation Award, IEEE SEGE 2015 Best Paper Award, UBICOMM 2010 She has led DAAD-funded international projects with universities in the USA, Korea, and China, and contributes to standardization as a member of DIN and ISO committees on security (SC 27) and AI (SC 42). She is also an IEEE Senior Member and active reviewer for top journals. Her research addresses real-world challenges in automotive security, smart energy, and digital transformation.
Georgios Ellinas is a Professor at the University of Cyprus, specializing in optical networking, machine learning for network management, and UAV swarm coordination. His research spans critical areas such as Elastic Optical Networks (EONs) , Physical Layer Security , and Multi-Agent Reinforcement Learning for autonomous systems. Recent work includes quantile regression models for handling uncertainty in network traffic, edge-assisted collision warning systems for urban mobility, and multi-task learning architectures for drone state identification. He applies distributed estimation techniques to jamming aerial targets and explores probabilistically robust trajectory planning for autonomous vehicles. His contributions extend to fair resource allocation in optical networks, UAV swarm coordination using ROS-LoRa integration, and quantum key distribution optimization. Collaborations with researchers like Tania Panayiotou and Panayiotis Kolios highlight his interdisciplinary approach.
Mario Fritz is a faculty member at the CISPA Helmholtz Center for Information Security , an honorary professor at Saarland University , and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) . Previously, he led a research group at the Max Planck Institute for Informatics (2011-2018) and conducted postdoctoral work at UC Berkeley. His research focuses on trustworthy artificial intelligence at the intersection of information security and machine learning , with recent projects including the EU-funded ELSA - European Lighthouse on Secure and Safe AI . Current roles: CISPA faculty (2018-present), Saarland University professor (2019-present) Past roles: Max Planck senior researcher (2011-2018), ICSI/UC Berkeley postdoc (2008-2010) Education: PhD from TU Darmstadt, computer science studies at FAU Erlangen-Nuremberg His research spans AI security , privacy-preserving machine learning , computer vision , and deep generative models . Recent work includes LLM-based code deobfuscation , certified malware detectors , and membership inference attacks on DocVQA models . He serves as Associate Editor for the journal IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and leads projects like Trustworthy Federated Data Analytics , PriSyn (synthetic health data with privacy), and ImageTox (automated toxicity detection). As of 2025, he has published over 100 scientific articles , including 80 in top venues like ICML, ICLR, and CVPR.
Viktor Kunčak is an Associate Professor with tenure at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He leads the Laboratory for Automated Reasoning and Analysis (LARA) and has been at EPFL since 2007 after completing his PhD at the Massachusetts Institute of Technology (MIT). His educational background includes: PhD from Massachusetts Institute of Technology (MIT), 2007 Professor Kunčak's research focuses on bridging the gap between human goals and computational realizations through program synthesis , verification , and automated reasoning . His work has significant applications in software development, formal methods, and programming languages. He is particularly known for developing practical tools like Leon and Stainless that implement theoretical advances in automated reasoning. His recent publications demonstrate a consistent focus on advancing program synthesis techniques, verification frameworks, and formal methods. The research trajectory shows progression from foundational theoretical work to practical applications and tools that can be used by developers. Many papers focus on making verification and synthesis more accessible and efficient for real-world programming tasks. His notable scientific achievements include: ACM SIGSOFT Distinguished Paper Award for work on automated testing European Research Council (ERC) Grant of 1.5M EUR (2012) Communications of the ACM Research Highlight for a PLDI paper Professor Kunčak has supervised 13 completed PhD theses and teaches courses on functional and parallel programming, compilers, and verification at EPFL. He has also co-taught a popular MOOC on Parallel Programming that reached over 100,000 learners worldwide. His research has been supported by significant funding including a 5-year ERC grant. He has served in leadership roles for major conferences including as program co-chair for FMCAD 2014 and VMCAI 2012. He leads the Laboratory for Automated Reasoning and Analysis (LARA) at EPFL, which develops tools like Stainless for program verification. The lab has established an international presence through collaborations including a European COST Action to establish standardized formats for verification and synthesis (Rich Model Toolkit).
Adam Doupé is a full professor at Arizona State University, specializing in cybersecurity and software security. His research focuses on automated vulnerability analysis, phishing detection, binary decompilation, and security in web technologies. Key research areas include: Cybersecurity Software Security Phishing and Fraud Detection Binary Analysis Automated Vulnerability Discovery His recent work explores large-scale phishing mitigation using machine learning (e.g., ScamNet), improving binary decompilation techniques (e.g., SAILR), and analyzing fraud browser detection via fingerprints. Trends show a strong emphasis on practical security tools, human-centric security analysis, and firmware vulnerability discovery. While no specific awards or grants are listed in the DBLP data, Doupé actively contributes to security education through frameworks like SENSAI and CTF-as-a-service. He collaborates extensively with researchers such as Yan Shoshitaishvili, Ruoyu Wang, and Tiffany Bao.
Henrik Walter is a Full Professor (W3) of Psychiatry with a focus on Psychiatric Neuroscience and Neurophilosophy at Charité – Universitätsmedizin Berlin. He serves as Director of the Mind and Brain Research Division and Deputy Medical Director (Research) at the Department of Psychiatry and Psychotherapy, Charité Campus Mitte. Walter is also a faculty member at the Berlin School of Mind and Brain , a faculty member of the Bernstein Computational Center Berlin , and a principal investigator at the Berlin Center for Advanced Neuroimaging . Clinical expertise: Schizophrenia and affective disorders Empirical research: Working memory, volition, reward mechanisms, emotion regulation, mentalization, imaging genetics, connectomics Philosophical research: Philosophy of mind, neurophilosophy, neuroethics, philosophy of psychiatry Research trends in his 15 most recent publications (2009-2024) emphasize connectome-based machine learning , dynamic network reconfiguration , self-control mechanisms , and predictive models for psychiatric relapse . His work explores the intersection of neural network organization , emotional processing , and philosophical frameworks in mental health. Walter oversees major projects including environMENTAL (data harmonization in large cohorts) and FOR5187 PREACT (personalized psychotherapy). His team actively trains students and researchers through internships, theses supervision, and doctoral programs.
