John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute, University of Amsterdam, working with the QuSoft research center. He co-authored the open-access book Picturing Quantum Software and developed the PyZX quantum compiler. His research spans quantum computation and quantum foundations, focusing on diagrammatic methods like the ZX-calculus and ZH-calculus. Quantum circuit optimization and verification Quantum foundations via algebraic/compositional methods Co-creator of PyZX His recent publications explore multi-qutrit systems, completeness of graphical calculi, and quantum state representations. Supervises students in quantum computing, including Lia Yeh and Sarah Li. Directs the new Master's program in Quantum Computer Science at UvA. Actively contributes to open-source projects and international conferences. Notable collaborations include Aleks Kissinger, Neil J. Ross, and QuSoft researchers. Uses GitHub for DiZX development (qudit extension of PyZX). No explicit scientific awards mentioned.
Ralph Luetticke is a Professor of Economics at the University of Tübingen, affiliated with the Centre for Economic Policy Research and the Stone Centre on Wealth Concentration at University College London. His research focuses on fiscal/monetary policy, business cycles, and computational methods, emphasizing household heterogeneity. Key contributions include analyzing liquidity channels of fiscal policy, military multipliers, and unconventional policy shocks. Recent work explores military spending multipliers, endogenous gridpoint methods for distributional dynamics, and the distributional impacts of monetary policy. His 2023 ERC Starting Grant ('AIRMAC') supports research into aggregate uncertainty in business cycles. Teaching includes advanced macroeconomics courses at UCL and Tübingen, emphasizing HANK models and policy analysis. Scientific awards include the ERC Starting Grant (2023). Research tools developed include the BASE for HANK toolbox (Julia) and open-source codes for heterogeneous agent modeling. His work bridges theoretical macroeconomics with empirical policy analysis, addressing modern challenges like inequality and pandemic stimulus effectiveness.
Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.
Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.
Sergey Tulyakov is the Director of Research at Snap Inc. , leading the Creative Vision team. His work focuses on enhancing creator capabilities through computer vision , machine learning , and generative AI , with applications in 2D/3D/4D video generation, editing, and personalization. He pioneered video generation frameworks like MoCoGAN and First Order Motion Model , and has been recognized for BEST IN SHOW AWARD at SIGGRAPH Real-Time Live! 2020. PhD (2012-2017): University of Trento, Italy MSc (2010): Belorusian State University of Informatics and Radioelectronics B.Eng (2009): Belorusian State University of Informatics and Radioelectronics His research interests span computer vision , generative models , 3D reconstruction , and personalization , with a focus on making large models efficient and mobile-compatible . Recent publications highlight advancements in 4D video generation , text-guided 3D composition , and lightweight architectures . Key scientific awards include the SIGGRAPH Real-Time Live! 2020 Best in Show for Interactive Video Stylization. He has also served on technical program committees for top-tier conferences like CVPR, ICCV, SIGGRAPH, and NeurIPS since 2022. His team organizes tutorials and keynotes, including courses on Deep Generative Models and Efficient Neural Networks . While no direct student names are listed, his collaborative work spans 60+ top-tier publications.
Ori Lahav is a faculty member in the School of Computer Science at Tel Aviv University. His research is generously supported by an ERC Starting Grant and an ISF Grant. He actively supervises PhD and MSc students, and seeks highly motivated candidates for postdoc, PhD, and MSc positions in programming language theory, concurrency, and formal methods. Dr. Lahav completed his PhD at Tel Aviv University under the supervision of Arnon Avron. In 2014, he was a postdoctoral researcher at Tel Aviv University hosted by Mooly Sagiv. From 2014 to September 2017, he was a postdoctoral researcher at MPI-SWS in Germany hosted by Viktor Vafeiadis and Derek Dreyer. His primary research areas focus on programming languages and verification, with specialization in concurrency and relaxed memory models. He also has significant interests in proof-theory, semantics of non-classical logics, and automated reasoning. His work bridges theoretical foundations with practical applications in programming language design and implementation. Dr. Lahav's publication record shows a consistent trajectory of high-impact research in top-tier conferences including PLDI, POPL, OOPSLA, and ESOP. His recent work (2023-2025) demonstrates continued leadership in memory models, concurrency semantics, and verification techniques. His research spans both theoretical contributions in denotational semantics and practical tools for verification. Best Paper Award DISC 2024 Best Student Paper Award DISC 2024 Distinguished Artifact Award ESOP 2022 Distinguished Paper Award OOPSLA 2021 Kleene Award for Best Student Paper LICS 2013 Dr. Lahav actively advises students including Yoav Ben Shimon, Yotam Dvir, Amir Karniel, and Roy Margalit (PhD students), Yuval Katsman Ezra (MSc student), and has alumni including Ori Saporta (MSc) and Abhishek Kr Singh (postdoc, now Assistant Professor at IIIT Hyderabad). He has organized significant events including VMCAI 2024 and Dagstuhl Seminars on persistent programming. His teaching portfolio includes courses on Shared Memory Concurrency Semantics, Programming Language Foundations, and Software Foundations in Coq.
Rainer Böckmann is a Professor of Computational Biology in the Department of Biology at Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany, where he leads the Group for Theoretical and Computational Membrane Biophysics. His research integrates molecular dynamics simulations with biophysical analysis to study membrane structure, dynamics, and function. Research Interests: His work focuses on computational biophysics, particularly lipid bilayers, membrane proteins, molecular dynamics, and structural bioinformatics. He investigates how lipid composition, cholesterol, and embedded peptides influence membrane organization, curvature, and permeability, with applications in antimicrobial strategies and mRNA vaccine delivery systems. Recent Research Trends: His recent publications reflect a strong emphasis on lipid nanoparticles (LNPs), particularly their phase behavior, pH-dependent protonation, and structural transitions relevant to mRNA vaccines. He also explores antimicrobial peptides, membrane domain formation, and the role of cholesterol in modulating membrane properties. His group develops and applies advanced simulation techniques, including constant-pH MD and coarse-grained modeling. Member of Editorial Board, Biophysical Journal (2024–present) Elected Member, DFG Review Board for Biophysics (2020–present) Chairman, Molecular Biophysics Section, German Biophysical Society (2011–2012) Leadership and Service: Böckmann is actively involved in academic governance, serving on editorial boards, DFG committees, and as a guest editor for special issues in Frontiers journals. He contributes to graduate education and high-performance computing initiatives at FAU, including the NHR@FAU and Life@FAU Graduate School. He has organized major conferences and workshops in biophysics and membrane modeling. Laboratory and Collaboration: He leads a research group focused on biomembrane physics, collaborating with experimentalists and theorists. His lab develops and applies simulation tools to study membrane systems, bridging computational insights with biological function.
Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)
Prof. Christian Liebscher is a Professor of Advanced Transmission Electron Microscopy at the Ruhr University Bochum , affiliated with the Faculty of Physics and Astronomy and the Research Center Future Energy Materials and Systems (RC FEMS). His work focuses on developing cutting-edge TEM techniques to understand energy-related materials' atomic-scale structure-functionality relationships. He combines aberration-corrected scanning TEM (STEM), 4D-STEM, and in-situ microscopy with machine learning to analyze complex material datasets. Education and Career: 2000–2006: Study of Materials Science at the University of Bayreuth. 2006–2010: PhD at the University of Bayreuth (summa cum laude) with a thesis on phase and dislocation analysis in superalloys. 2011–2014: Postdoc at the University of California, Berkeley, and the National Center for Electron Microscopy (Lawrence Berkeley National Laboratory). 2014–2015: Staff scientist at the University of Duisburg-Essen. 2015–2024: Group leader at the Max Planck Institute for Sustainable Materials in Düsseldorf. Research Interests: Prof. Liebscher’s research bridges microscopy innovation and materials understanding. He emphasizes atomic-scale characterization of interfaces, defects, and grain boundaries in metals and alloys using advanced STEM and 4D-STEM. His work addresses how structural features—like segregation, strain, and phase transitions—impact material properties. He also pioneers machine learning tools to automate data analysis from microscopy and tomography, advancing materials dataspaces. Key topics include energy materials (e.g., PEM fuel cells), high-entropy alloys, and nanomaterials for applications like semiconductors and electromagnetic absorption. Scientific Contributions: His publications highlight trends in grain boundary phase transitions, microstructure-property correlations, and integration of AI into microscopy. For example, recent work explores how grain boundary complexions affect mechanical strength in alloys and how in-situ TEM reveals deformation mechanisms under realistic conditions. He has contributed significantly to methodologies like scanning precession electron diffraction tomography and unsupervised machine learning for atomic-resolution datasets. Labs and Collaborations: Prof. Liebscher leads the Advanced Transmission Electron Microscopy group at RUB, building on his previous leadership at the Max Planck Institute. His lab collaborates with institutions like the Lawrence Berkeley National Laboratory and integrates interdisciplinary approaches combining experimental microscopy with computational modeling.
Christoph Heinzl is a Professor of Cognitive Sensor Systems at the University of Passau since September 2022. He leads the Knowledge-based Image Processing research group at the Fraunhofer Development Center X-ray Technology (EZRT) . His academic background includes a PhD in Informatics and a Habilitation in 2022 , both from TU Wien . Research Focus: Scientific visualization, visual analytics, immersive analytics, virtual/augmented reality, machine learning, and X-ray computed tomography (XCT). Key Trends: Development of novel visualization techniques for complex volumetric data (e.g., dynamic volume lines, visual coherence frameworks), parameter space analysis, and cross-virtuality collaboration tools. Applications: Aerospace component inspection, defect analysis in composites (CFRP, GFRP), porosity quantification, and 4DCT time-series exploration.
Ina Fichtner is a Professor at the Faculty of Digital Transformation of University of Applied Sciences HTWK Leipzig since 2022. Previously, she led the MINT department at the Institute for Applied Training Science (IAT) in Leipzig for 13 years (2009–2022), focusing on integrating mathematics, informatics, and natural sciences into sports research. Her work bridges computer science , biomechanics , and sports informatics , with extensive projects on athlete movement analysis, data systems (IDA), and digital tools for elite sports. PhD in Computer Science (2007) from TU Dresden and Leipzig University Diplom in Mathematics and Computer Science (2002) from Jena, Dresden, and Sheffield Her research spans data science , sports technology , and applied informatics , particularly in ski jumping , dive analysis , and athlete biomechanics . She has co-authored numerous publications in theoretical computer science and applied sports informatics , including studies on 3D body scanning , inertial sensors , and force-velocity profiling . She served as Alumni Representative and Treasurer of the Friends' Association at HTWK Leipzig, with memberships in German Mathematical Society and German Sports Science Association .
Christof Weiß is a Professor for Computational Humanities at the CAIDAS / Institute of Computer Science, Julius-Maximilians-Universität Würzburg (JMU), Germany. He serves as Head of the DFG-funded Emmy Noether group on Computational Analysis of Music Audio Recordings: A Cross-Version Approach. His academic journey includes previous positions as Visiting Researcher at University Télécom Paris (2021), Visiting Lecturer at Karlsruhe University of Music (2020, 2021), and Research Assistant at International Audio Laboratories Erlangen (2015-2022) and Fraunhofer Institute for Digital Media Technology (2012-2015). His educational background encompasses a PhD in Media Technology from University of Technology Ilmenau (2017), Concert Diploma in Composition from Würzburg University of Music (2012), Physics Diploma from University of Würzburg (2012), and Music Diploma in Composition from Würzburg University of Music (2011). This unique combination of technical and artistic training forms the foundation of his interdisciplinary research approach. Weiß's research operates at the critical intersection of computer science and musicology, developing novel computational methods for analyzing musical structures in audio recordings. His work bridges technical audio processing with musicological insights, creating methodologies for tonal analysis, key estimation, and cross-version comparison of musical performances. His approach combines deep learning techniques with music theory to extract meaningful patterns from large music corpora, enabling new forms of musicological corpus studies that were previously impossible. His recent publications reveal a clear research trajectory toward integrating advanced machine learning with fundamental musicological questions. The consistent theme across his work involves analyzing classical music structures through computational lenses, with particular emphasis on cross-version consistency in performances, tonal complexity measurement, and developing datasets that support computational musicology. His publications span both highly technical audio processing journals and musicology-focused venues, demonstrating his commitment to bridging these disciplines. Best paper award at the 4th conference on Computational Humanities Research (CHR), 2023 KlarText award for science communication of the Klaus Tschira Foundation, 2018 2nd prize at Festival Pablo Casals composition competition, Prades (France), 2013 Youth Cultural Advancement Award (Kulturförderpreis) of the city of Amberg, Germany, 2011 As principal investigator of the DFG Emmy Noether group, Weiß leads a multidisciplinary research team investigating computational analysis of music audio recordings through a cross-version approach. His research has secured significant funding including the prestigious Emmy Noether program, supporting doctoral and postdoctoral researchers working on various aspects of music information retrieval and computational humanities. His collaborative network spans institutions across Europe, including University Télécom Paris, Queen Mary University of London, and multiple German research centers. Weiß leads the Computational Humanities research group at CAIDAS, which focuses on developing computational methodologies for music analysis with particular emphasis on classical repertoire. The lab creates specialized datasets (including the Wagner Ring Dataset and Schubert Winterreise Dataset), develops algorithms for structural music analysis, and applies these tools to address musicological questions that require computational scale and precision. Their work bridges the gap between technical audio processing capabilities and humanities research questions, creating new pathways for understanding musical structure and evolution.
Prof. Dr.-Ing. Michael Möhring is a Professor of Data Science at Reutlingen University's Faculty of Informatics. He serves as Prodekan for the Herman Hollerith Zentrum (HHZ) and leads research in data analytics, Industry 4.0, and process mining. Previously, he held roles as an IT consultant, project manager at Bosch Group/BSH, and academic researcher. Education: Dr.-Ing. (PhD) in Business Informatics M.Sc. in Business Informatics B.Sc. in Business Informatics Research Interests: Focuses on leveraging structured/unstructured data for industrial applications, enterprise architecture management, digital twins integration, and AI-driven decision support. Specializes in bridging technical systems with organizational processes in manufacturing and service industries. Lab Affiliations: AI-Real Lab AIDA Future Mobility Lab Internet of Things Lab Virtual Reality Lab Articles Trends: Recent work emphasizes practical implementations of AI in production failure analysis (language models), energy optimization systems (HollerithEnergyML), and technical debt management in SMEs. Consistently explores data integration challenges across manufacturing, service ecosystems, and digital twin frameworks. Grants & Collaborations: Active in EU-funded projects like 5G-PreCiSe and bwHealthApp. Collaborates with industry partners on digital transformation initiatives through HHZ's applied research programs.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Dr. Hans Oechsner is a leading academic in agricultural engineering and bioenergy, holding multiple leadership positions including Head of the State Institute for Agricultural Engineering and Bioenergy (Landesanstalt für Agrartechnik und Bioenergie), Managing Director of the Baden-Württemberg Working Group on Agricultural Engineering and Rural Construction (ALB), and Project Leader for the International Graduate College focused on maize-based agricultural systems adaptation to limited phosphate reserves. He teaches advanced courses on biogas technology and bioenergy infrastructure at the Department of Agricultural Engineering in the Tropics and Subtropics. His research centers on optimizing bioenergy systems and sustainable resource management, with key interests in: Advanced biogas production techniques (e.g., mechanical pretreatment, two-stage digestion) Phosphorus recovery from organic waste streams Valorization of agricultural residues and livestock manure Development of circular bioeconomy models for crop systems Dr. Oechsner's recent publications (2022-2025) demonstrate a strong focus on enhancing biogas plant efficiency, nutrient recycling from digestate, and sustainable biomass utilization. His work consistently integrates full-scale applications with fundamental research, particularly in pretreatment optimization and waste stream valorization. He leads research infrastructure at the State Institute for Agricultural Engineering and Bioenergy, coordinating teams working on large-scale biogas optimization and agricultural waste management. His projects frequently involve international collaborations and industry partnerships.
