Find Academic Supervisors

• Fuzzy Systems
• Computational Intelligence
• Nonlinear Control
• +4 more

Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.

Andrew BassettView profile

Researcher

• Genome Engineering
• Neurodegenerative Diseases
• Stem Cell Research
• +5 more

Dr. Andrew Bassett serves as Head of the Cellular and Gene Editing Research group at the Wellcome Sanger Institute, where he develops cutting-edge genome engineering techniques using human pluripotent stem cells to investigate neurodegenerative diseases including Alzheimer's and Parkinson's. His work focuses on scaling genetic screening approaches and improving CRISPR specificity for modeling complex disease mechanisms. His academic training includes: PhD at the MRC Laboratory of Molecular Biology (MRC-LMB) with Andrew Travers on chromatin remodelling in heterochromatin formation Postdoctoral research with David Baulcombe at the University of Cambridge studying small RNA roles in chromatin modification Additional postdoctoral work with Chris Ponting at the MRC Functional Genomics Unit (MRC-FGU) in Oxford, where he pioneered CRISPR applications in Drosophila Bassett's research program centers on developing advanced genome engineering methodologies for precise modulation of gene expression networks during development and neurodegeneration. His group specializes in creating complex editing events (SNPs, paired knockouts, enhancer perturbations) within iPSC-derived models, with particular emphasis on epigenetic regulation and transcriptional control. Current projects integrate single-cell 'omics and phenotypic assays to decode genetic causes of neurodegenerative disorders through the OpenTargets consortium. Analysis of his 15 most recent publications reveals dominant trends in CRISPR technology development (35%), neurodegenerative disease modeling (30%), and single-cell functional genomics (25%). His work consistently bridges methodological innovation with disease mechanism studies, increasingly incorporating multi-omics approaches and expanding into cancer immunology and infectious disease applications since 2022. As group leader, Bassett mentors postdoctoral researchers and PhD students while securing major funding for genome engineering initiatives. His team operates within the Sanger Institute's Cellular Operations division and maintains critical partnerships with the OpenTargets consortium for therapeutic target validation. The laboratory specializes in high-throughput screening platforms using iPSC-derived neural and microglial models, with recent methodological advances including scSNV-seq and ONE-STEP tagging systems that significantly enhance precision genome editing capabilities.

Michael BoutrosView profile

Professor

• Functional Genomics
• Structural Genomics
• Wnt Signaling
• +7 more

Michael Boutros is a Full Professor at Heidelberg University and Head of Division at the German Cancer Research Center (DKFZ). He currently serves as Dean of the Medical Faculty at Heidelberg University (since 2023) and Director of the Marsilius Kolleg (since 2020). He has held leadership roles including Coordinator of the Functional and Structural Genomics Program at DKFZ (2014–2023) and Acting Scientific Director (2015–2016). His academic base is within the Medical Faculty, focusing on molecular oncology and functional genomics. PhD, Witten/Herdecke University (1993–1996) Postdoctoral Research, Harvard Medical School (1999–2003) MPA, John F. Kennedy School of Government, Harvard University (1999–2001) Additional training: Cold Spring Harbor Laboratory, SUNY Stony Brook His research centers on Wnt signaling, functional genomics, and cancer pathways. He leads major research initiatives such as CRC 1324 on Wnt signaling and the ERC Synergy Grant DECODE. His work integrates high-throughput screening, CRISPR, and systems biology to dissect signaling networks in cancer and development. He has pioneered genome-wide RNAi and CRISPR screens to identify novel regulators of Wnt signaling across models. The 15 most recent articles reflect a strong focus on Wnt pathway regulation using functional genomics in both Drosophila and mammalian systems. Themes include high-throughput screening, CRISPR-based validation, cross-species conservation, and therapeutic targeting. Keywords span Cancer Biology, Systems Biology, and Signal Transduction, with subfields like RNAi, ubiquitination, stem cell regulation, and machine learning in image analysis. Michael Boutros has received numerous scientific honors: Elected member, Leopoldina National Academy of Sciences (2022) Elected member, Heidelberg Academy of Sciences (2022) EMBO Member (2013) ERC Advanced Grant (2012) Johann-Georg Zimmermann Research Award (2007) EMBO Young Investigator (2005) Member, 'Die Junge Akademie' (2003) He has been a recipient of the Emmy-Noether Program, McCloy Fellowship, Boehringer Ingelheim PhD Fellowship, Studienstiftung Fellowship, and Fulbright Fellowship. As a mentor and research leader, he has supervised numerous early-career scientists and coordinated large collaborative grants including the FP7 'CancerPathways' project. He currently serves as Speaker of the Research and Strategy Commission at Heidelberg University and Managing Director of the Health and Life Science Alliance Heidelberg Mannheim. He leads the CRC 1324 on Wnt signaling and is Coordinating PI of the ERC Synergy Grant DECODE. He is also Spokesperson of DFG Research Group 1036 and Coordinator of the former FP7 Coordinated Project 'CancerPathways'. His lab employs cutting-edge functional genomics tools to decode signaling networks in cancer and development.

Christian WaldView profile

Research Fellow

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +3 more

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

Georg GuggenbergerView profile

Professor

• Soil Science
• Soil Chemistry
• Biogeochemistry
• +7 more

Professor Georg Guggenberger is a distinguished academic at Leibniz University Hannover, where he serves as Professor of Soil Chemistry within the Department of Soil Science at the Institute of Earth System Sciences, Faculty of Natural Sciences. In addition to his research and teaching responsibilities, he holds several significant administrative positions including Vice Dean for Research in the Dean's Office of the Faculty of Natural Sciences, Executive Management at both the Institute of Soil Science and Department of Soil Science levels, and Chair of the Selection Committee for the M.Sc. Landscape Sciences program. Prof. Guggenberger's research program focuses on fundamental processes governing soil organic matter dynamics across diverse ecosystems worldwide, from Arctic to tropical regions. His work investigates biotic and abiotic factors influencing the formation, transport, and stabilization of soil organic matter, with particular emphasis on soil fertility maintenance. He employs advanced methodologies including stable isotope approaches, biomarker analyses, microspectroscopic techniques (XPS, ESEM, NMR, NanoSIMS), and molecular biological methods to study biogeochemical interactions on mineral surfaces, dissolved organic matter dynamics, plant-mycorrhizal-soil interactions, permafrost soil organic matter, and impacts of land use and climate change on soil organisms. Analysis of Prof. Guggenberger's recent publications reveals a strong interdisciplinary research program spanning soil biogeochemistry, carbon cycling, soil microbiology, and climate change impacts. His work integrates field experiments along environmental gradients with laboratory manipulation studies to identify underlying mechanisms. The research shows particular strength in understanding organic matter stabilization mechanisms, microbial contributions to soil processes, and development of sustainable soil management practices that address climate change challenges. Prof. Guggenberger actively contributes to academic service through his leadership roles in examination boards and committees, including the Examination Board for Geosciences programs and the Selection Committee for Landscape Sciences. His administrative responsibilities as Vice Dean for Research demonstrate his commitment to advancing research within the Faculty of Natural Sciences at Leibniz University Hannover.

• Additive Manufacturing
• Laser Powder Bed Fusion
• Materials Science
• +2 more

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.

Roland ZengerleView profile

Professor

• Microfluidics
• Lab-on-a-Chip
• Bio-MEMS
• +6 more

Prof. Dr. Roland Zengerle serves as Full Professor for Application Development at the Institute of Microsystems Technology within the Faculty of Engineering at Albert Ludwigs University of Freiburg, concurrently holding the position of Director at Hahn-Schickard Institute for Microanalysis Systems in Freiburg. His academic leadership spans microsystems engineering with a focus on translational research bridging fundamental science and clinical applications. Zengerle's research expertise centers on Microfluidics, Lab-on-a-Chip systems, Bio-MEMS, Electrochemical Energy Systems, and Tomographic Reconstruction of Mesoporous Materials. He pioneers hybrid manufacturing techniques integrating molten metal printing with polymer processing to develop point-of-care diagnostic platforms and advanced energy systems. Current projects include UTI-Diag for urinary tract infection diagnosis and PhotonMed, a 32-million-euro medical technology initiative where his MEMS Applications Laboratory develops centrifugal microfluidic solutions. Analysis of his recent publications reveals a dominant trend toward multi-technology integration: centrifugal microfluidics combined with 3D bioprinting for organoid-based drug testing, molten metal printing for flexible electronics, and bead-based immunoassays for infectious disease detection. The work demonstrates strong clinical translation focus, particularly in cancer diagnostics (circulating tumor cell isolation), infectious disease testing (TB diagnostics), and regenerative medicine (spheroid/organoid handling). His laboratory has secured significant funding for high-impact projects including: UTI-Diag: Molecular diagnostics for urinary tract infections PhotonMed: Medical technology innovation consortium livMatS: Living, Adaptive and Energy-autonomous Materials Systems Zengerle actively mentors researchers through Freiburg's Master Lab program and Writer's Studio initiative while promoting young talent via Bootcamp training. His group maintains strategic alliances with Hahn-Schickard spin-offs and industry partners, leveraging university cleanroom facilities and specialized service centers for microfabrication. The MEMS Applications Laboratory operates as a hub for interdisciplinary innovation, combining microfabrication expertise with clinical insights to develop commercializable diagnostic solutions. Current infrastructure supports centrifugal microfluidic cartridge development, 3D-bioprinting of tissue models, and electrochemical sensor integration, with ongoing work focused on automating complex biological workflows for point-of-care applications.

Dirk ZiegenbalgView profile

Professor

• Photochemical Engineering
• Chemical Reaction Engineering
• Process Intensification
• +4 more

Prof. Dirk Ziegenbalg is a Professor of Chemical Engineering and Deputy Director at the Institute of Chemical Engineering at Ulm University. He holds a doctorate from Friedrich-Schiller-University Jena (2013) and previously served as Group Leader at the Institute of Chemical Technology, University Stuttgart (2012-2018). His research focuses on photochemical engineering, reactor design, and photocatalytic processes for applications in energy and environmental technology. Key contributions include advancements in photoreactor engineering, light-driven catalysis, and scalable water purification systems. He has been recognized with the Hanns Hofmann Award (2017) for innovations in microstructured reactor design. Teaching responsibilities include courses such as Strömungsmechanik , Chemische Prozesstechnik , and Industrial Catalysis . His research group, Photochemical Engineering, develops modular and industrially scalable photoreactor systems, emphasizing process intensification and dynamic irradiation control. Recent work addresses challenges in CO₂ conversion, selective Grignard reagent formation, and real-time process analytics using spectroscopy. Publications highlight interdisciplinary approaches, integrating chemical engineering with materials science and environmental applications. Ongoing projects involve 3D-printed catalytic materials, radiation field tomography for photoreactor characterization, and cross-disciplinary collaborations for sustainable water treatment technologies.

Fangzhou JinView profile

Assistant Professor

• Algebraic Geometry
• Pure motives
• Mixed motives
• +5 more

Fangzhou Jin is an Assistant Professor at the School of Mathematical Sciences, Tongji University. His research focuses on advanced topics in Algebraic Geometry , particularly in Motivic Homotopy Theory , Algebraic K-theory , and Real Algebraic Geometry . He has extensive experience in collaborative mathematical research, having worked with prominent mathematicians such as Frédéric Déglise, Enlin Yang, and Marc Levine. Education : Ph.D. in Mathematics (2016, École Normale Supérieure de Lyon), M.Sc. in Pure Mathematics (2012, Université Paris-Diderot), Diploma of ENS (2013) His work bridges Algebraic Cycles with Étale Cohomology , exploring Weight Structures in mixed motives and Chern Classes in motivic contexts. Jin has contributed to the theory of Trace Maps and Local Terms in motivic homotopy, as well as the study of Flat Cohomology and Real Schemes . Key trends in his publications include: 2025: Cohomology of singular varieties via real cycle class maps 2024: Trace maps and local terms in motivic homotopy 2023: Moving lemmas in homotopy colevel theory 2022: Quadratic conductors, perverse homotopy, and pro-Chern classes 2021: Rational motivic homotopy and Künneth formulas 2020: Gersten complexes and real flat cohomology 2018: Algebraic G-theory in motivic categories 2016: Borel-Moore homology and weight structures Scientific Awards & Grants : Fundamental Research Funds for Central Universities (2021-2025) National Natural Science Foundation of China Grants 12101455 (2022-2024), 12471014 (2025-2028) National Key R&D Program of China 2021YFA1001400 (2022-2026) DFG Priority Programme SPP 1786 & ERC Project QUADAG (2018-2020) Advising & Teaching : Jin has supervised exercise classes and seminars at multiple institutions, including Universität Duisburg-Essen and Tongji University. His teaching spans Algebraic Topology , Étale Cohomology , and Algebraic Geometry . Labs & Collaborations : Active in international collaborations, he is a key member of the Algebraic Geometry and Ramification Seminar at Tongji University and has participated in DFG Research Training Group 2553 during his postdoctoral period.

• Geometric Model Theory
• Algebraic Geometry
• Additive Combinatorics
• +3 more

Amador Martin-Pizarro is a Professor in the Department of Mathematical Logic at the Albert-Ludwigs-Universität Freiburg. His research focuses on geometric model theory and its applications to algebraic geometry, additive combinatorics, and number theory. He has contributed to foundational work on supersimplicity, stability, and definable groups in simple theories. Martin-Pizarro led the Freiburg group in the binational ANR-DFG project GeoMod. His work bridges model theory with algebraic structures, exploring connections between combinatorial patterns and logical frameworks. Recent studies include applications of model theory to arithmetic progressions, pseudofinite groups, and equational theories of fields.

Martin SpindlerView profile

Professor

• Econometrics
• Statistics with Applications to Finance, Insurance, and Health Economics
• Nonparametric Statistics
• +3 more

Prof. Dr. Martin Spindler is a Professor for Statistics at the Department of Statistics with Application in Business Administration, University of Hamburg Business School. His research bridges Econometrics, Statistics, and Machine Learning, focusing on high-dimensional methods, causal inference, and applications in finance, insurance, and health economics. Current position since 2016 Visiting Professor at University Mannheim (2016), Boston College (2015), and MIT (2015, 2013-2014) Senior Researcher at Max Planck Society (2012-2016) Education: PhD in Economics, University of Munich (2012) Master in Mathematics and Economics, University of Munich (2008) and Regensburg (2003) B.A. in Mathematics, University of Regensburg (2005) His methodological work includes L2Boosting for treatment effect estimation, double machine learning frameworks, and nonparametric approaches for asymmetric information. Applications span from fraud detection in claims management to pandemic shielding strategies and financial forecasting. Research Trends: Recent publications emphasize high-dimensional statistical methods, causal machine learning, and interdisciplinary applications. Key tools include double machine learning, attention networks, and transformation models. Collaborations: Active partnerships with institutions like MIT, Boston College, and Max Planck Society, alongside contributions to open-source software (e.g., DoubleML, hdm package).

• Structural Analysis
• Computational Mechanics
• Digital Twin Technology
• +4 more

Talhah Shamshad Ali Ansari is a Research Associate at the Chair of Structural Analysis and Dynamics at the Technical University of Munich (TUM) . He works on advanced computational methods, focusing on digital twins, adjoint-based system identification, and multiphysics simulations. His research addresses structural optimization, wind engineering, and robust meshing techniques. Research Highlights : Digital Twin technology for structural analysis Adjoint-based methods for system identification Multiphysics simulations in wind engineering Structural optimization for additive manufacturing Teaching : Contributed to courses in Theory of Plates and computational mechanics curricula Publications (2025): Developed adjoint-based thermal field recovery methods Analyzed algorithms for digital twin system identification Advanced high-fidelity simulations for structural weaknesses

Anna C. BalazsView profile

Professor

• Soft matter theory
• Polymer physics
• Smart materials
• +4 more

Anna C. Balazs is Distinguished Professor and John A. Swanson Chair of Engineering in the Department of Chemical Engineering at the University of Pittsburgh, with an adjunct appointment in Chemistry and visiting professorships at Scripps Research Institute, UT-Austin and Oxford University. In 2025 she receives the €10,000 Gutenberg Research Award from Johannes Gutenberg University Mainz (JGU) for her pioneering theoretical work on smart soft materials. She earned an A.B. in Physics from Bryn Mawr College (1975) and a Ph.D. in Materials Science from MIT (1981), followed by post-doctoral research at Brandeis, MIT and UMass. Research interests span theoretical and computational soft-matter physics, focusing on: Statistical-mechanical modelling of polymer blends and composites Self-oscillating and chemo-responsive hydrogels Active matter, enzyme-powered swimmers and self-propelling sheets Self-healing, shape-morphing and bio-inspired materials Computer simulation of colloidal and interfacial phenomena Recent publications (2023-2025) demonstrate a clear trend toward integrating chemistry, fluid mechanics and elasticity to create life-like, autonomous soft machines. Key contributions include: Harnessing enzyme pumps to drive macroscopic sheet locomotion Designing chemically communicating micro-post arrays Creating dissipative materials with programmable, hierarchical 3-D architectures Scientific awards include: Gutenberg Research Award 2025 Polymer Physics Prize, American Physical Society SF Boys-A. Rahman Award, Royal Society of Chemistry Langmuir Lectureship Award, American Chemical Society Election to the U.S. National Academy of Sciences (2021) She serves on the Advisory Board of the DOE-BES Materials Council and on editorial boards for Langmuir , Soft Matter and Polymer Reviews . Her group collaborates closely with experimental teams world-wide, including the DFG-NSF “Confine” partnership with JGU and the CoM2Life Cluster of Excellence initiative.

Lena KostujView profile

Researcher

• Entrepreneurship
• Digital Transformation
• Organizational Psychology
• +2 more

Lena Kostuj is a Research Associate at the Research Center for Digital Transformation and Doctoral Candidate in Business Administration (specializing in Entrepreneurship, Organization and Culture) at Leuphana University Lüneburg, with additional affiliation as Associate at the Leuphana Center for Organization & Social Transformation. Educational background: MA Cultural Studies: Culture & Organization (2020-2022) BA Cultural Studies (2018-2021) BSc International Business Administration & Entrepreneurship (2016-2021) Her research integrates entrepreneurship with digital and social transformation, examining how cultural dynamics and organizational structures drive innovation in sustainable business models. Work spans inclusive working environments, circular economy systems, and digital platform ecosystems through interdisciplinary lenses of psychology, cultural studies, and business administration. As LOST Community Manager since 2023, she coordinates knowledge exchange initiatives within transformation-focused research networks while advancing her doctoral work on socially innovative organizational change.

Daniel ViewegerView profile

Researcher

• Materials Engineering
• Additive Manufacturing
• Design Methods for Additive Manufacturing
• +2 more

Daniel Vieweger serves as a Research Scientist at the Chair of Materials Engineering of Additive Manufacturing within the TUM School of Engineering and Design at the Technical University of Munich (TUM). Based in Garching bei München, Germany, he operates from room B1.2.02 at Freisinger Landstraße 52, with contact details including email (daniel.vieweger@tum.de) and phone (+49 89 289 55347). His role centers on advancing additive manufacturing research under Prof. Dr. Peter Mayr's leadership. His research focuses on materials engineering for additive manufacturing processes, with specialized expertise in advanced design methodologies for metal 3D printing systems. This work directly supports TUM's Project Week: Fundamentals of Advanced Design Methods for Additive Manufacturing initiative, addressing critical challenges in geometric complexity, material efficiency, and structural integrity. His contributions bridge academic research with industrial applications through TUM.Additive and TUM.Idea collaborations. Within TUM's innovation ecosystem, Vieweger participates in cross-disciplinary teams developing next-generation additive manufacturing protocols. The chair maintains strategic partnerships with aerospace and automotive industries to optimize material deposition techniques and post-processing methods. His current work emphasizes computational modeling of thermal dynamics during laser-based metal additive processes, contributing to TUM's leadership in sustainable advanced manufacturing.