جستجوی استاد راهنمای دانشگاهی

Sebastian Muellerمشاهده پروفایل

استاد

• Asset Pricing
• Investor Behavior
• Digital Finance
• +۳ مورد دیگر

Professor Sebastian Mueller is a faculty member at the TUM School of Management, Technical University of Munich, Campus Heilbronn. He holds a PhD in Finance from the University of Mannheim and has served as Professor of Corporate Finance at the German Graduate School of Management and Law (Heilbronn) and as Assistant Professor in Finance at the University of Mannheim. His research focuses on asset pricing, digital finance, sustainable finance, and investor behavior. PhD in Finance (summa cum laude), University of Mannheim His empirical research combines quantitative methods with machine learning, text analysis, and generative AI to analyze financial market anomalies, economic linkages, and media impacts on investor decision-making. He teaches finance courses across TUM's Bachelor, Master, and Executive Education programs, including Corporate Finance, Digital Finance, and Sustainable Finance. His awards include the SUERF/UniCredit Foundation Research Prize (2019), Acatis Value Awards (2015, 2018), and the VHB Young Talent Award (2015). He supervises finance students through seminars and thesis projects while leading research initiatives at the Center for Digital Transformation and Global Center for Family Enterprise.

Majid Taie Semiromiمشاهده پروفایل

پژوهشگر

• Groundwater modeling
• Surface-groundwater interactions
• Isotope hydrology
• +۳ مورد دیگر

Majid Taie Semiromi is a Postdoctoral Researcher at the Hydrogeology Group, Institute of Geological Sciences, Department of Geosciences, The Free University of Berlin. His work integrates groundwater modeling, climate change impacts, and data-driven algorithms in hydrogeological simulation. Ph.D. in Civil Engineering-Geohydrology (University of Kassel, Germany, 2018) M.Sc. in Watershed Management Engineering (Tarbiat Modares University, Iran, 2010) B.Sc. in Range and Watershed Management Engineering (Gorgan University, Iran, 2007) His research focuses on groundwater modeling, surface-groundwater interactions, isotope hydrology, and the application of machine learning to forecast climate change impacts on water resources. He has led projects on drought monitoring and developed hybrid models for hydrological analysis. Scientific awards include DFG funding (2020–2023), DAAD scholarship (2014–2018), and a 2015 fellowship for drought workshops in China. He has advised M.Sc. and Ph.D. students on topics like kettle hole impacts, groundwater drought indices, and climate change in Iranian basins.

Uwe Rascherمشاهده پروفایل

استاد

• Photosynthesis
• Remote Sensing
• Plant Physiology
• +۵ مورد دیگر

Prof. Dr. Uwe Rascher is the Head of the Shoot Dynamics group at the Institute of Bio- and Geosciences (IBG) , Plant Sciences (IBG-2) within the Jülich Research Centre . His research bridges biophysical processes in photosynthesis with remote sensing applications. Research Focus: Spatiotemporal dynamics of photosynthesis Non-destructive physiological monitoring Solar-induced chlorophyll fluorescence (SIF) for ecosystem analysis Drought and stress response in crops Machine learning for agricultural decision support Integration of leaf-to-canopy scale observations Scientific Trends: Analysis of SIF for photosynthesis quantification, development of hyperspectral imaging systems, cross-scale stress detection (drought, heat), machine learning applications in plant phenotyping, and climate research collaborations. Technical Contributions: Development of HyScreen, FloX, and FluoMap systems for field spectroscopy, UAV-based sensor validation, and standardized ground measurement networks. His work emphasizes sensor fusion, light distribution models, and fractal geometry for fluorescence downscaling.

Tristan Bereauمشاهده پروفایل

استاد

• Computational Physics
• Biomolecular Simulations
• Machine Learning for Soft Matter
• +۴ مورد دیگر

Tristan Bereau is a Professor at Heidelberg University, affiliated with the Institute for Theoretical Physics. His research focuses on computational physics and multiscale modeling of soft matter and biomolecules, integrating physics-inspired machine learning techniques. Current affiliation: Professor, Institute for Theoretical Physics, Heidelberg University Research themes: Multiscale modeling, coarse-graining, data-centric materials science Research interests center on multiscale modeling , machine learning for molecular systems , and chemical space exploration . Key trends in his recent publications include physics-based diffusion models , high-throughput computational screening , and data-driven design of biomolecular systems . His lab has produced 15 recent works spanning free-energy estimation , coarse-grained parameterization , and machine learning for membrane interactions , with a focus on reproducibility and FAIR data principles in materials science.

Robert Rothمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Many-Body Physics
• Ab Initio Nuclear Structure Theory
• +۲ مورد دیگر

Prof. Dr. Robert Roth is a faculty member at the Institute for Nuclear Physics (Technische Universität Darmstadt), specializing in Theoretical Nuclear and Many-Body Physics , Ab Initio Nuclear Structure Theory , and Computational Physics . He leads a research group focused on ultracold atomic gases and nuclear structure simulations. Email: robert.roth@physik.tu-darmstadt.de Office: S2|11 204, Schlossgartenstraße 2, Darmstadt Roth's research involves collaborations with the Collaborative Research Center 1245 , Helmholtz Research Academy Hesse for FAIR , and Extreme Matter Institute . His recent work explores machine learning interatomic potentials for materials science applications, including phase diagrams and alloy modeling. Notable projects include automated workflows via the pyiron framework and studies on Cu-Zr alloys and silicon oxycarbides. His computational approaches bridge quantum mechanics and macroscopic material properties.

• Integrated Photonics
• Optical Communication Systems
• Silicon Photonics
• +۳ مورد دیگر

Dr. Alvaro Moscoso Martir is a Chief Engineer and primary contact at the Institute of Integrated Photonics, RWTH Aachen University . His work focuses on photonic integrated circuits for advanced optical communication systems, biomedical applications, and quantum technologies. Role: Chief Engineer Key Projects: Optical phased arrays, nonlinear frequency division multiplexing, Mach-Zehnder modulators Technologies: Silicon photonics, spectral overlap, resonant enhancement Research Interests span integrated photonics, optical communication systems, and biomedical imaging. His recent work emphasizes nonlinear Fourier transform-based transmission systems and ultra-wideband photonic devices. Publications highlight innovations in spectrally efficient architectures, soliton generation, and resonator design for reduced crosstalk. Technical Contributions include patents and designs for silicon photonic modulators, directional couplers, and six-port microwave receivers. His collaborations explore hybrid integration techniques and temperature-tolerant components for scalable optical networks. Contact: amartir@iph.rwth-aachen.de

• Graph Algorithms
• Network Analysis
• Scientific Workflow Scheduling
• +۳ مورد دیگر

Henning Meyerhenke is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He serves as Deputy Director of Teaching and Studies, focusing on graph algorithms , network analysis , and scientific workflow scheduling . His research spans complex systems modeling and parallel computing. Ranks: Professor Institution: Humboldt University of Berlin Research Interests include energy-efficient computing, heterogeneous systems, and scalable graph algorithms. Recent work explores network sparsification , workflow mapping , and graph robustness under edge deletions. His group also investigates climate science applications via climate networks . Publication Trends reflect advancements in parallel algorithms , distributed systems , and adaptive scheduling for scientific workflows. Key subfields include memory-aware computing , harmonic centrality , and edge sampling techniques. Student Advising : Michael Piechotta (PhD candidate, defended September 15, 2025)

Sadaf Gulshadمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Explainable AI
• +۴ مورد دیگر

Sadaf Gulshad is a Postdoctoral Researcher in Machine Learning and Computer Vision at MultixLab, University of Amsterdam. She completed her PhD (2022) in Computer Science at the University of Amsterdam under Prof. Dr. Arnold Smeulders, and holds a Master's degree in Electrical Engineering from KAIST (South Korea, 2017) and a Bachelor's in Electrical and Computer Engineering from COMSATS Institute (Pakistan, 2015). Research Interests: Sadaf focuses on improving AI transparency and robustness through explainable models. Her work addresses challenges like adversarial examples, medical imaging imperfections, and uncertainty estimation in clinical data. Key contributions include hierarchical prototype explainers (HIPE), transform-augmented networks, and attribute-based reasoning frameworks. Recent Publications: Her research spans explainable AI (CVPR 2023), robustness (NeurIPS 2021), medical imaging analysis (Medical Physics 2023), and uncertainty estimation (European Heart Journal 2021). She develops methods that bridge human cognition with machine learning explainability. Teaching & Supervision: Sadaf has served as Teaching Assistant for courses including "Applied Machine Learning" and "Multimedia Analytics" at the University of Amsterdam. She supervised multiple Master's theses on topics like green roof distribution analysis, semantic segmentation, and interpretable representation learning in collaboration with institutions such as UMC Utrecht and VU Medical Center.

• Computer Science
• Embedded Systems
• Neuromorphic Engineering
• +۳ مورد دیگر

Nikil D. Dutt is a Professor in the Department of Computer Science at the University of California, Irvine. His research focuses on embedded systems, neuromorphic engineering, healthcare informatics, and machine learning applications. He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (1989). His work spans interdisciplinary areas such as neuromorphic computing, wearable healthcare systems, and energy-efficient architectures. Notable contributions include frameworks for self-aware systems, neuromorphic hardware design, and data-driven healthcare solutions. He collaborates extensively with researchers in computer architecture, biomedical engineering, and cyber-physical systems. Recent publications highlight advancements in EEG analysis for traumatic brain injury, energy-efficient machine learning on wearables, and secure embedded systems for automotive applications. His research integrates theoretical models with practical implementations, addressing challenges in reliability, scalability, and adaptability.

• Power Electronics
• Electrical Engineering
• Model Predictive Control (MPC)
• +۴ مورد دیگر

José Rodríguez is a Professor in the Department of Computer Science at CINVESTAV-IPN in Mexico City, Mexico. His research focuses on power electronics, model predictive control (MPC), renewable energy systems, and electric drives. He has collaborated extensively with researchers worldwide, contributing to advancements in energy transition technologies and grid integration strategies. His work emphasizes improving efficiency, reducing computational complexity, and enhancing reliability in power conversion systems. Key areas of expertise include fault diagnosis in rotating machinery, optimization of photovoltaic systems, and the development of advanced control algorithms for electric drives and converters. Rodríguez has authored over 200 peer-reviewed publications, with a strong presence in top-tier journals like IEEE Transactions on Industrial Electronics and IEEE Access . His recent work explores hybrid models (e.g., CWT-LeNet-5-LSTM) for fault detection, low-cost sensor integration, and data-driven predictive control techniques. He also investigates multilevel inverter topologies and grid-connected converter systems, addressing challenges like transient DC offset mitigation and harmonic distortion reduction. Rodríguez’s contributions span both theoretical and applied research, bridging the gap between control systems and practical energy solutions. His interdisciplinary approach combines machine learning, optimization algorithms, and traditional engineering principles to tackle modern energy challenges.

• Accessibility Research
• Sign Language Recognition
• Human-Computer Interaction
• +۴ مورد دیگر

Danielle Bragg is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, specializing in accessibility research with a focus on technologies for deaf and hard-of-hearing communities. Her work bridges human-computer interaction, computer vision, and inclusive design to create practical solutions for sign language recognition, translation, and communication accessibility. Her research interests center on making technology accessible through innovative approaches to sign language processing, assistive communication systems, and inclusive design methodologies. She investigates how to build robust sign language recognition systems that work in real-world settings, addresses bias in AI models for sign language understanding, and develops community-centered approaches to dataset collection. Her work particularly emphasizes involving the Deaf community in the research and development process to ensure technologies meet actual needs. Dr. Bragg's publication record shows a consistent trajectory from her PhD work at the University of Washington (2018) through to current research in 2025, with increasing leadership in the field of accessibility technology. Her recent work explores cutting-edge applications of AI for sign language processing, gaze-based interfaces, and systematic analysis of how large language models are transforming human-computer interaction research. Her collaborative approach is evident in her extensive co-authorship network, working with both technical researchers and community stakeholders to advance the field of accessibility technology through interdisciplinary partnerships.

Nian-Ze Leeمشاهده پروفایل

استادیار

• Formal Methods
• Hardware Verification
• Software Verification
• +۳ مورد دیگر

Nian-Ze Lee is an Assistant Professor at the Department of Electrical Engineering of National Taiwan University, leading the Formal Methods and Analysis for Computing and Engineering Laboratory (ForMACE Lab). He is also a Guest Professor affiliated with the Software and Computational Systems Lab (SoSy-Lab) at Ludwig-Maximilians-Universität München (LMU Munich), Germany. He holds a Ph.D. in Electronics Engineering from National Taiwan University (2021). His research focuses on formal methods, hardware and software verification, and circuit-based analysis. Education: Ph.D. in Electronics Engineering, National Taiwan University (2021) Research Interests: Formal methods for hardware and software systems Model checking and verification frameworks Circuit-based program verification (e.g., Btor2C translator) Algorithm selection for verification tools Awards: ACM SIGSOFT Distinguished Paper Award (2025) Best Artifact Award (2024) DFG Grant for Bridging Hardware and Software Analysis (2024) Advising & Grants: Supervises Ph.D./Master’s students on topics like algorithm selection and verification frameworks Recipient of DFG grant enabling collaboration between NTU and LMU Munich Labs/Teams: ForMACE Lab (NTU) – Focuses on formal methods and system analysis SoSy-Lab (LMU) – Collaborations on verification tools and frameworks

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.

• Communications Engineering
• Signal Processing
• Information Theory
• +۳ مورد دیگر

Tobias Prinz is a Researcher at the Institute for Communications Engineering, Technical University of Munich (TUM), where he has been a Research Assistant since July 2016. He holds a Master of Science (2016) and Bachelor of Science (2013) in Electrical Engineering from TUM. His research focuses on advanced communication systems, including neural network-based interference cancellation, optical fiber communications, polar codes, and modulation techniques. He has been awarded the Prof. Dr. Ralf Kötter Memorial Award (2018) and the Heinrich Hecht Award (2019). Prinz has contributed to teaching courses such as 'Nachrichtentechnik 2', 'Machine Learning for Communications', and laboratory courses in telecommunications. His work spans theoretical and experimental studies in communications engineering, with a strong emphasis on practical applications of coding theory and signal processing. His publications address challenges in bandlimited channels, direct detection systems, and asymmetric channel capacity using polar codes. Recent research includes neural network-based successive interference cancellation and PAM-6 modulation optimization for fiber-optic links.

Chinmay Datarمشاهده پروفایل

پژوهشگر

• Scientific Computing
• Deep Learning
• Partial Differential Equations
• +۳ مورد دیگر

Chinmay Datar is a doctoral candidate at the Technical University of Munich (TUM), affiliated with the Institute of Advanced Study (IAS) in the focus group 'Scientific Machine Learning.' He holds an M.Sc. in Computational Engineering from Friedrich-Alexander University (FAU), Erlangen, and a Bachelor's in Mechanical Engineering from the University of Pune. His research focuses on integrating classical scientific computing methods with deep learning to solve Partial Differential Equations (PDEs). Teaching Responsibilities include: Summer Semester 2025: Seminar on High-Dimensional Methods for Scientific Computing and Lab Course on Machine Learning in Crowd Modeling/Simulation Winter Semester 2024/25: Courses on Scientific Computing & Machine Learning and a Seminar on Ethics in Science Research Interests: Novel neural architectures for dynamical systems simulation Backpropagation-free training of neural PDE solvers Domain decomposition combined with neural networks for PDE solutions Student Supervision: Ongoing: Eray Yildiz (Master's) and Ahmet Semiz (Master's) Completed: Rahma Atamert and Aditya Phopale (Master's) Recognition: FAU Graduation Scholarship (Japan internship support). Labs/Teams: Part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz.