Find Academic Supervisors

Jussi ÄngesleväView profile

Professor

• Media Art
• Interaction Design
• Digital Materiality
• +3 more

Jussi Ängeslevä is a Professor at Berlin University of the Arts and Creative Director at ART+COM Studios, specializing in media art, interaction design, and digital materiality. His career spans academic roles including Visiting Professor at Royal College of Art (2011-2018), Honorary Professor at UdK Berlin (2011-present), and Assistant Professor at UdK Berlin (2005-2007). He holds an MA in Audio-Visual Media Culture from the University of Lapland (2004) and an MA in Interaction Design from the Royal College of Art (2002). His research explores the intersection of computational design, physical interfaces, and spatial communication, with notable interests in kinetic sculptures, caustic light systems, and hybrid museum experiences. Projects like 'RGB|CMY Kinetic' and 'Pinnannousu' demonstrate his integration of mechatronics, computational optics, and environmental interaction. Ängeslevä has received over 20 international awards including a D&AD Black Pencil (2009), Cannes Lions Gold (2009), and Prix Ars Electronica (2003). His contributions to academic discourse include publications on prototyping methodologies, digital materiality, and embodied interaction in venues like Springer and Cambridge University Press. He actively participates in juries for Ars Electronica, D&AD, and Siggraph. Collaborations with institutions like EPFL and Museum für Naturkunde highlight his interdisciplinary approach. Current projects include 'NuForm' (hybrid museum experiences) and 'Place of Remembrance' (data-driven memorials using organic materials). He maintains affiliations with the Hybrid Plattform Berlin and contributes to design education globally.

Edward S. BoydenView profile

Professor

• Neuroscience
• Neuroengineering
• Optogenetics
• +5 more

Edward S. Boyden is the Y. Eva Tan Professor in Neurotechnology at MIT, where he holds appointments in the Department of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering. He is a full member of the McGovern Institute for Brain Research, co-director of the Center for Neurobiological Engineering and the K. Lisa Yang Center for Bionics, and an investigator at the Howard Hughes Medical Institute. Boyden joined the MIT faculty in 2007 and was awarded tenure as a full professor seven years later. Boyden's research spans multiple areas of neurotechnology, with groundbreaking contributions in optogenetics, expansion microscopy, deep brain stimulation, and multiplexed imaging. His work has transformed neuroscience by providing researchers with powerful tools to observe and manipulate brain activity at unprecedented resolution. Boyden's research integrates principles from physics, engineering, chemistry, and biology to develop novel approaches for understanding and treating brain disorders. His publications reveal a consistent focus on developing innovative technologies that push the boundaries of what's possible in neuroscience. The trend shows increasing clinical translation of his basic science discoveries, with recent work moving from fundamental tool development toward therapeutic applications, particularly in vision restoration through optogenetics and non-invasive brain stimulation for memory enhancement. Breakthrough Prize in Life Sciences (2016) The Brain Prize (2013) Rumford Prize (2019) National Academy of Sciences (2019) Gairdner Foundation International Award (2018) Warren Alpert Foundation Prize (2019) Wilhelm Exner Medal (2020) Boyden has mentored numerous students who have gone on to establish their own research programs, including Deblina Sarkar, Christian Wentz, and Kate Adamala. His research has been supported by significant grants from the NIH, NSF, and private foundations. Through his leadership of the Synthetic Neurobiology Group, Boyden has fostered interdisciplinary collaborations across multiple institutions and fields. Boyden leads the Synthetic Neurobiology Group at MIT, which brings together researchers from diverse backgrounds including neuroscience, engineering, physics, and computer science. The group operates state-of-the-art facilities for developing and testing new neurotechnologies, with close connections to clinical researchers for translational work.

• Crop Science
• Plant Physiology
• Agricultural Modeling
• +3 more

Prof. Tsu-Wei Chen serves as a Professor at the Albrecht Daniel Thaer Institute of Agricultural and Horticultural Sciences within the Faculty of Life Sciences at Humboldt University of Berlin. His office is located at Albrecht-Thaer-Weg 5, Room 201, with contact email tsu-wei.chen@hu-berlin.de and phone 030 2093-46295. His research focuses on crop science with particular emphasis on wheat and cucumber systems. Key interests include: Yield stability and breeding progress in cereals Canopy architecture and photosynthetic optimization Plant nutritional value and sustainable production systems Structural-functional modeling of crop growth Climate resilience through physiological adaptations Analysis of his 2020-2025 publications reveals a consistent focus on integrating field trials, computational modeling, and high-throughput phenotyping to address agricultural challenges. His work demonstrates particular expertise in wheat breeding for yield stability across environments and cucumber canopy physiology under stress conditions. No scientific awards were explicitly documented in the available materials. Information regarding student advisement, grant funding, or mentorship activities was not provided in the source texts. Prof. Chen leads research within the Intensive Plant Food Systems group, developing innovative approaches for plant production including tools like SIMBAL (structural-functional modeling) and Half-Farmer (AI-assisted seed germination analysis).

• Human Activity Understanding
• Computer Vision
• Human-Object Interaction
• +5 more

Yuankai Wu is a Researcher at the Chair of Media Technology , Technical University of Munich , focusing on Human Activity Understanding and Computer Vision . He received a B.Sc. in Electrical Engineering (2017) from Ruhr West University of Applied Sciences and an M.Sc. in Electrical Engineering and Information Technology (2020) from TUM. His work explores Human-Object Interaction Understanding and 3D Geometric Features for Assistive Robotics . Education : B.Sc. (Ruhr West), M.Sc. (TUM) Affiliation : Chair of Media Technology, TUM Research Themes : Human activity modeling, vision-driven robot assistance, action anticipation His publications (2022–2025) address topics like TSCL for action segmentation, MistSense for mistake detection, and Neural Painted Radiosity Fields for 3D reconstruction. Collaborative projects span embodied interaction , contextual alignment , and scan-to-CAD estimation .

• Computer Vision
• Machine Learning
• Teleoperation
• +3 more

Dong Yang is a Researcher and PhD candidate at the Chair of Media Technology, Technical University of Munich (TUM) , since October 2021. His work bridges Computer Vision, Machine Learning, and Robotics to advance haptic teleoperation systems. B.Eng in Electrical Engineering and Automation (Fuzhou University, China) B.Sc in Electrical Engineering and Information (Technical University of Kaiserslautern, Germany) M.Sc in Electrical Engineering and Information (TUM, Germany) Research focuses on haptic communication , teleoperation , and scene understanding for robots. Key contributions include: Developing SRI-Graph for scene-robot interaction Creating ISSC for semantic shared control Advancing depth estimation in adverse weather Designing HPF-SLAM for visual navigation His publications span top conferences like ICRA , IROS , and RO-MAN , addressing topics in haptic teleoperation , sensor fusion , and collaborative robotics . Supervises thesis projects including Diffusion Model-based Imitation Learning and Scene Graph-based Real-time Scene Understanding Collaborates on projects like Centre for Tactile Internet with Human-in-the-Loop (CeTI) and Teleoperation over 5G

• Autonomous Driving
• Multi-Object Tracking
• Sensor Calibration
• +3 more

Leah Strand is a Ph.D. candidate and research assistant at the Technical University of Munich (TUM) in the Department of Robotics, Artificial Intelligence and Real-time Systems under Prof. Knoll. She joined TUM in March 2020 and contributes to projects like Providentia++ , which focuses on creating real-time digital twins of traffic on the Autobahn A9. Education: M.Sc. in Mechatronics and Information Technology, B.Sc. in Engineering Science (both from TUM) Research interests include: Autonomous Driving Multi-Object Tracking Sensor Calibration and Fusion Her recent publications emphasize image enhancement , multi-camera calibration , and traffic surveillance datasets . She actively participates in teaching and project advising, with expertise in real-time systems and infrastructure-based perception for autonomous vehicles. Contact: leah.strand@tum.de

Yoav WaldView profile

Researcher

• Safe and robust machine learning
• Machine learning in healthcare
• Structured prediction
• +5 more

Yoav Wald is an incoming Faculty Fellow at New York University's Center for Data Science (CDS), joining in Fall 2023. Previously, he served as a Postdoctoral Fellow at Johns Hopkins' Whiting School of Engineering, where he focused on developing safe and robust machine learning systems with healthcare applications. He completed his PhD at the Hebrew University of Jerusalem under the supervision of Amir Globerson and was also a researcher at Google Research, working on structured prediction and neural network explainability in computer vision problems. Wald holds a BSc in Physics and Computer Science along with an MSc in Computer Science. His research interests center on improving the reliability of machine learning systems, particularly in healthcare contexts. He specializes in out-of-distribution detection, model calibration, and addressing spurious correlations in AI systems. Wald is deeply committed to making machine learning more transparent and trustworthy for real-world applications. His recent publications in top-tier conferences like UAI, ICCV, and NeurIPS demonstrate his contributions to understanding model robustness and generalization. Wald is the lead organizer of the Spurious Correlations, Invariance and Stability (SCIS) Workshop at the International Conference on Machine Learning (ICML), which he previously co-organized in 2022. Among his honors, Wald received excellence awards for teaching during his time at the Hebrew University of Jerusalem. He has established himself as a promising researcher in the machine learning community with a focus on foundational issues that impact real-world deployment of AI systems. Wald has collaborated with industry researchers at Google and is building connections across academia through his workshop organization and conference participation. His move to NYU CDS represents a significant step in his academic career, where he plans to advance research on safe machine learning in healthcare within CDS's diverse and dynamic environment.

Dmitri BershadskyyView profile

Assistant Professor

• Behavioral and Experimental Economics
• Artificial Intelligence
• Lie Detection
• +5 more

Dr. Dmitri Bershadskyy serves as Assistant Professor at the Vienna University of Economics and Business within the Institute for Digital Ecosystems, while maintaining an active research role as Senior Research Associate at the Chair of Economics (Faculty of Business and Economics) at Otto von Guericke University Magdeburg. His interdisciplinary work bridges economic theory with artificial intelligence and software engineering methodologies to investigate human behavior in digital environments. His research spans Behavioral and Experimental Economics, Artificial Intelligence, Lie Detection, Software Engineering, Algorithm Aversion, and Institutional Design. Employing laboratory and online experiments, surveys, and software engineering techniques, he examines deception detection mechanisms, human responses to algorithmic advice, and incentive structures in collaborative settings. His methodological approach emphasizes the integration of economic principles with computational tools. Recent publications reveal a strong interdisciplinary trajectory, with increasing collaboration between economics and computer science. Key thematic clusters include AI-driven lie detection systems, human-AI interaction dynamics in decision-making contexts, and the development of experimental frameworks for software engineering research. This work demonstrates consistent methodological innovation through the creation of specialized experimental tools like the Magdeburg Tool for Video Experiments (MTVE). Dr. Bershadskyy actively supervises undergraduate and graduate thesis work, offering dedicated seminars for Bachelor's and Master's candidates. He leads a major DFG-funded project on AI-based lie detection that was recently renewed for three additional years, involving collaboration with software engineers and psychologists across multiple institutions. As a core member of the MaXLab research group at OVGU Magdeburg, he contributes to the development of experimental infrastructure and participates in establishing industry standards, including contributions to official guidelines for software engineering experimentation protocols published by the ACM SIGSOFT Empirical Standards initiative.

Carina SilbererView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +5 more

Carina Silberer is an Assistant Professor at the Institute for Natural Language Processing within the Faculty of Computer Science, Electrical Engineering, and Information Technology at the University of Stuttgart. Her research bridges computational linguistics, computer vision, and artificial intelligence, focusing on multimodal language processing and semantic representation. Research Focus: Semantic compositionality, vision-language integration, and multimodal learning Expertise: Cross-modal understanding, computer vision, natural language processing Publications: 15 most recent works (2010–2025) span machine learning, semantic modeling, and multimodal cognition Her work explores how language and visual data interact, including emotion recognition in social media, object segmentation through referring expressions, and causal modeling in video analysis. She emphasizes empirical methods and dataset creation to advance multimodal research. Carina Silberer's publications demonstrate a strong focus on interdisciplinary approaches combining artificial intelligence with cognitive science and linguistics , particularly in grounding meaning through multimodal data. Her research contributes to understanding semantic representations, event modeling, and commonsense reasoning in vision-language systems.

Oliver RittmannView profile

Research Fellow

• Political Representation
• Gender Inequality
• Political Communication
• +3 more

Oliver Rittmann serves as a Lorenz von Stein Research Fellow at the Mannheim Centre for European Social Sciences (MZES), University of Mannheim. His research integrates computational analysis of audio, image, and video data to address core political science questions in political representation, inequality, and communication. Rittmann pioneers the application of computer vision in legislative studies, notably through his current project analyzing gender-based attention disparities in the Baden-Württemberg state parliament. Using AI-driven video analysis of plenary sessions, he demonstrates that female members receive systematically less attention than male counterparts—a pattern driven exclusively by male MPs' behavior. His methodology transforms unstructured audiovisual data into quantifiable political behavior metrics, bridging longstanding gaps between social theory and empirical evidence. His publication record (2020-2025) reveals three thematic pillars: audiovisual analysis of parliamentary behavior (body language, vocal pitch, attention patterns), statistical methodology for regression interpretation, and electoral system dynamics . These works consistently advance data science applications in political science, particularly through novel computational frameworks for previously unstructured data sources. Scientific Awards Lorenz von Stein Research Fellowship Rittmann leads interdisciplinary collaborations between political scientists and computer scientists, as evidenced by his parliamentary attention project. His research demonstrates how data science enables large-scale behavioral analysis impossible through traditional methods—processing thousands of speeches rather than selective case studies. Future work will likely expand audiovisual analysis to other legislative contexts while refining gender inequality metrics.

Alexander KellerView profile

Professor

• Ecology
• Biodiversity
• Bioinformatics
• +5 more

Prof. Dr. Alexander Keller is a Professor at Ludwig Maximilian University of Munich (LMU), based at the LMU Biocenter in Planegg-Martinsried, Germany. He leads the "Cellular and Organismic Networks" research group within the Graduate School Life Science Munich (LSM), integrating field ecology, bioinformatics, and next-generation sequencing technologies to study ecological networks. His research centers on biodiversity dynamics within the plant-microbe-pollinator triangle , examining mutualistic interactions, microbiome compositions, and genomic-level molecular processes. Key methodologies include DNA metabarcoding, phylogenetic reconstructions, and computational workflow development. His work addresses ecosystem resilience, biodiversity loss, and conservation strategies under environmental change, with emphasis on land-use impacts, urbanization, and climate gradients across tropical and temperate ecosystems. Recent publications (2025) demonstrate strong trends in pollination ecology, microbiome analysis, and biodiversity monitoring using advanced sequencing . His studies reveal how landscape heterogeneity affects bee-pollen networks, how host identity shapes microbiomes, and how urban bees adapt foraging strategies. Notable methodological contributions include the LEPY pipeline for morphological analysis and MetAnoDe for metabarcoding quality control, alongside extensive work on tropical forest regeneration and undescribed species cataloging. Scientific Awards: No awards were mentioned in the provided text. Information regarding student advising and research grants was not provided in the available text. The "Cellular and Organismic Networks" group develops open computational pipelines and laboratory protocols for ecological data analysis. They conduct fieldwork across Ecuadorian rainforests, European grasslands, and urban environments, maintaining databases for trait analysis and sequence curation while adhering to open science principles. Current projects investigate microbiome transmission in bee-flower networks, forest succession dynamics, and the molecular basis of ecological resilience under global change.

• Control Systems
• Robotics
• Unmanned Aerial Vehicles
• +3 more

Yuan Yuxia is a Researcher at the Chair of Autonomous Aerial Systems (TULRAAS) within the Technical University of Munich (TUM), actively contributing to both research and teaching missions at the Ottobrunn campus (Lise-Meitner-Str. 9). Her institutional affiliation places her within a leading European research group focused on advanced aerial robotics and control systems under Prof. Ryll's leadership. Her research spans Control Systems , Robotics , Unmanned Aerial Vehicles , Human-Robot Interaction , Brain-Computer Interfaces , and Rehabilitation Robotics , with dual specializations in UAV payload control and wearable assistive devices. She has pioneered dual quaternion methodologies for UAVs with cable-suspended loads to address dynamic instability, while simultaneously developing brain/muscle signal-driven exoskeletons that restore stair-climbing capability for mobility-impaired users. Her technical approach integrates nonlinear control theory, fuzzy neural networks, and reinforcement learning to solve real-world robotics challenges. Analysis of her 15 most recent publications (2018-2025) reveals two dominant research trajectories: (1) Advanced UAV control systems focusing on cable-suspended load manipulation through geometric approaches like dual quaternions, and (2) Rehabilitation robotics emphasizing human-in-the-loop control via physiological signals. These streams converge in her work on brain-controlled mobile robots and adaptive exoskeletons, demonstrating exceptional interdisciplinary synthesis across aerospace engineering, biomedical applications, and machine learning. Her publications consistently target high-impact control challenges with immediate practical relevance. Scientific Awards: No major scientific awards are documented in the provided sources. Advising and Grants: While specific student names and grant details aren't disclosed, her extensive publication record across robotics and rehabilitation domains indicates active supervision of graduate researchers and successful acquisition of competitive research funding. Her collaborations with medical and engineering teams suggest participation in multi-institutional projects addressing both industrial UAV applications and healthcare robotics. Laboratory and Team Affiliations: Yuan operates within TULRAAS's state-of-the-art Ottobrunn facilities specializing in UAV testing and simulation. The group maintains strong industry partnerships for technology transfer, particularly in aerial manipulation and medical robotics. Her position within this internationally recognized chair enables cutting-edge research on autonomous systems with direct pathways to real-world deployment in logistics, search-and-rescue, and clinical rehabilitation settings.

Alain PaganiView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Image Understanding
• +6 more

Alain Pagani is a Principal Researcher and Deputy Director of the Augmented Vision department at the German Research Center for Artificial Intelligence (DFKI) . He leads a team of 10 PhD students and researchers, focusing on Augmented Reality, Computer Vision, and Machine Learning. Research Interests include 3D Computer Vision, Image-based Scene Reconstruction, Object Detection and Tracking, and Extended Reality (AR/VR) applications. His work spans both industrial and academic projects, such as the EU initiatives dAIEDGE , CORTEX² , and FLUENTLY . Scientific Awards include a Google Research Award, Best Paper Award (INI-GraphicsNet 2007 Honorable Mentions), and Best Paper Award (INI-GraphicsNet 2008 Honorable Mentions). Projects like dAIEDGE (AI at the Edge) and CORTEX² (Collaborative XR) highlight his leadership in distributed AI and real-time extended reality systems. He has also coordinated EU projects such as LARA (Outdoor AR using Galileo) and Eyes of Things (Low-power Computer Vision).

• Artificial Intelligence
• Business Informatics
• Semantic Technologies
• +6 more

Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .

Jovita LukasikView profile

Researcher

• Computer Vision
• Machine Learning
• Neural Architecture Search
• +4 more

Jovita Lukasik is a Ph.D. candidate in Computer Science at the University of Mannheim , affiliated with the Data and Web Science Group and the Computer Vision and Machine Learning Group at the Max Planck Institute for Informatics . Supervised by Prof. Dr.-Ing. Margret Keuper , her research focuses on Neural Architecture Search (NAS) and its applications in computer vision and graph-based learning systems. Education Ph.D. (2019–present), Computer Science, University of Mannheim M.Sc. (2016–2019), Mathematics in Business and Economics, University of Mannheim B.Sc. (2012–2015), Mathematical Finance, University of Konstanz Lukasik's research bridges generative NAS , graph neural networks , and inverse problem solutions . She has developed novel approaches for variational graph embeddings and stability analysis in NAS frameworks, contributing to the democratization of efficient deep learning architecture discovery. Her publication trends reveal expertise in automated machine learning with specific attention to search space optimization , graph-based NAS , and stability in differentiable architecture search . She has presented at premier venues including European Conference on Computer Vision and International Conference on Learning Representations . Lukasik actively contributes to academic communities through workshop organization (NAS @ ICLR 2021) and virtual seminar leadership on AutoML advancements.