Find Academic Supervisors

• Haptic Teleoperation
• Control Algorithms
• Network Communication
• +5 more

Zican Wang is a Scientific Staff member and Ph.D. candidate at the Chair of Media Technology within the School of Computation, Information and Technology at Technical University of Munich (TUM). He joined TUM in 2021 after completing his M.Sc. in Mechanical Engineering from the Robotics Laboratory at Shanghai Jiao Tong University, where he had previously earned dual B.Sc. degrees in Mechanical Engineering and Computer Science. Wang's research focuses on haptic teleoperation systems, control algorithms, network communication, and related technologies including digital twins and perceptual signal quality assessment. His work specifically targets the optimization and assessment of control methods and quality of experience in teleoperation over time-delayed networks. He employs data processing, machine learning, robotics, and virtual reality as assistive tools for his primary research objectives. Wang's publications demonstrate expertise in haptic dataset augmentation, teleoperation control systems, and subjective experience prediction. His recent publications reveal a strong emphasis on practical applications of haptic technology in robotics, with particular attention to variable impedance control, human stiffness estimation, and GAN-based approaches for haptic data processing. Wang frequently collaborates with Professor Eckehard Steinbach and colleagues including Dong Yang, Xiao Xu, and Basak Gülecyüz on projects related to the Centre for Tactile Internet with Human-in-the-Loop (CeTI). IEEE Sensors Journal publication (2024) on haptic sensor-aided variable impedance control Multiple IROS and RO-MAN conference papers (2022-2023) on haptic dataset augmentation and teleoperation optimization Wang also supervises student research, including a project on GAN-based subjective haptic signal quality assessment database augmentation. His work contributes to TUM's research in haptic communication, video communication, and human activity understanding as part of broader projects including the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and DFG-funded research on Teleoperation over 5G.

• Behavioral Ecology
• Collective Behavior
• Wildlife Conservation
• +3 more

Dr. Blair Costelloe is a behavioral ecologist and Postdoctoral Fellow at the Max Planck Institute for Animal Behavior and University of Konstanz. She leads the Herd Hover project, applying drone technology and computer vision to study collective animal behavior and conservation. Max Planck Institute for Animal Behavior, University of Konstanz (Current Institution)

Alexander EckerView profile

Professor

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +3 more

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +4 more

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

• Computer Vision
• Machine Learning
• Augmented Reality
• +3 more

Tom Drummond is a Professor and Head of the Department of Electrical and Computer Systems Engineering at Monash University. He serves as Monash Node Leader and a member of the Executive Committee of the ARC Centre of Excellence for Robotic Vision. His work focuses on real-time computer vision, machine learning, and robust methods with applications in augmented reality, robotics, assistive technologies, and medical imaging. Education: BA in Mathematics from the University of Cambridge; PhD in Computer Science from Curtin University. Research Interests: Dr. Drummond specializes in real-time processing of video data, developing algorithms for computer vision and robust systems. His research bridges theoretical advancements with practical applications in robotics and human-centered technologies. Scientific Awards: Könderink prize IEE International Symposium on Mixed and Augmented Reality (ISMAR) 10-year impact award Grants: Throughout his career at the University of Cambridge and Monash University, he has secured over $30M AUD in research and industry grants.

Lena Maier-HeinView profile

Professor

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +4 more

Lena Maier-Hein serves as one of Helmholtz Imaging's center coordinators at the German Cancer Research Center (DKFZ), head of the Intelligent Medical Systems division, managing director of the Data Science and Digital Oncology cross-topic program, and is a full professor at Heidelberg University. She also holds the position of managing director at the National Center for Tumor Diseases (NCT) Heidelberg. Her research focuses on machine learning-based biomedical image analysis with specific emphasis on surgical data science, computational biophotonics, and validation of machine learning algorithms. As a fellow of both the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and the European Laboratory for Learning and Intelligent Systems (ELLIS), she leads significant initiatives including presidency of the MICCAI special interest group on challenges and chairing the international surgical data science initiative. Her publication portfolio demonstrates strong focus on algorithm validation, surgical data science, and medical imaging AI, with recent work addressing critical issues in reproducibility, bias in medical AI, and practical clinical implementation. The articles reveal consistent themes around establishing rigorous validation frameworks, developing practical surgical AI tools, and addressing real-world deployment challenges in medical imaging. Her notable scientific recognition includes: 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) 2017/18 Berlin-Brandenburg Academy Prize European Research Council (ERC) starting grant (2015-2020) ERC consolidator grant (2021-2026) She serves on the editorial boards of prestigious journals including Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Medical Image Analysis, demonstrating her leadership in establishing standards for medical imaging research. Her work bridges fundamental AI research with practical clinical applications, particularly in surgical settings where real-time decision support is critical.

• Signal processing
• Digital image processing
• Nonlinear dynamic systems
• +5 more

Professor Kristina Kelber is a faculty member at HTW Dresden (Hochschule für Technik und Wirtschaft Dresden), holding the Chair of Communications Engineering within the Faculty of Electrical Engineering. She is also a member of the Faculty Council, contributing to academic governance at the institution. Her research focuses on signal processing , digital image processing , and the analysis and design of nonlinear dynamic systems . Her work spans theoretical aspects of communications engineering and practical applications in diverse fields including music technology for accessibility applications, bird song identification, film restoration, and visitor center information systems. Her publication record demonstrates consistent expertise from the 1990s to present, with evolving applications from foundational work in chaos-based encryption systems to more recent applied research with social impact. Professor Kelber has secured research funding for projects such as "sprechAktiv," a child-friendly interactive language learning media initiative developed in cooperation with Linguwerk GmbH, Dresden, and funded by the BMBF's SME innovative program from 2013-2015. Her research methodology combines theoretical analysis with practical implementation, as evidenced by publications ranging from mathematical analysis of chaotic systems to applied projects in music technology and image processing. Member of the Faculty Council at HTW Dresden Associated with the Communication Technology laboratory Active researcher with publications spanning over two decades Professor Kelber teaches courses in systems theory, signals and systems, audio and video technology, signal coding, and digital image processing across multiple degree programs. She offers both traditional classroom instruction and distance learning options through the Bildungsportal Sachsen (OPAL) platform, demonstrating adaptability to different educational delivery methods and commitment to accessible education.

Meir FederView profile

Professor

• Information Theory
• Signal Processing
• Communication Systems
• +1 more

Meir Feder is a Professor in the Electrical Engineering department at Tel Aviv University holding the Information Theory Chair, and serves as a Visiting Professor in the Department of EECS at MIT. His research bridges theoretical foundations with practical applications across signal processing, communication systems, and machine learning. His scholarly focus centers on information-theoretic frameworks for data prediction, compression, and transmission. Key contributions include universal prediction algorithms that optimize data handling under uncertainty, with recent work extending these principles to neural network optimization and AI infrastructure. This research demonstrates consistent translation from theoretical breakthroughs to real-world engineering solutions. Major recognitions include: IEEE Information Theory Society best paper award for universal prediction research Israeli Defense Forces "creative thinking" award for technological innovation Research Prize of the Israeli Electronic Industry presented by the President of Israel Professor Feder maintains deep industry engagement through technology entrepreneurship. He founded Peach Networks (acquired by Microsoft) and Amimon (acquired by Vitec), developing wireless video transmission systems. His renewed machine learning focus led to co-founding Run:ai, which creates virtualization platforms for AI infrastructure acceleration. These ventures exemplify his commitment to transforming academic research into commercial technologies while securing substantial venture funding.

Andrew BegelView profile

Associate Professor

• Software Engineering
• Human-Computer Interaction
• Accessibility
• +3 more

Andrew Begel is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Principal Researcher at Microsoft Research from 2006 to 2022. He received his Ph.D. in Computer Science from the University of California at Berkeley and holds a Master of Engineering from MIT. Dr. Begel is internationally recognized for his pioneering research on cooperative and human aspects of software engineering, combining empirical studies of professional software engineers with organizational behavior. His work focuses on accessibility, neurodiversity, and human-computer interaction, with particular emphasis on creating more inclusive technology workplaces. He was the first to study emotions in software engineering and conducted groundbreaking research on autistic software developers, identifying their unique strengths and challenges. His research portfolio shows a clear trajectory toward understanding human factors in software development, with increasing focus on neurodiversity and accessibility. Early work examined code comprehension through eye tracking and fMRI studies, while recent publications focus on creating inclusive environments for autistic software engineers and developing tools that support mixed-ability collaboration. His work spans empirical software engineering, human-computer interaction, and accessibility research, often bridging these domains to address real-world challenges in software development environments. Best Paper Award — SIGCSE (2021) ACM Distinguished Member (2019) Best Paper Honorable Mention Award — CSCW (2019) Best Paper Award — ICSE, Software Engineering In Practice (2019) Most Influential Paper Award (10 Years) — ICER (2019) Best Paper Honorable Mention Award — CHI (2017) Dr. Begel has been deeply involved in academic service, organizing and serving on program committees for top software engineering conferences. He has run FSE and ICSE's ACM Student Research Competitions and ICSME's Doctoral Symposium. He created the Autism at Work Research Workshop series, bringing together researchers, practitioners, and autism self-advocates to help autistic engineers find and keep jobs. As a member of Microsoft's AI Ethics Board, he contributed to disseminating best practices for responsible AI engineering. His teaching includes courses at the University of Washington's iSchool and coding camps for autistic youth. Dr. Begel leads the Autism at Work Research Workshop series and has established himself as a prominent figure in creating more inclusive environments for neurodiverse software engineers. His work bridges academic research with practical applications in industry settings, particularly through his collaborations with Microsoft and other technology companies.

Antonio FilieriView profile

Visiting Associate Professor

• Static analysis
• Privacy
• Automated test generation
• +5 more

Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.

Monique MeuschkeView profile

Associate Professor

• Medical Visualization
• Narrative Visualization
• Medical Storytelling
• +3 more

Monique Meuschke serves as a Privatdozent (equivalent to Associate Professor) at the Institute of Simulation and Graphics, Otto-von-Guericke University Magdeburg, where she leads pioneering research at the intersection of medical visualization and narrative storytelling. Her work transforms complex clinical data—particularly hemodynamic patterns in cerebral aneurysms—into accessible visual narratives for diverse audiences ranging from neurosurgeons to patients and the general public. Her research program centers on three interconnected pillars: Medical Visualization Fundamentals : Developing novel techniques for blood flow analysis, aneurysm characterization, and vascular structure representation through systems like Aneulysis and COMFIS Narrative Storytelling Frameworks : Creating structured methodologies for 'disease stories' that communicate pathological processes through visual narratives, including AI-generated characters and virtual reality experiences Public Health Translation : Bridging clinical data with community health literacy through projects like Raccoon for risk communicators and visual disease stories for epidemiological data Meuschke's publication trajectory reveals accelerating impact since 2020, with 18 first-author/co-author papers in 2024-2025 alone spanning top venues including IEEE Transactions on Visualization and Computer Graphics, Medical Image Analysis, and Eurographics workshops. Her work demonstrates consistent focus on making medical data both scientifically rigorous and humanly comprehensible, with recent innovations in AI-assisted narrative generation and real-world validation through platforms like YouTube comment analysis. She maintains active collaboration within Otto-von-Guericke University's visualization ecosystem while extending impact through clinical partnerships and public health initiatives. Her research directly addresses critical gaps in medical communication—from surgical planning support to population-level health literacy—through technically sophisticated yet accessible visual solutions.

Philipp BruneView profile

Professor

• Artificial Intelligence
• Facial Emotion Recognition
• Cybersecurity
• +5 more

Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.

• Industrial Organization
• Consumer Choice
• Marketing Strategy
• +7 more

Xinlei Chen serves as Professor of Marketing at Cheung Kong Graduate School of Business (CKGSB), with prior faculty appointments at Shanghai Advanced Institute of Finance (SAIF) and University of British Columbia. His academic foundation includes a B.E. from Tsinghua University and Ph.D. in Marketing from University of Minnesota. His educational background features: B.E. in Engineering, Tsinghua University Ph.D. in Marketing, University of Minnesota (Carlson School) Chen's research centers on empirical industrial organization and consumer decision modeling, with specific expertise in pricing strategy, advertising effectiveness, channel management, and entertainment economics. His work bridges theoretical structural modeling with real-world applications in digital platforms, video gaming, and social networks, emphasizing how consumers process information and firms optimize competitive strategies in dynamic markets. Recent investigations examine gender dynamics in online competition and advertising spillovers in short-form video ecosystems. Analysis of his publication trajectory reveals consistent focus on marketing science fundamentals while evolving toward digital economy applications. Early work established foundations in movie economics and private label competition, transitioning to contemporary investigations of platform markets, mobile advertising fraud, and social network effects. His methodology combines rigorous econometric techniques with field experiments, particularly evident in 2025 ByteDance research and mobile ad fraud studies. His scientific recognition includes: MSI Young Scholar (2009) Two-time Long Term Impact Award Finalist (Society for Marketing Science, 2018 & 2020) Carlson School Dissertation Award (2003-2004) Multiple doctoral consortium fellowships (INFORMS, AMA-Sheth) Chen demonstrates extensive teaching impact across executive education, MBA, and doctoral programs at CKGSB, SAIF, and UBC, complemented by industry experience as project manager at ABB and China-Machine Building International. His business-oriented scholarship extends to authored books on Chinese internet markets and regular commentary in major business media. Current research activities include field experiments on cross-media advertising and pandemic exercise behavior through digital platforms.

Annegret WengView profile

Professor

• Financial Mathematics
• Insurance Mathematics
• Number Theory
• +4 more

Annegret Weng serves as Professor and Dean of the School of Applied Mathematics and AI at Stuttgart University of Applied Sciences since 2009, with board membership in the German Society for Financial and Insurance Mathematics (DGVFM) since 2023. Her career bridges industry expertise from Allianz and SV Sparkassenversicherung with academic leadership in quantitative disciplines. Diploma in Mathematics, University of Frankfurt PhD, University of Essen (1999) Postdoctoral Research, Universities of Essen and Mainz (1999-2001) Her research integrates pure mathematics with financial applications, focusing on number theory (De Bruijn sequences, cyclic matrices) and actuarial science (Solvency II compliance, disability risk modeling). Recent work explores AI's role in educational assessment and regulatory frameworks for insurance algorithms, demonstrating consistent innovation across theoretical and applied domains. Publication trends (2015-2024) reveal dual expertise: advancing actuarial methodologies through parameter uncertainty modeling while pioneering AI-driven educational tools. Her work consistently connects recreational mathematics (e.g., magical properties of linear recursions) with industry-critical applications in insurance risk assessment. As head of the Laboratory for Quantitative Methods of Actuarial Mathematics , she leads research in computational risk modeling. Her industry engagement includes the German Actuarial Association's Big Data in Life Insurance Working Group (since 2024) and DGVFM's School Working Group (since 2019), driving collaboration between academia and insurance regulators.

Thomas KopinskiView profile

Professor

• Machine Learning
• Deep Learning
• AI Safety
• +6 more

Prof. Dr. Thomas Kopinski is a Professor at the Faculty of Engineering and Economics, South Westphalia University of Applied Sciences in Meschede, Germany. He leads the AI Safety and Collective Intelligence Lab, focusing on cutting-edge research in machine learning applications for industrial and automotive systems. His work bridges academic research and industry collaborations, notably with BMW AG. Research Focus: His team explores: Deep learning architectures for real-time gesture recognition and automotive HMI AI safety protocols and collective intelligence frameworks Industrial applications including predictive maintenance and anomaly detection 3D programming and sensor fusion techniques Team & Students: Current advisees include PhD candidates working on: Bayesian deep learning for predictive maintenance (Felix Neubürger) Generative models for image synthesis (Yasser Saeid) Object recognition in crash test videos (Daniel Gierse) Key Projects: Actively directs WiTraPres and Core Transformer initiatives, with upcoming R&D in AI Safety launching in 2025. Industrial collaborations focus on automotive safety systems and manufacturing optimization.