Philippos Mordohai is Professor in the Department of Computer Science at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He earned his PhD in Electrical Engineering from the University of Southern California and conducts research in geometric computer vision, 3D reconstruction, and robotic perception. His research combines machine learning and parallel programming to develop novel approaches for depth estimation, scene understanding, and autonomous navigation. Current projects include real-time dense mapping for underwater environments and domain generalization for stereo matching algorithms. Dr. Mordohai serves as associate editor for IEEE Transactions on Pattern Analysis and Machine Intelligence and has chaired multiple international computer vision conferences. His research has been supported by NSF, DOE, and Google, with applications in robotics, mixed reality, and autonomous systems.
Prof. Alexander Geissler holds the position of Full Professor of Health Care Management at the School of Medicine (Med-HSG) within the University of St. Gallen. His research focuses on health systems research, health economics, and health policy, with particular emphasis on digital transformation in healthcare and patient-reported outcomes. He has contributed extensively to studies on healthcare quality improvement, public reporting systems, and the integration of artificial intelligence in medical diagnostics and screening programs. His work spans topics like optimizing hospital digital maturity (e.g., German DigitalRadar project), analyzing surgical outcomes (robotic vs. open prostatectomies), and evaluating patient empowerment through quality information. He has pioneered methodologies for interpreting patient-reported outcomes (e.g., EQ-5D-3L) and designing clinical dashboards to enhance care delivery. Recent research highlights include investigating AI applications in breast cancer screening and cost-effectiveness of remote patient monitoring post-joint replacement surgery. Geissler’s publications demonstrate a strong focus on healthcare policy implications, such as hospital capacity planning, payment systems for specialized care, and cross-country comparisons of healthcare transparency initiatives. His work frequently bridges academic rigor with practical policy recommendations, particularly in Switzerland and Germany. Notably, he has addressed low-value care reduction, price sensitivity in healthcare demand, and the socio-demographic factors influencing healthcare utilization. While no specific awards are listed, his prolific research output reflects sustained leadership in health systems analysis. His academic contributions are disseminated through the Alexandria Research Platform and international peer-reviewed journals.
Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants
Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.
Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.
Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.
Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.
Jennifer Gibbs is a Professor and Graduate Director in the Department of Communication at the University of California, Santa Barbara. Her research focuses on collaboration in global teams, distributed work arrangements, and the impact of technologies like AI and digital media on organizational practices. She holds affiliated appointments in TMP and CITS. Education: Ph.D. (2002) in Organizational Communication from the University of Southern California; B.A. (1992) in Philosophy from Pomona College. Research interests include distributed work dynamics, global team management, remote work challenges, and technology's role in organizational transformation. Her work combines qualitative and quantitative methods, emphasizing real-world field studies to explore communication processes and technological impact. Recent studies address boundary management in remote work, well-being in virtual teams, and AI's role in legal and organizational contexts. Her articles highlight trends in global work dynamics, technological affordances, and equity perceptions among remote workers. Gibbs has served as Editor of Communication Research and on editorial boards of top-tier journals. She teaches Organizational Communication, Communication Technology, and Qualitative Research at both undergraduate and graduate levels. Her contributions bridge theory and practice, addressing critical issues like work-life balance in digital environments and the future of hybrid workplaces.
Bernhard Rumpe is a Professor and Chair of Software Engineering at the Department of Computer Science 3, RWTH Aachen University, Germany. He leads a research group focused on model-based software engineering, domain-specific languages, and digital twins, with strong industrial collaborations and applications in embedded systems, AI, IoT, and autonomous vehicles. His research centers on improving software development through model-driven engineering, generative techniques, and formal modeling using UML, SysML, and the MontiCore language workbench. Key interests include digital twins, variability modeling, model composition, and the integration of cyber-physical systems with information systems. The recent publications highlight a consistent focus on model-driven digitalization, language workbenches, and system integration. Trends show increasing emphasis on digital twins in manufacturing and societal systems, formal verification of model transformations, and educational applications of model-driven low-code platforms. His work bridges theoretical foundations with industrial applicability. Keynote Speaker, OOPSLE 2025 General Chair, GPCE 2023 Session Chair, MODELS 2020 Program Committee Member, SLE, GPCE, ICSE, ECMFA He advises master’s and doctoral students and leads a vibrant research team that has successfully executed over 100 research projects. His group develops foundational tools like MontiCore and applies them in industrial contexts, contributing to software quality and developer efficiency. No formal grants are listed, but sustained project funding is evident. He is involved in several research labs and teams centered around the Software Engineering Chair at RWTH Aachen, focusing on language workbenches, model-driven development, and digital twin systems. The team actively contributes to open research through publications, tools, and industrial partnerships.
Piotr Winkielman is a Professor of Psychology at the University of California, San Diego (since 2007), with affiliations at Warwick Business School (UK) and the Warsaw School of Social Sciences and Humanities (Poland). His research focuses on the interplay of emotion, cognition, embodiment, and consciousness in social cognition, employing methods from social/cognitive psychology and neuroscience. Key interests include unconscious emotion processing, facial mimicry, decision-making under emotional influence, and the neural basis of social perception. Education: PhD in Social Psychology (1997), University of Michigan MSc in Psychology (1991), University of Bielefeld, Germany BSc in Psychology (1988), University of Warsaw, Poland Research Interests: Winkielman explores how fleeting affective reactions influence behavior without conscious awareness, studying facial feedback mechanisms, emotional contagion, and the role of physiological signals in decision-making. His work bridges theories like Affect-As-Information and Facial Feedback, while challenging assumptions about the necessity of conscious processes in emotion. Grants & Funding: Supported by NSF Grant BCS-0350687 for studies on unconscious emotion and physiological feedback. Collaborations include projects on mimicry, stress effects, and cross-modal perception. Labs & Teams: Leads research at UCSD on embodied cognition and social neuroscience, with studies involving EEG, EMG, and behavioral experiments. International collaborations span UK, Poland, and Germany.
Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.
M.Sc. Maximilian Mühlbauer is a researcher at the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems at Technische Universität München (TUM), part of the Faculty of Computer Science. His work focuses on robotics, artificial intelligence, and space robotics, particularly in areas like in-orbit manufacturing, virtual fixtures, and human-robot interaction. He contributes to projects such as the ACOR initiative and the AI-In-Orbit-Factory, exploring fault-tolerant processes and adaptive robotic systems for space applications. Research Interests: Maximilian’s research emphasizes AI-driven robotics , space robotics , and control systems . He develops methodologies for virtual fixtures , reconfigurable robotic systems , and teleoperation with shared control . His work integrates probabilistic models and machine learning for resilient systems in challenging environments like space. Publications: His recent work spans topics from in-orbit manufacturing and force-sensitive space manipulators to multi-modal haptic teleoperation , reflecting a focus on practical robotic applications in aerospace and industry. Grants/Advising: Maximilian oversees available theses on topics like mixture of experts fixture learning and virtual fixture adaptation , inviting collaboration on AI-driven robotics projects. He collaborates with Prof. Alin Albu-Schäffer and contributes to TUM’s research initiatives in autonomous systems. Labs: He is part of the Sensor-Based Robot Systems lab, advancing robotics for human-centric and space-oriented applications.
Dr. Yelda Turkan is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University, where she leads research in automation, computer vision, and machine learning for sustainable infrastructure. She holds a PhD from the University of Waterloo and dual BS degrees in Civil Engineering and Geomatics Engineering from Istanbul Technical University. Her work focuses on leveraging lidar, digital twins, and BIM to improve construction operations and decision-making in the built environment. She has secured over $4M in grants from NSF, FHWA, and other agencies, and currently leads the NSF Convergence Accelerator-funded 'Deep Reality' project for AI-driven infrastructure management. Education: Ph.D., Civil Engineering, University of Waterloo, 2012 M.S., Engineering Informatics & Remote Sensing, Istanbul Technical University, 2006 B.S., Civil Engineering (double major in Geomatics Engineering), Istanbul Technical University, 2005/2003 Professional Roles: Vice President, International Association for Automation and Robotics in Construction (IAARC) Chair, ASCE Computing Division Education Committee Associate Editor, ASCE OPEN Journal Her research emphasizes automation in construction quality control, infrastructure inspection via drones and lidar, and immersive education tools using VR/AR. Recent projects include automated curb ramp compliance analysis, wildfire impact modeling, and digital twin development for timber structures. She has published over 80 peer-reviewed articles and actively promotes computing integration in civil engineering education and professional practice.
Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.
Teng-Fong Wong is a Research Professor in the Department of Geosciences at Stony Brook University, where he has been a faculty member since 1982. His research focuses on the intersection of rock mechanics, earthquake processes, and environmental applications, making significant contributions to understanding deformation mechanisms in geological materials. Education: Sc.B., Brown University, 1973 M.S., Harvard University, 1976 Ph.D., Massachusetts Institute of Technology, 1981 Research Interests: Professor Wong's research centers on rock mechanics with emphasis on earthquake mechanics, energy resources, and environmental applications. He investigates both phenomenological and micromechanical aspects of rock deformation and fluid flow using an integrated approach combining high-pressure deformation experiments, quantitative microstructure characterization, and theoretical analysis. His work spans brittle-ductile transitions in porous rocks, permeability evolution, strength properties of fault zone materials from SAFOD and TCDP drilling projects, and submarine groundwater discharge systems. Publication Trends: Wong's recent publications (2006-2008) demonstrate a consistent focus on strain localization mechanisms in porous rocks, particularly examining compaction bands and deformation bands in sandstones. His work integrates advanced imaging techniques (X-ray radiography, CT scanning) with mechanical testing to understand the micromechanics of rock failure. A significant thread connects his research on fault zone properties from major drilling projects (SAFOD, TCDP) with fundamental rock deformation processes. Scientific Recognition: U.S. Patent 6,874,371 for Ultrasonic Seepage Meter (2005) U.S. Patent 7,107,859 for Ultrasonic Seepage Meter (2006) Co-author of "Experimental Rock Deformation - The Brittle Field" (2nd Edition, Springer-Verlag, 2005) Professional Activities: Professor Wong maintains an active international research profile with numerous visiting appointments including at Australian National University, MIT, ETH Zurich, and institutions in China and France. His work involves extensive collaboration with USGS and international research teams on major fault zone drilling projects. He has developed specialized equipment like the ultrasonic seepage meter for measuring submarine groundwater discharge. Research Infrastructure: Wong's laboratory utilizes advanced capabilities including high-pressure deformation equipment, 3D visualization through laser scanning confocal microscopy and synchrotron microCT, and integrates these with analytic modeling and numerical simulation techniques (finite element and discrete element methods) to investigate micromechanics of dilatant and compactant failure in geological materials.
