Dr. Aniket Bera is an Associate Professor in Computer Science at Purdue University and holds an Adjunct Associate Professor role at the University of Maryland at College Park (UMIACS). He directs the IDEAS Lab at Purdue and previously served as a Research Assistant Professor at UNC Chapel Hill. His research focuses on Affective Computing, Computer Graphics (AR/VR), AI & Robotics, Social Robotics, and medical AI applications for mental health diagnostics. Affiliations: Purdue University (Primary), University of Maryland (Adjunct), UMIACS Career: Joined Purdue in 2017, extensive industry collaborations with Disney Research, Intel, and C-DAC Research Interests: Affective Computing: Emotion perception via gait analysis, speech, and facial/body expressions AR/VR: Redirected walking, virtual environments, and human motion modeling Medical AI: AI-driven mental health detection systems (e.g., VidSole dataset) in collaboration with medical schools Key Contributions: Developed Project Dost (mental health initiative) Received 2020 Brain & Behavior Seed Grant ($X) for emotion-gait research Authored 65+ papers (1,800+ citations) with awards at IEEE VR 2021 Funding & Leadership: Serves as Senior Editor for IEEE RA-L (Planning/Simulation) Conference Chair for ACM SIGGRAPH MIG 2022 Labs/Teams: IDEAS Lab (Purdue), UMD GAMMA Group
Ming-Hsuan Yang is a Professor in the Department of Computer Science & Engineering at the University of California, Merced , where he also serves as the Graduate Chair for the Electrical Engineering and Computer Science (EECS) graduate group. His research spans computer vision , machine learning , and pattern recognition , with a focus on image and video restoration, object tracking, and 3D scene understanding. Ph.D., University of Illinois at Urbana-Champaign (2000) M.S., University of Texas at Austin (1994) M.S., University of Southern California (1992) B.S., National Tsing-Hua University, Taiwan (1991) His research interests include computer vision (object tracking, image deblurring, saliency detection), machine learning (transfer learning, sparse representation), and 3D reconstruction (Gaussian splatting, scene generation). He has pioneered methods in diffusion models , transformer architectures , and multi-modal vision-language systems . Recent publication trends show leadership in 3D mesh generation (ICCV 2025), video diffusion (CVPR 2025), and image restoration (PAMI 2025), with interdisciplinary applications in medical imaging (TMI 2024) and human motion analysis (WACV 2025). Scientific awards include Nvidia Fellowships and EECS Rising Stars recognitions for advisees, with Meta , Google DeepMind , and Adobe alumni placements. He has advised 18 PhD students and 13 MS students since 2009, with notable fellowships including Chancellor's Graduate Fellowship and GSOP Fellowship . His Visual Tracking and Learning Lab produces high-impact work in object tracking , image enhancement , and semantic segmentation , supported by NSF grants and industry collaborations . Lab alumni now lead R&D at top tech companies like Stability AI and Meta .
Kathleen M. Carley is a full professor at Carnegie Mellon University's School of Computer Science with courtesy appointments in Engineering and Public Policy, Heinz School, and Electrical and Computer Engineering. As director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social-Cybersecurity (IDeaS) , she leads interdisciplinary research at the intersection of network science, cognitive modeling, and cybersecurity. Ph.D. in Sociology from Harvard University SB degrees in Economics and Political Science from MIT Her research focuses on Dynamic Network Analysis (DNA) and Social-Cybersecurity (SC) , developing tools like ORA (network analysis), AutoMap (semantic mining), Construct (influence simulation), and BotHunter (bot detection). She has over 400 publications and 15+ active research projects addressing disinformation, cognitive security, and organizational resilience. Recent work examines LLM-powered bots , multi-platform misinformation dynamics , and public health analytics . As an IEEE Fellow, she contributes to standards in computational social science while teaching courses on network analysis and complex socio-technical systems.
Edward Andò is a Principal Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) , with affiliations to the IMAGING group and the College of Engineering (ENAC) . His work bridges software development, experimental geomechanics, and educational initiatives in image analysis. Principal Scientist, IMAGING-GE (EPFL) Lecturer, Sciences et Génie Civil (SGC-ENS) Lecturer, Enseignement à la Défense (EDEE-ENS) Research Interests Andò specializes in 3D image analysis , with a focus on X-ray tomography , digital volume correlation (DVC) , and micromechanical modeling of granular materials. His work addresses geomechanical failure mechanisms, soil dynamics, and open-source software tools like SPAM for practical material analysis. Publication Trends His recent articles (2025–2023) emphasize X-ray tomography for studying granular deformation , rock failure , medical imaging , and soft particle compaction . Topics span geomechanics, computational modeling, and software development for experimental validation. Labs and Teams Andò contributes to the IMAGING group at EPFL, where he co-develops the SPAM (Software for Practical Analysis of Materials) . His teaching includes courses like Fundamentals of Image Analysis and Quantitative Imaging for Engineers , which integrate hands-on training with theoretical frameworks.
Tobias Sjöblom serves as Professor and Head of Department at Uppsala University's Department of Immunology, Genetics and Pathology, where he leads the Cancer Precision Medicine research program. His work bridges clinical oncology and molecular biology, with particular focus on translating genomic findings into clinical applications for cancer patients. His research interests center on cancer genomics, precision medicine, and molecular diagnostics, with extensive work on colorectal cancer biology and treatment. Sjöblom's laboratory investigates how genetic variations influence cancer development, progression, and response to therapy, particularly focusing on pharmacogenomic biomarkers that can guide personalized treatment decisions. His work spans from basic molecular mechanisms to clinical applications, with strong emphasis on translating research findings into clinical practice. Analyzing his recent publication record reveals a clear trajectory toward precision oncology applications, with increasing focus on liquid biopsy technologies, AI-assisted diagnostics, and biomarker-driven treatment strategies. His research demonstrates a consistent pattern of investigating how specific genetic alterations (particularly in NAT2, CYP2D6, and other metabolic enzymes) can be leveraged for targeted cancer therapies. The interdisciplinary nature of his work is evident in collaborations spanning molecular biology, clinical oncology, bioinformatics, and medical imaging. Sjöblom has established significant research infrastructure through initiatives like the U-CAN biobank, creating valuable resources for cancer research across Sweden. His leadership extends to developing novel methodologies for cancer genomics and diagnostics, including advanced techniques for mutation detection and tissue analysis. His laboratory maintains strong clinical connections, working closely with oncologists and surgeons to ensure research questions address real clinical challenges. This translational approach has resulted in numerous publications in high-impact journals including Nature, Science, and Cell Death and Disease, demonstrating the significance and quality of his contributions to cancer research.
Lifeng Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Zhou Lab focused on advancing robustness and reliability in multi-robot systems through integration of foundation models. His research addresses real-world challenges in environmental monitoring, disaster response, and urban mobility. Education PhD, Electrical and Computer Engineering, Virginia Tech, 2020 MS, Control Science and Engineering, Shanghai Jiao Tong University, 2016 BS, Automation, Huazhong University of Science and Technology, 2013 Research Focus Dr. Zhou's work integrates robotics, algorithms, game theory and machine learning to develop secure and scalable autonomous systems. Primary research thrusts include: Resilient multi-robot coordination in adversarial environments Large language model integration for robotic decision-making Game-theoretic resource allocation strategies Risk-aware planning for autonomous vehicles Publication Trends Recent work (2024-2025) demonstrates strong focus on large language model applications in multi-robot systems, with 12/15 articles exploring LLM integration for flocking, scene segmentation, and decision-making. Additional emphasis includes adversarial robustness in target tracking (5 articles) and autonomous driving applications (4 articles). Awards and Recognition Best Paper Award, WACV 2025 LLVM-AD Workshop Professional Service Associate Editor, ICRA Conference Editorial Board Laboratory Focus The Zhou Lab develops foundational algorithms for secure and scalable multi-robot systems, with current projects spanning environmental monitoring drones, disaster response coordination, and autonomous vehicle perception systems.
Mustafa Gül is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta’s Faculty of Engineering. He also serves as Director of Internationalization at the Faculty of Engineering’s Deans Office. His research focuses on smart, sustainable, and resilient cities, with an emphasis on infrastructure monitoring and energy-efficient systems. Education: PhD in Civil Engineering (University of Central Florida, 2009), MSc in Electrical Engineering (University of Central Florida, 2011), MSc in Civil Engineering (Boğaziçi University, 2004), BSc in Civil Engineering (Boğaziçi University, 2002). Dr. Gül’s research spans two primary domains: Crowdsensing-based Monitoring of Built and Natural Environments (CoMBiNE) using AI, signal processing, and data analytics for infrastructure health; and Energy-Efficient Smart Cities through solar PV integration, IoT applications, and net-zero energy homes. His work bridges structural engineering, machine learning, and sustainable urban development. Recent publications highlight advancements in smartphone-based damage detection, UAV-assisted disaster assessment, and AI-driven energy systems. His team’s work on crowdsensing bridges, solar PV optimization, and smartphone analytics has been widely recognized in journals like Structural Control and Health Monitoring and Energy and AI . Notably, his 2017 paper earned the Best Paper Award at ISARC. Students supervised include Azim, R. , Keskin, M. , and Do, N. T. , among others. Scientific Awards: Best Paper Award, 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) Dr. Gül’s projects often involve interdisciplinary collaboration, leveraging sensor networks, computer vision, and optimization algorithms to address urban resilience and energy sustainability. His leadership extends to course CIV E 779 and ongoing research initiatives in Alberta, Canada.
Dr. Siwei Lyu is a SUNY Distinguished Professor and SUNY Empire Innovation Professor in the Department of Computer Science and Engineering at the University at Buffalo. He serves as Co-Director of the Center for Information Integrity (CII) and Director of the UB Media Forensic Lab (UB MDFL). His research focuses on digital media forensics, computer vision, and machine learning, with significant contributions to counter-deepfake technologies. Education includes a PhD in Computer Science from Dartmouth College (2005), MS from Peking University (2000), and BS in Information Science from Peking University (1997). He has held academic positions at the University at Albany and New York University. His work spans media forensics, adversarial machine learning, and AI security. Notable achievements include developing the Celeb-DF dataset, leading NSF-funded projects, and testifying before U.S. and NYS legislative bodies on disinformation threats. Over $11.3M in grants have supported his research on AI-generated media detection, including a $5M NSF Convergence Accelerator grant. Key awards include IEEE and IAPR Fellowships, Google Faculty Award, and SUNY Chancellor's Research Award. He has authored 230+ papers, 4 patents, and serves on editorial boards of top journals and conferences (e.g., CVPR, ICCV).
Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.
Niko Troje is a Professor at York University, affiliated with the Departments of Psychology, Biology, and Electrical Engineering & Computer Science. He holds cross-appointments and leadership roles, including Director of the BioMotion Lab. His research focuses on perceptual representations, biological motion, and vision science. Education: Ph.D. in Biology (1994) - Albert-Ludwigs Universität B.Sc. in Biology (1990), Physics & Mathematics (1987) - Albert-Ludwigs Universität Research Interests: Troje investigates how the brain processes biological motion, perception of human and animal movement, and applications in virtual reality. His work bridges neuroscience, psychology, and computer science. Key Contributions: Pioneered point-light displays for motion perception, explored gait analysis in mental health, and developed tools for motion capture and analysis (e.g., bmlTUX). Awards: Humboldt Research Prize (2014) NSERC Steacie Fellowship (2008-2009) Canada Research Chair (2003-2013) Grants & Labs: Led funded projects on movement perception, collaborated with institutions like the Max Planck Institute, and directs the BioMotion Lab at York University.
David Wettergreen is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science , where he has been a faculty member since 2000. He directs the PhD Program in Robotics and holds a courtesy appointment in Mechanical Engineering. His research focuses on robotic exploration systems for extreme environments, spanning planetary surfaces, underwater caves, and terrestrial deserts. Key areas include autonomous navigation , science autonomy , multi-modal perception , and resource-cognizant planning . Field validation drives his work, with deployments in the Atacama Desert, Antarctic volcanoes, and lunar analog sites. Co-founder of Mesh Robotics LLC for off-road autonomy Former Research Fellow at Australian National University Former National Research Council Research Associate at NASA Ames Research Center His 15 most recent publications (2023-2025) demonstrate expertise in autonomous path planning , machine learning applications , terrain modeling , and science-driven exploration . Collaborations span planetary science, environmental monitoring, and space systems engineering. He has advised 17 PhD and 33 MS students , many of whom now work in space exploration or field robotics, and teaches courses in Robotics Systems Engineering . Current projects include the MoonRanger lunar micro-rover and technologies for autonomous resource mapping .
Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.
Achilleas Psyllidis is an Assistant Professor of Urban Mobility and Director of the Urban Analytics Lab at TU Delft. He also leads the Social Urban Data Lab at Amsterdam Institute for Advanced Metropolitan Solutions and is affiliated with the LDE Centre for BOLD Cities. His roles include membership in TU Delft's Transport & Mobility Institute, the Mobility Futures Vision Team, and serving on the Executive Board of CUPUM. Education: PhD in Spatial Data Science (TU Delft, Faculty of Architecture and the Built Environment) Master of Science in Spatial Planning (National Technical University of Athens) Engineering Diploma in Architectural Engineering (National Technical University of Athens) Research Interests: Focuses on accessibility, walkability, land-use dynamics, and travel behavior. Develops computational methods for analyzing access equity, spatial segregation, and human mobility. Leads projects on sustainable urban mobility, environmental exposures, and the 15-minute city concept. Awards: CTwalk Map: Best Demo Award (ICT.Open 2024) ROUTE Ontology of Urban Transportation Entities (2015) Grants & Projects: Involved in initiatives like PERISCOPE (Social Resilience Design), Horizon2020 'Equal-Life' (Environmental Health), and SocialGlass (Urban Analytics Dashboard). Active in research collaborations across Europe and Asia. Labs & Teams: Directs Urban Analytics Lab and Social Urban Data Lab, focusing on data-driven urban solutions. Engages in interdisciplinary teams addressing mobility futures, urban health, and sustainable design.
John Nassour is a Researcher at the Technical University of Munich's School of Computation, Information and Technology, affiliated with the Chair of Cognitive Systems. He holds engineering degrees from Tishreen University (electronics), a Master's in intelligent systems from University of Cergy-Pontoise/École Nationale Supérieure de l'Électronique, and a joint PhD from University of Versailles/TUM. His interdisciplinary research focuses on computational cognitive systems applied to robotics, including wearable devices, humanoid robots, soft robotics, and robot learning for locomotion/manipulation. Before joining TUM in 2020, he was a lecturer/researcher at Chemnitz University of Technology. He teaches courses in cognitive systems, neuro-inspired engineering, and soft robotics.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
