Sara Beery is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on leveraging computer vision for environmental sustainability and conservation challenges. Beery's work spans computational vision, self-supervised learning, and multimodal AI systems. She has contributed to advancements in neural latent dynamics, face recognition bias analysis, and spatio-temporal dataset creation for autonomy. Her recent publications emphasize computer vision applications in conservation, medical imaging, and robotics. Notable topics include synthetic image generation, denoising low-SNR video, and cell segmentation using foundational AI models. Scientific Awards Resnick Graduate Scholar
Stephen Huang is Professor of Computer Science at the University of Houston's College of Natural Sciences and Mathematics. He directs the Center for Cybersecurity Research and has held administrative roles including Department Chairman and Director of Graduate Studies. He received his PhD in Computer Science from the University of Texas-Austin and served as National Research Council-NASA Senior Research Associate at NASA Goddard Space Flight Center. His 40+ year career encompasses cybersecurity research, algorithm design, and academic leadership. Research focuses on intrusion detection systems, malware analysis, network security, and applied algorithms. His work integrates machine learning with security applications, particularly in anomaly detection and behavioral analysis. Recent projects include NSA-funded research on automated intrusion detection through metadata traffic analysis. Publications demonstrate progression from foundational algorithms to advanced AI applications in security. Recent articles focus on VPN detection, Android malware analysis, and large language model interpretability, consistently applying computational theory to cybersecurity challenges. He leads the GAANN Fellowship Program in Cybersecurity and Research Experience for Undergraduates (REU) initiatives. His laboratory develops practical security solutions through collaborations with government agencies and industry partners.
Prof Wai Lok Woo is a Chair in Machine Learning at Northumbria University's Computer and Information Sciences Department. He serves as Faculty Postgraduate Research Director (Engineering and Environment) and Head of the Artificial Intelligence and Digital Technology Research Cluster. With a PhD in Statistics and Machine Learning from Newcastle University, he has over 450 publications and holds IET and IEEE Fellowships. Education PhD in Statistics and Machine Learning, Newcastle University, UK MSc in Communications Engineering, Newcastle University, UK BEng (Hons) in Electrical and Electronic Engineering, Newcastle University, UK Research Interests His work focuses on machine learning foundations, signal/image processing, and smart sensing for applications like non-destructive testing, energy systems, and mental health monitoring. Core themes include: AI & Machine Learning: Deep neural networks, ethical AI, reinforcement learning Data Science: Latent variable analysis, blind signal separation Sustainable Development: Smart grids, precision farming, disaster resilience Awards Fellow of IET and IEEE IEE Prize British Commonwealth Scholarship Grants & Projects Leads the EU-funded CO VER project (€6M) on UAV-based emergency response systems. Active in UK/EU grant evaluations and has supervised 8 PhD students. Labs & Teams Directs the AI & Digital Technology Research Cluster, collaborating globally on UN SDGs like affordable energy and climate action.
Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.
Muhammed Enes Atik is an Assistant Professor in the Department of Geomatics Engineering at Istanbul Technical University (ITU), affiliated with the Faculty of Civil Engineering. His academic roles include teaching and research supervision. He holds a PhD (2018), MSc (2016), and BSc (2011) in Geomatics Engineering from ITU. His research focuses on remote sensing, deep learning applications in geospatial data, UAV-based photogrammetry, and point cloud processing. Education: PhD in Geomatics Engineering, Istanbul Technical University, 2011–2018 MSc in Geomatics Engineering, Istanbul Technical University, 2016–2018 BSc in Geomatics Engineering, Istanbul Technical University, 2011–2016 Research Interests: Enes Atik specializes in integrating deep learning techniques with geomatics engineering to enhance photogrammetric applications, UAV data analysis, and point cloud segmentation. His work bridges remote sensing, GIS, and computer vision to address challenges in urban planning, disaster management, and infrastructure monitoring. Key areas include: 3D modeling and semantic segmentation of point clouds Optimization of UAV-based photogrammetric workflows Machine learning for remote sensing image classification Flood susceptibility analysis using GIS and AHP Cultural heritage documentation via laser scanning and photogrammetry Awards: IEEE GRSS Türkiye Thesis Competition Award (2022) Muhammed Enes Atik Prize for Student Excellence (2021) 3-Minute Thesis Competition Award (IEEE GRSS, 2024) Grants & Projects: Principal Investigator: 'Deep Learning-Based Disparity Map Estimation and DEM Generation from UAV Imagery' (2023–2025) Co-Investigator: '3D Modeling of Historical Instruments' (2021–2023) Lead Researcher: 'Flood Simulation Using Game Engines' (2024–ongoing) Teaching: Enes Atik instructs courses in programming fundamentals, photogrammetry, geomatics projects, and laser data engineering at both undergraduate and graduate levels.
Feng Liu is an Assistant Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He previously served as a Postdoctoral Researcher at Michigan State University's Department of Computer Science and Engineering. His research spans computer vision, machine learning, and biometric recognition, with a focus on 3D scene understanding, generative AI, and human-AI interaction. Research Interests: 3D Computer Vision: 3D object/scene understanding, 3D generation, VR/AR, 3D vision+language understanding 3D Human Digitization: Modeling, reconstruction, rendering, biomechanics Generative AI: Explainability, generalization, controllability in generative models, DeepFake detection Biometric Recognition: Face and gait recognition, person re-identification AI + X: Applications in Education and Healthcare Publication Trends: Dr. Liu's recent publications demonstrate a strong focus on advancing biometric recognition systems, particularly in open-set scenarios and video-based person re-identification. His work integrates 3D vision, diffusion models, and large-scale benchmarking, with increasing emphasis on real-world challenges such as aerial-ground integration, long-range recognition, and controllable synthetic data generation for training robust models. Scientific Recognition: Area Chair for BMVC 2025, ACM MM 2025, FG 2025, IJCNN 2025 Area Chair for FG 2024 Advising and Engagement: Dr. Liu is actively recruiting PhD students and interns, indicating an expanding research group. He regularly presents at major conferences including CVPR, NeurIPS, and WACV, and participates in workshops such as ELFA, demonstrating active engagement with the research community. His collaborations span multiple institutions, including Michigan State University, Queensland University of Technology, and others. Labs and Teams: While specific lab names are not mentioned in the provided texts, Dr. Liu leads a research group focused on computer vision and AI, with projects involving 3D reconstruction, biometrics, and generative models. His work on AG-VPReID and HAMoBE suggests leadership in developing large-scale benchmarks and hierarchical recognition systems.
Tim Polzehl is a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin's Speech and Language Technology (SLT) Department. He holds a PhD in technical communication sciences from TU Berlin (2014), focusing on automatic personality prediction from speech/user data. Previously, he led the Next-Generation Crowdsourcing group as a postdoc at TU Berlin's Quality and Usability Lab, overseeing projects like the Crowdee crowdsourcing platform. His current research spans speech anonymization, disinformation detection, deepfake analysis, and AI ethics. He actively supervises doctoral students and collaborates on EU-funded projects. Education: PhD in Technical Communication Sciences, TU Berlin (2014) Studies in Technical Communication Sciences, TU Berlin Research Interests: Focuses on applying AI to speech technology, privacy-preserving systems, and combating disinformation through machine learning. Specializes in multimodal systems, ethical AI deployment, and human-AI collaboration frameworks. Publications: Recent work emphasizes regulatory challenges of AI (e.g., EU AI Act compliance), privacy in clinical speech data, and personality-aware chatbots. Key areas include disinformation detection via LLMs and technical cybersecurity for speech systems. Grants & Awards: Participated in BMBF-funded leadership programs and EIT-Digital EU projects. Current projects involve developing frameworks for ethical AI integration in critical applications like elections. Labs/Teams: Leads SLT Department initiatives on autonomous AI agents and voice cloning at DFKI. Collaborates with TU Berlin's Quality and Usability Lab on ongoing research.
Prof. Manuel Eisner is the Wolfson Professor of Criminology and Director of the Institute of Criminology at the University of Cambridge, and a retired Professor of Sociology at the University of Zurich. His research focuses on explaining societal and historical patterns of interpersonal violence, psychological and social mechanisms influencing violent behavior, and evidence-based prevention strategies. He leads the Violence Research Center and founded the Zurich Project on Social Development (z-proso). Eisner has authored 15 books and over 100 journal articles, with work spanning English, German, Spanish, and French. His advisory roles include collaborations with the WHO, UNICEF, and World Bank. Notable awards include the Sellin-Glueck Award from the American Society of Criminology. Education: Studied history at the University of Zurich, earned a doctorate in sociology. His research integrates criminology, sociology, and public health, examining topics like adolescent victimization impacts, historical homicide patterns, and global violence reduction frameworks. He co-organized the 2014 WHO global violence reduction conference at Cambridge. Research interests emphasize transdisciplinary approaches to violence prevention, combining longitudinal cohort studies with innovative methods like ecological momentary assessment and machine learning. His work bridges academic theory with practical policy, influencing international initiatives such as the WHO INSPIRE framework. Eisner's team includes researchers like Denis Ribeaud and Margit Averdijk, advancing understanding of violence dynamics through large-scale data analysis.
Ileana Buhan is an Assistant Professor at Radboud University Nijmegen's Digital Security Group and a member of the CESCA Lab. Her research focuses on hardware security, particularly advancing tools for secure hardware design and mitigating side-channel vulnerabilities. She previously held roles at Riscure (2011–2020) as a security evaluation manager and product manager, and at Philips Research (2008–2010) as a senior scientist. She earned her Ph.D. in Cryptography with Noisy Data from the University of Twente in 2008, recognized with the 2008 EBF European Biometrics Research Industry Award. Her work emphasizes practical security evaluation methods, automated leakage modeling (e.g., ABBY tool), and hardware-software co-design for resistance against side-channel attacks. She actively contributes to conferences like CHES, FDTC, and CARDIS, often in program committee roles. Recent invited talks include topics such as AI-driven vulnerability prediction, architecture-level simulators for root cause analysis, and automated tools for cryptographic implementation security. Her research spans RISC-V processors, microarchitecture analysis, and the intersection of machine learning with hardware security. Notable contributions include frameworks for leakage detection, fault simulation, and explainable side-channel analysis. She balances academic rigor with industry relevance, aiming to bridge gaps between theoretical security and real-world implementation challenges.
Turgay Celik is a full Professor at the Department of Information and Communication Technology , University of Agder (Norway). His research focuses on machine learning applications in remote sensing, explainable AI, and data analysis . He leads projects related to radiometric normalization, sentiment analysis for low-resource languages, and biomedical prediction models. Research Interests : Machine learning for geospatial data, explainable NLP, adaptive learning systems, and domain adaptation frameworks Recent Publications : 15+ articles on topics spanning remote sensing image processing, multilingual NLP, and counterfactual credit scoring explanations Collaborations : Active in international research with co-authors from institutions in Norway, Iran, South Africa, and China His methodological work includes Trust-Region Reflective algorithms, Laplacian Pyramid Fusion, and SAM transfer learning for water segmentation tasks. He contributes to open-source frameworks evaluation and systematic reviews in computer vision and financial AI.
Dr. Nick Pitropakis is an Associate Professor at the School of Computing Engineering and the Built Environment (Edinburgh Napier University). He actively contributes to cybersecurity research through 39 publications, focusing on threat intelligence sharing , cloud security , IoT security , and privacy-preserving technologies . CyberHunt - Automated Threat Hunting for Critical Infrastructures (Funder: Norway RC) TRUST Platform - Crossborder Data Federation (Funder: UKRI) AI-based Biometric Authentication (Funder: Data Lab) His research combines graph theory for attack path analysis and blockchain for secure threat intelligence sharing. Key publication areas include: LLMs in threat hunting Chaotic encryption schemes Adversarial machine learning Distributed ledger applications Scientific contributions span 19 journal articles, 15 conference proceedings, and 2 book chapters. Collaborates with Prof Bill Buchanan and Dr Pavlos Papadopoulos on cybersecurity standardization and trusted computing .
Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.
Dr. Ipek Oruc is an Associate Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, leading the NOVA Lab. Her interdisciplinary research focuses on applying computational techniques, AI, and data science to advance visual health, particularly in developing low-cost diagnostic tools for retinal imaging and rehabilitation strategies for eye/brain disorders. Her work emphasizes equity in healthcare access for underserved communities. Research interests include AI-driven medical diagnostics, neuroimaging analysis, visual perception mechanisms, and understanding face processing deficits in autism spectrum disorder (ASD). The lab’s recent studies highlight atypical facial exposure patterns in ASD and novel retinal biomarker discovery using deep learning. Current projects aim to democratize AI-based healthcare through virtual health integration. Key achievements include a HIFI Award (2024) for Alzheimer’s detection research and a Killam Teaching Award (2024) for Todd Kamensek. The lab collaborates widely, with postdoctoral fellows (e.g., Gulce Ozturan) and doctoral candidates (e.g., Todd Kamensek, Parsa Delavari) advancing projects in AI ethics, small-data classification, and neuroimaging. NOVA Lab locations include the Blusson Spinal Cord Centre (Vancouver General Hospital), with ongoing clinical partnerships. Research outputs span 80+ peer-reviewed articles, emphasizing translational applications of AI in vision science and neurology.
Dr. Isa Inuwa-Dutse is a Senior Lecturer in Computer Science at the University of Huddersfield , and a Visiting Lecturer at the University of Hertfordshire . He contributes to the Centre for Autonomous and Intelligent Systems , focusing on ethical AI applications across domains. Education: PhD in Computer Science, Edge Hill University (2016-2019) MSc in Computer Science, University of Manchester (2012-2013) BSc in Computer Science, Bayero University (2005-2010) His research integrates Natural Language Processing and Machine Learning , with emphasis on explainability , responsible AI , and social engagement through argumentative frameworks. Recent work explores deepfake detection , wearable biometric security , and human-AI team dynamics . Publications highlight trends in AI ethics , deep learning , and social network analysis , particularly in cybersecurity contexts. He contributes to UN Sustainable Development Goals through educational technologies and digital literacy initiatives. Advising: Accepting PhD students in AI/ML domains Activities: Independent assessor for Northumbria University (2023-present), external examiner for BPP University (2023-2025)
Dr. Nik Nailah is a Lecturer at the Malaysia School of Information Technology, Monash University. Her research focuses on applying cognitive science and multi-agent systems to develop cost-effective AI solutions for underserved populations, particularly in healthcare. She specializes in creating tools for rural patients with chronic illnesses like heart failure and cancer. Notable contributions include the MARIA agent system for medication adherence and frameworks for standardizing clinical data using FHIR. Key Awards: Recipient of the Pro Vice Chancellor Award for Excellence in Education (2015) for pioneering project-based learning initiatives. Also recognized with the Ke Arah Komuniti Bebas Diabetes Prize (2021) for community-focused diabetes management systems. Research Impact: Her work has been featured in STAR newspaper for analyzing hospital systems and improving human-computer interface designs. She has spoken globally, including at Google Tech Talks (2008) and the 'Technovisionaire' event in Milano (2009), where she was a finalist for the Women and Technology Award. Current Projects: Leading three major initiatives including biometric cryptosystems for security, deep learning for child behavior analysis, and intelligent medicine frameworks. Her research addresses UN Sustainable Development Goals, particularly in health and technology access. Collaborations: Active in international projects with institutions in Malaysia, China, Australia, and Europe, focusing on AI ethics, healthcare informatics, and secure communication protocols.
