Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.
Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.
Shahriar Nirjon is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research focuses on Embedded Intelligence, developing end-to-end systems that make resource-constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. Dr. Nirjon received his Ph.D. from the University of Virginia in 2014. Before joining UNC Chapel Hill in 2015, he worked as a Research Scientist at HP Labs (2014-2015) and as a Research Intern at Microsoft Research (Summer 2013) and Deutsche Telekom Lab (Summer 2010). His primary research interest is Embedded Intelligence, with recent works broadly categorized into embedded deep learning and multi-modal sensing techniques. Applications of his research span wearables and implantables, long-term monitoring and control systems, smart home environments, and mobile health solutions. Dr. Nirjon's research bridges theoretical foundations with practical implementations, resulting in systems that have real-world impact in healthcare, safety, and everyday computing. His work has been highlighted in prominent media outlets including IEEE Spectrum, The Economist, New Scientist, and BBC. Dr. Nirjon's publication record demonstrates a strong focus on mobile computing systems, embedded sensor networks, and wireless technologies. His recent work shows increasing integration of machine learning and artificial intelligence with embedded systems, particularly in healthcare applications. There's a clear trajectory toward more sophisticated, energy-efficient systems capable of on-device intelligence, with growing emphasis on privacy-preserving techniques and real-world deployments in healthcare settings. Best Paper Award, Challenges in AI and Machine Learning for IoT (AIChallengeIoT '20) Best Presentation Award, Pervasive and Ubiquitous Computing (Ubicomp '20) Best Paper Award, Distributed Computing in Sensor Systems (DCOSS '19) Best Presentation Award, Vehicular Networking Conference App Contest (VNC '18) Best Demo Runner Up, Vehicular Networking Conference App Contest (VNC '18) Best Paper Nomination, Embedded Wireless Systems and Networks (EWSN '17) Best Demo Runner Up Award, Embedded Networked Sensor Systems (SenSys '16) Best Paper Award, Mobile Systems, Applications, and Services (MOBISYS '14) Best Paper Award, Real-Time and Embedded Technology and Applications Symposium (RTAS '12) Dr. Nirjon has advised numerous PhD students including Chong Shao (Google), Shiwei Fang (Assistant Professor at Augusta University), Tamzeed Islam (Research Staff at Amazon), Bashima Islam (Assistant Professor at Worcester Polytechnic Institute), Seulki Lee (Assistant Professor at UNIST, Korea), and Yubo Luo (Black Sesame Technologies Inc.). He currently advises Mahathir Monjur, Zhenyu Wang, and Louie Lu who are in various stages of their PhD programs. His research is supported by significant grants including an NSF CAREER award ($561K), an NSF SCH grant ($941K), and multiple other NSF-funded projects totaling over $2 million. His active projects include Audio Privacy, Pedestrian Safety, IoT Data Privacy, and HVAC Acoustic Fingerprinting. Dr. Nirjon leads research in the Embedded Intelligence Lab at UNC Chapel Hill, where his team develops cutting-edge technologies in mobile computing, embedded systems, and wireless networks. His work spans multiple domains including healthcare (mobile health systems), safety (pedestrian safety applications), and smart environments (smart homes). He collaborates with researchers across disciplines, particularly in healthcare through the Carolina Health Informatics Program (CHIP), and is actively involved in the Be-A-Maker (BeAM) network of makerspaces at UNC.
Professor Johan Lilius is a Full Professor of Embedded Systems at Åbo Akademi University's Faculty of Science and Engineering, Department of Information Technology. He has held this position since 2001 and currently serves as Head of Research. Prof. Lilius has demonstrated extensive leadership throughout his career, having served as director of the Turku Center for Computer Science (TUCS) and multiple terms as Head of the Department of Information Technology, where he led significant departmental restructuring and educational reform efforts through two major university reorganizations. He is currently a member of the steering group for Digivisio2030, a national initiative involving all Finnish higher education institutions aimed at building a new educational ecosystem. Prof. Lilius's research focuses on energy-efficient software, safety in autonomous systems, and neuro-symbolic computing. His work contributes to the UN Sustainable Development Goals, particularly in technology and environmental sustainability. His research interests span autonomous navigation, data-parallel computing, energy-aware systems, and maritime technology. His work bridges theoretical computer science with practical applications in embedded systems, particularly in maritime contexts. Analysis of his recent publications shows a strong trend toward autonomous systems, particularly in maritime applications, with significant work on energy efficiency in embedded architectures. His research increasingly integrates AI and machine learning approaches with traditional software engineering, focusing on safety-critical systems where reliability is paramount. The fingerprint analysis of his work reveals strong connections to convolutional neural networks, autonomous navigation algorithms, and scenario-based testing for complex systems. Ten-Year Most Influential Paper Award at the ACM/IEEE Conference on Model Driven Engineering Languages and Systems Several Best Paper Awards 2015 Gadd Prize for Research Excellence at Åbo Akademi Prof. Lilius has demonstrated exceptional commitment to doctoral education, having supervised over 10 completed doctoral theses and currently mentoring several PhD students. He leads the TUCS Graduate program and has co-organized numerous academic workshops, summer schools, and conferences. His research is supported by significant projects including EDISS (Engineering of Data-intensive Intelligent Software Systems), IoT Reboot Factory, DECATRIP (Decarbonizing Transport Corridors), SMARTER (Sea4Value Smart Terminals), and AutoMare EduNet (Autonomous Maritime Education Network), funded by the European Commission, Business Finland, and other organizations. Prof. Lilius collaborates extensively through the Turku Center for Computer Science (TUCS) and participates in national initiatives like Digivisio2030. His work often involves interdisciplinary teams focusing on the intersection of software engineering, AI, and practical maritime applications, with strong industry partnerships that ensure real-world impact of his research.
Tengda Han is a research scientist at Google DeepMind , focusing on video understanding and visual-language models . He previously completed his PhD at the University of Oxford under Andrew Zisserman and earned a Bachelor of Engineering from Australian National University in mechanical & material engineering, with prior studies in business administration and law at Renmin University of China . Research Interests: His work explores neural networks for video analysis, including self-supervised learning , video captioning , object counting , and prompt engineering . Key projects include AutoAD for movie description, Temporal Alignment Networks , and Dense Predictive Coding for video representation. Scientific Awards: Best Paper Award at ACCV 2024 Best Poster Award at BMVC 2023 BMVA Sullivan Doctoral Thesis Prize Runner-up Collaborations & Students: He has collaborated with researchers like Andrew Zisserman , Max Bain , and Arsha Nagrani , and mentored students Junyu Xie , Toby Perrett , and Niki Amini-Naieni on projects including Shot-by-Shot and Unique Video Captioning .
Thomas Jean-Hugh is a Professor and Teacher-Researcher at Le Mans Université , affiliated with the Laboratoire d’Acoustique de l’Université du Mans (LAUM) , a leading research institute in acoustics in France. His role involves both teaching and active research in acoustic imaging, signal processing, and structural acoustics. Education PhD in Acoustics and Signal Processing (exact institution and year not specified in text) Research Interests Thomas Jean-Hugh’s research focuses on acoustic source localization and imaging , particularly through the use of microphone arrays , beamforming , and inverse problem solving . His work spans: Acoustic holography for non-stationary and confined sources UAV acoustic detection and tracking using sparse sensor arrays Vibro-acoustic diagnostics for automotive and naval structures Speech processing in meeting environments using distant microphone arrays Non-destructive testing using acoustic emission and imaging techniques Research Trends His recent publications (2022–2025) reveal a strong focus on machine learning-enhanced acoustic imaging , real-time UAV detection , and robust speech processing . He integrates genetic algorithms , Bayesian regularization , and circular harmonics into acoustic array processing, with applications in autonomous systems , marine biology , and automotive acoustics . Scientific Contributions Over 70 peer-reviewed publications in journals and conferences Active supervision of PhD students and postdocs in acoustics and signal processing Participation in international conferences such as Interspeech , ICSV , GRETSI , and ICA Collaborations & Labs He collaborates extensively within LAUM and with external partners including CNRS , ISCA , and international research groups. His lab work involves experimental acoustics , sensor array design , and real-time signal processing .
Gelareh Naseri is a Post-Doctoral Associate at the Center for Data Science (CDS) at New York University, collaborating with Professor Yann LeCun. Her research bridges deep learning and music/audio generation, focusing on self-supervised representation learning via Joint Embedding Predictive Architectures (JEPA). Ph.D. in Music Composition, University of California, Riverside Master’s in Music Composition, University of Art, Iran Bachelor’s in Computer Engineering, Shahid Beheshti University, Iran Her research spans deep learning , self-supervised learning , and music representation learning , with applications in audio synthesis and machine learning . She works within the CDS lab at NYU, contributing to advanced AI research in music contexts.
Sagie Benaim is an Assistant Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Previously, he was a postdoctoral researcher at DIKU (Department of Computer Science, University of Copenhagen) working with Professor Serge Belongie and was a member of the Pioneer Center for AI. He completed his PhD at Tel Aviv University in the Deep Learning Lab under the supervision of Professor Lior Wolf. Dr. Benaim's research spans computer vision, machine learning, and computer graphics, with a particular emphasis on generative models, neural signal representations, and inverse graphics. His work explores how disentangled representations can be leveraged to better understand and manipulate visual content. He has made significant contributions to 3D scene manipulation, image-to-video generation, and neural rendering techniques. His recent publications reveal a strong trajectory toward advancing generative AI capabilities, particularly in 3D understanding, multimodal generation, and video synthesis. His research consistently bridges theoretical foundations with practical applications, demonstrating innovation in neural representation manipulation for both creative and analytical purposes. Dr. Benaim is actively seeking excellent students and postdocs to join his research group, indicating his commitment to mentoring the next generation of researchers in computer vision and machine learning. His position at the Hebrew University of Jerusalem places him within a vibrant academic community focused on advancing the frontiers of computer science.
Luigi Celona is an Assistant Professor in the Department of Informatics, Systems and Communication (DISCo) at the University of Milano-Bicocca, Italy. He is affiliated with the Intelligent Sensing Laboratory under Prof. Paolo Napoletano and serves as Associate Editor for Elsevier Neurocomputing and Springer Image, Signal and Video Processing journals. Education: BSc in Computer Science from University of Messina; MSc and PhD in Computer Science from University of Milano-Bicocca (supervised by Prof. Raimondo Schettini and Prof. Paolo Napoletano). Research Interests encompass signal/image/video analysis and understanding, with a focus on perceptual metrics for quality, aesthetics, and memorability. His work includes: Visual quality assessment and enhancement Facial analysis and monitoring Speaker recognition and emotional state analysis Applications of computer vision and machine learning Technical expertise spans computer vision , image dehazing , pattern recognition , and deep learning methodologies.
Luxi Zhao is a Research Fellow at Technische Universität München's Embedded Systems and Internet of Things department, specializing in Time-Sensitive Networking (TSN) and real-time network calculus. Working under the supervision of Prof. Sebastian Steinhorst, Zhao contributes to projects like ReMiX and 6G-Life, focusing on security, performance analysis, and configuration optimization of deterministic networks. Research Focus: Worst-case latency analysis in TSN networks Runtime configuration and reconfiguration problems Hybrid scheduling of processing and communication Network calculus modeling for heterogeneous systems Security challenges in industrial IoT and autonomous systems Interoperability of IoT systems in Industry 4.0 Teaching Activities: Secure Autonomous Systems (2025) Software Architecture for Distributed Embedded Systems (2025) IoT Security (2025) IoT Remote Lab practical courses across multiple semesters Advanced Seminar series (2020-2025)
Dr Jiahong Zhao is a Lecturer in Acoustics at the University of Southampton. His research focuses on signal processing and audio technologies. Research interests include microphone array signal processing, spatial audio, machine learning, and computer vision. Current supervisor for PhD student Yaoxiang Yu in Engineering and Environment. Contact: Jiahong.Zhao@soton.ac.uk
Elgar Fleisch is a Professor at the University of Zurich, specializing in interdisciplinary research at the intersection of Internet of Things (IoT), Radio-Frequency Identification (RFID), Mobile Health (mHealth), and Energy Informatics. His work bridges theoretical and applied domains, focusing on real-time data analytics, wearable technologies, and behavioral interventions. Key research areas include sensor-based health monitoring systems Development of transparent computing frameworks for AI/data collection Consumer behavior analysis through IoT-enabled retail and energy systems Machine learning applications for circadian rhythm analysis and respiratory disease detection Recent publications demonstrate expertise in federated learning for cough classification, circadian rhythm biomarkers, and driver behavior analytics. While no specific awards are listed in this dataset, his collaborations with institutions like ETH Zurich and University of St. Gallen indicate broad academic engagement.
Dr Amin Karami is an Associate Professor in the School of Computer Science and Digital Technologies (CDT) at the University of East London, within the School of Architecture, Computing and Engineering. He serves as course leader for MSc Big Data Technologies and leads postgraduate programs, having secured £1.23 million in funding from the Office for Students to develop inclusive AI and Data Science courses for non-STEM and far-STEM graduates. His research spans Artificial Intelligence, Big Data Analytics, Blockchain, and Optimization, with focus on Industry 5.0 applications. Current work addresses federated learning heterogeneity, smart contract security, healthcare fraud detection, and ethical AI implementation. He develops cloud-based platforms for large-scale data processing and computational intelligence solutions for real-world industry challenges. Recent publications demonstrate strong trends in federated learning techniques, blockchain vulnerability mitigation, and big data applications across healthcare, finance, and social media. His work consistently bridges academic research with industry needs through partnerships with Multiverse and Cambridge Spark, emphasizing practical solutions for credit risk assessment, satellite telemetry, and personalized marketing. Scientific recognition includes: UEL Vice-Chancellor & President Impact & Innovation Award for Industry 4.0 readiness (2023) Fellow of the Higher Education Academy (FHEA) Dr Karami actively supervises UG/PG/PhD students while leading curriculum innovation through externally funded projects. His Chainlink Bootcamp initiative connects academia with industry practitioners, and he serves as external examiner and conference program chair. Significant grant achievements include developing diversity-focused STEM pathways that enhance graduate employability through industry-aligned training in AI and Data Science.
Dr Saeed Sharif is an Associate Professor in the Computer Science department at the School of Architecture, Computing and Engineering , University of East London . He leads the Intelligent Technologies Research Group and serves as Course Leader for MSc Computer Science programs. With a PhD in artificial intelligence from Brunel University London , his work bridges Artificial Intelligence , Medical Technology , and Smart Infrastructure . His research interests span Medical Image Analysis , Intelligent Diagnosis Systems , Big Data Analysis , and IoT Security . Collaborations with NHS Trusts and global institutions focus on improving healthcare systems through machine learning and biomedical signal processing. 2018-2022: £268,000 KTP project with Innovate UK Multiple Research Internship Schemes at UEL (2017-2021) European Research Centre collaboration (2015) As a technical committee member for conferences like IEEE CIT and a journal Guest Editor , he drives academic discourse. He has supervised numerous PhD students and served on examination panels, while maintaining 53+ publications in venues including IEEE Access , Applied Soft Computing , and Computer Methods and Programs in Biomedicine .
Prof. Dr. Oğuzhan ERDEM is a faculty member at Trakya University Faculty of Engineering , Department of Electrical and Electronics Engineering since 2023. He earned his PhD in Electrical and Electronics Engineering from Middle East Technical University in 2011 and served at various academic roles across 22 years, including Department Head since 2021. PhD: Middle East Technical University (2011) Visiting Scholar: University of Southern California (2010-2011) His research focuses on machine learning applications in biomedical diagnostics and FPGA-based network processing . Recent publications explore Parkinson's disease detection through keystroke analysis, cough sound diagnostics, and hardware-accelerated network traffic classification. Key article trends show: 2023-2025: Deep learning for medical diagnostics 2016-2020: Hardware-optimized network processing 2010-2015: FPGA-based IP lookup architectures Awards: Middle East Technical University 2011 Doctoral Thesis of the Year He has supervised 6 graduate theses and led 2 major projects including Early Detection of Parkinson's Disease Using Multimodal Signals (2021). Patent co-inventor in systolic array architectures.
