Michael Orchard is a Professor in the Department of Electrical and Computer Engineering at Rice University. He holds affiliations with the Digital Signal Processing (DSP) Group and the Center for Multimedia Communication (CMC). His research focuses on image/video modeling, compression, data science, and systems engineering. Education: Ph.D. in Electrical Engineering, Princeton University (1990) M.A. in Electrical Engineering, Princeton University (1988) M.S. in Electrical Engineering, San Diego State University (1986) B.S. in Electrical Engineering, San Diego State University (1980) His work has earned prestigious recognitions, including early-career awards from NSF and ARO, and IEEE Fellowship. His technical contributions emphasize advancing compression algorithms and multimedia systems.
Florian Metze is an Adjunct Professor at the Language Technologies Institute (LTI) within the School of Computer Science at Carnegie Mellon University. His work focuses on advanced speech and audio processing, multimodal learning, and machine learning applications in under-resourced languages. He leads research in speech recognition, generative audio models, and cross-modal understanding. His research interests emphasize leveraging audiovisual data for robust speech recognition, developing scalable solutions for low-resource languages, and advancing multimodal systems through innovations like diffusion-based text-to-audio generation (Audio-Journey) and modular neural architectures (Legonn). His contributions include foundational work on wake-word detection, speaker verification (MASV), and context-aware error correction in ASR systems. Collaborative projects span speech technology for unwritten languages, child phonetic acquisition modeling, and integrating visual context into speech processing pipelines. His work often bridges theoretical advancements with practical applications in edge computing, security, and biomedical signal analysis (e.g., heart sound monitoring). Metze's research outputs include over 100 peer-reviewed articles since 2018, with recent emphasis on large language models for multitalker scenarios, efficient neural architectures, and cross-modal representation learning. He actively contributes to open-source tools like the ACLEW DiViMe diarization toolkit.
Christopher Metzler is an Assistant Professor in the Department of Computer Science at the University of Maryland (UMD), with appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and a courtesy appointment in the Electrical and Computer Engineering Department. He leads the UMD Intelligent Sensing Laboratory, focusing on computational imaging, machine learning, and wireless communications. His research develops novel systems and algorithms for imaging through scattering media, multimodal sensor fusion, and self-supervised AI techniques. Education: PhD in Electrical and Computer Engineering (Rice University, 2019), MSEE (2014), BSEE (2013). Postdoctoral Fellowship: Stanford Computational Imaging Lab (2020). Awards: AFOSR Young Investigator Program (2022), NSF CAREER (2023), ARO Early Career Award (2024), and multiple fellowships (NSF GRFP, NDSEG). Research interests include computational imaging, machine learning, statistical signal processing, and AI-driven sensor fusion. His work addresses challenges in non-line-of-sight imaging, turbulence mitigation, and hardware-aware algorithms. He advises 10 PhD students and collaborates on projects funded by the Air Force, NSF, and other agencies. Key contributions include neural wavefront shaping, adversarial sensing frameworks, and high-resolution non-line-of-sight imaging. His lab develops open-source software and datasets, such as the Transmission Matrix Dataset and learned compressive sensing tools.
Amitabh Varshney is the Dean of the College of Computer, Mathematical, and Natural Sciences and Professor of Computer Science at the University of Maryland, College Park. He previously directed the UMD Institute for Advanced Computer Studies (2010–2018) and served as interim Vice President for Research (2016–2017 and 2021). His research focuses on virtual/augmented reality (VR/AR), scientific visualization, molecular graphics, and high-performance computing. Collaborations include NVIDIA, Honda, IBM, and the University of Maryland, Baltimore (UMB). Education: B.Tech. (IIT Delhi, 1989), M.S. and Ph.D. (UNC Chapel Hill, 1991 and 1994). Research highlights include molecular surface algorithms, GPU computing, and immersive technologies for healthcare and education. Awards include the NSF CAREER Award (1995), IEEE Visualization Technical Achievement Award (2004), and IEEE Fellow (2010). He leads the NVIDIA CUDA Center of Excellence and co-founded the Maryland Blended Reality Center. Recent work explores nanophotonics for AR/VR displays, VR medical training, and bias detection in AI systems. His interdisciplinary projects address challenges in personalized medicine, pain management, and implicit bias training through extended reality (XR). Key Projects: Augmentarium (immersive infrastructure), CHIB (healthcare bioinformatics), Immersive Media Design Program. Grants: NSF, NIH, industry partnerships. Awards: NSF CAREER, IEEE Technical Achievement Award, IEEE Fellow.
Sanghamitra Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland College Park (since Fall 2022). She is affiliated with the Department of Computer Science, the Center for Machine Learning (CML) at UMIACS, and the Values-Centered Artificial Intelligence (VCAI) initiative. Previously, she was a Senior Research Associate at JPMorgan Chase AI Research’s Explainable AI Centre of Excellence (XAI CoE) and earned her PhD and Master’s from Carnegie Mellon University (2021) and B.Tech. from IIT Kharagpur (2015), all in Electrical and Computer Engineering. Her research focuses on trustworthy machine learning, addressing challenges in fairness, explainability, privacy, and reliability through foundational approaches rooted in information theory, causality, statistics, and optimization. Notable contributions include work on coded computing for distributed machine learning and fair-lending model reviews at JPMorgan. She has been featured in New Scientist and Montreal AI Ethics Brief . Education PhD, Carnegie Mellon University, 2021 Master’s, Carnegie Mellon University, 2021 B.Tech. (Hons.), Indian Institute of Technology Kharagpur, 2015 Research Interests Trustworthy Machine Learning Explainability & Fairness Information Theory & Optimization Algorithmic Ethics Awards 2024 NSF CAREER Award 2023 Northrop Grumman Faculty Research Award 2023 JPMorgan Faculty Research Award Grants & Advising Recipient of NSF Career Award, JPMorgan, and Northrop Grumman grants Guiding UMD graduate students in machine learning and distributed systems Labs & Teams Core member of the Values-Centered Artificial Intelligence (VCAI) group Collaborates with industry (e.g., JPMorgan) on AI ethics and reliability
Dr. Frederick Li is an Associate Professor in the Department of Computer Science at the University of Durham. He holds a Ph.D. in Computer Graphics from City University of Hong Kong and has held academic roles at The Hong Kong Polytechnic University prior to his current position. His research focuses on Computer Graphics, Machine Learning, Visual Aesthetics, and Educational Technologies. He serves as an Associate Editor for Frontiers in Education and Virtual Reality & Intelligent Hardware, and has organized major conferences such as ICWL 2022 and ISVC 2021. Education: B.A. (Hons.) in Computing Studies from The Hong Kong Polytechnic University, M.Phil. in Computer Science, and Ph.D. in Computer Graphics. Research interests include mesh saliency, human-object interaction recognition, cloud modeling, and student performance analytics. He has published extensively in top venues like IEEE Transactions, ACM SIGGRAPH, and ECCV. Notable awards include Best Paper (ITiCSE 2014) and Outstanding Paper (ICALT 2013). He currently chairs the Undergraduate Board of Examiners and serves as an external examiner for Northumbria University's MSc program. His work spans educational technologies, collaborative virtual environments, and visual aesthetics, with applications in VR therapy, face forgery detection, and artistic style transfer. He supervises numerous Ph.D. students and contributes to Durham’s academic governance, including curriculum development and postgraduate management.
Hongyang Zhang is an Assistant Professor at the University of Waterloo's David R. Cheriton School of Computer Science (part of the Faculty of Mathematics) and a faculty member of Vector Institute for AI. His research focuses on machine learning theory and applications, including inference acceleration for large language models (e.g., EAGLE series), world models for robotics and autonomous systems, and AI security. He leads the SafeAI Lab and is affiliated with the AI Institute and Cybersecurity and Privacy Institute. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2019) Postdoc at Toyota Technological Institute at Chicago (2019–2021) Bachelor's degree from Peking University (2015) Research Interests: Inference acceleration (e.g., EAGLE-3 achieving 5× speedup) World models for infinite-horizon video generation (The Matrix) AI security, adversarial robustness, and watermarking System-2 LLMs for alignment and reasoning Awards & Recognition: 1st place in multiple adversarial vision challenges (NeurIPS 2018, CVPR 2021) AAAI New Faculty Highlights (2023) IEEE Senior Member (2024) Amazon Research Award and WAIC Yunfan Award Academic Leadership: Area Chair for ICML, NeurIPS, ACL, and ICLR Action Editor for Data-centric Machine Learning Research (DMLR) Teaching: Introduction to ML, Robustness of ML, and AI Security courses Labs & Teams: Leads the SafeAI Lab, collaborating on projects like EAGLE, The Matrix, and zkLLM.
Professor Son Lam Phung is a faculty member at the University of Wollongong (UOW), holding the position of Professor in the School of Electrical, Computer and Telecommunications Engineering (SECTE) since 2022. He earned his B.Eng. (First-Class Honours) and Ph.D. in Computer Engineering from Edith Cowan University, Australia, where he was awarded the University Medal for academic excellence in 2000. His research focuses on image and signal processing, machine learning, and artificial intelligence, with applications in defense, healthcare, and autonomous systems. He has secured over 22 external grants from organizations such as the Australian Research Council (ARC), Qatar National Research Fund, and the Department of Foreign Affairs and Trade. Research Interests: Image and video processing Pattern recognition Machine learning and deep learning Assistive navigation systems for vision-impaired individuals Radar and sonar imaging for defense and environmental monitoring Grants & Projects: ARC Discovery Projects: 'Assistive Micro-navigation for Vision Impaired People' and 'Dynamic Visual Scene Gist Recognition' Qatar National Research Fund: 'Big Crowd Data Analytics' Defence Science and Technology Group: 'New Deep Networks for Iris-based Post-Mortem Identification' Awards & Recognition: Recipient of multiple best paper awards at IEEE conferences (ICASSP-2016, DICTA-2014) Highly Commended Supervisor Award (2012 Canon Extreme Imaging Competition) UOW Outstanding Contribution to Teaching Award (2010) Editorial Roles: Associate Editor, IEEE Access (IF: 3.4) Section Editor, Sensors Journal (Sensing and Imaging Section) Teaching & Supervision: Supervised 30 HDR students (20 PhD, 10 MPhil) to completion Teaching awards include the OCTAL Early Career Award (2010) Subjects include Digital Signal Processing, Embedded Systems, and Image Processing Labs & Teams: He leads the Centre for Signal and Information Processing (CSIP), focusing on algorithm development for defense, healthcare, and autonomous systems. His lab collaborates on projects involving AI-driven solutions for agriculture, environmental monitoring, and medical imaging.
Edwin Ren is an Associate Professor in the School of Computing Sciences at the University of East Anglia (UEA), UK. His research focuses on Internet of Things (IoT), 5G/6G networks, cloud computing, and cybersecurity. He holds a PhD in Information Communication Technology from the University of Agder, Norway, and has held postdoctoral positions at National Chiao Tung University, Taiwan. Ren is a member of the Cyber Intelligence and Networks and Data Science & AI groups at UEA. **Education**: - PhD in Information Communication Technology, University of Agder, Norway (2012) - Postdoctoral Fellow at National Chiao Tung University (2012–2017) **Research Interests**: - IoT and Smart Systems - Network Function Virtualization (NFV) and SDN - Blockchain-based Security Protocols - Mobile Edge Computing and 5G/6G Architectures - Privacy-Preserving AI and Federated Learning **Key Achievements**: - Best Paper Award at IEEE MDM 2012 - Active in interdisciplinary projects like the Training DIGIT initiative (EPSRC-funded) and Smart Environments Research Facility **Grants & Collaborations**: - EPSRC-funded projects on Digital Innovation and Cyber-Physical Systems - Royal Society-supported AIoT platform research **Labs/Teams**: - Leads the Cyber Intelligence and Networks research group at UEA - Involved in the Smart Environments Research Facility, a multidisciplinary EPSRC project
Feng Luo is a Professor at the School of Computing , Clemson University, and founding director of the Clemson AI Research Institute for Science and Engineering (AIRISE). He earned his Ph.D. in Computer Science from the University of Texas at Dallas in 2004 and held a postdoctoral position at the University of Texas Southwestern Medical Center before joining Clemson in 2006. Research Focus: Deep Learning, High-Throughput Biological Data Analysis, Computational Genomics Laboratory: BigData Analytics Research Group ( BigData@Clemson ) His research spans Deep Learning applications in bioinformatics, with emphasis on genomic data analysis and network biology . Recent work includes genome assembly algorithms, methylation detection, and AI-driven cybersecurity for IoT devices. His 15 most recent publications (2024-2025) focus on advanced AI/ML methods for 3D genome reconstruction , molecular structure generation , video denoising , and diffusion model security , reflecting interdisciplinary work bridging Computer Science with Genomics and Materials Science . Dr. Luo has advised numerous graduate students and postdocs, several of whom now hold positions at companies like Amazon , Meta , and LinkedIn . He actively serves on university and professional committees as a Senior IEEE Member and ACM Member . His funded projects include NSF , DOE , and NIFA grants addressing citrus disease resistance , off-road autonomy , and AI-enabled healthcare .
Prof. Juraj Gazda is a Professor at the Faculty of Electrical Engineering and Informatics, Technical University of Kosice. His research focuses on cognitive radio networks, artificial intelligence, green communications, and agent-based modeling. He holds a PhD in engineering from TUKE (2010) and has extensive industry experience as an external consultant at Nokia Networks (2011–2014), advising on LTE wireless systems. He has been a guest researcher at institutions like Ramon Llull University (Spain), Technical University of Hamburg (Germany), and Delft University of Technology (Netherlands). His teaching includes stochastic modeling and data analysis courses. Gazda’s work bridges academia and industry, emphasizing network security, autonomous systems, and 6G technologies. Research highlights include contributions to UAV-assisted networks, blockchain-based spectrum management, and AI-driven edge computing. His projects address real-world challenges in 5G/6G networks, including energy efficiency, secure communication, and autonomous vehicle coordination. Gazda actively collaborates with global tech firms like Verizon Wireless, contributing to network design and optimization. Publications span AI applications in healthcare, vehicular networks, and quantum computing. He advocates for interdisciplinary research, integrating machine learning, blockchain, and metaverse technologies to address future mobility and communication needs.
Dr. Miroslav Michalko is an Associate Professor at the Department of Computer Networks and Security, Faculty of Electrical Engineering and Informatics, Technical University of Košice. His research focuses on Cybersecurity, Multimedia Content Delivery, IoT for eHealth, Smart Cities and Homes, Web Technologies, and EU Project Management. He teaches courses such as Computer Networks, Security of Computer Networks, and CCNA Security. He actively coordinates the Girls in IT @ FEEI TUKE initiative and serves as the National Guarantor for Information Technologies and Telecommunications within Slovakia's Qualifications Verification System. His research interests span Cybersecurity methodologies, IoT integration in healthcare, Smart City infrastructure, and innovative educational technologies. Notable projects include developing UAV-based monitoring systems, smart home solutions, and cybersecurity educational platforms. He has contributed to over 50 peer-reviewed publications, emphasizing practical applications of computer networks and IoT. Dr. Michalko's recent work highlights advancements in autonomous UAV control via machine learning, intrusion detection in IoT environments, and real-time water leak detection systems. His teaching emphasizes hands-on learning through simulations and industry-aligned certifications like Cisco's NetAcad program.
Ioannis D. Schizas is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Arlington (UTA), with a primary appointment in the Nedderman College of Engineering. He joined UTA in 2011, progressing from Assistant to Associate Professor before being promoted to Full Professor (effective September 2024). His research focuses on machine learning, signal processing, distributed systems, and data mining, with applications in sensor networks, remote sensing, and graph-based learning. Education: He holds a Diploma in Computer Engineering and Informatics (University of Patras, Greece, 2004), an M.Sc. (2007), and a Ph.D. (2011) in Electrical and Computer Engineering from the University of Minnesota. Research Interests: Schizas' work spans machine learning algorithms, statistical signal processing, and distributed optimization. Key areas include clustering techniques, kernel methods, dimensionality reduction, and applications in wireless sensor networks, hyperspectral imaging, and multi-target tracking. Publications: His recent work emphasizes scalable machine learning (e.g., kernel-based clustering, neural network pruning for IoT), distributed sensor data processing, and graph filtering for data reduction. Notable trends include fusion of modalities (e.g., hyperspectral unmixing) and real-time systems (e.g., video tracking). Awards: He received the Outstanding Advisor Award (2024) and Advisor Excellence Award (2023) at UTA. His research has been supported by grants from the Army Research Office, AFOSR, and NSF, totaling over $1M in funding. Advising & Grants: Schizas has advised numerous PhD/Master’s students on topics like kernel learning, distributed tracking, and data-driven techniques. His grants include projects on dynamic data-driven systems (e.g., multi-threat tracking, DDROSSIL framework). Labs/Teams: His research group collaborates on projects involving distributed algorithms, sensor networks, and machine learning applications. Active collaborations exist with organizations like Adobe Systems and the Air Force Office of Scientific Research.
Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.
Angeliki Katsenou is a Marie Sklodowska-Curie Research Fellow at the Visual Information Lab, University of Bristol, UK. She holds a Ph.D. in Computer Science and Engineering from the University of Ioannina (2014), an MSc from the University of Patras (2007), and a Diploma in Electrical Engineering and Computer Technology from the same university (2004). Her research focuses on video compression, communications, and sensor networks, with expertise in resource allocation and cross-layer design for wireless networks. She has contributed to European and international projects as a freelance researcher since 2007. Her affiliations include the Visual Information Lab (University of Bristol) and the Computer Science Department at the University of Ioannina, where she maintains contact information. Research interests span interdisciplinary topics such as video analysis models, visual sensor networks, and QoS optimization in multiaccess systems. No specific scientific awards or grants are listed, though her work emphasizes collaborative projects. She has no listed advisees at this time.
