جست‌وجوی استاد و پژوهشگر

Thierry Artièresمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.

Mehdi Khazaeliمشاهده پروفایل

دانشیار

• Sport Analytics
• Data Analytics
• Machine Learning
• +۴ مورد دیگر

Mehdi Khazaeli serves as Associate Professor in the Engineering Management Program within the School of Engineering and Computer Science at University of the Pacific, concurrently holding the position of Assistant Dean for Innovation and Professional Development. In this administrative capacity, he directs the corporate affiliates program encompassing CO-OP initiatives, sponsored research partnerships, and career services for engineering students. His instructional portfolio includes advanced courses in Design and Innovation, Decision Making, and Data Analytics. His academic credentials include: Ph.D. in Engineering Science (Information Technology Engineering concentration), Louisiana State University, 2013 M.S. in Product Design and Management, University of Liverpool, 2009 B.S. in Industrial Engineering, Isfahan University of Technology, 2005 Dr. Khazaeli maintains active professional certifications as a California-licensed Professional Engineer (Industrial Engineering) and Project Management Professional (PMP). His research program demonstrates exceptional interdisciplinary range: Sport Analytics Core : Pioneering AI-driven performance analysis across golf, soccer, and basketball through biomechanics and video processing Technical Integration : Merging Building Information Modeling with wireless network analysis for construction innovation Educational Engineering : Developing entrepreneurial frameworks for bioengineering curriculum design Recent publications (2021-2024) reveal a dominant research trajectory applying deep learning to athletic performance optimization, with secondary streams in construction informatics and engineering education reform. His work consistently bridges theoretical AI advances with practical sports and industrial applications. Professional recognition includes: Outstanding Innovation Product Award in Product Design and Development Project Expo (University of Liverpool) As Assistant Dean, he architects industry-academia partnerships through corporate affiliations and CO-OP programs, while his consulting engagements span R&D sectors, venture capital firms, and research institutions. His leadership directly shapes professional development pipelines connecting student talent with industry innovation demands. Administrative initiatives under his direction cultivate cross-sector collaboration networks that accelerate technology transfer from academic research to industrial implementation, with particular emphasis on data-driven decision systems across sports and construction domains.

• Artificial Intelligence
• Interactive Storytelling
• Games
• +۲ مورد دیگر

Edirlei Everson Soares de Lima is an Assistant Professor at Universidade Europeia – IADE, where he coordinates the Games Development course. His research intersects Artificial Intelligence , Interactive Storytelling , and Game Design , focusing on adaptive narratives and player modeling. Ph.D. in Computer Science, Pontifical Catholic University of Rio de Janeiro (PUC-Rio) M.Sc. in Computer Science, Federal University of Santa Maria (UFSM) B.Sc. in Computer Science, Contestado University (UnC) His research explores Interactive Narratives in digital entertainment, spanning from game AI to virtual reality horror mechanics. Recent work emphasizes Machine Learning for fear modeling and Procedural Content Generation in quests. Publications and teaching roles since 2011 reflect expertise in Game Programming , Player Experience , and Computer Graphics , with notable awards including multiple SBGames Best Paper accolades and ITU honors. Best paper awards at SBGames (2016–2020), ICEC (2015), WebMedia (2014) ITU Honorable Mentions: 'Innovation' (2011), 'Interactivity' (2012) SBGames 2010: Best game and honorable mentions for 'Krimson' He has supervised multiple Master’s students in projects related to Interactive Design , Game Technology , and Narrative Systems , with over a decade of teaching experience across institutions like PUC-Rio and UERJ/IPRJ.

KRASEN KIROV ANGELOVمشاهده پروفایل

استاد

• Wireless Communication Systems
• 5G Networks
• Internet of Things (IoT)
• +۷ مورد دیگر

Krassen Kirov Angelov is a full professor at the Department of Communication Equipment and Technologies within the Faculty of Electrical Engineering and Electronics at Technical University - Gabrovo . He has led and participated in 15 research projects, focusing on wireless communication platforms, IoT systems, and optical network optimization. Head, Department of Communication Equipment and Technologies Project Manager for 5G/IoT research (2022) Co-author of 3 textbooks on communication circuits Research Interests include: 5G network design and optimization IoT communication protocols Optical network modeling Secure data transmission Satellite broadcasting systems Smart infrastructure development His recent publications (2021-2025) demonstrate expertise in: Chaotic encryption techniques Neural network applications LoRaWAN coverage planning DVB-S2 signal transmission Passive optical network modeling Radio frequency optimization PhD Students : Plamen Tashev (Radio Channel Monitoring) Mikhail Angelakis (Wireless Resource Management) Victor Krustev (Security Identification) Ivan Zlatev (Network Resource Allocation) Evelina Vaseva (Beekeeping Monitoring)

Raza Ul Mustafaمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Machine Learning
• +۵ مورد دیگر

Raza Ul Mustafa is an Assistant Professor in the Department of Mathematics and Computer Science at Loyola University New Orleans . Prior to this, he was a Postdoctoral Fellow in Large Language Models (LLMs) at American University in Washington, DC. Education: Ph.D. in Computer Engineering (2018-2022), University of Campinas Research Interests span Natural Language Processing , Large Language Models , Machine Learning , and Quality of Experience (QoE) in multimedia systems. His work combines 5G networks and deep learning to optimize video streaming performance, while also exploring hate speech detection in online platforms using advanced NLP techniques. Publications focus on two primary areas: (1) 5G network optimization for video QoE, including ML-based stall prediction and QoS-to-QoE mapping, and (2) hate speech analysis using LLMs to detect coded terms in extremist social media. These studies demonstrate his dual expertise in telecommunications and social media NLP . Industry Experience includes six years as a full-stack developer, which enriches his teaching with practical insights. His research is reproducible and has been published in top-tier venues like IEEE WCNC , DSAA , and ACM QoSS conferences.

Ali Abbasiمشاهده پروفایل

استاد

• Embedded Systems Security
• Hardware Security
• Firmware Vulnerability Analysis
• +۳ مورد دیگر

Dr. Ali Abbasi is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He leads the Embedded Systems Security (EMSEC) research group, focusing on hardware-software co-design for security, including fuzzing techniques , side-channel analysis , and mission-critical real-time systems . His work bridges cybersecurity , space systems , and automotive security through innovative research. PhD , Eindhoven University of Technology (2017–2018) Postdoctoral Researcher , Ruhr-University Bochum (2019–2021) His research integrates hardware security and systems security , with recent publications addressing firmware vulnerabilities , satellite communication security , and PLC defense mechanisms . Key article trends include: 2025 : Differential testing of video hardware stacks 2024 : Memory safety in bootloaders, space system sandboxing, and industrial control system security 2023 : Standardized metrics for PLC defenses and experimental satellite security analysis Scientific Awards : Distinguished Paper Award, IEEE Symposium on Security and Privacy (2023) He has taught advanced courses on Foundations of Firmware Security (2025), Systems Security (2022–2024), and Reverse Engineering (2023), with practical outcomes including student-led 0-day vulnerability discovery in real-time operating systems.

• Computer Graphics
• Computer Vision
• Video Processing
• +۴ مورد دیگر

Prof. Dr. Thorsten Thormählen is a University Professor for Computer Graphics and Multimedia Programming in the Department of Mathematics and Computer Science at Philipps University Marburg, Germany, a position he has held since October 2012. Prior to this, he served as Substitute Professor for Interactive Graphics Systems at the University of Tübingen (2011-2012) and led the independent research group "Image-based 3D Scene Analysis" at the Max Planck Center for Visual Computing and Communication (2007-2012). His academic background includes a PhD (Dr.-Ing.) from the University of Hannover (2000-2005), where he also worked as a full-time scientific assistant, and a Diploma in Electrical Engineering from the University of Duisburg (1994-1999). Prof. Thormählen's research centers on Visual Computing , spanning Computer Graphics , Computer Vision , Video Processing , and Human-Computer Interaction . His work pioneers tools for 3D reconstruction (e.g., Multi-View Photometric Stereo), video manipulation (e.g., MovieReshape), and multimedia processing (e.g., GSN Composer), with groundbreaking contributions to accessibility through haptic and auditory interfaces for blind 3D modelers. His lab develops practical systems that bridge theoretical computer vision with real-world applications. Analysis of his 15 most recent publications reveals a consistent focus on photometric stereo techniques for 3D reconstruction, video processing innovations, and accessibility-driven interfaces. His work increasingly emphasizes inclusive design, particularly for visually impaired users, while maintaining technical rigor in camera calibration, surface modeling, and multi-sensor fusion. At the Max Planck Center, he established and led the "Image-based 3D Scene Analysis" research group, which advanced methods for reconstructing 3D environments from video sequences. His current work at Marburg continues this trajectory, integrating computer vision, graphics, and human-centered design to solve complex problems in digital content creation and accessibility.

• Accessibility Research
• Sign Language Recognition
• Human-Computer Interaction
• +۴ مورد دیگر

Danielle Bragg is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, specializing in accessibility research with a focus on technologies for deaf and hard-of-hearing communities. Her work bridges human-computer interaction, computer vision, and inclusive design to create practical solutions for sign language recognition, translation, and communication accessibility. Her research interests center on making technology accessible through innovative approaches to sign language processing, assistive communication systems, and inclusive design methodologies. She investigates how to build robust sign language recognition systems that work in real-world settings, addresses bias in AI models for sign language understanding, and develops community-centered approaches to dataset collection. Her work particularly emphasizes involving the Deaf community in the research and development process to ensure technologies meet actual needs. Dr. Bragg's publication record shows a consistent trajectory from her PhD work at the University of Washington (2018) through to current research in 2025, with increasing leadership in the field of accessibility technology. Her recent work explores cutting-edge applications of AI for sign language processing, gaze-based interfaces, and systematic analysis of how large language models are transforming human-computer interaction research. Her collaborative approach is evident in her extensive co-authorship network, working with both technical researchers and community stakeholders to advance the field of accessibility technology through interdisciplinary partnerships.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Andreas Savakis is a Professor in the Department of Computer Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). His expertise spans Artificial Intelligence, Computer Vision, and Machine Learning with a focus on domain adaptation, deep learning, and aerial imagery analysis. He holds a BS and MS from Old Dominion University and a PhD from North Carolina State University. His research emphasizes robust algorithms for object detection, tracking, and domain adaptation in challenging environments like aerial surveillance and medical imaging. Notable contributions include resilient deep networks, Grassmann manifold optimization, and semantic pose estimation frameworks. He has published extensively in top venues such as CVPR, ICIP, and IEEE journals. Key achievements include recognition in Stanford’s top 2% scientists (2022) for citation impact. His work bridges theoretical advancements with practical applications in autonomous systems, healthcare, and environmental monitoring. Current teaching includes machine learning fundamentals and analytical methods in computer engineering. Research trends in his articles highlight domain adaptation for varying conditions (e.g., weather, sensors), efficient neural network quantization, and multi-person pose estimation in complex scenes. His lab explores cross-modal learning (e.g., SAR-optical fusion) and continual learning frameworks for evolving data distributions.

John Mageeمشاهده پروفایل

دانشیار

• Computer Vision
• Human-Computer Interaction
• Assistive Technology
• +۲ مورد دیگر

John Magee is Associate Professor of Computer Science at Clark University and serves as Provost and Vice President of Academic Affairs. He holds a Ph.D. from Boston University with research focusing on computer vision, human-computer interaction, and assistive technologies that empower individuals with disabilities. His publications demonstrate strong focus on accessibility innovations, educational technologies, and computer vision applications. Recent work (2022-2024) includes affect-aware educational systems, accessibility tools like Camera Mouse, and security applications of computer vision. His research consistently addresses real-world challenges in disability access and learning technologies. Dr. Magee's work includes developing adaptive interfaces that respond to users' physical abilities and cognitive states, particularly for individuals with profound disabilities. His research extends to interdisciplinary applications combining computer vision with education, security, and healthcare domains.

• Multiprocessor Systems
• Real-Time Embedded Computing
• Distributed Signal Processing
• +۳ مورد دیگر

Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.

• Natural Language Processing
• Machine Learning
• Social Media Analysis
• +۳ مورد دیگر

Dr. Fatima Haouari is a Research Fellow in the School of Computer Science at the University of Sheffield, specializing in Machine Learning and Natural Language Processing (NLP). She is a core member of the Natural Language Processing research group, focusing on advancing methodologies for misinformation detection, rumor verification, and social media analysis. Her work integrates computational linguistics with real-world applications, including developing multilingual systems for Arabic-language content analysis. Haouari has pioneered datasets such as ArCOV-19 and ArCOV19-Rumors , which provide foundational resources for studying Arabic social media content during the pandemic. She is a key contributor to the CLEF-CheckThat! initiative, leading tasks on check-worthiness estimation, authority identification, and adversarial robustness in fact-checking systems. Her technical work includes optimizing resource allocation for live streaming and transcoding systems, reflecting a dual focus on theoretical NLP and practical system design. Her research trends emphasize cross-lingual challenges in information verification across platforms like Twitter, with a focus on Arabic language processing. Recent work highlights include real-time claim verification systems (e.g., Tahaqqaq) and frameworks for detecting stance of authoritative voices in rumors. While not explicitly noted, her contributions span both academic publications and applied systems development. She collaborates extensively in international research networks and has designed benchmark evaluation tasks for NLP challenges.

• Computer Engineering
• Parallel Computing
• Field-Programmable Gate Arrays (FPGAs)
• +۵ مورد دیگر

Zuofu Cheng is a Teaching Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His research focuses on parallel computing, FPGA-based machine learning acceleration, GPU optimization, and innovative engineering pedagogy. He has contributed to curriculum development in computer engineering education and advanced mesh optimization algorithms for heterogeneous computing systems. Research interests include: Hardware acceleration for AI/ML systems Parallel algorithm design for multi-core architectures Interactive engineering education methodologies Real-time acoustic simulation engines Publications highlight trends in FPGA/GPU optimization, IoT applications of machine learning, and computational thinking integration in undergraduate curricula. Over 75 citations reflect notable impact in hardware-accelerated computing domains. Active collaborations include work with IEEE conferences and HPC training initiatives.

Michael Zinkمشاهده پروفایل

استادیار

• Network Protocols
• Multimedia Distribution
• Wireless Networks
• +۵ مورد دیگر

Michael Zink is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst and an adjunct assistant professor in the Manning College of Information and Computer Sciences (CICS). He is affiliated with the Microwave Remote Sensing Laboratory and has held roles including assistant research professor in computer science. His research focuses on network protocols, multimedia distribution systems, and high-bandwidth data networks, with contributions to the KOMSSYS streaming platform and FPGA-based network architectures. Zink's work spans topics like secure video streaming, LED wall calibration, and federated learning for sustainable UAV operations. He received the Best Paper Award at Multimedia Computing and Networking (2008). Education: PhD from Darmstadt University of Technology. Research Interests: Sensor networks, wireless and wired multimedia distribution, network protocol optimization, video streaming security, and distributed cloud platforms. Recent work includes innovations in hue-preservation for virtual production and carbon-efficient federated learning (CEFL). Technical Contributions: Developed systems for real-time route configuration of entertainment content, scanner noise elimination in film restoration, and profile-based HDR content generation. His team explores network-attached FPGAs in cloud environments and edge-to-cloud workflows for scientific applications. Labs & Teams: Active in the Microwave Remote Sensing Laboratory and collaborates with the Open Cloud Testbed (OCT) for cloud infrastructure research. Engaged in federated cyberinfrastructure projects like the ERN Cryo-EM Pilot.

Pedro Sarmentoمشاهده پروفایل

پژوهشگر ارشد

• Sound and Music Computing
• Deep Learning
• AI in Music
• +۳ مورد دیگر

Pedro Sarmento is a Research Fellow at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on Sound and Music Computing , leveraging deep learning and AI techniques for symbolic music generation, particularly in guitar tablature formats. He leads projects like Guitar-Oriented Neural Music Generation , exploring prescriptive notation in fretted instruments using datasets like dadaGP and GigaMIDI. His work integrates transformer models for tasks such as emotion-conditioned music generation, style transfer, and live coding performances. Notable applications include mapping smart city sensor data (e.g., air quality) to musical outputs. Collaborations span music informatics, interdisciplinary projects in progressive metal composition, and AI ethics in creative processes. Research highlights include conditional music generation, guitarist style modeling, and dataset development for expressive music performance analysis. Pedro contributes to the Centre for Digital Music (C4DM) and actively publishes in AI-driven music technology, bridging computational methods with artistic creativity.