جست‌وجوی استاد و پژوهشگر

• Audio and Speech Processing
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Serkan KESER is an Associate Professor in the Department of Electrical and Electronics Engineering at the Faculty of Engineering and Architecture, Ahi Evran University, Turkey. He has been serving as a full-time faculty member since 2018 and previously served as Head of Department from 2018-2021. His educational background includes a PhD in Electrical and Electronics Engineering from Eskişehir Osmangazi University (2009-2018), a Master's degree in the same field from the same institution (2005-2008), and a Bachelor's degree in Electrical and Electronics Engineering from Mustafa Kemal University (1999-2005). Dr. KESER's research focuses on three main areas: Audio and Speech Processing : Including speaker identification, isolated word recognition, and speech coding techniques Signal Processing : With applications in fiber optic sensor systems and acoustic positioning Image Processing : Covering face recognition, image compression, and denoising techniques His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods. He has made significant contributions in applying subspace methods to various domains including speech recognition, image processing, and sensor technologies. His work often bridges theoretical signal processing concepts with practical applications in areas like smart home systems, medical imaging, and environmental monitoring. Dr. KESER has successfully supervised five Master's students to completion, with thesis topics spanning deep learning applications for class attendance systems, photovoltaic systems, speaker identification, brain tumor classification, and speech-controlled robotic arms. He has led four research projects, including development of fiber optic motion sensors, smart home models using Arduino microcontrollers, and science outreach initiatives. His teaching portfolio includes advanced courses in digital image processing, artificial neural networks, digital signal processing, and machine learning at both undergraduate and graduate levels. Dr. KESER's research impact is reflected in his publication metrics: 30 total publications with 117 citations (h-index 5) through the UNIS system, 25 publications with 180 citations (h-index 6) on Google Scholar, and strong representation in Scopus and Web of Science databases.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۷ مورد دیگر

David Martins de Matos is an Associate Professor at Instituto Superior Técnico (IST), Universidade de Lisboa , and a senior researcher at INESC-ID Lisbon within the Human Language Technology Lab . With a career spanning over three decades, he has taught subjects such as Compilers and Object-Oriented Programming since 1993. His research focuses on Natural Language Engineering , Automatic Natural Language Generation , Music Information Retrieval , and Machine Learning Applications in Healthcare . Education: B.Sc. in Electrical and Computer Engineering (IST, 1990) M.Sc. in Electrical and Computer Engineering (IST, 1995) on object-oriented programming in distributed systems Ph.D. in Systems and Computer Science (IST, 2005) on automatic natural language generation Research Interests: His work bridges Natural Language Processing and Computational Music Analysis , with applications in Health Informatics . He investigates semantic frame induction, dialog act recognition, and multimodal systems for chronic pain assessment, Alzheimer's detection, and music generation. His recent articles explore cross-modal retrieval, deep learning for pain narratives, and embodied semantics via fMRI. Scientific Contributions: He has published over 161 works, including 15 recent articles on chronic pain datasets, dialog act recognition, and music-language correlations. His awards include Senior Member status in ACM (SIGMM, SIGIR) and IEEE (Signal Processing Society, Computer Society) , and membership in the Order of Portuguese Engineers . Advising & Collaborations: He has supervised 111 doctoral and master's theses, mentoring students in topics like Visual Story Generation , Music Summarization , and Health Informatics . He collaborates with institutions such as IBM Research , Northwestern University's Feinberg School , and Universidade de Lisboa .

Jaumin Ajdariمشاهده پروفایل

استاد

• Parallel Processing
• Wavelet Transforms
• Machine Learning
• +۷ مورد دیگر

Jaumin Ajdari is a Full Professor at the Faculty of Contemporary Sciences and Technologies at South East European University in Tetovo, Macedonia. He holds a Doctor of Mathematical Sciences degree from the University of Tirana, with a focus on parallel processing and orthogonal wavelet transforms. Education: PhD in Mathematical Sciences (University of Tirana, 2011), MSc in Mathematics (University of Tirana, 2006), MSc in Mathematics (University of Zagreb, 1993), Engineer in Applied Mathematics (University of Zagreb, 1993) His research spans parallel computing , machine learning , database systems , IoT applications , and natural language processing , particularly for low-resource languages. His recent publications focus on predictive modeling, smart agriculture using IoT, cloud computing challenges in education, and hate speech detection in Albanian social media. Key article trends include: Machine learning applications in education and agriculture Cloud computing adoption studies IoT sensor data analysis NLP for Balkan languages Database optimization techniques Parallel algorithm implementations

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.

Anne Göhringمشاهده پروفایل

پژوهشگر

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +۵ مورد دیگر

Anne Göhring is an Academic Associate at the University of Zurich, working within the Language, Technology and Accessibility research team at the Institute of Computational Linguistics. With over a decade of experience in computational linguistics and natural language processing, she has established herself as a specialist in machine translation, sign language processing, and German language analysis. Her work bridges theoretical linguistics with practical applications, particularly focusing on accessibility technologies and resource-poor languages. Her educational background includes: 2001-2010: Studies in Spanish Linguistics and Literature, Computational Linguistics 2006-2007: Erasmus study year at Universidad Complutense Madrid Dr. Göhring's research spans multiple domains of computational linguistics with a strong emphasis on practical applications. She has made significant contributions to machine translation systems, particularly for hybrid approaches and low-resource languages like Quechua. Her recent work has increasingly focused on sign language processing, developing corpora and translation systems to improve accessibility. She also conducts important research in semantic role labeling, sentiment analysis, and German language processing, with applications ranging from social media analysis to veterinary text mining. Analysis of her recent publications reveals a clear trajectory toward accessibility-focused NLP research, particularly in sign language technologies. While maintaining expertise in German language processing and machine translation, her 2023-2024 work shows a strong emphasis on sign language corpora development (SwissSLi) and pose-based identification systems. Earlier work focused more on semantic analysis, sentiment detection, and cross-lingual processing, demonstrating remarkable versatility across NLP subfields. Dr. Göhring has been actively involved in numerous research projects: 2011-2014: SNSF project SQUOIA on Hybrid Machine Translation 2015: CTI project SentiSpider on expectation-based sentiment analysis 2016-2018: What's up, Switzerland? corpus project 2018-2022: Sentiment inference research 2021-2024: NCCR Evolving Language Project: IMAGINE 2022-2026: Flagship IICT Throughout her career, Dr. Göhring has supervised numerous student projects and taught courses ranging from introductory computational linguistics to specialized seminars on machine translation and NLP for medicine. Her teaching portfolio demonstrates commitment to both foundational knowledge and cutting-edge applications in the field. She is an active member of the Language, Technology and Accessibility research group at the University of Zurich, collaborating extensively with researchers like Manfred Klenner, Sarah Ebling, and Martin Volk on diverse NLP challenges.

Michele H. Jacksonمشاهده پروفایل

استاد

• Communication
• Organizational Communication
• Group Communication
• +۴ مورد دیگر

Michele H. Jackson is a Professor at Michigan State University, specifically affiliated with Lyman Briggs College. She holds the title of West Holmes Professor and is a Science and Society Fellow at the Center for Teaching and Learning Innovation. Her work bridges communication studies, technology, and education, with a particular focus on how emerging technologies affect human communication and learning environments. Dr. Jackson earned her Ph.D. in Speech Communication with an emphasis on communication and technology from the University of Minnesota. She also holds an M.A. in Speech Communication with a focus on women's rhetoric from the same institution, and a B.A. cum laude in Speech Communication from Macalester College. Her research primarily explores the social dimensions of technology, with recent emphasis on machine learning and generative AI in educational contexts. She investigates how communication technologies shape human interaction, particularly in group settings and academic environments. Her work spans communication theory, organizational behavior, educational technology, and the social construction of technology, with significant contributions to understanding how technologies influence group dynamics and learning processes in computer science education. Analysis of Dr. Jackson's publication record reveals consistent engagement with the intersection of communication and technology over several decades. Her work shows an evolution from foundational studies on communication technology theory to contemporary research on AI and machine learning in education. Key themes across her publications include the social construction of technology, group communication dynamics, technology adoption in educational settings, and the impact of emerging technologies on organizational and educational environments. Over 55 scholarly publications across multiple domains Research spans communication theory, computer science education, and emerging technologies Recent focus on generative AI implications for teaching and learning Extensive work on group communication in engineering and computational contexts Dr. Jackson actively welcomes student research collaborators, particularly those interested in the social and organizational implications of artificial intelligence. Her work indicates involvement in grant-funded research projects, including an NSF-funded IT Workforce grant mentioned in her publications on computer science classroom environments. She has collaborated with numerous researchers across disciplines, demonstrating an interdisciplinary approach to studying technology and communication. Her research appears to involve extensive interdisciplinary collaboration across communication, computer science, and education domains, with particular emphasis on how communication technologies shape learning environments and workplace dynamics. While specific lab structures aren't detailed in the available information, her work on classroom technologies and group communication suggests engagement with both theoretical frameworks and practical implementations of communication technologies.

Kamal Premaratneمشاهده پروفایل

استاد

• Conspiracy Theory Research
• Social Media Analysis
• Machine Learning
• +۵ مورد دیگر

Kamal Premaratne serves as a Professor in the Department of Electrical & Computer Engineering at the College of Engineering, University of Miami. His scholarly work uniquely bridges technical expertise in machine learning and signal processing with social science research on conspiracy theories and political extremism. With numerous publications in both technical and social science journals, he demonstrates an exceptional interdisciplinary approach to understanding complex contemporary phenomena. Professor Premaratne's research spans two primary domains: advanced computational methods and social/political analysis. In computational methods, he focuses on quantum tensor networks for time series analysis, graph neural networks for gesture recognition, and uncertainty quantification in machine learning systems. His social science research examines conspiracy theories including the "White Replacement" theory, QAnon, and "white genocide" narratives, investigating their sociodemographic correlates and relationship to political extremism. His work often employs mixed-methods approaches combining qualitative analysis with quantitative content analysis and network analysis. Analysis of Premaratne's recent publications reveals a strong focus on understanding how conspiracy theories spread on social media platforms and their relationship to political behavior. His technical work shows innovative applications of quantum physics concepts to machine learning problems, creating more interpretable models while maintaining performance. This dual focus demonstrates how computational methods can be applied to social science questions and vice versa. Professor Premaratne actively collaborates with researchers across disciplines including psychology, political science, and communication studies. His research has been published in high-impact journals such as Scientific Reports, Journal of Politics, and Political Science Quarterly. Though specific grant information isn't detailed in the available materials, his extensive publication record suggests substantial research activity. His work has significant implications for understanding political polarization, misinformation spread, and developing more interpretable AI systems.

Chang Y Chooمشاهده پروفایل

استاد

• Computer Vision
• Digital Signal Processing
• FPGA Design
• +۷ مورد دیگر

Dr. Chang Y Choo is a Professor of Electrical Engineering at San José State University, where he also serves as Director of the AI/ML FPGA/DSP Systems Laboratory. His academic career spans over three decades, with previous positions at Worcester Polytechnic Institute and industry experience at Altera Corp. (now Intel). Dr. Choo maintains an active research program focusing on hardware acceleration for AI and signal processing applications, with particular emphasis on FPGA-based implementations for real-world systems. Dr. Choo's educational background includes: Ph.D. in Computer and Systems Engineering, Rensselaer Polytechnic Institute (1986) M.S. in Operations Research and Statistics, Rensselaer Polytechnic Institute (1982) B.S./M.S. in Engineering, Seoul National University, Korea Dr. Choo's research interests center on the intersection of hardware design and artificial intelligence. His work focuses on implementing computer vision, deep learning, and digital signal processing algorithms on specialized hardware platforms including FPGAs, GPUs, and custom ASICs. Current projects include developing real-time illumination/view-independent object recognition systems for autonomous vehicles, wideband acoustic echo cancellation for wearable technology, and FPGA-based accelerators for medical imaging applications. His research bridges theoretical algorithm development with practical hardware implementation constraints. Analysis of Dr. Choo's recent publications reveals a clear trajectory toward increasingly sophisticated hardware-accelerated AI systems. His work has evolved from foundational research in digital signal processing and image compression to cutting-edge applications of deep learning on specialized hardware. Recent publications demonstrate expertise in implementing CNN architectures on FPGAs, developing metabolic syndrome prediction models, and creating food object detection systems using transformer models. This progression reflects the broader field's shift toward hardware-aware AI development. Dr. Choo's significant scientific contributions include multiple patents that have advanced the state of the art in several domains: U.S. Patent No. 9,025,763 (2015): 'Apparatus and Method for cancelling wideband acoustic echo' U.S. Patent Nos. 7,058,675 (2006) and 7,124,161 (2006): 'Apparatus and method for implementing efficient arithmetic circuits in programmable logic devices' U.S. Patent Nos. 5,943,096 (1999) and 6,621,864 (2003): 'Motion vector based frame insertion process' U.S. Patent Nos. 5,832,131 (1998) and 5,991,455 (1999): 'Hashing-based vector quantization' U.S. Patent No. 5,587,710 (1997): 'Syntax based arithmetic coder and decoder' Throughout his career, Dr. Choo has been actively involved in both academic and industry collaborations. He has served as a technical consultant for numerous Silicon Valley companies including National Semiconductor (now Texas Instruments), Philips Semiconductor, Skybox Imaging (acquired by Google), Novariant (now AgJunction), and Ricoh Innovations. His industry experience informs his teaching approach, which emphasizes practical implementation considerations alongside theoretical foundations. Dr. Choo has also served as an expert witness in intellectual property court cases involving audio and video compression algorithms and FPGA hardware. Dr. Choo directs the FPGA/DSP AI/DL Laboratory at San José State University, which focuses on developing hardware-accelerated solutions for real-time AI applications. The lab maintains strong connections with Silicon Valley technology companies and provides students with hands-on experience in cutting-edge hardware design methodologies. Current research directions include autonomous vehicle navigation systems, medical imaging applications, and edge AI deployment strategies.

• Computer Networks
• Network Security
• Internet of Things
• +۳ مورد دیگر

Utz Roedig is a Full Professor of Computer Science at University College Cork (UCC), Ireland, and a Principal Investigator at the CONNECT Centre. Previously, he served as Professor at Lancaster University, UK, leading the Academic Centre of Excellence in Cyber Security Research (ACE-CSR), and held research positions at UCC and Darmstadt University of Technology, Germany. Education: Dipl.-Ing in Engineering from Darmstadt University of Technology Dr.-Ing (Doctor of Engineering) from Darmstadt University of Technology His research spans computer networks and network security , with over 150 publications in IoT security, industrial control systems, 5G networks, and voice assistant vulnerabilities. Recent work integrates machine learning for intrusion detection and fault prediction while addressing human factors in secure coding. Industry collaborations have yielded multiple patents. Analysis of 2023-2025 publications reveals dominant themes in industrial IoT security (e.g., resilient time-sensitive networking), 5G infrastructure protection, and voice assistant threats (wake word jamming/spoofing). His team develops countermeasures using protocol design and ML-driven anomaly detection, with growing emphasis on human-centric security challenges. Scientific Awards: No specific awards are documented, though research impact is evidenced by patents and sustained funding from major international bodies. Advising and Grants: Secured funding from EU, EPSRC, and industry partners. Serves as grant reviewer for EPSRC (UK), ESF (EU), and FWO (Belgium), and on TPCs for DCOSS, EWSN, and IPSN conferences. Student supervision details are unavailable, but research leadership implies active mentoring. Grants: EU, EPSRC, Industry Review Roles: EPSRC, ESF, FWO Labs and Teams: Leads research at UCC's CONNECT Centre (telecommunications security). Previously directed Lancaster University's ACE-CSR, a UK government-designated cybersecurity research hub.

Muskaan Singhمشاهده پروفایل

مدرس

• Natural Language Processing (NLP)
• Computational Linguistics
• Artificial Intelligence (AI)
• +۹ مورد دیگر

Muskaan Singh is a Lecturer in Data Analytics at the Intelligent Systems Research Centre (ISRC) within the School of Computing, Engineering and Intelligent Systems at Ulster University . A member of the Cognitive Analytics Research Lab (CARL) , her work bridges Natural Language Processing (NLP) , Artificial Intelligence , and Practical Applications in domains ranging from machine translation to biomedical diagnostics. Education: PhD in Machine Translation (Thapar Institute of Engineering and Technology, 2016-2020) Master’s in Machine Translation (IIIT Hyderabad, India) Her research spans NLP and AI with applications in code-switched language modeling , depression detection , social media analytics , and medical diagnostics . She has developed multilingual tools for automatic minuting, including DeepCon and ALIGNMEET , and contributed to EU-funded projects like ROXANNE (criminal network analysis) and ELITR (European Live Translator). Key scientific awards include first prizes in international NLP competitions (EVAL4NLP, LT-EDI, SMM4H) and recognition at EMNLP , ACL , and COLING . She received the Inclusion and Diversity Grant (EMNLP 2021) and GHC Scholarship (2019). Current projects include AI-EPOCMON (AI-Enabled Point-of-Care Monitoring) and T3-NCP (crime prevention for safer communities). Dr. Singh has supervised grants from UKRI and Alzheimer’s Research UK , focusing on AI for health and IT operations . Her team at ISRC collaborates globally with institutions in Switzerland , Czech Republic , and India . She also leads research for the Center for Data Science and Artificial Intelligence at IIIT Lucknow, India.

• Machine Learning
• Artificial Intelligence
• Federated learning
• +۹ مورد دیگر

Rajesh M. Hegde is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research spans multiple domains within artificial intelligence, signal processing, and communication systems. He has established himself as a leading researcher in multimodal information processing and has contributed significantly to speech and audio processing techniques. Education: PhD in Computer Science and Engineering, IIT Madras (2005) ME in Electronics Engineering, Bangalore University (1988) BE in IT Engineering, Mysore University Professor Hegde's research focuses on the intersection of machine learning, artificial intelligence, and signal processing, with particular emphasis on multimodal information fusion. His work bridges theoretical foundations with practical applications in speech recognition, wireless communication, and pervasive computing. He has made significant contributions to group delay analysis in speech processing and developed innovative approaches for speaker segregation and multimodal system design. His research has practical implications for improving human-computer interaction through more natural and context-aware interfaces. Analysis of his recent publications reveals a consistent focus on multimodal systems and speech processing, with increasing attention to federated learning approaches and wireless network applications. His work demonstrates a strong theoretical foundation combined with practical implementation expertise, particularly evident in his prototyping of multimedia acquisition systems. Scientific Awards: P.K Kelkar Research Fellowship (2009-2013) Mentor for best undergraduate engineering design project award at UC San Diego (2005-06) ISCA Grant for INTERSPEECH 2004 IIT Madras PhD thesis recommended for IBM best thesis award (2005) Programme Committee member for ISCA Winter schools Commendation by IIT Kanpur for teaching excellence Professor Hegde has supervised multiple research projects and mentored students in developing innovative multimedia systems. His work on multimodal situation awareness systems has received recognition for its interdisciplinary approach. He has secured research funding through fellowships and institutional support, enabling his laboratory to maintain cutting-edge research facilities. His laboratory is located in ACES 203 and 204 at IIT Kanpur, where his research team works on multimodal information processing, wireless communication systems, and speech/audio processing applications. The lab maintains strong connections with international research institutions, particularly through his involvement with ISCA and previous collaborations with UC San Diego.

Richard H. Granger, Jr.مشاهده پروفایل

استاد

• Computational Neuroscience
• Cognitive Neuroscience
• Brain Evolution
• +۷ مورد دیگر

Richard H. Granger, Jr. is a Professor at Dartmouth College , affiliated with the Thayer School of Engineering and the Psychological and Brain Sciences department. His work spans computational and cognitive neuroscience, focusing on brain circuit analysis, neuroimaging, robotics, and brain engineering. B.S., Massachusetts Institute of Technology Ph.D., Yale University His research integrates artificial intelligence, neurobiology, and mathematical modeling to understand fundamental mechanisms of learning, memory, and brain evolution. He develops algorithms inspired by cortical-subcortical loops and explores brain-inspired hardware architectures for efficient computing. Recent publications highlight his innovations in neural network design (Hamiltonian bitwise architecture), neuroimaging analyses, and cognitive modeling. His work bridges theoretical neuroscience with practical applications in medical diagnostics and robotic systems. Invited talk at UC San Diego (2018) Keynote speaker at Artificial General Intelligence Conference (2014) TEDx talk at Dartmouth (2010) Contact: Richard.Granger@dartmouth.edu , Moore Hall, Dartmouth College.

• Intelligent Transport Systems
• Location-Based Services
• Software and Hardware Integrated Navigation Systems
• +۷ مورد دیگر

Saleh Hadi Mohammed is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE), where he has worked since 2015. With over 10 years of academic experience, his expertise spans software engineering, blockchain systems, and intelligent transport technologies. Candidate of Technical Sciences (2013) Engineer in 'Computers, Complexes, Systems and Networks' (2008) Professional training in Moodle-based distance learning Research interests include: Intelligent transport systems and location-based services Software/hardware navigation integration and blockchain applications Cloud services, mobile app development, and MLOps platforms His recent publications analyze MLOps scalability, blockchain donation tracking, and AI-driven navigation for visually impaired users. Notable collaborations involve Springer, IEEE, and CEUR Workshop Proceedings. Scientific awards : Gratitude from HSE Faculty of Computer Science (2024) As a thesis supervisor , he has guided 15+ students in projects ranging from fraud detection to mobile education apps. He also leads SmartMLOps , HSE’s AI service deployment platform (2024), and participated in the Administrative Personnel Reserve Program (2024-2025).

Francesc Alías Pujolمشاهده پروفایل

استاد

• Speech Processing
• Acoustic Signal Processing
• Text-to-Speech Synthesis
• +۷ مورد دیگر

Francesc Alías Pujol is a Professor at the Department of Engineering, Ramon Llull University (La Salle Campus Barcelona), where he also serves as Director of Teaching and Research Staff Policies since September 2019 and coordinates the PhD Program in Information Technologies since February 2022. He is a researcher in the Human-Environment Research (HER) group, focusing on speech and acoustic signal processing for natural human-machine interaction. His educational background includes: BSc in Telecommunications Engineering (1997) MSc in Electronics Engineering (1999) PhD in ICT and their application in Management (2006) MBA in Business Administration (2013) University Expert Program in Digital Transformation (2023) Dr. Alías's research primarily focuses on signal processing, with special emphasis on speech and acoustic signal processing to achieve natural interaction between humans, machines, and their environment. His work spans from Text-to-Speech (TTS) synthesis to vocal biomarkers for digital health applications, numerical voice simulation, and environmental acoustics. He has developed expertise in expressive speech analysis and synthesis, sound source identification, and smart city applications through wireless acoustic sensor networks. His publication record shows a consistent trajectory in speech and acoustic processing, with recent work focusing on the impact of global events like the COVID-19 pandemic on urban soundscapes, advanced techniques for glottal source analysis, and the development of algorithms for anomalous noise event detection in smart city applications. His research bridges theoretical signal processing with practical applications in environmental monitoring and human-computer interaction. Dr. Alías has received numerous scientific awards including: 3rd six-year research merit (2018-2023) from AQU Catalunya Most cited paper in Noise Mapping (2021) for his work on COVID-19's impact on urban noise 2nd six-year research merit (2012-2017) from AQU Catalunya Best academic record award for his MBA (2013) Multiple best paper awards from the Spanish Thematic Network on Speech Technology He has led and participated in numerous research projects including DISTRESIA (stress biomarker identification), FEMVoQ (3D voice simulation), SUARAMAP (acoustic monitoring for dementia detection), GENIOVOX (expressive voice generation), and DYNAMAP (dynamic noise mapping). His work has been supported by various funding bodies including the European Commission, Spanish Ministry of Science, and Catalan Government. As coordinator of the PhD Program in Information Technologies and former Director of the Department of Engineering (2014-2021), Dr. Alías plays a significant role in academic leadership at La Salle-URL. His research group focuses on the intersection of speech processing, environmental acoustics, and smart city applications, contributing to both theoretical advancements and practical implementations in these fields.

He David Zhangمشاهده پروفایل

استادیار

• Computer Vision
• Robotics
• SLAM
• +۳ مورد دیگر

He David Zhang is an Assistant Professor of Computer Science at Governors State University and a Postdoctoral Associate at Virginia Commonwealth University (VCU) Robotics Lab. His research focuses on computer vision, robotics, and assistive technologies for visually impaired individuals. Ph.D. in Electrical & Computer Engineering, University of Arkansas at Little Rock (2018) B.S. in Computer Science & Technology, Chinese University of Mining and Technology at Beijing (2009) Study Certificate in Computer Architecture, University of Chinese Academy of Sciences (2010) Dr. Zhang's research integrates vision-based algorithms with inertial sensors to create robust systems for indoor localization, 3D mapping, and human-robot interaction. Key projects include Smart Cane, CoRobotic Cane (CRC), Wearable Robotic Object Manipulation Aid (W-ROMA), and Quadrupedal Human-Assistive Robotic Platform (Q-HARP), which aim to enhance mobility for the visually impaired and elderly populations. His recent publications emphasize improving Visual-Inertial Odometry (VIO) accuracy through depth uncertainty modeling and hybrid perspective-n-point methods. He has contributed to benchmark datasets like VCU-RVI and explored SLAM applications in real-time assistive robotics. Awards include Best Conference Paper honors and competitive recognitions in IROS and 3MT competitions. Best Conference Paper Award, IEEE HSI 2019 2nd Place, IROS 2020 UZH-FPV VIO Competition System Engineering Award for Excellent Ph.D. Student, UA Little Rock (2018)