جست‌وجوی استاد و پژوهشگر

• Theoretical computer science
• probabilistic programming
• probability theory
• +۲ مورد دیگر

Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.

Reza Jafariمشاهده پروفایل

دانشیار

• Data mining and data analytics
• Statistical modeling & forecast
• Machine learning and deep learning for prediction & classification
• +۲ مورد دیگر

Reza Jafari is a Collegiate Associate Professor of Computer Science at the Virginia Tech Innovation Campus. He previously served as faculty and program director of the ECE program at East Coast Polytechnic University and as a professional lecturer in the data science program at George Washington University. His research focuses on data mining, statistical modeling, machine learning/deep learning for prediction/classification, recurrent neural networks, and stability analysis of neural-based systems. Education: Ph.D., Computer and Electrical Engineering, Oklahoma State University (2012) M.S., Applied Mathematics, Oklahoma State University (2011) M.S., Mechatronics, American University of Sharjah (2005) B.S., Engineering, University of Tehran (2001) Research Interests: Jafari's work spans data analytics, statistical forecasting, machine learning, and neural networks. He emphasizes practical applications, such as temporal forecasting using deep learning and stability analysis of control systems. His contributions include advancements in recurrent neural networks and their hardware implementations. Publications: His recent work includes comparative studies of deep learning architectures for temporal forecasting (2025), precision robotics control (2024), and stability analysis of neural network-based controllers (2023). These studies highlight a trend toward integrating traditional methods with modern machine learning techniques. Awards: Innovator of the Year Award (2014) Innovator of the Year Award (2016) Professional Roles: Jafari has served as a peer reviewer for IEEE Transactions , IJCNN , and Automatic Control . His research bridges theoretical foundations and applied systems, with a focus on interdisciplinary solutions in data science and engineering.

Monia Del Pintoمشاهده پروفایل

پژوهشگر ارشد

• Urban Planning
• Disaster Risk Reduction
• Heritage Studies
• +۳ مورد دیگر

Dr. Monia Del Pinto is a Vice-Chancellor Independent Research Fellow at Loughborough University's Department of Architecture. Her work intersects architecture, urban planning, heritage studies, and disaster risk reduction. She leads the DAEDALHUSS project, developing global methodologies to assess vulnerability in heritage cities facing climate-induced hazards. Her doctoral research advanced spatial vulnerability assessment using space syntax and GIS modeling for earthquake-prone settlements. Education: MSc in Architectural Engineering PhD in Architecture Research Focus: Innovating spatial methodologies for disaster resilience, particularly in historical urban environments. Key projects include DAEDALHUSS and prior work on earthquake vulnerability. Her interdisciplinary approach integrates urban morphology with disaster policy and climate adaptation. Grants & Activities: Loughborough University Doctoral Prize Fellowship Labs/Teams: Engaged with Architecture research groups focusing on cultural heritage and disaster resilience.

Ronak R. Mohantyمشاهده پروفایل

عضو هیئت علمی

• Human-Computer Interaction
• Extended Reality (XR)
• Haptic Technology
• +۳ مورد دیگر

Dr. Ronak R. Mohanty is a Research Scientist in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison , specializing in Human-Computer Interaction and Applied Perception . His work focuses on human-centric design principles to develop innovative Extended Reality (XR) and haptic technologies for applications in health technology, orthopedic surgery training, and XR learning. He has contributed to cutting-edge research at Meta Reality Labs , particularly in haptics-based wearable devices and human augmentation technologies. Primary Affiliation: University of Wisconsin-Madison Department: Industrial and Systems Engineering Ronak R. Mohanty’s research spans multisensory experience design, with a focus on haptic rehabilitation , augmented reality (AR) for triage training, and spatial audio accessibility solutions. His work bridges academic and industrial domains, emphasizing inclusivity and precision in XR systems. Scientific publications highlight trends in XR technology , haptics , medical simulations , and assistive interfaces . His studies explore motor strategies in virtual spaces, bone-drilling analytics, and speech-driven 3D modeling, reflecting interdisciplinary expertise in human augmentation and user-centered design . Contact: ronak.mohanty@wisc.edu

Olivier Lartillotمشاهده پروفایل

پژوهشگر

• Computational Music Analysis
• Artificial Intelligence
• Music Information Retrieval
• +۶ مورد دیگر

Olivier Lartillot is a Researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. His work focuses on computational music and sound analysis, artificial intelligence, and music information retrieval. He developed the MIRtoolbox and MiningSuite frameworks, tools widely used in music analysis. Previously, he held roles including Academy of Finland Research Fellow (2009–2014) and postdoctoral positions at the Finnish Centre of Excellence in Interdisciplinary Music Research. Education: PhD in Music and Acoustics from IRCAM/UPMC Paris (2000–2004); Master’s in Musicology from Sorbonne University (1996–1999). Research Projects: MIRAGE (AI-based music analysis), SoundTracer (Norwegian folk music transcription), HIGH-M (music and health interaction). His research interests span computational models of music structure, cross-cultural music analysis, and applications in music therapy and digital heritage. He has authored over 80 articles (3,000+ citations) and contributed to initiatives like the Norwegian Folk Music Archive digitization. He is a member of the editorial board for Transactions of the International Society for Music Information Retrieval and an expert evaluator for EU Horizon 2020 programs. His work bridges technical innovation with musicological theory, addressing challenges in automated transcription, segmentation, and expressive performance analysis.

Loukas Hadellisمشاهده پروفایل

استاد

• Industrial Communication
• Microcontrollers
• Industrial IoT
• +۵ مورد دیگر

Loukas Hadellis is a Professor in the Department of Electrical Engineering and Computer Technology at the University of Patras. He holds a PhD from the same department (2002) and a Master of Engineering from Carleton University, Canada (1983). His research focuses on Industrial Communication, Smart Grid technologies, Sound Engineering, and Fiber Optic Communication. He advises on projects related to IoT, Energy Management, and Automation Systems. Research interests include fieldbuses, microcontrollers, industrial IoT, interoperability, distributed control, and smart grid DSM/ADR. He has contributed to advancements in audio electronics, optical communication systems, and context-aware control frameworks. His work integrates theoretical models with practical applications, such as optimizing energy costs in smart homes and developing demand response systems for smart grids. He maintains active collaborations in both academic and industrial sectors.

Albert Qiaochu Jiangمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +۳ مورد دیگر

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.

Annegret Falknerمشاهده پروفایل

استادیار

• Neuroscience
• Behavioral Neuroscience
• Social Behavior
• +۳ مورد دیگر

Annegret Falkner is an Assistant Professor at the Princeton Neuroscience Institute (Princeton University). Her research focuses on the neural mechanisms underlying social behavior, social affective states, and hormone-dependent neuroplasticity in adults. She holds a BA in Biology and Biochemistry from Oberlin College (2002), a PhD in Neuroscience from Columbia University (2012), and completed postdoctoral training at NYU. Her work integrates experimental and computational approaches to study aggression circuits, social learning, and stress responses. Education: B.A. Biology & Biochemistry, Oberlin College (2002) PhD in Neuroscience, Columbia University (2012) Postdoctoral Fellowship, NYU (2012–2016) Research Interests: Dr. Falkner’s lab investigates how neural circuits mediate social interactions, aggression, and hormonal influences on behavior. Key themes include: Neuroendocrine regulation of social motivation Aggression circuits and behavioral persistence Neural substrates of social learning Stress and resilience mechanisms Her recent studies explore dopamine’s role in resilience, oxytocin’s role in maternal behavior, and computational tools for multi-animal tracking. Recent Research Trends: Her articles highlight advancements in aggression circuit dissection, neuroendocrine systems analysis, and behavioral tracking technologies. Work on the lateral habenula and dopaminergic signatures underscores her focus on stress and resilience pathways. Lab & Collaborations: Her lab employs rodent models, electrophysiology, and machine learning (e.g., SLEAP system). Collaborations emphasize interdisciplinary approaches to neurobehavioral problems.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

Jing Zhangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

Sidi Luمشاهده پروفایل

استادیار

• Edge Computing
• Autonomous Vehicles
• Artificial Intelligence
• +۳ مورد دیگر

Sidi Lu is an Assistant Professor in the Department of Computer Science at William & Mary. His research focuses on edge computing, autonomous vehicles, and applied AI, emphasizing reliable and efficient computing systems for transportation and IoT. He joined William & Mary in August 2023 after completing his PhD at Wayne State University, where he specialized in vehicle computing and edge intelligence. Education: PhD in Computer Science, Wayne State University (2023) Exchange Undergrad in Electrical and Computer Engineering, University of California, Riverside B.Eng in Electronic Information Engineering, Xidian University Research Interests: Sidi explores edge computing frameworks for connected vehicles, teleoperation in autonomous systems, and AI-driven solutions for real-world challenges. His work bridges theory and practice, addressing scalability, security, and efficiency in vehicle-edge-cloud ecosystems. Awards & Recognition: Class of 2024 Influencer Recognition (William & Mary) NSF CRII Award (2023) Ralph H. Kummler Award (2022) Dr. Michael E. Conrad Graduate Research Award (2021) Grants & Services: Sidi leads NSF-funded projects and serves on technical committees for top conferences like ACM/IEEE SEC and IEEE MOST. He mentors students in research and organizes events at Grace Hopper Celebrations. Labs & Teams: Director of the Vehicle Computing Lab at William & Mary, focusing on advancing autonomous systems and edge-enabled applications.

Gregory F. Welchمشاهده پروفایل

استاد

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +۴ مورد دیگر

Gregory F. Welch is the Pegasus Professor and AdventHealth Endowed Chair in Simulation at the University of Central Florida (UCF). He holds appointments in the College of Nursing, Department of Computer Science, and the Institute for Simulation & Training. As Co-Director of the Synthetic Reality Laboratory, his research focuses on virtual/augmented reality, human-computer interaction, and healthcare/defense applications. Welch earned a Ph.D. in Computer Science (UNC Chapel Hill, 1997) and a B.S. in Electrical Engineering Technology (Purdue University, 1986). Prior roles include work at NASA's Jet Propulsion Laboratory and Northrop-Grumman. His awards include IEEE Virtual Reality Academy induction (2022), IEEE VR Technical Achievement Award (2018), and ACM SIGGRAPH Pioneer recognition. He maintains an influential Kalman Filter website with over 12,000 citations. Welch serves as an IEEE Technical Expert for VR/AR and Vice Chair for Conferences in the Visualization and Graphics Technical Community. Notable contributions include the physical-virtual patient system for medical training and innovations in augmented reality interfaces. His work bridges computer science with healthcare and defense sectors, emphasizing human-centric design and cross-disciplinary collaboration.

Lichao Sunمشاهده پروفایل

استادیار

• Artificial Intelligence Security
• AI Privacy
• Machine Learning
• +۷ مورد دیگر

Lichao Sun is an Assistant Professor of Computer Science and Engineering at Lehigh University. He holds a Ph.D. in Computer Science from the University of Illinois at Chicago (2020), and bachelor's and master's degrees in Computer Science and Engineering from the University of Nebraska-Lincoln. His research focuses on AI security and privacy, addressing vulnerabilities in AI systems, particularly in large language models and multimodal systems. He also explores data mining, federated learning, and reinforcement learning applications. His educational background includes: Ph.D., Computer Science, University of Illinois at Chicago (2020) M.S., Computer Science, University of Nebraska-Lincoln B.A., Computer Science and Mathematics, University of Nebraska-Lincoln Research interests emphasize mitigating security threats in AI, including adversarial attacks (backdoors, token-level vulnerabilities), robust watermarking, and privacy-preserving techniques in federated learning. His work integrates vision-language models, multimodal reasoning, and ethical AI safety. Recent studies highlight innovations in secure embedding aggregation, generative AI defenses, and strategic reasoning in LLMs. Publications (2025) reflect a focus on AI security, multimodal systems, and reinforcement learning applications, with contributions to conferences like CCS, NeurIPS, and AAAI. His research bridges theoretical advancements with practical cybersecurity solutions for modern AI architectures. No scientific awards explicitly mentioned in the provided text. His advising and grants details are not detailed here, though his prolific publication record suggests active research funding. No lab/teams explicitly referenced in the input data.

Ruth Farrarمشاهده پروفایل

عضو هیئت علمی

• Sound Art
• Binaural Technology
• Immersive Audio
• +۵ مورد دیگر

Dr Ruth Farrar is a Reader in Creative Media and Enterprise at Bath Spa University's Bath School of Art, Film and Media. She serves as Co-Director of the Centre for Cultural and Creative Industries (CCCI), co-investigator on the £46M MyWorld project, and Knowledge Exchange Lead in her school. Her research focuses on immersive audio, binaural technology, and public engagement strategies, with notable projects like the Immersive Audio Network and the award-winning 'Dear Carnegie Hall' app. She founded Shextreme Film Festival and leads the NET Lab, actively supporting academic-industry partnerships. A Fellow of the Higher Education Academy, her work bridges creative practice with academic research. Education: Completed an AHRC-funded PhD by practice at the University of Exeter (2011-2016) on binaural technology applications. Research interests include sound art, spatial audio innovation, and leveraging technology for cultural preservation. Her work has been exhibited internationally, including at the Moving Sounds Festival in New York and Cannes Film Festival. Recent projects emphasize collaborative approaches to immersive experiences with partners like the Roman Baths. Grants: MyWorld (2018-2023), AHRC PhD funding. Awards: Recognized for impactful public engagement and feminist filmmaking contributions. Professional roles include membership in Film Hub South West and leadership in global adventure media networks.

Austin Mooreمشاهده پروفایل

مدرس ارشد

• Dynamic Range Compression
• Music Production Techniques
• Distortion in Audio
• +۲ مورد دیگر

Austin Moore is a Senior Lecturer in music production and sound engineering at the University of Huddersfield, where he leads courses in Sound Engineering and Creative Music Production. His research examines distortion, compression, and non-linear signal processing in music production. His work analyzes sonic signatures of audio hardware, perceptual effects of dynamic range compression, and spatial audio techniques. He investigates how distortion shapes musical aesthetics across genres from rock to electronic music. He co-edited Distortion in Music Production: The Soul of Sonics (Routledge 2023) and co-edits the Journal of Music Production Research. He teaches modules on sound engineering, mixing, synthesis, and electronic music production.