جست‌وجوی استاد و پژوهشگر

Reyan Ahmedمشاهده پروفایل

استادیار

• Graph Theory
• Algorithms
• Network Visualization
• +۳ مورد دیگر

Reyan Ahmed serves as an Assistant Professor of Practice at The University of Arizona, holding a Ph.D. from the institution with office GS 831. His academic profile bridges theoretical computer science and practical visualization systems. Ph.D., The University of Arizona Dr. Ahmed's research centers on graph-theoretic foundations with emphasis on sparsification techniques, spanner constructions, and geometric optimization. His work advances computational geometry through polygon-based facility assignments and stress-invariant metrics, while pioneering neural network integrations for Steiner tree approximations and vertex-splitting algorithms. Key contributions include multi-level graph sketching frameworks and scalable layout methodologies addressing NP-hard combinatorial problems. Analysis of his 2021-2025 publications reveals three dominant threads: (1) Algorithmic innovations in graph sparsification using machine learning-assisted search, (2) Theoretical advancements in additive spanners with lightness guarantees, and (3) Practical visualization systems for evolving networks. His methodology consistently merges discrete mathematics with gradient-based optimization for real-world network applications. Scientific recognition: No awards documented in source materials Research leadership includes development of Monte Carlo tree search frameworks for graph optimization and semantic zooming architectures for massive network visualization. Current projects focus on scale-invariant stress metrics and multi-priority sparsification, with ongoing exploration of neural network accelerators for classical combinatorial problems. No student advising or grant details were provided. Lab infrastructure supports large-scale graph processing through custom visualization pipelines, though specific team compositions remain undocumented.

• Reinforcement Learning
• Computational Decision-Making
• Machine Learning
• +۳ مورد دیگر

Carlo D'Eramo is a Professor in Reinforcement Learning and Computational Decision-Making at the Center for Artificial Intelligence and Data Science (CAIDAS) of Julius-Maximilians-Universität Würzburg (JMU). He leads the LiteRL research group and the hessian.AI independent research group, focusing on lightweight methods for adaptive autonomous agents in complex real-world environments. B.Sc., M.Sc. in Computer Engineering - Politecnico di Milano (2011, 2015) Double M.Sc. in Computer Science - University of Illinois at Chicago (2015) Ph.D. in Information Technology - Politecnico di Milano (2019) His research spans multiple RL domains including multi-task learning , curriculum learning , adversarial RL , options learning , and multi-agent coordination . Recent work explores physics-informed ML, neural network distillation, and uncertainty-driven exploration. 2025 publications highlight advancements in: Bellman update optimization via adaptive distillation uncertainty propagation in tree search multi-agent policy gradients real-world educational applications The group's work shows increasing specialization in physics-integrated RL and efficient neural architectures. Spotlight Presentation - ICML (2025) Spotlight Presentation - ICLR (2024) Oral Presentation - NeurIPS (2020) As Senior Area Chair for RLC and Area Chair for major AI conferences (AAAI, NeurIPS, ICLR), he contributes to academic governance. His team includes researchers like Ahmed Hendawy, Théo Vincent, and Georgia Chalvatzaki, with collaborations spanning TU Darmstadt and hessian.AI.

• Artificial Intelligence
• Blockchain
• Wearable Devices
• +۴ مورد دیگر

Dr. Yining Hu is a Lecturer at the School of Computer Science , University of Technology Sydney since January 2024. Her research and teaching focus on Artificial Intelligence , Blockchain , Cloud Computing , and Wearable Technology . She holds a PhD from the University of New South Wales and has collaborated with industry partners on blockchain applications for IoT security and sustainability. PhD: University of New South Wales Current role: Lecturer at UTS School of Computer Science Key research areas: Wearables, Blockchain, Cloud Computing Dr. Hu's publications demonstrate her technical expertise in blockchain-IoT integration, with notable works on secure transaction frameworks and carbon offset management. Her recent 2025 paper in Electronics explores blockchain-DNN synergy for IoT security. Earlier works cover wearables' security protocols and adaptive fitness tracking frameworks. She contributes to academic service as a member of the Editorial Board for the International Journal on Data Science and Technology (2024-2027). Her teaching portfolio includes advanced cloud computing and software development courses, with availability for Masters/PhD supervision . Current funded research includes blockchain for food traceability and a 2025-2028 LLM-driven text-to-SQL grant with Hampton Capital.

• Trustworthy computing
• Energy harvesting
• Intelligent system design
• +۷ مورد دیگر

Youhua Shi is a full Professor in the Faculty of Science and Engineering at Waseda University, Japan. He obtained his Doctor of Engineering from Waseda in 2005 and is an active member of IEICE, IPSJ, IEEE, and two Japanese academic societies. His research portfolio integrates trustworthy computing, hardware security of AI accelerators, energy-harvesting interface circuits for triboelectric nanogenerators, and low-power VLSI design-for-test methodologies. Education: Doctor of Engineering, Waseda University (2005) Graduate studies, Waseda University, Division of Engineering (completed 2005) Research Interests: Prof. Shi pursues trustworthy and secure silicon systems, spanning hardware Trojans in automated AI-accelerator flows, radiation-hardened latch design for soft-error resilience, and power-efficient CNN accelerators exploiting zero-gating and data-reuse techniques. Parallel work targets energy-autonomous IoT through advanced interface circuits for triboelectric nanogenerators, achieving record energy-per-cycle beyond the classical CMEO limit. Publication Trends: Recent articles (2024-2025) emphasize two thrusts: (i) security of AI/FPGA accelerators—proposing stealthy hardware-Trojan frameworks embedded within design-space-exploration flows that can misclassify up to 97% of inputs—and (ii) power electronics for triboelectric harvesters—introducing dual-output rectifiers and Bennet-doubler biasing that multiply output power >150× over conventional full-wave rectifiers, enabling battery-free IoT nodes. Scientific Awards: APCCAS Best Student Paper Award – 2020 IEEK Best Paper Award – 2012 Students & Collaboration: He has mentored numerous doctoral and master’s scholars, including Yirui Su, Chao Guo, Jinghao Ye, Lin Ye, Saki Tajima, and Masaru Oya, many of whom serve as first authors on his high-impact publications, indicating an active and productive advising role. Labs & Teams: While the text does not name a specific laboratory, his continued affiliation with Waseda University’s Faculty of Science and Engineering and his extensive project output imply he leads a research group focused on secure & energy-efficient VLSI systems, collaborating closely with colleagues such as Prof. Masao Yanagisawa and Prof. Nozomu Togawa.

• Medical Informatics
• Statistical Science
• Bioinformatics
• +۵ مورد دیگر

Dr. Masato Inoue is a Professor at the Faculty of Science and Engineering , School of Advanced Science and Engineering at Waseda University. He holds a Doctor of Medical Science from Kyoto University. Education: 2003 - Kyoto University Graduate School of Medicine 2003 - Kyoto University His research spans multiple disciplines at the intersection of Medical Informatics , Bioinformatics , and Statistical Mechanics . Key areas include: Medical Imaging : Developing Bayesian super-resolution algorithms and Prior Ensemble Learning for improved MRI reconstruction Voice Analysis : Creating innovative voice quality quantification systems for clinical diagnostics Genetic Analysis : Advancing haplotype inference methods and gene network modeling Signal Processing : Applying statistical mechanics to diverse problems from coding theory to neuroscience His recent publications (2021-2012) demonstrate consistent contributions to medical imaging algorithms , voice disorder classification , and genetic data analysis . Notable collaborations include work with Kyoto University researchers , Swedish medical institutions , and cross-disciplinary teams in bioengineering.

Sourav Duttaمشاهده پروفایل

پژوهشگر ارشد

• Model Order Reduction
• Computational Fluid Dynamics
• Scientific Machine Learning
• +۴ مورد دیگر

Sourav Dutta is a Research Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin, where he works with Clint Dawson in the Computational Hydraulics Group. Prior to joining UT Austin in October 2022, he was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center (ERDC) from September 2017 to August 2022. Dr. Dutta's research lies at the intersection of physics-based computational methods and data-driven machine learning techniques for environmental flow problems. His work focuses on developing efficient numerical approximations by combining physical principles with modern machine learning algorithms. His primary research areas include: Model Order Reduction Computational Fluid Dynamics Scientific Machine Learning Uncertainty Quantification Applied Mathematics His publication record reveals a strong trajectory in reduced order modeling for environmental hydrodynamics, particularly for advection-dominated problems that challenge traditional numerical methods. His research has evolved from foundational work on hybrid numerical methods for porous media flows to cutting-edge applications of deep learning and neural ODEs in computational hydraulics. Dr. Dutta has presented his research at major venues including SIAM conferences, Computational Methods in Water Resources meetings, and the HydroML Symposium. In September 2022, he co-organized a minisymposium on Machine Learning and Data-Driven Methods for Forward and Inverse Problems at the SIAM Mathematics of Data Science meeting. His educational background includes a PhD in Mathematics from Texas A&M University (2017), supervised by Dr. Prabir Daripa, and an Integrated BSc & MSc in Mathematics & Computing from the Indian Institute of Technology, Kharagpur (2010). At the Oden Institute, Dr. Dutta is currently working on compound flood simulation capabilities within Adaptive Hydraulics (AdH), reduced order modeling for coastal engineering applications, and physics-based operator learning frameworks for environmental flows. He has also developed pyNIROM, an open-source Python package for non-intrusive reduced order modeling of time-dependent problems.

Trung Leمشاهده پروفایل

مدرس

• Deep Generative Models
• Optimization for Machine Learning
• Kernel Methods
• +۶ مورد دیگر

Trung Le is a Lecturer in the Department of Data Science & AI at Monash University. His research focuses on deep generative models, kernel methods, optimization in machine learning, and Bayesian inference, with applications to supervised learning, adversarial learning, and cyber security. He holds a PhD in Computer Science from the University of Canberra (2013). Key projects include the Australian Research Council-funded initiatives such as 'Can Machines Unlearn? Toward Next-Generation Safe Artificial Intelligence' (2025–2029) and 'Trustworthy Generative AI: Towards Safe and Aligned Foundation Models' (2024–2026). His work contributes to UN SDG 4 (Quality Education) through advancements in AI ethics and safety. Education: PhD in Computer Science, University of Canberra (2013) Research Areas: Continual Learning, Diffusion Models, Domain Adaptation, Adversarial Machine Learning Awards: KDD 2023 Best Student Paper Award, Imagine Cup 2010 Australia First Prize Trung has published extensively in top-tier venues like NIPS, ICLR, and JMLR, with over 100 outputs since 2009. His recent work emphasizes robustness in AI systems, including projects on adversarial defense and ethical concept erasure in generative models.

Helmut Bölcskeiمشاهده پروفایل

استاد

• Applied Mathematics
• Machine Learning Theory
• Mathematical Signal Processing
• +۳ مورد دیگر

Prof. Dr. Helmut Bölcskei is a Full Professor of Mathematical Information Science at ETH Zurich's Department of Information Technology and Electrical Engineering. He holds a joint affiliation with the Department of Mathematics. His academic journey includes a Dipl.-Ing. and Dr. techn. from Vienna University of Technology, followed by postdoctoral research at Stanford University and industry roles at Iospan Wireless and Celestrius AG. He has been at ETH Zurich since 2002, contributing to applied mathematics, machine learning theory, signal processing, and statistics. Education : 1994: Dipl.-Ing., Vienna University of Technology 1997: Dr. techn., Vienna University of Technology Industry Experience : Co-founder of Iospan Wireless (acquired by Intel) and Celestrius AG His research focuses on applied mathematics , machine learning theory , and data science , with emphasis on neural network approximation, metric entropy, and signal processing. Recent work explores theoretical limits of deep learning and nonlinear system identification. His publications highlight advancements in quantization, compression, and system complexity analysis. Prof. Bölcskei has received numerous accolades, including IEEE Fellow status, the 2010 Vodafone Innovations Award, and the ETH 'Golden Owl' Teaching Award. He served as Editor-in-Chief of the IEEE Transactions on Information Theory (2010–2013) and has held editorial roles in multiple journals. His leadership includes roles on the Board of Governors of the IEEE Information Theory Society and as a delegate for faculty appointments at ETH Zurich. Labs/Teams : Mathematical Information Science Group at ETH Zurich's Department of Information Technology and Electrical Engineering

Michael Carbinمشاهده پروفایل

دانشیار

• Programming Systems
• Probabilistic Programming
• Quantum Computing
• +۳ مورد دیگر

Michael Carbin is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research centers on developing programming systems that handle uncertainty through probabilistic programming, quantum computing, and neural networks. Carbin's work spans programming languages, systems, and machine learning, with themes including uncertainty management, efficiency optimization, and formal verification. His publications demonstrate a strong focus on probabilistic inference methods, neural network optimization, and quantum programming frameworks. Awards and Honors: Sloan Research Fellowship (2020) Multiple Best Paper Awards (OOPSLA 2013, 2014; ICLR 2019) NSF CAREER Award (2018) Google Faculty Research Award (2018) As the head of the Programming Systems Group, he advises 10+ graduate students and postdocs, focusing on cutting-edge systems research. He has secured grants including Facebook Research Awards and NSF funding.

George Drettakisمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Rendering
• Image-Based Rendering
• +۱۲ مورد دیگر

George Drettakis is a Senior Researcher at INRIA Sophia-Antipolis and leads the GRAPHDECO research group. He has held professorial roles at institutions including MIT, University of Reims, University of Toronto, and École Normale Supérieure. His research focuses on rendering for computer graphics and sound, with emphasis on image-based rendering, perceptual rendering, and audio-visual cross-modal effects. He has also explored interactive illumination, shadows, relighting, and generative models. Current Students: G. Kopanas (Neural Rendering), N. Violante (Generative Models), A. Petitjean (co-supervised), Y. Poirier-Ginter (co-supervised), P. Panantonakis (starting fall 2023). Postdoctoral Researchers: A. Gauthier at INRIA. His recent work includes 3D Gaussian Splatting , Diffusion-based Relighting , and Neural Radiance Fields . He has received the Eurographics Outstanding Technical Contributions Award (2007) and was named an Eurographics Fellow . He manages projects like ERC Advanced Grant FUNGRAPH and has participated in H2020 EMOTIVE , ANR SEMAPOLIS , and CROSSMOD . His group collaborates internationally and has hosted researchers from institutions such as UC Berkeley, Imperial College London, and TU Wien.

Elena Simona Lohanمشاهده پروفایل

استاد

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +۷ مورد دیگر

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

V. Menkovskiمشاهده پروفایل

دانشیار

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

Yang Suiمشاهده پروفایل

پژوهشگر

• Efficient AI
• Machine Learning Systems
• Model Compression
• +۵ مورد دیگر

Yang Sui is a Postdoctoral Research Associate in the Department of Computer Science at Rice University, collaborating with Professors Xia (Ben) Hu and Hanjie Chen. His research focuses on Efficient AI and Trustworthy AI, including deep neural networks, large language models (LLMs), diffusion models, and algorithm-hardware co-design. He holds a PhD from Rutgers University (2024), an MS from Jilin University (2019), and a BS from Jilin University (2016). Education: PhD, Computer Science, Rutgers University, 2024 MS, Computer Science, Jilin University, 2019 BS, Computer Science, Jilin University, 2016 Research Interests: Efficient AI: Model Compression (pruning, quantization, low-rank decomposition), Generative AI (diffusion models, LLMs), and algorithm-hardware co-design. Trustworthy AI: Adversarial robustness (backdoor attacks, vulnerability detection). He has interned at Snap Research (2024), Tencent America (2022), and Baidu (2018), contributing to projects like BitsFusion quantization and Paddle-Lite framework. Awards: Paul Panayotatos Scholarship (2024) Best Paper Runner-Up Award (DCAA Workshop at AAAI 2023) First Place in ESWEEK Classification Track (2023) SGS Travel Award (2023) Advising & Grants: Advises students on topics like LLM quantization and multimodal models. Collaborates with industry and academia on grants related to efficient AI and hardware co-design. Led projects like Rice’s “Efficient Deep Learning Reading Group” (2023). Labs & Teams: Contributes to Snap’s Creative Vision team, Rutgers’ research groups, and co-design initiatives with industry partners like Baidu and Tencent.

Marc Mezardمشاهده پروفایل

استاد

• Statistical Physics
• Machine Learning
• Information Theory
• +۵ مورد دیگر

Marc Mezard is a Professor of Theoretical Physics at Bocconi University, where he leads the newly established Department of Computational Sciences. Previously, he served as Research Director at CNRS in Paris and held roles at Université Paris Sud. He earned his PhD in Physics from École Normale Supérieure in Paris in 1984. His research focuses on statistical physics of disordered systems, with applications to machine learning, information theory, computer science, and biophysics. His work bridges theoretical physics and interdisciplinary fields, including neural networks and deep learning, where he explores the impact of data structure on learning strategies. He teaches undergraduate courses in statistical and quantum physics and a doctoral course on complex systems. Key research themes include emergent phenomena in complex systems, with contributions to spin glass theory, compressed sensing, and algorithmic solutions for random satisfiability problems. His publications span foundational topics in statistical mechanics and modern applications in data science. Marc Mezard has collaborated extensively with institutions and researchers globally, contributing to the theoretical foundations of computational and physical sciences. His academic leadership includes directing École Normale Supérieure from 2012 to 2022, fostering interdisciplinary research initiatives.

Mandy Plumbمشاهده پروفایل

دانشیار

• Motor Control
• Neurodevelopmental Disorders
• Pediatrics
• +۳ مورد دیگر

Mandy Plumb is an Associate Professor in Clinical Exercise Physiology at the School of Science and Technology, University of New England (UNE), Armidale Campus. Her work integrates neuroscience, motor control, and sports science to address critical gaps in pediatric neurodevelopmental disorders and female athlete development, with strong community engagement through The Female Co initiative. Education: Bachelor of Clinical Exercise Physiology (2023), University of New England PhD in Orthopaedic Surgery (2005), University of Aberdeen, Scotland, UK BSc (Hons) in Sports Science (2000), University of Wales, Bangor, UK Research Interests: Dr. Plumb's primary focus spans two interconnected domains: (1) Pediatric neurodevelopmental disorders, where she investigates motor proficiency, sleep patterns, and mental wellbeing in children with ADHD, Autism, and Developmental Coordination Disorder using fNIRS brain imaging and biomechanical analysis; and (2) Female athlete development, particularly for Aboriginal and Torres Strait Islander women and girls from remote Australia, through The Female Co's holistic programs combining athletic testing, strength conditioning, and recovery monitoring. Her work bridges clinical exercise physiology with community-based interventions to address health disparities. Publication Trends: Analysis of her 15 most recent publications (2019-2024) reveals three dominant research thrusts: motor control in neurodevelopmental disorders (35% of articles), sports injury epidemiology in cricket (27%), and female athlete performance optimization (20%). Methodologically, she increasingly employs neuroimaging (fNIRS) alongside biomechanical analysis, with growing emphasis on community-level interventions and health equity for underserved populations. Scientific Awards: Senior Fellow, Higher Education Academy Advising and Grants: Dr. Plumb has successfully supervised 3 PhD, 50 Master's, and 46 Honours students to completion, with current mentorship of 1 MPhil and 1 Master's candidate. Her research is supported through strategic partnerships with Exercise and Sports Science Australia (serving on Course Accreditation Committee since 2021), Sports Medicine Australia, and The Female Co, focusing on grants related to neurodevelopmental interventions and athlete development programs for remote communities. Labs and Teams: As Board Member and Athletic Performance Lead for The Female Co, she directs comprehensive programs including baseline athlete testing, sleep monitoring, strength and conditioning, and recovery protocols for emerging female athletes from discrete remote regions. Her campus-based work utilizes UNE's motor control laboratories equipped for fNIRS, motion capture, and biomechanical analysis, with strong ties to the Applied Agricultural Remote Sensing Centre for community health mapping.