جستجوی استاد راهنمای دانشگاهی

Charlotte Williamsمشاهده پروفایل

استاد

• Sustainable Polymer Chemistry
• Catalyst Development
• Renewable Resources Utilization
• +۴ مورد دیگر

Professor Charlotte Williams is a leading academic in sustainable materials science at the University of Oxford. Her research focuses on developing catalysts to transform renewable resources and CO₂ into polymers, aiming to replace petrochemicals in scalable materials production. She collaborates with Unilever on the Clean Future initiative, pioneering recyclable and biodegradable polymers for consumer products. Her work was recognized with the 2021 Unilever Clean Future Supplier Brilliance Award, jointly awarded with Professors Rosseinsky and Cooper. Williams’ research spans heterodinuclear catalyst design, ring-opening copolymerization, and chemical recycling of polymers. She leads a multidisciplinary team including scientists, engineers, and policy experts to decarbonize chemical supply chains, contributing to the UK’s net-zero goals. Key collaborations include the EPSRC Prosperity Partnership with the Universities of Liverpool and Oxford, focusing on low-carbon laundry detergent materials. Education: Not explicitly detailed in text. Grants: EPSRC Prosperity Partnership (2021–2026), Unilever-funded projects. Labs/Teams: Collaborations with Liverpool University and industry partners like Unilever. Future Work: Expanding applications of CO₂-derived polymers in consumer goods and advancing recyclable materials. Her publications emphasize catalyst innovation, sustainable polymer synthesis, and materials lifecycle analysis, reflecting her commitment to bridging academic research with industrial sustainability challenges.

• Wireless Networks
• Signal Processing
• 5G/6G Communications
• +۳ مورد دیگر

Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.

Eoin Delaneyمشاهده پروفایل

مدرس

• Explainable AI
• Trustworthy AI
• Computer Vision
• +۶ مورد دیگر

Eoin Delaney is a Lecturer in the School of Computer Science and Statistics at Trinity College Dublin (as of Spring 2025). Previously, he was a Postdoctoral Researcher at the Oxford Internet Institute (OII), University of Oxford (October 2023 – March 2025), focusing on Trustworthiness Auditing in AI. His research spans Explainable AI, algorithmic fairness, computer vision, and sustainable AI applications. He holds a PhD from University College Dublin (UCD), advised by Dr. Derek Greene and Prof. Mark T. Keane, where he developed counterfactual explanations for time series and image data. His work bridges technical AI research with societal impact, including collaborations with Accenture Labs and the VistaMilk SFI Research Centre. Key achievements include developing the OxonFair toolkit (accepted to NeurIPS 2024), winning the 2022 AI Ireland Award for Best Application of AI in a Student Project, and authoring papers in top venues like NeurIPS, AIJ, and IJCAI. He is actively involved in academic service, serving on program committees for FAccT 2025 and AAAI workshops. Eoin has supervised students at the University of Oxford and organizes workshops on AI accountability, such as the Auditing Accountability in Trustworthy Artificial Intelligence in personalized medicine. His teaching and outreach efforts include promoting STEM education through initiatives like an educational website for children, emphasizing cryptography and probability. He continues to explore fairness in AI systems, user-centric explanations, and scalable machine learning frameworks (PyTorch, TensorFlow).

Agostino Capponiمشاهده پروفایل

استاد

• Systemic Risk
• Financial Technology
• Blockchain and Crypto Economics
• +۴ مورد دیگر

Agostino Capponi is a Professor of Industrial Engineering and Operations Research at Columbia University, affiliated with Columbia Engineering and the Data Science Institute (DSI). He holds academic fellowships at the Luohan Academy (Alibaba Group) and the Fintech@Cornell Center. His research focuses on systemic risk, financial technology, blockchain economics, and machine learning applications in finance. He has authored a best-selling book on machine learning in financial markets and received prestigious awards including the NSF CAREER Award and the JP Morgan AI Faculty Research Award. Education: Master's and PhD in Computer Science and Applied & Computational Mathematics from Caltech (2006-2009). Professional roles include Editor of Management Science , co-editor of Mathematics and Financial Economics , and leadership positions in the Bachelier Finance Society and INFORMS Finance Section. His research has been funded by NSF, DARPA, J.P. Morgan, Ethereum Foundation, and others. Research interests span blockchain governance, decentralized finance protocols, and systemic risk mitigation in financial networks. Notable contributions include work on liquidity risk, crypto-economic systems, and causal inference in financial modeling. Media coverage includes American Banker, Vox, and Chicago Booth Review. He holds a patent in military network tracking and served as a visiting scholar at the Federal Reserve Bank of New York.

Andrea Santilliمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +۱۰ مورد دیگر

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.

June-Yub Leeمشاهده پروفایل

استاد

• Numerical Analysis
• Inverse Problems
• Computational Fluid Dynamics
• +۳ مورد دیگر

June-Yub Lee is a Professor of Mathematics and Vice President at Ewha Womans University, leading the Office of University Planning and Coordination. He is also a member of the Institute of Mathematical Sciences. His academic background includes a Physics major and Mathematics minor from KAIST, followed by a Ph.D. in Mathematics from New York University's Courant Institute. Prof. Lee specializes in numerical analysis, inverse problems, computational fluid dynamics, and phase-field modeling. His research focuses on developing robust numerical methods for partial differential equations, with applications in materials science and fluid dynamics. Notable contributions include work on operator splitting methods, energy-stable schemes for phase-field models, and inverse problem solutions in electrical impedance tomography. He has organized major international conferences like ICM2014 and ICIP series, and serves on editorial boards of journals such as the Journal of the Korean Society for Industrial and Applied Mathematics (KSIAM). His work bridges theoretical mathematics with computational science, emphasizing interdisciplinary applications. Prof. Lee has actively contributed to academic administration, including serving as Director of the Division of Natural Sciences at the National Research Foundation (NRF). His research lab focuses on scientific computation, with recent projects exploring long-time simulations of phase-field crystal models and high-order numerical methods for multiphase systems.

Oliver Kennedyمشاهده پروفایل

دانشیار

• Databases
• Programming Languages
• Data Science
• +۲ مورد دیگر

Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.

Stephan Leitnerمشاهده پروفایل

دانشیار

• Agent-Based Modeling and Simulation
• Organizational Dynamics
• Complex Systems
• +۵ مورد دیگر

Associate Professor at the University of Klagenfurt , affiliated with the Department of Management Control and Strategic Management under the Faculty of Economics and Law . Research focuses on agent-based modeling applied to organizational dynamics , complex systems , and managerial economics . Holds a doctoral degree in Social Sciences and Economics (2012) and venia docendi in Business Economics (2018) . Core faculty member in the Self-Organizing Systems research cluster Academic editor for PLoS ONE and editorial board member for multiple journals Recipient of the 2021 Advancement Award (Humanities/Social Sciences) from Carinthian government Research integrates computational simulation with organizational theory , examining phenomena like decentralized task allocation , incentive mechanisms , and reproducibility in social sciences . Teaching portfolio includes business analytics , management control , and scientific modeling at undergraduate and graduate levels. Recent publications explore organizational resilience , team coordination dynamics , and financial modeling using agent-based simulation techniques. Active participant in international conferences like Social Simulation Conference and European Conference on Operational Research .

Vagelis Papalexakisمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Vagelis Papalexakis is an Associate Professor and Ross Family Chair in the Computer Science & Engineering Department at the University of California, Riverside. His research focuses on data science, machine learning, and tensor methods, with applications in multi-aspect/multi-modal data analysis. He holds a Ph.D. from Carnegie Mellon University and a Diploma/M.Sc. from the Technical University of Crete. Affiliations: Ross Family Chair, Bourns College of Engineering, UCR Education: Ph.D. in Computer Science, Carnegie Mellon University M.Sc./Diploma in Electronic & Computer Engineering, Technical University of Crete His work emphasizes interpretable insights from complex datasets, including tensor-based defenses against adversarial attacks, graph representation learning, and scalable algorithms for high-dimensional data. Notable awards include the NSF CAREER Award (2021), IEEE DSAA Next Generation Award (2021), and ICDM Tao Li Award (2022). Grants include NSF funding for railway safety (CISE MSI: RPEP CPS), USDOT transportation research, and NVIDIA GPU grants. He leads projects in AI ethics, misinformation detection, and gravitational wave analysis. His lab collaborates with industry (e.g., Cisco, Instacart) and national labs (e.g., Lawrence Livermore).

Simon Puglisiمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Data Compression
• +۳ مورد دیگر

Simon Puglisi is a Professor at the University of Helsinki's Department of Computer Science, within the Faculty of Science. His research focuses on algorithms, bioinformatics, data compression, and string processing, with notable contributions to genomic data analysis and efficient indexing techniques. He holds the Alberto Apostolico Best Paper Award (2021) and leads the WILL # CHAIR # BOSSA project (2025–2029). His work includes scalable k-mer indexing tools like Themisto and advancements in Lempel-Ziv compression and suffix tree algorithms. He frequently collaborates internationally, participates in editorial roles for journals like the ACM Journal of Experimental Algorithmics , and contributes to conferences such as the International Symposium on Combinatorial Pattern Matching. Research Interests: Algorithm design for string processing and bioinformatics Efficient data structures for genomic data Compression techniques (e.g., Lempel-Ziv, Burrows-Wheeler) Dynamic and space-efficient algorithms Grants & Projects: WILL # CHAIR # BOSSA (2025–2029) Ongoing collaborations with institutions like the University of Melbourne and King's College London

Rita Hendersonمشاهده پروفایل

استاد

• Water Quality and Treatment
• Algal Biotechnology
• Environmental Engineering
• +۴ مورد دیگر

Professor Rita Henderson is a Professor in the School of Chemical Engineering at UNSW and Deputy Dean (Societal Impact & Translation) at UNSW Engineering. Her research focuses on water quality and treatment, algal biotechnology, and sustainable engineering solutions. She leads the Algal and Organic Matter (AOM) Lab, collaborating closely with the Australian water industry to address challenges in algal blooms, membrane fouling, and cyanobacteria management. She serves as Editor for Water Research and AWWA Water Science , and chairs UNSW’s Sustainable Development Goal (SDG) Steering Committee. Education: PhD in Water Sciences (Cranfield University, 2008), MSc in Water Pollution Control Technology (2004), and MChem in Environmental Chemistry (University of Edinburgh, 2002). Her work integrates advanced analytical techniques, machine learning, and innovative separation methods to improve water treatment efficiency. Research interests include organic matter characterization, membrane technology optimization, and real-time monitoring of cyanobacterial blooms. Her contributions span algal harvesting via PosiDAF flotation systems, disinfection by-product formation, and sustainable wastewater pond technologies. She actively promotes equity, diversity, and inclusion in engineering education. Grants and collaborations highlight her industry partnerships, particularly in developing scalable solutions for water security. Her lab’s innovations aim to bridge gaps between research and practical applications, ensuring robust water treatment strategies for global challenges.

• Embedded Systems
• System-on-Chip (SoC) Design
• Computer Architecture
• +۵ مورد دیگر

Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.

• Neural Video Communication
• Smart Cameras
• Computational Vision Models
• +۳ مورد دیگر

Zhan Ma is a Professor and PhD Advisor at the School of Electronic Science and Engineering, Nanjing University. He leads research in Neural Video Communication, Smart Cameras, and Computational Vision Models. His work focuses on end-to-end learning for compression, networking, and hardware-software co-design. Dr. Ma holds a PhD from New York University's Tandon School of Engineering (2010), and prior to his current role, he served as Senior Staff Researcher at Huawei (2013-2015) and Senior Researcher at Samsung (2011-2013). Research highlights include pioneering work in point cloud compression (adopted into IEEE standards) and dual-camera systems for high-resolution video acquisition. His algorithms are deployed in WeChat/WeChat Video for rate-quality optimization and in ISO standards for video complexity indicators. Recent work emphasizes machine learning-driven approaches for image/video compression and adaptive streaming frameworks. Honors include the 2023 IEEE CAS Society Outstanding Young Author Award and multiple best paper awards at IEEE WACV, BMSB, and other venues. He leads the Vision Lab at Nanjing University and collaborates with industry partners on practical implementations of his research.

John D. Kubiatowiczمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +۷ مورد دیگر

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.

• Sleep Disorders
• Obstructive Sleep Apnea
• Digital Health
• +۷ مورد دیگر

Dr Bastien Lechat is a Research Fellow at Flinders Health and Medical Research Institute (FHMRI): Sleep Health, within the College of Medicine and Public Health at Flinders University. He is also a Full Member of the College of Science and Engineering and the Medical Device Research Institute. As an NHMRC Emerging Leadership Fellow, he leads innovative research at the intersection of sleep medicine, artificial intelligence, and wearable technology. Education: PhD in Sleep Health, Adelaide Institute for Sleep Health, Flinders University (2018–2021) Bachelor of Engineering in Engineering Science/Acoustics, Université du Maine, France (2014–2017) Dr Lechat’s research focuses on understanding the physiological mechanisms and consequences of obstructive sleep apnea (OSA), particularly night-to-night variability and patient subtypes. He develops AI-driven tools for efficient and accurate diagnosis using wearables and signal processing. His work aims to create a scalable, low-cost model of care for sleep-disordered breathing, addressing global diagnostic gaps. His recent publications reveal a strong trend in digital health innovation, with a focus on machine learning for OSA detection, circadian rhythm modeling, cardiovascular risk prediction, and climate impacts on sleep. His research has been published in top journals including Nature Communications , Journal of Sleep Research , and Sleep Medicine , demonstrating interdisciplinary reach. Scientific Awards and Recognition: NHMRC Emerging Leadership Fellow (2023) Helen Bearpark Memorial Scholarship (2022) Emerging Research Leader Award, Flinders University (2021) Multiple early-career awards from Sleep Down Under, Australasian Sleep Association, and Adelaide Sleep Retreat Ranked in the top 5% of international authors in sleep apnea by Expertscape Dr Lechat has secured over $2.5 million in competitive research funding and actively supervises and mentors junior researchers. He serves on the program committee of the American Thoracic Society meetings and contributes to clinical guidelines. He collaborates globally with industry and academic partners to translate research into clinical practice. Laboratories and Research Teams: He co-leads the 'Novel use of digital innovations & technology development' theme at FHMRI: Sleep Health, working closely with Professor Danny Eckert. His team integrates expertise in biomedical engineering, data science, and clinical sleep physiology to advance digital sleep medicine.