جست‌وجوی استاد و پژوهشگر

Yuxin Chenمشاهده پروفایل

استاد

• Machine Learning Theory
• Diffusion Models
• Large Language Models (LLM)
• +۷ مورد دیگر

Yuxin Chen is a Professor of Statistics and Data Science and Electrical and Systems Engineering at the University of Pennsylvania (UPenn), where he has been since 2022. Prior to UPenn, he served as an Assistant Professor at Princeton University (2017–2021) and a postdoc at Stanford University (2015–2017). His research spans machine learning theory , diffusion models , reinforcement learning , nonconvex optimization , and high-dimensional statistics . Education : Ph.D. in Electrical Engineering (Stanford, 2015) Awards : SIAM Activity Group on Imaging Science Best Paper Prize (2024), IEEE Transactions on Power Electronics Prize Paper Award (2024), ICCM Best Paper Award (Gold Medal, 2017), Alfred P. Sloan Fellowship His recent work focuses on theoretical foundations of diffusion models , including convergence analysis, generalization behavior, and sampling efficiency. He has developed probability flow distance (PFD) to quantify distributional generalization and explored low-dimensional adaptation in diffusion processes. His research also addresses implicit regularization in nonconvex statistical estimation, with applications to phase retrieval and matrix completion. Key scientific applications of his work include controllable image editing, inverse problems in scientific imaging, and robustness in generative models. Collaborative projects with Prof. Qing Qu and Liyue Shen at ICML 2025 highlight his leadership in bridging theory and practice of generative AI. He has mentored several Ph.D. students who now hold academic positions, including Zihan Zhang (HKUST), Yuchen Zhou (UIUC), Hong Hu (WUSTL), Joshua Agterberg (UIUC), and Yuling Yan (UW-Madison). His grants include Google Research Scholar and Amazon Research Awards, supporting advancements in theoretical foundations of AI.

Ashley Headمشاهده پروفایل

پژوهشگر

• metal oxides
• heterogeneous catalysis
• surface science
• +۷ مورد دیگر

Ashley Head is a Materials Scientist in the Interface Sciences/Catalysis Group at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy Office of Science User Facility at Brookhaven National Laboratory. She joined CFN in January 2018 after completing postdoctoral research appointments at Lawrence Berkeley National Laboratory and Lund University in Sweden. PhD in Physical Chemistry, University of Arizona (2011) Bachelor's of Science in Chemistry, James Madison University (2005) Dr. Head specializes in surface science and catalysis with expertise in metal oxides, heterogeneous catalysis, and advanced spectroscopic techniques. Her research focuses on understanding atomic-scale processes at interfaces under realistic conditions. She pioneered the integration of ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) with infrared reflection absorption spectroscopy (IRRAS), enabling comprehensive surface analysis under atmospheric pressures. Her work spans fundamental surface science to applied research on gas mask filtration and CO 2 hydrogenation. Analysis of Dr. Head's publications reveals consistent focus on catalytic processes involving metal oxides, particularly copper and molybdenum oxides. A key trend is her development of multimodal characterization approaches that combine spectroscopic techniques to provide complementary insights into complex surface reactions under operating conditions. Her recent work extends to metal-organic frameworks and dimensional engineering of catalysts. James Madison University College of Science and Mathematics Alumna of the Year (2018) Dr. Head actively mentors students and postdoctoral researchers through CFN's user programs, collaborating with academic institutions including Stony Brook University and University of California, Davis. Her research is supported by the Department of Energy Office of Science and has led to collaborations with the U.S. Army Edgewood Chemical Biological Center. She serves on the Users' Executive Committee for Berkeley's Advanced Light Source, advocating for user facility improvements. Based in Building 735 at Brookhaven National Laboratory, Dr. Head leads research using state-of-the-art surface science instrumentation including lab-based AP-XPS systems and infrared spectrometers. She collaborates closely with CFN's Electron Microscopy and Theory & Computation groups, and with researchers at NSLS-II to conduct synchrotron-based experiments. Her laboratory is developing next-generation capabilities for studying chemistry inside porous materials and at liquid-solid interfaces.

Miriam Botrosمشاهده پروفایل

پژوهشگر

• Materials Science
• Electrochemistry
• Solid-State Batteries
• +۳ مورد دیگر

Dr. Miriam Botros serves as Group Leader for Energy Materials and Process Design within the Materials for Electrochemistry Research Unit (RU Janek) at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Germany. Her laboratory operates across Buildings 640/0-354 and 717/204 at the Eggenstein-Leopoldshafen campus, with office hours conducted remotely on Tuesdays and Fridays. Her research centers on high-entropy materials and solid-state electrochemistry , specializing in compositionally complex systems for energy storage. Key focus areas include garnet-type solid electrolytes , high-entropy oxides/fluorides , and advanced cathode architectures , with particular emphasis on doping effects, grain boundary engineering, and structural transformations in battery components. Recent work demonstrates expertise in photonic synthesis techniques and mechanochemical processing for novel material discovery. Analysis of her 15 most recent publications reveals a dominant research trajectory in solid-state battery materials (73% of works), with significant contributions to high-entropy systems (60%) and electrocatalysis (27%). Her collaborative network spans 12+ international institutions, consistently publishing in high-impact journals including Nature Reviews Materials , Science , and Energy & Environmental Science . Dr. Botros maintains active laboratory operations within KIT's Institute of Nanotechnology infrastructure, directing research on energy materials synthesis and characterization. Her team employs advanced techniques including nebulized spray pyrolysis, field-assisted sintering, and in-situ electrochemical analysis to develop next-generation battery components.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Polyhedral Compilation
• Performance Programming
• +۷ مورد دیگر

Tobias Grosser is an Associate Professor in Compiler Design at the Department of Computer Science and Technology, University of Cambridge. His research spans compilation, programming language design, and performance programming with applications to climate science, quantum computing, and hardware design. He leads an active research group focusing on making compilers more modular, predictable, and trustworthy while bridging the gap between developers and compilers. His research interests include: Compilers and polyhedral compilation Static and dynamic program analysis High-performance computing and loop optimization Domain-specific compilation for accelerators (GPU, FPGA) Formal verification for compiler correctness Machine learning applications to compiler design His recent publications demonstrate significant contributions to multi-level intermediate representations (MLIR), compiler verification using Lean, and performance modeling. His work on xDSL provides a Python-native compiler framework that enables rapid prototyping of compiler infrastructure. His research group has developed tools like FPL (Fast Presburger Library) for loop optimization in deep learning and scientific computing. Scientific awards include two Amazon Research Awards (2023-2024) for work in automated reasoning, a Google PhD Fellowship, and an Ambizione Fellowship at ETH Zurich. He has advised numerous PhD students and researchers who have gone on to positions at Google, ETH Zurich, NVIDIA, and Intel. He actively collaborates with industry through the Amazon Scholars program and hosts regular Compiler Social events in Cambridge. His research vision emphasizes connecting compiler technology with societal challenges like climate change while maintaining a strong commitment to open-source development and diversity in computer science.

• Computer Vision
• Computer Audition
• Machine Learning
• +۷ مورد دیگر

Dr. Yapeng Tian is an Assistant Professor in the Computer Science Department within the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas. He leads the Computer Vision and Multimodal Computing (CVMC) Lab, where his research focuses on solving core problems in computer vision, computer audition, and machine learning with applications to multisensory perception, computational photography, AR/VR, accessibility, and healthcare. Dr. Tian received his PhD in Computer Science from the University of Rochester in 2022, Master's in Electronic Engineering from Tsinghua University in 2017, and Bachelor's in Electronic Engineering from Xidian University in 2013. Prior to joining UT Dallas, he completed research internships at Facebook and Adobe Research, and served as a Research Assistant at both University of Rochester and Tsinghua University. His research interests span audio-visual scene understanding, audio-visual scene generation, AI for accessibility and healthcare, and image/video processing. He has made significant contributions to multimodal learning, particularly in audio-visual integration, where his work has been recognized with numerous awards including the Amazon Research Award, Cisco Faculty Research Award, and AAAI New Faculty Highlights. Analysis of his recent publications reveals a strong focus on diffusion models for multimodal generation, audio-visual learning, accessibility applications, and efficient multimodal processing. His work increasingly integrates healthcare applications, particularly in autism assessment and assistive technologies for people with disabilities. Key awards and recognitions: Amazon Research Award [2024] UIST Belonging & Inclusion Best Paper Award [2024] ACCV Best Paper Honorable Mention Award [2024] IEEE ISMAR IDEATExR workshop Best Paper Award [2024] Cisco Faculty Research Award [2023] AAAI New Faculty Highlights [2023] Dr. Tian actively mentors students at multiple levels, including PhD candidates, undergraduate researchers, and even K-12 students through outreach programs. His lab has secured significant grant funding including an NIH R01 grant and an Amazon Research Award. Current research directions include developing AI/AR-assisted vision for people with low vision and creating tools for kitchen accessibility through wearable AR.

Ramesh Harjaniمشاهده پروفایل

استاد

• Analog Circuit Design
• RF Circuit Design
• Wireless Communications
• +۷ مورد دیگر

Ramesh Harjani is the Edgar F. Johnson Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. He maintains an active research program with numerous ongoing projects and publications, and is currently accepting PhD students. His office is located in Kenneth H. Keller Hall on the University of Minnesota campus in Minneapolis. Dr. Harjani received his educational training at prestigious institutions globally: B.Tech in Electrical and Electronics Engineering from Birla Institute of Technology, Pilani, India (1982) MTech in Electrical Engineering from Indian Institute of Technology, New Delhi (1984) PhD in Electrical and Computer Engineering from Carnegie Mellon University (1989) Professor Harjani's research focuses on analog and RF circuit design for communication systems with particular emphasis on CMOS technologies. His work spans both wireless and wired communication domains, with current efforts directed toward high-performance front-end components for broadband and multi-standard wireless systems, as well as next-generation high-speed wired communication systems. His research group also investigates specialized areas including data converters, sensor interface circuits, analog circuit synthesis, and micro-power analog design. Recent publication trends show continued innovation in analog circuit design methodologies, with emphasis on improving performance metrics while addressing modern semiconductor challenges like FinFET self-heating effects. His work bridges theoretical circuit design with practical implementation challenges in advanced technology nodes, particularly 65nm CMOS processes. The research demonstrates strong industry relevance with applications in wireless communications, signal processing, and high-speed data conversion. Professor Harjani leads multiple significant research projects: Scalable Ultra-Low Variation Analog-Time Neural Network (S-UATN) Accelerator (2025-2025) Passive Tile Architecture for MAX (2023-2024) ALIGN Analog Layout Design Suite (2023-2024) Automated Layout Of Analog Arrays In Advanced Technology Nodes (2022-2024) ALIGN: Analog Layout, Intelligently Generated from Netlists (2018-2023) He maintains the Harjani Research Group, which collaborates extensively with industry partners including BAE SYSTEMS, NORTHWESTERN UNIVERSITY, SPAWAR, and THE NATIONAL SCIENCE FOUNDATION, demonstrating strong connections between academic research and practical engineering applications.

• Reinforcement Learning
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

C. H. T. Child is an Associate Professor in the Department of Computer Science at City, University of London, with a distinguished research career spanning over two decades. The academic maintains an active research profile with publications extending through 2025, demonstrating continued scholarly contributions across multiple domains of computer science and artificial intelligence. Child's primary research interests center around Reinforcement Learning , Artificial Intelligence , and Computer Vision , with specialized expertise in non-Euclidean geometry applications for gaming environments, medical informatics, and human-computer interaction. The research portfolio reveals a consistent trajectory from foundational work in reinforcement learning algorithms to applied research in game AI, medical informatics, and scene representation learning. A notable research thread involves the application of non-Euclidean geometry to create novel gaming experiences, while another significant strand focuses on developing advanced AI systems for non-player characters with personality modeling capabilities. Analysis of the most recent publications (2019-2025) shows an evolution toward more sophisticated deep learning architectures applied to complex problems in medical informatics and scene representation. The work has increasingly incorporated transformer networks, attention mechanisms, and advanced feature extraction techniques, reflecting broader trends in the field while maintaining the researcher's distinctive focus on reinforcement learning fundamentals. The publication record demonstrates successful translation of theoretical AI concepts into practical applications across gaming, healthcare, and computer vision domains. Child has supervised multiple doctoral students and research collaborators, with several co-authors appearing consistently across publications as first authors on significant works. The research has been supported through university resources and likely external funding, though specific grant details are not provided in the available materials. The academic maintains an active presence in both theoretical and applied research communities, with publications spanning top conferences and journals in computer science and AI. The researcher leads work in the Extreme AI Personality Engine project and contributes to computer vision research with applications in hand pose estimation and medical imaging. These research streams are well-integrated, with reinforcement learning principles providing a unifying theoretical framework across diverse application domains.

Mauro Jaskelioffمشاهده پروفایل

استاد مدعو

• Functional Programming
• Algebraic Effects
• Monad Transformers
• +۴ مورد دیگر

Mauro Jaskelioff is an Adjunct Professor at the College of Exact Sciences, Engineering and Surveying of the National University of Rosario (Argentina), where he heads the Computer Science Department . He is also an adjunct researcher at CIFASIS-CONICET. His research focuses on functional programming, monad transformers, algebraic effects, and program calculation. Education : PhD in Computer Science (University of Nottingham, 2009), Licenciateship in Computer Science (National University of Rosario, 2004), and prior studies in Electronic Engineering. Research Highlights : Developed the Monatron extensible monad transformer library, formalized modular operational semantics, and advanced algebraic effect handling. His work bridges category theory and practical Haskell implementations. Teaching : Courses in Data Structures & Algorithms, Programming with Categories, and Programming Languages. Students : Supervised PhD/MS students including Martín Ceresa, Guido Martínez, Exequiel Rivas, and Pablo Buiras. Publications : Key contributions in monad transformers, applicative traversals, and effect systems. Collaborations with Tom Schrijvers, Nicolas Wu, and Graham Hutton.

• Enterprise Integration
• Enterprise Architecture
• Systems Engineering
• +۷ مورد دیگر

Dr. Peter Bernus is an Associate Professor at the School of Information and Communication Technology , Griffith University . With a background in engineering design and computer-integrated manufacturing, he has contributed to enterprise integration frameworks like GERAM (ISO 15704) and led international standards development for enterprise architecture. His work spans academia, industry, and defense, focusing on complexity management, situational awareness, and sustainable decision-making. Education : PhD in Engineering Sciences (Hungarian Academy of Sciences, 1986) Bachelor of Electronic Technology (Budapest University of Technology, 1974) Research Interests : Enterprise Architecture and Modelling Resilience, Agility, and Ilities in Systems Artificial Intelligence and Situational Awareness Human-Computer Interaction and Decision Making Green Virtual Enterprises and Sustainable Development Funded Research : Lead projects on agile command and control systems (QDSA, 2022-2024) Contributed to modeling trust in autonomous systems (TAS DCRC, 2020-2023) Developed decision support systems for enterprise architecture (internal grants) Participated in European consortia like ECOLEAD and GLOBEMAN 21 Teaching & Supervision : Teaches Enterprise Architecture Frameworks and Information Systems Supervises PhD candidates in areas like smart monitoring, biometric identification, and self-evolving systems Professional Roles : Standards Australia representative to ISO/IEC/IEEE 42042 Co-Editor of International Handbooks on Information Systems (Springer) Member of ISO TC184 SC5, IFAC Technical Committee on Integration

Yang Haoمشاهده پروفایل

استاد

• Antenna Engineering
• Transformation Optics
• Material Discovery
• +۷ مورد دیگر

Professor Yang Hao is a distinguished academic at Queen Mary University of London, where he holds the position of Professor of Antennas and Electromagnetics. He serves as Deputy VP for Strategic Research and Deputy Head and Research Lead of The Centre for Electronics within the School of Electronic Engineering and Computer Science. His research group spans multiple centers including the Centre for Electronics, Centre for Multimodal AI, and Centre for Networks, Communications and Systems. Professor Hao earned his Ph.D. from the Centre for Communications Research at the University of Bristol in 1998, followed by postdoctoral research at the University of Birmingham (1998-2000). He joined Queen Mary University of London in 2000 as a lecturer, was promoted to Reader in 2005, and to Professor in 2007. He currently holds the QinetiQ/Royal Academy of Engineering Research Chair and has completed executive leadership training at Oxford's Saïd Business School. His research spans antenna engineering, transformation optics, material discovery, bioengineering, metamaterials, and communication engineering. Professor Hao's pioneering work in metamaterials and transformation optics has reshaped electromagnetic device possibilities. His research laid the foundation for discovering unconventional material properties through AI/ML and robotic-controlled lab automation, enabling precise wave control. Beyond theoretical advancements, he has made significant contributions to wireless communication, wearable electronics, and in-body radio channels for medical applications, demonstrating the societal impact of his work. His recent publications reveal a strong focus on metamaterials, AI-driven material discovery, and practical applications in satellite communications and medical devices. There's a clear evolution from fundamental electromagnetic theory toward integrated systems with machine learning components, particularly in antenna design, material characterization, and wireless communication for next-generation (6G) systems. Fellow of the Royal Academy of Engineering (2020) Fellow of the IEEE (2013) Fellow of the IET (2010) Fellow of the ERA Foundation (2007) Royal Society Wolfson Research Merit Award (2013-2018) IET AF Harvey Research Prize BAE Chairman's Silver Award Professor Hao has supervised over 70 PhD students and postdocs since 2000, with many now working at leading global institutions and companies including Samsung, Airbus, and Isotropic Systems. He has secured approximately £60M in research funding from UKRI, Royal Society, Royal Academy of Engineering, ERC, and EU. Current projects include an EPSRC prosperity partnership project of £2.6M. His leadership extends to editorial roles including Editor-in-Chief for EPJ Applied Metamaterials and past Editor-in-Chief for IEEE Antennas and Wireless Propagation Letters (2014-2017). His research group operates across multiple domains including the Centre for Electronics, where they develop advanced antenna systems and metamaterials; the Centre for Multimodal AI, focusing on AI-driven material discovery; and the Centre for Networks, Communications and Systems, working on next-generation wireless communication architectures. The team has successfully translated research into practical applications, including co-founding Isotropic Systems (All.Space), which has created over 100 engineering jobs in Reading.

Maria Liakataمشاهده پروفایل

استاد

• Natural Language Processing
• Semantics and Discourse
• Social Media Analysis
• +۷ مورد دیگر

Maria Liakata is a Professor of Natural Language Processing at Queen Mary University of London, where she serves as Deputy Centre Lead and 'Health and Wellbeing' Theme Lead for the Centre for Human-Centred Computing within the School of Electronic Engineering and Computer Science. She leads multiple research initiatives focusing on applying NLP techniques to healthcare applications, particularly mental health monitoring through social media and longitudinal user-generated content analysis. Her research interests span Natural Language Processing, Semantics and Discourse, Social Media analysis, Machine Learning, BioNLP, and NLP for Mental Health. Dr. Liakata has made significant contributions to knowledge discovery from corpora, automation of scientific experimentation, and automatic extraction of information from scientific literature. Her work has practical applications in mental health monitoring, dementia diagnosis, and rumor verification systems. Her recent publications demonstrate a strong focus on longitudinal analysis of language for mental health applications, temporal modeling of user-generated content, and healthcare AI. She has developed novel techniques for detecting changes in user representations over time, which have applications in mental health monitoring and early dementia diagnosis. Her research bridges the gap between technical NLP advancements and real-world healthcare challenges. EPSRC/UKRI Turing AI fellowship award on Creating Time Sensitive Sensors from Language & Heterogeneous User-Generated Content (2019-2025) IBM Faculty Award for work on emotion sensing from heterogeneous mobile phone data EU Project PHEME co-investigator (2014-2017) studying the spread of rumours in social media Early Career Fellowship from the Leverhulme Trust (2010-2013) on reasoning with scientific articles Dr. Liakata supervises a diverse group of PhD students working on cutting-edge NLP applications for healthcare, including mental health monitoring, dementia diagnosis, and medical imaging analysis. She leads multiple substantial research grants totaling millions of pounds from EPSRC, MRC, Wellcome Trust, and industry partners. Her current projects include 'Addressing socio-technical limitations of LLMs for medical and social computing' and 'Social media, Smartphone use and Self harm in Young People (3S-YP study)'. She leads the 'Health and Wellbeing' research theme within the Centre for Human-Centred Computing and collaborates closely with the Centre for Multimodal AI. Her interdisciplinary research team combines expertise in NLP, machine learning, clinical psychology, and healthcare informatics to develop practical AI solutions for real-world health challenges.

• Digital Holography
• Image Processing
• Deep Learning
• +۵ مورد دیگر

Bruno Cornelis is a postdoctoral researcher at Vrije Universiteit Brussel's Electronics and Informatics school, with a focus on biomedical signal processing, digital holography, and deep learning applications. His work spans both fundamental and applied research projects. Education : Master's thesis on Quantization-based Watermarking (2007) and Digital Holographic Image Processing (2007) Research Interests include: Deep Learning for Image Restoration and Biomedical Applications Sparse Signal Representations in Holography and Anomaly Detection Photoplethysmography (PPG) Signal Analysis Art Classification via Mathematical Signal Models Recent Publication Trends show a shift towards real-time traffic data disaggregation, PPG-based health monitoring, and deep learning architectures for image reconstruction. His work often bridges theoretical signal processing with practical biomedical/transportation applications. Honors : BARCO/FWO Master Thesis Award (2007) Collaborations involve EU-funded initiatives like GEAR and Tech4Health, with cross-disciplinary teams in Belgium and China. He has presented at IEEE conferences on transportation systems (ITSC 2022) and medical imaging (DATE 2025).

Celal Alpay Havleمشاهده پروفایل

استادیار

• Digital Transformation
• Multi-Criteria Decision Making
• Fuzzy Sets and Extensions
• +۷ مورد دیگر

Dr. Celal Alpay Havle is an Assistant Professor in the Pilot Training Department at Özyeğin University's Faculty of Aviation and Astronautics. His academic career began in 2015 while pursuing his doctorate at Galatasaray University, where he graduated top of his class in Industrial Engineering in 2022. B.Sc., Anadolu University (2014) - Industrial Engineering (Major), Electrical & Electronics Engineering (Minor) M.Sc., Galatasaray University (2015) - Industrial Engineering Executive MBA, University Canada West (2023, ongoing) His research focuses on Digital Transformation in aviation and other sectors, integrating Fuzzy Multi-Criteria Decision Making and Computational Intelligence . His recent work explores Artificial Intelligence , Metaverse , and Change Management frameworks. Dr. Havle has received multiple academic distinctions, including graduating as valedictorian from both his bachelor's and Ph.D. programs with high honors.

• Functional Nanomaterials
• Materials Science
• Catalysis
• +۶ مورد دیگر

Jing Yang is an Assistant Professor at the College of Chemistry and Chemical Engineering of Xiamen University , focusing on functional nanomaterials synthesis and characterization. Their research bridges materials science with clean energy applications. Ph.D. : Queensland University of Technology (2011) Bachelor : Beijing Normal University (2007) Research interests center on: Functional Nanomaterials : Controllable synthesis of nanostructures for catalysis, optical/electrical properties, and multi-functional devices Mineral-based Nanomaterials : Developing materials from mineral resources for energy and clean chemical applications Recent publications demonstrate expertise in: Nanostructure morphology control via adsorbate assistance Phase transitions in transition metal oxyhydroxides Spectroscopic characterization of nanomaterial thermal behavior Doping strategies for enhanced material performance Surfactant-free synthesis of gallium oxide particles

Soheil Mohammadiمشاهده پروفایل

استاد مدعو

• Disaster Recovery
• Urban Resilience
• Multi-hazard Risk Assessment
• +۲ مورد دیگر

Soheil Mohammadi is an Adjunct Professor at the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa. He teaches Multi-Hazard Impact and Risk Assessment in the Master's program in Engineering for Natural Risk Management. His research focuses on: Disaster recovery methodologies for urban systems Multi-hazard risk assessment integrating floods, seismic events, and pandemics Urban resilience planning using tools like Fuzzy Cognitive Mapping Development of recovery frameworks for sustainable settlements Mohammadi's publications (2020-2025) emphasize resilience-based solutions for cities exposed to natural hazards, with recurring themes in seismic/flood recovery, participatory planning, and critical infrastructure interdependencies. No awards, grants, or supervised students are documented.