جستجوی استاد راهنمای دانشگاهی

Huaxiu Yaoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Robotics
• +۷ مورد دیگر

Huaxiu Yao is an Assistant Professor at the University of North Carolina at Chapel Hill, holding a joint appointment in the School of Data Science and Society and the Department of Computer Science (College of Arts & Sciences). His research focuses on building reliable large-scale AI models (foundation models) with applications in healthcare, robotics, genomics, and transportation. He leads the AIMING Lab, which explores adaptive intelligence through alignment, interaction, and learning. Education: Ph.D. from Pennsylvania State University (2021), Postdoctoral Scholar at Stanford University (hosted by Chelsea Finn). Research Interests: Generalizable AI agents, preference alignment, out-of-distribution generalization, embodied AI, and multimodal reasoning. Key applications include biomedicine, robotics, and vision-language systems. Notable Awards: KDD Best Paper Award (2024), Amazon Research Awards (2025), TMLR Outstanding Paper Award (2024). Advising & Labs: Recruits Ph.D. and intern students. Leads the AIMING Lab, affiliated with UNC NLP Group. Organizes workshops on foundation models (ICML 2024) and trustworthy AI systems. Publications: Over 40 peer-reviewed papers, including top venues like ICLR, NeurIPS, and ICML. Focuses on AI alignment, multimodal systems, and domain generalization.

Tommaso Marcatoمشاهده پروفایل

مدرس

• Materials Science
• Optoelectronics
• Nanotechnology
• +۳ مورد دیگر

Dr. Tommaso Marcato is a Lecturer at the Department of Chemistry and Applied Biosciences, ETH Zürich. He earned his MSc in Chemical and Bioengineering from ETH Zürich after completing his BSc in Chemical Engineering at Politecnico di Milano, Italy. His research focuses on perovskite light-emitting diodes (PeLEDs), emission anisotropy, and light-outcoupling efficiency, with a particular emphasis on transition dipole moment orientation and nanocrystal self-assembly. His publications span topics such as Stabilization of CsPbI₃ nanoplatelets via metal iodide doping Molecular orientation engineering in OLEDs Ultraflexible self-powered optoelectronics Electroluminescent metasurfaces using perovskite nanocrystals His work aims to overcome photon trapping in devices by manipulating emitter orientation and exploring quantum confinement effects. Dr. Marcato's recent articles (2019–2025) demonstrate expertise in Hybrid perovskite systems Quantum dot applications Photonic device architecture Surface passivation techniques . He is part of the Shih group at ETH Zurich and has received funding from ETH-33 18-2 for his research on graphene-templated molecular alignment in LEDs.

Massimo Piccardiمشاهده پروفایل

استاد

• Natural Language Processing
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

• Psycholinguistics
• Discourse Pragmatics
• Perspective-Taking
• +۷ مورد دیگر

William S. Horton (Sid) is an Associate Professor in the Department of Psychology at Northwestern University , where he has taught since 2005 and directed the Cognition and Communication Lab (Cresap 108). Starting in September 2025, he will serve as Chair of the Department of Linguistics . His research focuses on socio-cognitive mechanisms in language use, particularly discourse pragmatics , perspective-taking , and non-literal language processing . Education: B.S. in Psychology/German from Duke University (1991), M.A. and Ph.D. in Cognitive Psychology from University of Chicago (1994/1999) Previous roles: Postdoctoral research at SUNY-Stony Brook, Cognitive Aging at Georgia Tech Research trends from his 20+ years of publications include common ground , memory processes , figurative language , and age-related communication changes . His work increasingly incorporates computational text analysis and real-time digital communication studies. Awards : Fellow, Society for Text and Discourse Students : Supervised Ph.D. theses on conversational alignment, common ground activation, and digital communication. Undergraduate honors projects explore inhibitory deficits, speech internalization, and perspective-taking in bilingualism. Cognition and Communication Lab investigates language processing in spoken/written contexts using experimental methods and computational models.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Peng Xiongمشاهده پروفایل

استاد

• Mesoscale Physics
• Spintronics
• Organic/Solid-State Hybrid Structures
• +۱ مورد دیگر

Peng Xiong is a Professor in the Department of Physics at Florida State University, with a research focus on electron and spin transport in low-dimensional quantum materials. He is affiliated with the Integrative NanoScience Institute (INSI) and has made significant contributions to mesoscale physics, spintronics, and organic/solid-state hybrid systems. B.S. in Physics (1987, University of Science and Technology of China) Ph.D. in Physics (1993, Brown University) Postdoctoral Fellowship (1993-1997, University of California at San Diego) Research Interests: Mesoscale Physics: Quantum phase transitions and fluctuation effects in 2D and 1D systems, semiconductor nanowires, carbon nanotubes, and nano-magnetism. Spintronics: Spin-polarized transport in hybrid structures (ferromagnet/normal metal, ferromagnet/superconductor, ferromagnet/semiconductor), magnetic semiconductors, and spin injection/detection. Organic/Solid-State Hybrids: Nanoscale biosensors utilizing magnetic and electrical principles, bio-mechanical devices, organic/solid interfaces, and template-directed nanostructure self-assembly. Publication Trends: His recent work spans superconducting fluctuations in ultrathin films, chirality-induced spin transport in semiconductors, interplay between structural chirality and spin-orbital effects, ion migration dynamics in 1D hybrids, quantum interference in nanowire loops, and modulation of nanomaterial properties through surface defect engineering. These studies often combine material synthesis, nanofabrication, cryogenic transport, and tunneling measurements. Scientific Recognition: Alfred P. Sloan Research Fellowship (1998) University Teaching Award (2003) PAI Award for Excellence (2004) Developing Scholar Award (2007) Fellow of the American Physical Society (2012) Advising Legacy: He has mentored numerous graduate students including Jeffrey Parker (Ph.D. 2003) Yongqing Li (Ph.D. 2003) Tianhan Liu (Ph.D. 2021) Jacob Hudis (Ph.D. 2021) and continues to guide current candidates like Yuwaraj Adhikari and Zhenqi Hua. Experimental Facilities: The lab at FSU features advanced equipment for nano-fabrication, including clean rooms, mask aligners, and thin film deposition systems. Cryogenic capabilities extend to dilution refrigerators (15 mK) and He3/He4 cryostats for ultralow-temperature studies of magnetic and superconducting systems.

Mohammad Mohammadi Amiriمشاهده پروفایل

استادیار

• Large Language Models
• Data Valuation
• Federated Learning
• +۵ مورد دیگر

Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.

• Combinatorial optimization
• Design and analysis of algorithms
• Field-programmable systems
• +۷ مورد دیگر

Martin D F Wong serves as the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean for the College of Engineering at the University of Illinois at Urbana-Champaign. He is affiliated with the Coordinated Science Laboratory and has been instrumental in advancing electronic design automation research. His educational background includes: B.Sc. in Mathematics, University of Toronto (1979) MS in Mathematics, University of Illinois at Urbana-Champaign (1981) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1987) Wong's research centers on combinatorial optimization and algorithm design for VLSI systems, with particular expertise in lithography-aware physical design, field-programmable systems, and electronic packaging. His work bridges theoretical algorithms with practical semiconductor manufacturing challenges as feature sizes shrink below 20 nanometers. Current research focuses on integrating chip design with next-generation lithography technologies including triple-patterning, self-aligned double patterning, directed self-assembly, and extreme ultraviolet processes. His publication record shows consistent contributions to electronic design automation, with emphasis on manufacturing-aware physical design algorithms and circuit optimization techniques. Recent work addresses the critical interface between circuit layout and lithography processes as semiconductor technology advances to 14nm and beyond. Scientific recognition includes: Fellow of IEEE and ACM 2000 IEEE Donald O. Peterson Best Paper Award Multiple Best Paper Awards at DAC, ICCD, and ICCAD conferences IBM Faculty Awards (2000, 2004) NSF Research Initiation Award Wong has secured significant research funding including a $450,000 NSF grant for lithography-aware physical design and has supervised over 49 PhD students. His work continues the legacy of integrated circuit innovation at Illinois, building on foundational contributions like Jack Kilby's integrated circuit invention. Current research initiatives focus on optimizing chip design for next-generation manufacturing processes where optical interference challenges require co-design of layout and fabrication. He leads research within the Coordinated Science Laboratory, focusing on electronic design automation algorithms that address the growing complexity of semiconductor manufacturing at nanometer scales.

Vishal Sharmaمشاهده پروفایل

مدرس ارشد

• Cyber-Physical Systems (CPS)
• 5G and Beyond Security
• Internet of Things (IoT)
• +۴ مورد دیگر

Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.

Brice Kuhlمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Memory
• Cognitive Control
• +۳ مورد دیگر

Brice Kuhl is a Professor in the Department of Psychology at the University of Oregon , where he leads the Kuhl Lab. His research focuses on the cognitive neuroscience of memory formation, retrieval, and forgetting , utilizing advanced neuroimaging techniques like fMRI and EEG combined with machine learning algorithms to analyze distributed neural activity patterns. His work explores mechanisms of memory interference resolution , forgetting , and neural representation transformation . Recent publications emphasize spaced learning , temporal memory dynamics , and memory-cognitive control interactions . The lab has received attention for decoding perceptual content from neural activity and reconstructing face images based on brain states. Current lab members include graduate students Anisha Babu , Tongle Cai , and America Romero , alongside postdocs like Soroush Mirjalili and Yoonjung Lee . The lab frequently presents at conferences like CNS and SFN , and maintains active collaborations in memory research and neuroimaging methodology . Notable projects include investigations into hippocampal pattern differentiation and parietal cortex roles in memory . The lab also contributes to open science initiatives with publicly available experimental codes and data .

Farhad Aslaniمشاهده پروفایل

دانشیار

• Structural Engineering
• Concrete Engineering
• Sustainable Construction Materials
• +۵ مورد دیگر

Dr. Farhad Aslani is an Associate Professor in the Department of Civil, Environmental and Mining Engineering at the University of Western Australia (UWA), serving as Director of the Materials and Structures Innovation Group. He leads the $250M Australian Composites Manufacturing CRC and co-leads UWA's Engineering Materials Research Cluster. His research focuses on innovative construction materials, including self-sensing concrete, 3D-printed composites, and fire-resistant materials. He has secured over $20M in CRC funding and holds leadership roles in national/international conferences. Key awards include the 2024 Concrete Institute WA Rising Star Award and multiple citation milestones. His work aligns with sustainable development goals, emphasizing eco-friendly materials and infrastructure resilience. Education: PhD in Structural Engineering, University of Technology Sydney (2014) Certificates in Leadership, Research Commercialization, Public Policy, and Project Management from Curtin University, Queensland University of Technology, RMIT, and UTS. Research Interests: Smart materials, sustainable concrete technologies, additive manufacturing, blast/fire resistance, and composites for infrastructure. His lab develops self-sensing concretes, lightweight engineered composites, and electromagnetic shielding materials. Grants & Projects: $4.3M ACM CRC project on composite repairs ARC grants for nanocomposite coatings and fire facilities Main Roads WA funding for timber bridge strengthening Expertise: Structural design, composite materials, and industrial collaborations (e.g., Woodside, Holcim). He advises on major projects like the Morley Ellenbrook Rail Line and Forrestfield Airport Link. Awards: 2024 Concrete Institute WA Rising Star Highly Cited Scholar (ScholarGPS, 2024) Most Cited Paper Awards (2018–2020)

Trey Portoمشاهده پروفایل

استاد مدعو

• Quantum Physics
• Ultra-cold Atoms
• Quantum Optics
• +۵ مورد دیگر

Trey Porto is an Adjunct Professor at the University of Maryland, affiliated with the Joint Quantum Institute (JQI) and NIST. His research focuses on ultra-cold atoms, quantum optics, and quantum information science. He leads projects on Rydberg atoms, optical lattices, and quantum networking, leveraging cold atom systems to explore novel quantum phenomena and control strategies. Research areas include ultra-cold Rb/Yb mixtures for studying Bose-Einstein condensates and engineered dissipation, as well as photon-photon interactions using Rydberg-dressed polaritons. His work bridges quantum simulation, quantum computing, and precision measurement, with applications in quantum networking and many-body physics. Key achievements include the 2023 UMD Quantum Invention of the Year Award for developing photon-counting methods that preserve quantum states. Porto collaborates with groups such as RQS and JQI, contributing to advancements in subwavelength optical potentials and Floquet-engineered systems. He mentors graduate students in experimental and theoretical aspects of cold atoms and quantum technologies. Publications highlight breakthroughs in Rydberg blockade enhancement, prethermal Bose-Einstein condensation, and compact auto-alignment systems for experimental setups. His lab is based in the Physical Sciences Complex on the UMD campus, with ongoing projects exploring quantum dissipation and photon-atom hybrid systems.

Yuri Rostovtsevمشاهده پروفایل

استاد

• Quantum Coherence
• Electromagnetically Induced Transparency
• Free Electron Lasers
• +۲ مورد دیگر

Dr. Yuri Rostovtsev is a Professor at the University of North Texas, specializing in quantum optics and atomic physics. He holds a Ph.D. from the Russian Academy of Sciences (1991). His office is located in GAB 525I and he can be contacted at (940) 565-3281. Research Interests: Dr. Rostovtsev's research focuses on quantum coherence phenomena, electromagnetically induced transparency, and matter-field interactions. His work spans theoretical and experimental investigations in quantum optics, including studies of quantum refraction, biophotons, and ultrafast processes in atomic and molecular systems. Recent Publications: His recent articles explore advanced quantum phenomena including single-photon interactions with atoms, quantum state engineering, plasmonic structures, and ultrafast dynamics in molecular systems. These publications demonstrate a consistent focus on quantum coherence effects and light-matter interactions at the quantum level. Scientific Awards: No awards mentioned in the provided text. Advising and Labs: No information available about students or research laboratories.

Laurie Wilcoxمشاهده پروفایل

استاد

• Vision Science
• Neuroscience
• Binocular Vision
• +۴ مورد دیگر

Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.

Irina Rishمشاهده پروفایل

استاد

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +۱۳ مورد دیگر

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.