جست‌وجوی استاد و پژوهشگر

• Neuromorphic Engineering
• Neural Networks
• Edge Computing
• +۳ مورد دیگر

Shih-Chii Liu holds the rank of Privatdozent (Associate Professor) in the Department of Information Technology and Electrical Engineering at ETH Zürich. He is affiliated with the Institute of Neuroinformatics , a joint institute between the University of Zurich and ETH Zurich. His research focuses on neuromorphic engineering, bio-inspired neural hardware, and edge computing systems, emphasizing energy-efficient algorithms and sensor technologies. Key research areas include neuromorphic sensors for real-time data processing, sparsity-aware neural networks, and adaptive computing architectures for edge devices. His work spans applications such as speech enhancement, wearable health monitoring, and bio-inspired keyword spotting systems. He leads the Sensors Research Group, which develops neuromorphic systems integrating novel sensors, spiking neural networks, and low-power hardware accelerators. Recent projects include the DeltaKWS low-power keyword spotting IC, EFLOP computational cost metrics for spiking networks, and NeuroBench benchmarking frameworks for neuromorphic systems. His contributions emphasize bridging biological neural principles with practical engineering solutions for IoT and embedded systems. Liu teaches courses such as Neuromorphic Engineering I and collaborates on cross-disciplinary projects involving neuroprosthetics, smart wearables, and multimodal sensor fusion. His work is characterized by hardware-software co-design approaches to tackle challenges in real-time, low-latency, and energy-constrained computing environments.

• Deep Learning
• Automatic Speech Recognition (ASR)
• Spoken Language Modeling
• +۳ مورد دیگر

Mikko Kurimo is a Full Professor at Aalto University's Department of Information and Communications Engineering, School of Electrical Engineering. He earned his M.Sc., Lic.Tech., and D.Sc.(Tech.) from Helsinki University of Technology (1992, 1994, 1997) and pioneered neural networks for automatic speech recognition (ASR) in his PhD thesis. After research roles at IDIAP (Swiss AI center) and visiting positions at University of Colorado, Edinburgh, SRI, ICSI, and Nitech, he leads Aalto's ASR group since 2000. His work focuses on unsupervised subword modeling for morphologically complex languages (Finnish, Estonian, Turkish, Arabic) and large speech foundation models. PhD in Neural ASR (Helsinki University of Technology, 1997) Research Scientist at IDIAP (Switzerland) Visiting Fellow at University of Colorado, Edinburgh, SRI, ICSI, Nitech Head of Aalto ASR Group (2000-present) His research spans deep learning for ASR, spoken language modeling , and low-resource language solutions . Recent work explores continued pre-training of self-supervised models, multimodal emotion recognition, and pronunciation assessment using LLMs. He led the winning team in the 2017 Multi-Genre Broadcast challenge and secured competitive funding in Tekes Challenge Finland and EC's H2020-ICT-2017. Key article trends include: Advancements in children's speech recognition and dysarthric speech processing Integration of generative AI for language learning feedback Specialization in low-resource Uralic languages (Finnish, Northern Sámi) Development of robust ASR systems for complex phonetic environments Scientific Awards ACM Multimedia 2023 Computational Paralinguistics Challenge Prize First place in MGB3 2017 Arabic ASR Challenge ISCA Best Student Paper Award (2011) Professeur Invité at Université de Saint-Etienne (2005-2006) Royal Society International Short Visit Fellowship (2004) Professor Kurimo leads the Speech Recognition Group at Aalto, collaborating with COIN (Centre of Excellence in Computational Inference) and AIRC (Adaptive Informatics Research Centre). His projects like CaptainA mobile app demonstrate practical applications of ASR in language education. He has supervised numerous publications with co-authors in domains spanning bandwidth extension, stuttering detection, and speech sound disorder assessment.

Lin Zhongمشاهده پروفایل

استاد

• Computer Science
• Quantum Computing
• Operating Systems
• +۳ مورد دیگر

Lin Zhong is the Joseph C. Tsai Professor of Computer Science at Yale University, leading the Efficient Computing Lab. He holds a Ph.D. from Princeton University and M.S./B.S. degrees from Tsinghua University. Previously, he served at Rice University from 2005 to 2019. His research focuses on optimizing computing efficiency, quantum error correction, operating systems, and mobile systems. Education: Ph.D., Princeton University M.S., Tsinghua University B.S., Tsinghua University Research Interests: His work spans quantum computing (e.g., decoding algorithms for surface codes), operating systems (safety, correctness, and lightweight kernels), and mobile/networking systems (massive MIMO, energy-efficient designs). Recent trends include integrating large language models (LLMs) into robotics and securing cloud-based AI workflows. Awards: NSF CAREER Award ACM SIGMOBILE RockStar (2014) and Test of Time (2022) Fellowships from IEEE and ACM Best Paper Awards at ACM MobileHCI, IEEE PerCom, ACM MobiSys, and more Lab & Teams: His Efficient Computing Lab explores systems for quantum error correction (e.g., FPGA-based decoders), secure embedded systems, and LLM-driven robotics. Projects include TimelyLLM (real-time LLM serving) and Blindfold (confidential memory management).

Larry Mooreمشاهده پروفایل

استاد

• Environmental Engineering
• Water Quality Modeling
• Wastewater Treatment Optimization
• +۳ مورد دیگر

Dr. Larry Moore is a Professor of Civil Engineering at the University of Memphis, specializing in environmental engineering with a focus on wastewater treatment optimization, water quality modeling, and energy conservation in water treatment plants. He holds a B.S. from the University of South Alabama (1973) and M.S./Ph.D. in Environmental Engineering from Mississippi State University (1974/1983). His career spans over 33 years of teaching and applied research, addressing wastewater challenges for over 200 industries in Tennessee through the UT Center for Industrial Services. Research interests include water quality modeling of streams (e.g., Loosahatchie River studies), optimization of municipal/industrial wastewater treatment plants, and energy efficiency improvements in water treatment facilities. Collaborative projects with the Tennessee Department of Environment and Conservation and the U.S. Department of Energy have driven energy savings in over 24 treatment plants. Dr. Moore has served as President of the Kentucky-Tennessee Water Environment Association (KTWEA) and received the prestigious Leary Jones Award (2009) for his contributions to the wastewater profession, inducting him into the KTWEA Hall of Fame. Teaching responsibilities include advanced graduate courses such as Biological Wastewater Treatment, Water Quality Modeling, and Solid Waste Management. Awards include multiple 'Superior Performance in University Research' recognitions (1985-1989) and the 1990 Civil Engineering Outstanding Research award. He actively mentors graduate students in applied wastewater research and has advised numerous M.S. and Ph.D. candidates focusing on treatment plant optimization and emerging technologies. Professional Affiliations: Life Member of Water Environment Federation, KTWEA Pretreatment Certification Committee (since 2000), Tennessee Water and Wastewater Operator Certification Board (since 2007) Key Projects: Operational guidance for Memphis' Maxson WWTP, energy conservation studies across 24 plants, and aeration system design improvements Community Impact: Operator training programs, NPDES permit compliance support, and industrial wastewater problem resolution

Keith A. Brownمشاهده پروفایل

دانشیار

• Hierarchical Soft Matter
• Autonomous Experimentation
• Scanning Probe Techniques
• +۴ مورد دیگر

Keith A. Brown is an Associate Professor in Mechanical Engineering at Boston University's College of Engineering with additional appointments in Materials Science & Engineering and Physics. He serves as Associate Chair for Graduate Programs in ME and leads the interdisciplinary KABLab research group. Education: PhD, Harvard University Dr. Brown's research centers on hierarchical soft matter systems including polymers and smart fluids. His group develops innovative approaches to accelerate materials research through nanocombinatorics , autonomous experimentation , and scanning probe lithography . Key focus areas include connecting nanoparticle properties to bulk smart fluid behavior, designing 3D-printed structures with programmed mechanics, and creating self-driving laboratories for materials discovery. His recent publications (2024-2025) demonstrate a strong emphasis on autonomous experimentation systems integrating machine learning with physical research. This work spans energy-absorbing foam design, nanoscale fluid manipulation, and physics-informed modeling for mechanical systems, establishing new paradigms in accelerated materials development. Scientific Awards: The Early Career Research Excellence Award, College of Engineering, 2021 Professor of the Year, Mechanical Engineering, 2020 Frontiers of Materials Award, The Minerals Metals and Materials Society (TMS), 2020 Dean’s Catalyst Award (2018) Dean’s Catalyst Award (2020) Moorman-Simon Interdisciplinary Career Development Professor, 2016 Dr. Brown teaches undergraduate courses including Fluid Mechanics (ME 303), Introduction to Materials (ME 306), and Nanomanufacturing (ME/MS 576). His research is supported by: Federal Grants : AFOSR MURI, NSF Nanomanufacturing, ACS Petroleum Research Fund Foundations : Gordon and Betty Moore Foundation Industry : Google Faculty Research Award University : BU Dean's Catalyst Award, Nanotechnology Innovation Center The KABLab employs interdisciplinary teams to develop novel instrumentation for hierarchical soft matter research, with particular expertise in autonomous experimentation platforms that combine scanning probe techniques with machine learning for accelerated materials discovery.

Lev Sarkisovمشاهده پروفایل

استاد

• Carbon Capture
• Material Informatics
• Molecular Simulation
• +۶ مورد دیگر

Lev Sarkisov is a Professor of Chemical Engineering at the University of Manchester, leading the Sarkisov Research Group. His work focuses on advancing porous materials for carbon capture, energy storage, drug delivery, and sensing through multiscale computational workflows integrating molecular simulation, machine learning, and process modeling. He holds a Ph.D. from the University of Massachusetts Amherst (2001) and held roles at the University of Edinburgh, including Head of Chemical Engineering. Notable achievements include securing a £1M Wolfson Foundation grant for sustainable engineering (2022) and receiving the 2013 Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Education: Ph.D., Chemical Engineering, University of Massachusetts Amherst, 2001 M.Sc./B.Sc., Moscow Lomonosov Academy of Fine Chemical Technologies, 1995-1997 Research Interests: The group develops porous materials using AI-driven approaches for carbon capture, energy-efficient separations, and material informatics. Key areas include MOFs, adsorption phenomena, and open-source software for reproducible research. Grants & Awards: £1M Wolfson Foundation Grant (2022) Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2013) Edinburgh University Student Union Teaching Award (2019) Labs & Collaborations: The group collaborates globally, emphasizing open-source tools and reproducibility. Projects include CRAFTED (MOF adsorption database) and PoreBlazer v4.0.

• Climate Change Adaptation in Agriculture
• Land Use Modeling
• Biodiversity Conservation
• +۳ مورد دیگر

Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.

• Sustainable Transportation Systems
• Shared Mobility
• Public Transit Planning
• +۷ مورد دیگر

Dr. XiaoYue Cathy Liu is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah. She holds a PhD in Transportation Engineering from the University of Washington and advanced degrees in Transportation Planning and Electronics Engineering. Her research focuses on sustainable transportation systems, shared mobility, public transit optimization, managed lanes, and intelligent transportation systems. She actively serves on committees including the Transportation Research Board's Highway Capacity Quality of Service (HCQS) Committee and chairs its Technology Transfer Subcommittee. She also advises the Utah Model Advisory Committee and previously served on Salt Lake City’s Transportation Advisory Board. Dr. Liu is a licensed professional engineer in Utah. Education: PhD, Transportation Engineering, University of Washington (2013) MA, Transportation Planning & Management, Texas Southern University BS, Electronics & Electrical Engineering, Beijing Jiaotong University Graduate Certificate, Global Trade & Logistics, University of Washington (2011) Research Interests: Electric vehicle infrastructure and charging networks Smart transportation systems and agent-based modeling GIS-based asset management for transportation infrastructure Snowplowing operations optimization and winter maintenance Managed lanes and high-occupancy toll (HOT) lane analysis Public transit equity and accessibility Her recent publications emphasize data-driven approaches to transportation challenges, including EV demand forecasting, drone delivery networks, and resilience in interdependent transit systems. She collaborates on projects addressing Utah’s unique transportation needs, such as optimizing snowplow routes and integrating renewable energy into public transport systems. Professional Contributions: Board member, Utah Model Advisory Committee Former Chair, Salt Lake City Transportation Advisory Board (2013-2016) TRB Managed Lane Committee member TRB Transit Capacity & Quality of Service (TCQS) Committee member (2016-2019) Dr. Liu’s work bridges transportation engineering with computational methods, focusing on equity, sustainability, and operational efficiency. She leads research initiatives involving large-scale modeling, geospatial analytics, and interdisciplinary solutions for modern mobility challenges.

• Quantum Photonics
• Computational Electromagnetics
• Quantum Amplifier Design
• +۳ مورد دیگر

Yongjie Yuan is a Researcher at the Technical University of Munich under the Associate Professorship of Computational Photonics within the TUM School of Computation, Information and Technology. His research focuses on quantum cascade devices, Josephson traveling-wave parametric amplifiers, and noise modeling in superconducting circuits. Institution: Technical University of Munich School: TUM School of Computation, Information and Technology Academic Rank: Researcher Email: yongjie.yuan@tum.de Yuan's work integrates quantum mechanics with computational photonics , emphasizing the modeling of superconducting circuits and microwave parametric amplifiers . He investigates dissipative-dispersive systems , flux-driven amplifiers , and nonlinear junction topologies to enhance quantum device performance. Recent publications highlight his contributions to quantum amplifier design , Josephson junction modeling , and noise dissipation analysis in microwave photonics. His research is associated with the EU Project QOMBS, focusing on quantum cascade devices and parametric amplification. Quantum Cascade Devices Josephson Traveling-Wave Parametric Amplifiers Quantum Noise Modeling Nonlinear Circuit Analysis Microwave Photonics Yuan actively participates in teaching and supervising activities, contributing to courses on computational photonics , quantum engineering , and partial differential equations for electrical engineering. He collaborates with researchers like Christian Jirauschek and Michael Haider on quantum device simulations.

John F. Bradyمشاهده پروفایل

استاد

• fluid mechanics
• transport processes
• complex and multiphase fluids

John F. Brady is the Chevron Professor of Chemical Engineering and Mechanical Engineering at the California Institute of Technology. He earned his B.S. from the University of Pennsylvania (1975), M.S. (1977) and Ph.D. (1981) from Stanford University, and has held academic roles at Caltech since 1985, including Executive Officer for Chemical Engineering (1993-99; 2013-19). His research focuses on fluid mechanics, transport processes, and complex/multiphase fluids. Elected to the National Academy of Sciences (20XX) Elected to the American Academy of Arts and Sciences (20XX) Brady's publications reveal expertise in active matter dynamics, microrheology, and non-equilibrium systems. His work spans fundamental fluid mechanics to applied biomedical device design, with a strong emphasis on computational modeling and experimental validation in colloidal and soft matter physics.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.

Huiyan Liمشاهده پروفایل

دانشیار

• Biosensors
• Lab-on-a-chip Technologies
• Cancer Diagnostics
• +۴ مورد دیگر

Huiyan Li, PhD, P.Eng., is an Associate Professor in the Department of Biomedical Engineering at the University of Guelph. Her research focuses on developing micro/nanoscale biosensors and lab-on-a-chip technologies for cancer diagnostics and personalized medicine. Dr. Li holds a Ph.D. in Biomedical Engineering from McGill University and completed postdoctoral training at Harvard Medical School/Massachusetts General Hospital. Her multidisciplinary research integrates biosensing, micro/nanofabrication, bio-optics/electronics, and computational tools to study cancer molecular complexity. Current openings exist for M.A.Sc. students interested in biosensing research. Key research areas include extracellular vesicle analysis, multiplexed immunoassays, magnetic/nanoparticle-enhanced bioassays, and graphene-based biomedical sensors. Recent work emphasizes point-of-care diagnostics and enhanced protein detection via novel material integration. Teaching responsibilities include ENGG 6301 (Advanced Micro/Nano Biotechnology) and undergraduate courses in bio-instrumentation and biomedical signal processing. Her work spans biomaterials classification, microfluidic systems, and antimicrobial nanocomposite development. Research outputs emphasize scalable microarray formats, EV-based biomarker discovery, and sensor sensitivity enhancement through nanomaterial innovations. Current projects address EV concentration measurement, 3D antibody microarrays, and magnetic-responsive hydrogel discs for bioassay improvements.

Stephan Staudacherمشاهده پروفایل

استاد

• Aircraft Propulsion
• Gas Turbine Engineering
• Thermodynamics
• +۳ مورد دیگر

Prof. Dr.-Ing. Stephan Staudacher is the Director of the Institute of Aircraft Propulsion at the University of Stuttgart. His work focuses on aircraft propulsion systems, gas turbine performance, and turbomachinery design. He holds a professorship in the Faculty of Mechanical Engineering and Aerospace, leading research in advanced engine technologies, erosion effects, and fault detection algorithms. Research interests include engine reliability, computational fluid dynamics (CFD), and experimental validation of propulsion systems. His publications emphasize topics like neural network applications for fault detection, ice crystal icing simulations, and particle transport in additive manufacturing processes. Recent studies highlight the optimization of composite-cycle engines and assessment of mission severity caused by erosion. Key contributions include advancements in engine condition monitoring, transient performance analysis, and the development of Stuttgart University’s Altitude Test Facility (ATF). His work bridges theoretical models with industrial applications, addressing challenges in both civil and military aviation propulsion systems.

Kunhee (KC) Choiمشاهده پروفایل

استاد

• Artificial Intelligence in Transportation
• Construction Management
• Infrastructure Sustainability
• +۶ مورد دیگر

Kunhee (KC) Choi is a Professor in the Department of Construction Science at Texas A&M University, holding the History Maker Homes Endowed Professorship and serving as a Chancellor EDGES Fellow. His research focuses on enhancing transportation systems through cyber-enabled solutions, including digital twinning, artificial intelligence, and data-driven modeling. B.E. in Architectural Engineering, Korea University (1999) M.S. in Construction Science, Texas A&M University (2002) Ph.D. in Civil and Environmental Engineering, University of California, Berkeley (2008) Dr. Choi’s work addresses four key areas: (1) AI-powered mobility and safety solutions for highway projects, (2) cost-time-risk tradeoff analyses under alternative contracting methods, (3) infrastructure sustainability through life-cycle assessment, and (4) stochastic modeling for civil planning and management. His research has been funded by NSF, USDOT, NCHRP, and TxDOT. Recent publications highlight his emphasis on predictive analytics, flood resilience, pavement engineering, and AI-driven disaster recovery frameworks. The i²dEAS Lab, which he directs, has produced 130+ peer-reviewed publications and secured $500k+ in annual research funding. Chancellor EDGES Fellow (2022) NSF Grant Recipient for Road Safety Research Texas Sea Grant Program Award (NOAA Funding) for AI-based evacuation route modeling Dr. Choi’s teaching philosophy emphasizes integrative learning and interdisciplinary critical thinking in construction management. His lab collaborates with academia, government, and industry to optimize transportation infrastructure performance through safer, more efficient management strategies.

Jian Zhaoمشاهده پروفایل

دانشیار

• Information Visualization (InfoVis)
• Human-Computer Interaction (HCI)
• Data Science
• +۶ مورد دیگر

Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.