جست‌وجوی استاد و پژوهشگر

Zechuan Linمشاهده پروفایل

مدرس

• Computational Biology
• Neurogenomics
• Genomics
• +۷ مورد دیگر

Zechuan Lin is a Lecturer at the Department of Neurology , Yale School of Medicine, and a member of the Adams Center for Parkinson's Disease Research . He previously held a postdoctoral research fellowship at Harvard Medical School/Brigham and Women's Hospital in 2023 and earned his PhD from Peking University, College of Life Sciences in 2019. His research bridges computational biology , genomics , and plant genetics , focusing on genetic improvement in crops like rice and maize. Key methodologies include heterosis analysis , transcriptomic profiling , and QTL mapping to dissect agronomic traits and environmental adaptation. Lin's publications highlight advancements in hybrid rice breeding , genome-wide selection , and computational tools for allelic imbalance and daylength-sensing models. His work integrates bioinformatics and genetic networks to address both fundamental and applied biological questions. He is affiliated with Scherzer's Lab , which employs interdisciplinary approaches to neurogenomics and personalized medicine for neurological disorders like Parkinson's disease. His contributions to big data analysis and cross-species genetic studies reflect a unique intersection of plant and neurogenomics.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Ningyuan Caoمشاهده پروفایل

استادیار

• Machine Learning Acceleration
• Real-Time Systems
• Low-Power Circuits
• +۷ مورد دیگر

Ningyuan Cao is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame, College of Engineering. He leads the Circuit and System Intelligence Research Lab , focusing on the intersection of advanced hardware design and real-time/low-power machine learning applications. Education : Ph.D., Electrical and Electronics Engineering, Georgia Institute of Technology (2020) M.S., Electrical Engineering, Columbia University (2015) B.S., Electrical and Electronics Engineering, Shanghai Jiao Tong University (2013) His research investigates custom analog/mixed-signal circuits , digital architecture , and micro-system design for machine learning acceleration, distributed intelligence, and data-driven IC design automation. Key application domains include Internet-of-Everything, tactile internet, and mixed reality systems. Recent publications highlight work on Bayesian neural networks , privacy-preserving bio-signal encoders , transformer-based surrogate models , and compute-in-memory architectures . Technical themes span neuromorphic computing, uncertainty quantification, and hardware security.

Kyu Y. Rheeمشاهده پروفایل

استاد

• Tuberculosis
• Antibiotic Resistance
• Metabolomics
• +۳ مورد دیگر

Kyu Y. Rhee is a Professor of Medicine and Professor of Microbiology and Immunology at Weill Cornell Medical College . His research focuses on Mycobacterium tuberculosis , with emphasis on metabolic pathways , antibiotic resistance mechanisms , and drug development . Research highlights include: Multi-omic approaches to TB drug discovery Mechanistic studies of antibiotic action Deciphering TB transmission genetics Metabolomics-driven target identification Current funding includes: Bill & Melinda Gates Foundation : AI/ML-assisted bacterial permeability platform National Institute of Allergy & Infectious Diseases : UM1 TB drug regimen design consortium National Heart, Lung, & Blood Institute : Studies on M. tuberculosis PE/PPE proteins and fructose-induced cancer He has authored over 50 publications on TB metabolomics, drug development, and pathogen persistence. His work bridges systems biology , chemical biology , and clinical research to address antimicrobial resistance.

• Network Tomography
• Software-Defined Networking
• Cyber-physical Systems
• +۶ مورد دیگر

Ting He is a Professor in the Department of Computer Science and Engineering, specializing in interdisciplinary research at the intersection of network sciences, energy systems, and cybersecurity. Their work addresses critical challenges in network tomography, software-defined networking, and cyber-physical systems, with a strong emphasis on advancing edge computing and decentralized learning paradigms. NSF-funded research on Distributed Edge Intelligence (2024–2025) Collaborative projects on Overlay Networks and Adversarial Reconnaissance in SDN Recent publications analyze network topology inference, energy-efficient decentralized learning, and secure cloud file systems. Their research aligns with UN SDGs through contributions to sustainable energy systems and secure IT infrastructure. Key collaborations with Silvestri, La Porta, and Chaudhuri Active in Smart Grid resilience and cascading failure mitigation

Max Pellertمشاهده پروفایل

استادیار

• Computational Social Science
• Digital Traces
• Affective Expression
• +۹ مورد دیگر

Max Pellert is a computational social scientist and cognitive scientist with faculty appointments at multiple institutions. Since 2022, he has served as an Assistant Professor at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim . He previously held an interim Professor position at the University of Konstanz and an Assistant Researcher role at Sony Computer Science Laboratories Rome. His work bridges computational methods with social science theory. Education M.Sc., University of Vienna (2017) in Middle European interdisciplinary Master's program in Cognitive Science (with distinction) Ph.D., Medical University of Vienna (2022) in Medical Informatics, Biostatistics & Complex Systems Research interests center on Computational Social Science , Digital Traces , and Natural Language Processing for emotion and sentiment analysis. He develops Temporal Adapters for tracking longitudinal emotional patterns and FAULTANA pipeline for polarization studies. His AI Psychometrics framework assesses personality-like traits in large language models. Recent publications include: (1) 2025 ACL work on political bias in LLMs; (2) 2025 ICWSM study of temporal emotion analysis; (3) 2024 Perspectives on Psychological Science paper on LLM psychometrics; (4) 2024 PNAS Nexus polarization analysis; (5) 2023 Emotion cross-cultural study of pandemic emotions. Scientific Awards Habilitation candidate status at University of Mannheim Teaching includes IS 616: Large Scale Data Analysis , IS 809: Advanced Text Mining Lab , and IS 723: Data Science Seminar at master’s and PhD levels. His Barcelona Supercomputing Center role focuses on principal investigator duties for computational social science projects.

Valentyn Panchenkoمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Dependence Measures and Copulae
• +۱۱ مورد دیگر

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

• Cognitive/Systems Neuroscience
• Computation and Theory
• Neurobiology of Learning and Memory
• +۷ مورد دیگر

Larry Abbott is the William Bloor Professor of Theoretical Neuroscience at Columbia University, with joint appointments in the Department of Physiology and Cellular Biophysics (within Biological Sciences) and the Mortimer B. Zuckerman Mind Brain Behavior Institute. He serves as Co-Director of the Center for Theoretical Neuroscience and is a Senior Fellow at HHMI Janelia Farm. PhD in Physics (1977), Brandeis University His research focuses on computational and mathematical modeling of neurons and neural networks, emphasizing spike-timing-dependent plasticity, sensory encoding in olfaction, and dynamics of internally generated neural activity. He explores how chaotic neural activity is harnessed for motor output and how perception involves dynamic inference and synaptic plasticity. Recent publications highlight applications of recurrent neural networks, hierarchical control mechanisms, and sensory-motor integration. Collaborative work spans institutions like MIT, Hebrew University, and the Allen Institute for Brain Science. Awards include the NIH Director’s Pioneer Award and the Swartz Prize in Theoretical Neuroscience. NIH Director’s Pioneer Award (2004) Swartz Prize (2010) First Annual Prize in Mathematical Neuroscience (2013) Irving Institute Mentor of the Year (2013)

Joseph Francoisمشاهده پروفایل

استاد

• International Economics
• Trade Policy
• Globalization
• +۴ مورد دیگر

Joseph Francois is a Professor of Economics and Director at the World Trade Institute (WTI) , affiliated with the Economic Institute at the University of Bern. He previously served as Deputy Director of the NCCR Trade Regulation (2015–2017) and held professorships at Johannes Kepler Universität Linz, Erasmus University Rotterdam, and other institutions. His research spans international economics, trade policy, globalization, environmental economics, and computable general equilibrium (CGE) modeling. Key focus areas include cross-border production chains, trade in services, open economy competition policy, financial market integration, and the interplay between trade agreements and sustainability goals. Scientific distinctions include a Fellowship at the Centre for Economic Policy Research (London). His methodological expertise includes CGE modeling, econometric estimation of large nonlinear systems, and policy analysis of trade disputes, carbon border adjustments, and non-tariff measures. Education: Ph.D. in Economics, University of Maryland BA in Economics and History, University of Virginia He has contributed extensively to policy briefs and technical reports, particularly in analyzing trade impacts on employment, economic development, and environmental outcomes.

Jaana Kaarina Bäckمشاهده پروفایل

استاد

• Forest Ecology
• Atmospheric Sciences
• Plant Ecophysiology
• +۴ مورد دیگر

Professor Jaana Kaarina Bäck is affiliated with the University of Helsinki, serving in the Faculty of Agriculture and Forestry's Department of Forest Sciences. She leads the 'Ecosystem processes' research group and oversees ecosystem research at the SMEAR measurement network. Her work bridges forest ecology, atmospheric science, and climate change mitigation, focusing on forest-atmosphere interactions and biogenic volatile organic compounds (BVOCs). PhD in Physiological Plant Ecology (University of Oulu, 1994) Docentship in Forest-Atmosphere Interactions (University of Helsinki, 2010) Docentship in Plant Ecophysiology (University of Oulu, 1998) Bäck's research explores how boreal forests influence atmospheric composition, carbon cycling, and climate dynamics. She investigates BVOC emissions, ecosystem responses to climate change, and feedback mechanisms between vegetation and the atmosphere. Her recent publications (277 total) analyze carbon dynamics after forest thinning, aerosol production in boreal ecosystems, and harmonized environmental observations via eLTER. These works span soil functions, air quality, and climate risk research. Knight, First Class, Order of the White Rose of Finland (2021) Open Science Award (University of Helsinki, 2018) Pro Scientia Award (Finnish Academy of Sciences and Letters, 2017) Bäck supervises 3 postdoctoral researchers, 4 doctoral students as main supervisor, and 12 as co-supervisor. She has secured over 6 M€ in research funding, including EU Horizon grants and Academy of Finland projects.

• 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.

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics (CFD)
• +۴ مورد دیگر

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.

Tom Conteمشاهده پروفایل

استاد

• Computer Architecture
• Compiler Optimization
• Manycore Architectures
• +۴ مورد دیگر

Tom Conte is an academic leader with a joint appointment in the School of Electrical & Computer Engineering and School of Computer Science at Georgia Institute of Technology. As the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH), he specializes in computer architecture and compiler optimization. His work focuses on manycore architectures, energy-efficient microprocessor design, and embedded system architectures. Prior to Georgia Tech, he directed the Center for Embedded Systems Research at North Carolina State University. He holds IEEE Fellow status and served as 2015 President of the IEEE Computer Society, co-leading the IEEE Rebooting Computing Initiative since 2011. Dr. Conte earned his bachelor’s degree in Electrical Engineering from the University of Delaware (1986), followed by M.S. and Ph.D. degrees in Electrical Engineering from the University of Illinois at Urbana-Champaign (1988 and 1992). His research has been recognized with prestigious awards including the IEEE Computer Society’s Golden Core Member award and the National Science Foundation’s CAREER Award (1996). His research interests span quantum computing, 3D chip architectures, energy-efficient processing, and post-Moore computing innovations. He has pioneered initiatives like the Superstrider architecture and CREEPY energy-efficient processing frameworks. Recent work includes advancements in quantum programming languages (e.g., Qwerty) and hybrid quantum-classical systems. Awards: IEEE Fellow, Young Alumni Achievement Award, CAREER Award Leadership: IEEE Computer Society President (2015), CRNCH Director Key Projects: Rebooting Computing Initiative, Superstrider Architecture His lab’s contributions include novel compiler optimizations for manycore systems, smart NIC offloading techniques, and thermodynamically inspired computing models. Conte’s work bridges academic research with industry needs through interdisciplinary collaborations and standardization efforts.

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.