جست‌وجوی استاد و پژوهشگر

Ali Hirsaمشاهده پروفایل

استاد

• Algorithmic Trading
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Ali Hirsa is a Professor of Professional Practice at Columbia Engineering within the Department of Industrial Engineering and Operations Research. He has also served as an Adjunct Professor at Columbia University since 2000 and is the Managing Partner at Sauma Capital, LLC, a New York-based hedge fund. PhD in Applied Mathematics from University of Maryland College Park (advised by Professors Howard C. Elman and Dilip B. Madan) Research Interests : Ali’s research spans algorithmic trading , machine learning , data mining , computational finance , quantitative finance , and optimization . He focuses on developing learning algorithms for signal extraction from financial data, with applications in trading strategies and financial modeling. Scientific Awards and Affiliations : He was a Fellow at NYU’s Courant Institute Mathematics of Finance Program (2004–2014) and holds affiliations with Columbia’s Data, Media and Society , Financial and Business Analytics , and Foundations of Data Science centers. Publications and Patents : Ali authored Computational Methods in Finance (2012) and co-authored the third edition of An Introduction to Mathematics of Financial Derivatives . He co-invented US Patent 8,799,146 for fair post-trade allocation algorithms. He also serves as editor of the Journal of Investment Strategies . Leadership and Governance : He has been Vice Chair of the Board of Visitors at the University of Maryland’s College of Computer, Mathematical, and Natural Sciences since 2016 and previously served on the Board of Visitors at the A. James Clark School of Engineering (2008–2018) and as a trustee of the University of Maryland College Park Foundation (2011–2016).

Arno Onkenمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

• Operations Research
• Management Science
• Dynamic Pricing
• +۴ مورد دیگر

Arash Asadpour Rahimabadi is an Associate Professor at the N. P. Loomba Department of Management within the Zicklin School of Business at Baruch College, CUNY . His research bridges Operations Research and Management Science , focusing on algorithmic design, dynamic pricing, and optimization in gig economy platforms. Education: Ph.D. in Operations Research, Stanford University (2010) BSc in Computer Engineering, Sharif University of Technology (2004) His research interests include stochastic optimization , submodular maximization , marketplace stability , and fair allocation . Recent work explores dynamic pricing in extreme value regimes , shared ride sustainability , and regulation of gig economy platforms . His scientific contributions span algorithmic game theory, combinatorial optimization, and resource allocation, with key publications in Management Science and Operations Research . Current projects analyze escrow payment mechanisms , shared mobility efficiency , and hotel reservation systems . Scientific Awards Best Paper Award, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010 1st Rank in Iran’s National Graduate Entrance Exam in Computer Engineering, 2004 Silver Medals in Iranian National Olympiads in Informatics, 1999–2000 He serves on graduate and PhD committees at CUNY and has reviewed for journals including Management Science and Operations Research . His teaching includes courses like Decision Models and Analytics and Advanced Discrete Optimization .

Stephen Brownمشاهده پروفایل

استاد

• Field-Programmable Gate Arrays (FPGAs)
• Computer Architecture
• CAD Algorithms
• +۴ مورد دیگر

Stephen Brown is a Professor at the University of Toronto within the Department of Electrical and Computer Engineering under the Faculty of Applied Science and Engineering. He earned his B.A.Sc and M.A.Sc in Electrical Engineering from the University of Toronto and New Brunswick, respectively, and a Ph.D. in Electrical Engineering from the University of Toronto (1992). His career spans over two decades in academia and industry collaboration. Education : B.A.Sc, University of New Brunswick M.A.Sc, University of Toronto Ph.D, University of Toronto Professor Brown’s research focuses on field-programmable gate arrays (FPGAs) , CAD algorithms , and computer architecture , with applications in machine learning and high-level synthesis . He is a principal investigator in the LegUp project , an open-source high-level synthesis framework that bridges software and hardware design. His work also extends to optimizing FPGA interconnect delays, physical synthesis, and logic block architectures. Key trends in his publications include advancements in high-level synthesis tools, FPGA architecture evaluation, and timing-driven design methodologies. His contributions often intersect with design automation , resource sharing , and embedded systems . Scientific Awards : NSERC 1992 Doctoral Prize Hart Professorship for Innovation in Teaching (2017) Multiple teaching excellence awards Best Paper Award at ICCAD 1990 Best Paper Award nomination at Canadian Conference on VLSI (1989) As Director of the FPGA University Program at Intel Corporation, he leads industry-academia initiatives. His teaching portfolio includes courses like ECE253 (Digital Logic) and ECE1733F (Switching Theory).

Christian Flachslandمشاهده پروفایل

استاد

• Climate Policy
• Energy Policy
• Sustainability Governance
• +۳ مورد دیگر

Christian Flachsland is Professor of Climate Policy and Director of the Centre for Sustainability at the Hertie School. He also serves as a Research Fellow at the Mercator Research Institute on Global Commons and Climate Change (MCC) , where he previously led the Governance working group. His research focuses on climate policy design, energy governance, and the political economy of sustainability transitions, with particular emphasis on Germany and the European Union. Flachsland's recent publications analyze climate institutions across Germany, UK, Sweden, and Australia, and develop frameworks for anticipatory policy mix construction in road transport. His work combines machine learning literature mapping with comparative institutional analysis , appearing in top journals like Science and Nature Climate Change . Current projects examine EU carbon border adjustment mechanisms and reflexive governance in climate policy evaluation. As co-coordinator of Governance research in the BMBF-funded Kopernikus-Ariadne project , he assesses climate policy options for Germany and Europe through multi-country institutional comparisons and policy pathway assessments . His work bridges academic research with policy advice, including participation in the IPCC Scoping Meeting for its Sixth Assessment Report.

André B.J. Kokkelerمشاهده پروفایل

استاد

• Cognitive Radio
• Beamforming
• MIMO Systems
• +۵ مورد دیگر

André B.J. Kokkeler is a Full Professor at the Digital Society Institute and affiliated with the Radio Systems department at the University of Twente. His research focuses on wireless communication systems, signal processing, and mmWave technology, particularly in applications like beamforming, cognitive radio, and error-resilient algorithms. Recent research outputs highlight his work on: Hybrid beamforming techniques for full-duplex integrated sensing and communication (ISAC) systems Energy-efficient iterative algorithm implementations Single-bit angle-of-arrival (AoA) localization methods mmWave channel characterization in reverberation chambers Radar-driven human gait modeling His work contributes to advancements in wireless systems for IoT, automotive radar, and energy-constrained environments. Collaborations span multiple institutions and focus on propagation modeling, antenna characterization, and sensing applications.

Ting Sunمشاهده پروفایل

دانشیار

• Urban climate modelling
• Climate change impacts and adaptation
• Extreme weather events in cities
• +۵ مورد دیگر

Dr Ting Sun is an Associate Professor in Climate & Meteorological Hazard Risks at University College London , Department of Risk and Disaster Reduction. He earned his BEng (2009) and PhD in Hydrology (2013) from Tsinghua University , followed by a visiting period at Princeton University (2011–2012). After postdoctoral appointments at Tsinghua and the University of Reading , he held a NERC Independent Research Fellowship at Reading (2017–2022) before joining UCL in May 2022. Education PhD in Hydrology, Tsinghua University, 2013 BEng in Hydraulic Engineering, Tsinghua University, 2009 Visiting PhD Student, Princeton University, 2011–2012 Research Interests Dr Sun’s work converges on urban climate modelling across scales —from neighbourhood blocks to global grids—focusing on the impacts of weather and climate extremes such as heat waves and extreme rainfall in cities. He is the lead developer of the Surface Urban Energy and Water balance Scheme (SUEWS) and its Python wrapper SuPy , developed in collaboration with Prof Sue Grimmond’s micromet group. He also contributes as a core member of the Urban Multi-scale Environmental Predictor (UMEP) development team. His multidisciplinary expertise integrates hydro-climate dynamics, computational modelling, machine learning, built-environment processes, and public-health linkages . Research Trends from Recent Publications Across the 15 most recent articles, a clear trajectory emerges from high-resolution urban-process modelling toward integrated socio-environmental assessments . Studies published in 2024–2025 couple atmospheric models (WRF-SUEWS) with global building-morphology datasets (GLAMOUR) to quantify how cities alter rainfall patterns, temperature sensitivity, and heat-related mortality. Earlier works progressively refined SUEWS’s physical parameterisations and Python accessibility, while recent outputs leverage deep-learning remote-sensing tools (SHAFTS) and hybrid hydrological-neural architectures to deliver actionable insights for urban planning and climate adaptation. Scientific Awards & Fellowships NERC Independent Research Fellowship , University of Reading, 2017–2022 HEA Fellowship , University College London, 2023 Professional Service & Editorial Roles Topic Editor , Geoscientific Model Development (from 2025) Editorial Board Member , Scientific Data (from 2024) Peer review and consultancy for journals, conferences, and policy bodies Supervision of taught-course projects and research degrees External examining and mentoring Labs, Teams & Collaborations Dr Sun leads and collaborates within the UCL Department of Risk and Disaster Reduction , working closely with the micromet group at the University of Reading (Prof Sue Grimmond) on SUEWS/SuPy development. He is an active member of the UMEP consortium and maintains extensive international collaborations spanning Tsinghua University, Princeton, and numerous European research centres, underpinning a vibrant, interdisciplinary research network focused on urban climate resilience.

Mohammed J. Zakiمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Graph-structured Data Analysis
• +۶ مورد دیگر

Mohammed J. Zaki is a Professor and Department Head at the Department of Computer Science , Rensselaer Polytechnic Institute . He co-directs the NSF IUCRC Center for Research Towards Advancing Financial Technologies (CRAFT) and has authored the textbook Data Mining and Machine Learning (2nd Ed, Cambridge University Press, 2020). Research Focus : Novel data mining techniques for graph-structured and textual data with applications in bioinformatics, personal health, and financial analytics. Leadership : Co-chair of BIOKDD workshops, Associate Editor for multiple journals, and Program Co-chair for SIGKDD, SDM, ICDM, and other top conferences. Scientific Awards include: EDBT'24 Test of Time Award NSF CAREER Award DOE Early Career Award HP Innovation Research Award Google Faculty Research Award Fellowships: IEEE, ACM, AAAS, SIAM Recent Publications highlight advancements in: Knowledge Graph Completion with Directed Attention Multi-Sense Embeddings for Language Models Financial Trend Prediction via Graph Neural Networks Health-Guided Recipe Recommendation Systems Triplet Interaction in Molecular Graph Learning Temporal Personal Health Data Analysis

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Psychological Safety
• Teaming
• Organizational Learning
• +۳ مورد دیگر

Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School, holding a chaired position dedicated to human interactions in successful enterprises. She has been consistently ranked among Thinkers50's top management thinkers since 2011, achieving #1 status in 2021 and 2023. Her research focuses on psychological safety, teaming, and organizational learning, with significant contributions to understanding how organizations learn from failure. Key publications include The Fearless Organization (2019) and Right Kind of Wrong (2023), the latter winning the Financial Times and Schroders Best Business Book of the Year award. Her work spans organizational behavior, healthcare management, and innovation science, with articles in top journals like Administrative Science Quarterly and Harvard Business Review . Recent research trends show increasing focus on psychological safety dynamics in constrained environments, cross-boundary teaming, and the science of intelligent failure. Her 15 most recent articles (2021-2025) demonstrate expanding applications from healthcare to general management contexts, with growing emphasis on temporal dimensions of team learning and data-driven decision pitfalls. Major awards include: Thinkers50 #1 Management Thinker (2021, 2023) Thinkers50 Breakthrough Idea Award (2019) Financial Times Best Business Book Award (2023) Accenture Award for California Management Review (2003) Edmondson advises on organizational transformation through numerous Harvard Business School cases, including culture change initiatives at Microsoft, LEGO, and Cleveland Clinic. Her research has secured funding for studies on teaming in pharmaceutical development, smart city projects, and healthcare innovation. Current work examines psychological safety erosion in new hires and cross-boundary team dynamics in complex innovation projects.

Joergen Kornfeldمشاهده پروفایل

پژوهشگر

• Connectomics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish

Sarah Hernandezمشاهده پروفایل

دانشیار

• Transportation Systems Engineering
• Freight Transportation Data Analysis
• Intelligent Transportation Systems (ITS)
• +۳ مورد دیگر

Sarah Hernandez is an Associate Professor in the Civil Engineering Department at the University of Arkansas , specializing in transportation systems engineering. Her research focuses on advanced data collection and analysis for freight planning, and she teaches graduate courses in transportation planning and data analysis. Ph.D. in Civil and Environmental Engineering, University of California, Irvine M.S. in Civil Engineering, University of California, Irvine B.S. in Civil Engineering, University of Florida Her research integrates Intelligent Transportation Systems (ITS) technologies to address freight data gaps, including: Development of tools for freight performance measures Fusion of GPS, WIM, and lock performance data Weather impact on freight traffic Lidar-based truck classification Key trends in her publications include: Advancing sensor technologies for freight analytics Improving long-range infrastructure planning Addressing data gaps in commercial vehicle operations Enhancing freight network efficiency through modeling Scientific awards: Private Sector Applicability Award, TRB Intermodal Freight Committee (2018) As founder of the Freight Transportation Data Research Lab , she leads initiatives on unbiased freight planning and workforce diversity. Her outreach includes mentoring middle and elementary school STEM programs.

Shihab Jimaaمشاهده پروفایل

دانشیار

• AI-based performance enhancement of LP-WAN technologies for IoT
• 6G wireless communications
• Atrial Fibrillation Data Analysis and Validation
• +۳ مورد دیگر

Dr. Shihab Jimaa is an Associate Professor at the Department of Computer and Information Engineering , Khalifa University, and currently serves as the Associate Dean of UG Studies at the College of Computing and Mathematical Sciences. He has held academic positions including Research Fellow, Senior Lecturer, and Assistant Professor at institutions such as Loughborough University, Warwick University, and Queen Mary University of London. PhD : Digital Communications, Loughborough University (1990) MSc : Digital Communication Systems, Loughborough University (1986) PGCE : Post Graduate Certificate in Higher Education, University of Hertfordshire (1998) Dr. Jimaa’s research focuses on energy-efficient wireless communication systems , AI-driven 6G technologies , and machine learning applications for early detection of Atrial Fibrillation . His work spans adaptive filtering, channel estimation, and signal processing in both telecommunications and biomedical engineering domains. Current projects include optimizing LP-WAN technologies for IoT scalability and developing deep learning-based scheduling techniques. His research also investigates terahertz communications , optical wireless networks , and ultra-massive MIMO for next-generation systems. He has contributed to 3 book chapters and over 100 conference/journal papers. IEEE Senior Member HEA Fellow Recipient of 1 patent Dr. Jimaa has supervised 30 MSc, 3 MPhil, and 4 PhD students . He actively participates in academic leadership as TPC Chair for IEEE conferences and previously served as Guest Editor for the International Journal of Communication Systems . Affiliated centers include the 6G Research Center and Center for Cyber-Physical Systems .

• Machine Learning
• Power Systems
• Physics-Informed Machine Learning
• +۳ مورد دیگر

Priya L. Donti is an Assistant Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and Laboratory for Information and Decision Systems (LIDS). She co-founded and chairs Climate Change AI , a global nonprofit focusing on climate-AI intersections. Education: Ph.D. in Computer Science & Engineering & Public Policy, Carnegie Mellon University (advised by Zico Kolter and Inês Azevedo) Undergraduate in Computer Science & Math, Harvey Mudd College Research Focus: Machine learning for high-renewables power grids, incorporating physics and constraints into deep learning. Key areas include robust optimization, control systems, and climate-AI alignment. Article Trends: 2024-2022 works emphasize climate mitigation through AI (keywords: environmental science, AI ethics, policy modeling), while 2019-2021 studies focus on grid stability (power engineering, optimization) and hybrid AI-logic systems (symbolic reasoning, constraint handling). Scientific Recognition: MIT Technology Review 35 Innovators Under 35 (2021) Vox Future Perfect 50 (2023) Schmidt Sciences AI2050 Early Career Fellowship ACM SIGEnergy Doctoral Dissertation Award (2022) Best paper/poster awards at ACM e-Energy 2021 and PECI 2019 Her group at MIT develops physics-informed ML for power grids, with funding from NSF, DOE, and Center for Climate and Energy Decision Making. She actively advises prospective students through MIT EECS applications.

• Artificial Neural Networks
• Chaotic Oscillators
• Circuit Analysis and Modeling
• +۹ مورد دیگر

Fernando Corinto is a Research Fellow at the Department of Electronics and Telecommunications (DET) , Polytechnic University of Turin , and a member of the SmartData@PoliTO Big Data and Data Science Laboratory. He holds a European Doctorate in Electronics and Communications Engineering (2005) and was a Marie Curie Fellow (2004) at University College Dublin, focusing on cardiac fibrillation modeling and chaotic systems. Education : Laurea (2001) and Ph.D. (2005) in Electronics and Communications Engineering from Politecnico di Torino His research spans nonlinear dynamical systems , memristor devices , and complex network modeling , with over 50 publications. Key projects include RECOMMEND (2024–2027) and COSMO (2020–2024), where he served as Scientific Director . His recent work involves memristor-based neuromorphic systems and nonlinear circuit applications in biomedical and industrial contexts. He supervises PhD students Rosanna Cavazzana and Davide Rossetti and teaches Nonlinear Systems for Engineering (Mathematical Engineering) and Memristor-based Neuromorphic Systems (Electrical Engineering). His scientific contributions include the Flux-Charge Analysis Method and Bifurcations without Parameters in memristor circuits. He holds a national/international patent for skin ulcer classification algorithms and has led commercial research projects in biomedical and packaging systems.