جست‌وجوی استاد و پژوهشگر

Dong Ngoduyمشاهده پروفایل

دانشیار

• Connected and Autonomous Vehicles
• Traffic Flow Theory and Simulation
• Data Fusion
• +۴ مورد دیگر

A/Professor Dong Ngoduy is the Head of Transport Engineering and an Associate Professor in Transport Engineering at Monash University's Department of Civil and Environmental Engineering. He holds a PhD in Traffic Flow Theory from TU Delft (Netherlands) and an MSc from Linköping University (Sweden). His research focuses on Connected and Autonomous Vehicles (CAVs), Traffic Flow Theory, Data Fusion, and Urban Network Optimization. He received the EPSRC Advanced Fellow Award (2011-2016) and chairs the Connected Traffic Systems Lab. His current work aims to develop smart city platforms for multi-modal transport infrastructure management. Education: MSc in Traffic Engineering, Linköping University PhD in Traffic Flow Theory, Delft University of Technology Research Expertise: Traffic Flow Theory Data Fusion Techniques Urban Network Optimization CAV Dynamics Grants & Awards: EPSRC Advanced Fellow Award (2011-2016) EU Horizon 2020 Project (~$1.2M) Australian Research Council (ARC) Assessor Teaching: Courses include Transport and Traffic Engineering (CIV2282) and Autonomous Vehicle Systems (CIV4100). Labs & Teams: Leads the Connected Traffic Systems Lab and collaborates on projects like Digital Twins for urban networks.

• Health Care Management
• Operations Research
• Patient Scheduling
• +۵ مورد دیگر

Professor Patrick Jonathan is a Full Professor at the Telfer School of Management, University of Ottawa. His research focuses on applying operations research methods to healthcare management challenges, particularly in patient scheduling, capacity planning, and disaster response logistics. He holds a B.A.Sc. from McMaster University, M.Sc. and Ph.D. from the University of British Columbia (UBC). Key research areas include healthcare system efficiency, stochastic optimization, approximate dynamic programming, and resource allocation in emergency and long-term care settings. He has contributed to projects like optimizing inpatient unit staffing during pandemics and developing robust logistics models for disaster relief operations. Education: B.A.Sc. (McMaster), M.Sc. (UBC), Ph.D. (UBC) Affiliations: INFORMS, CORS, Centre for Health Care Management (UBC) Recent work emphasizes pandemic-related healthcare operations, including staff scheduling under crisis conditions and elective patient admission strategies during the COVID-19 pandemic. His research also addresses dynamic home care routing and multi-priority patient scheduling policies. He has secured over $2.3 million in research funding through grants from NSERC, MITACS, and the National Research Council. Notable projects include the 'Resilience and Adaptation to Climatic Extreme (RACE) Wildfires' initiative and 'Stochasticity in Approximate Dynamic Programming.' Labs/Teams: Collaborates with interdisciplinary teams on healthcare logistics and emergency department optimization projects.

Hao Wangمشاهده پروفایل

مدرس ارشد

• Applied Machine Learning
• Smart Grid Optimization
• Data Analytics for Energy Systems
• +۴ مورد دیگر

Hao Wang is a Senior Lecturer in the Department of Data Science and Artificial Intelligence at Monash University, affiliated with the Monash Energy Institute and Monash Data Futures Institute. He holds an ARC DECRA Fellowship and leads interdisciplinary research on applied machine learning and optimization in smart grids, energy systems, and smart cities. His work focuses on leveraging AI for energy data analytics, EV integration, and distributed energy resource management. Education: PhD from The Chinese University of Hong Kong, supervised by Prof. Jianwei Huang. Postdoctoral research at Stanford University (Prof. Ram Rajagopal) and University of Washington (Prof. Baosen Zhang). Research interests include reinforcement learning for energy systems, decentralized control, and incentive mechanisms for prosumer participation. He serves on editorial boards of journals like Energy Conversion and Economics and IET Renewable Power Generation , and organizes conferences like ACM e-Energy and IEEE SmartGridComm. Awards: Best Paper Awards at IEEE PECON 2016, IEEE ICC 2017, and IEEE SmartGridComm 2020. Current projects include the Human-in-the-loop Microgrid Project and AI for Clean Energy and Sustainability initiative.

Malcolm Heywoodمشاهده پروفایل

استاد

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +۱۲ مورد دیگر

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

• Power System Analysis, Operation and Planning
• Power System Economics
• Electricity Markets
• +۱۶ مورد دیگر

Ivana Kockar is a Reader in the Department of Electronic and Electrical Engineering at the University of Strathclyde, within the Faculty of Engineering. She is affiliated with the Institute for Energy and Environment and leads research on the integration of Distributed Energy Resources (DERs) into power systems, focusing on optimization, market design, and policy analysis. PhD, McGill University, 2004 Her research interests include active network management, TSO-DSO coordination, agent-based modeling, Multi-Criteria Decision Analysis (MCDA), and virtual power plants. She develops tools to support the transition to low-carbon power systems and improve grid flexibility through demand participation and energy storage. The recent publications highlight a strong trend in analyzing DSO risk, flexibility markets, and human behavior in energy planning. Her work increasingly integrates socio-technical dimensions, examining how corporate and individual behaviors impact grid operations. There is also a consistent focus on business models for virtual power plants and the economic impacts of forecasting accuracy, indicating a blend of technical and market-oriented research. Outstanding Engineer Award, 2024 Best Innovation Project, December 2015 Special Issue inclusion, International Journal of Electrical Power and Energy Systems, 2008 She has supervised multiple PhD and MSc students and is Principal Investigator on several major research projects, including EPSRC-funded doctoral training and industrial knowledge exchange initiatives. Her work is supported by grants from EPSRC, Ofgem, and energy companies such as SSEN and Scottish Power. She actively collaborates with Management Science colleagues and contributes to policy development. She leads a research team at the Institute for Energy and Environment, working closely with industry partners and international research consortia such as the EU Horizon 2020 SmartNet project. Her team applies advanced computational methods, including high-performance computing and agent-based simulations, to real-world energy challenges.

Lena Mamykinaمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Human-Computer Interaction
• Ubiquitous and Pervasive Computing
• +۵ مورد دیگر

Lena Mamykina is an Associate Professor of Biomedical Informatics at Columbia University's Vagelos College of Physicians and Surgeons. As a member of the Data, Media and Society Committee and Health Analytics Co-Chair, she develops technologies to empower individuals and communities in health management. Georgia Institute of Technology: M.S. and Ph.D. in Human-Computer Interaction and Human-Centered Computing Columbia University: M.A. in Biomedical Informatics Ukrainian State University of Maritime Technology: B.S. in Computer Science Her research in the Action Research for Collective Health (ARCH) group focuses on: Biomedical Informatics and Human-Computer Interaction Ubiquitous/Pervasive Computing for health monitoring Computer-Supported Collaborative Work in clinical teams Personalized health coaching systems using AI Recent publications highlight trends in: AI-driven diabetes management and glucose forecasting Context-aware mobile health applications Conversational agents for behavioral interventions Data assimilation techniques with sparse patient data Equity-focused health informatics design Human-AI collaboration in clinical settings

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)
• +۴ مورد دیگر

Nikos Komninos is a researcher at City, University of London , specializing in cybersecurity, network security, and privacy-preserving systems. His work spans Internet of Things (IoT) , mobile ad hoc networks , and cloud computing security. Research Areas : Cybersecurity frameworks, machine learning for threat detection, quantum-resistant encryption, and privacy-preserving authentication systems. His recent publications focus on ransomware detection under concept drift, DDoS mitigation in IoT, and attribute-based encryption for fog computing. He has contributed to IEEE Transactions and journals like Computers & Security , with a trend toward real-time adaptive security systems and Bayesian risk assessment .

Noah Gansمشاهده پروفایل

استاد

• Service Operations
• Stochastic Processes
• Queueing Systems
• +۳ مورد دیگر

Noah Gans is the Anheuser-Busch Professor of Management Science at the Wharton School of the University of Pennsylvania, where he serves as Professor in the Operations, Information and Decisions department. His research focuses on service operations with particular emphasis on call center management, stochastic processes, and queueing system control. Department Editor, Stochastic Models and Simulation at Management Science President of Manufacturing and Service Operations Management Society (MSOM) PhD Program Coordinator for the OID Department His academic work spans diverse domains including healthcare technology pricing, container inspection security, workforce optimization, and revenue management. He has pioneered adaptive clinical trial designs and developed novel models for customer demand sensitivity in overbooking scenarios. Key research areas include: Bayesian sequential learning for multi-arm clinical trials Value-based pricing under uncertainty Stochastic control in service systems Security policy analysis for global supply chains Risk-sharing mechanisms in healthcare Scientific honors include NSF CAREER Award (1998), INFORMS George E. Nicholson Prize (1995), and multiple teaching awards from the Wharton MBA program (2004, 2010-2011, 1997-2001). His publications bridge theoretical operations research with practical implementation across healthcare, transportation, and service industries.

Selçuk Karabatıمشاهده پروفایل

استاد

• Retail Operations Management
• Sustainable Operations Management
• Supply Chain Management
• +۳ مورد دیگر

Selçuk Karabatı is a Professor of Operations Management at the College of Administrative Sciences and Economics, Koç University (Turkey). His research spans retail operations, sustainable supply chains, and production systems optimization. He holds a PhD from the University of Texas at Austin and has previously taught graduate courses in Service Operations Management , Operations Strategy , and Sustainable Operations Management at Koç University. Education : PhD (University of Texas at Austin), MS (University of Southern California), BS (Boğaziçi University) Editorial Roles : Senior/Associate Editor at Production and Operations Management and IIE Transactions His research focuses on Supply Chain Management , Retail Operations , and Sustainable Operations , with applications in inventory control, pricing strategies, and production planning. Recent and historical publications reveal expertise in optimization frameworks , dynamic pricing , portfolio rebalancing , and retail analytics . While no explicit scientific awards are listed in the provided text, his work has consistently addressed challenges in inventory substitution, auction mechanisms, and logistics coordination.

Mihai Marasteanuمشاهده پروفایل

استاد

• Asphalt Pavement Engineering
• Fracture Mechanics
• Viscoelasticity
• +۳ مورد دیگر

Mihai Marasteanu serves as Professor and Miles Kersten Chair in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, with affiliations at the Center for Transportation Studies. His research bridges fundamental material science and practical pavement engineering solutions for Minnesota's infrastructure network. His primary research focuses on asphalt pavement engineering , specializing in fracture mechanics and viscoelasticity applied to low-temperature cracking analysis. Key interests include recycled material integration , asphalt binder-mixture property relationships , and innovative testing methodologies for quality control. His work emphasizes cost-effective solutions for local roads while advancing predictive models for pavement performance. Recent publications (2022-2024) demonstrate strong trends in asphalt mixture optimization , probabilistic density modeling , and nanomaterial-enhanced asphalt (e.g., graphene nanoplatelets). The research consistently targets Minnesota-specific challenges including cold-climate durability, recycled material validation, and field-compaction efficiency. Professor Marasteanu maintains active funding through 9 current projects including: Tools to improve asphalt pavement durability (MN DOT, 2025-2027) Asphalt lift thickness impact on density (MN DOT, 2024-2026) Sawing/sealing joints for cracking control (MN DOT, 2023-2026) EV data for pavement quality assessment (FHWA, 2023-2025) His national leadership includes coordinating pooled fund studies with Wisconsin, Iowa State, and Illinois researchers on low-temperature cracking. While specific lab names aren't documented, his team operates within University of Minnesota's testing facilities, utilizing advanced rheometers and computational models to validate size-effect theories and representative volume element concepts for asphalt mixtures.

Mauricio Bustamanteمشاهده پروفایل

استادیار

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
• +۵ مورد دیگر

Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

• High-dimensional probability theory
• Data Science
• Machine Learning
• +۵ مورد دیگر

Sjoerd Dirksen is a Professor of Mathematics for Data Sciences at Utrecht University since May 2025, having previously served as an Associate Professor for Applied Mathematics (2019-2025) and Junior Professor at RWTH Aachen University (2014-2019). He is affiliated with the Mathematical Institute within the Faculty of Science at Utrecht University, where his office is located in the Hans Freudenthal Building. His research interests focus on high-dimensional probability theory and its applications in data science, machine learning, and signal processing. Specifically, he investigates randomized data dimension reduction methods using structured random matrices, theory for deep learning including random neural networks, high-dimensional covariance estimation for wireless communication systems, and statistical postprocessing of weather forecasts in collaboration with the Royal Netherlands Meteorological Institute (KNMI). Previously, he worked on compressed sensing, sharp estimates for stochastic processes in Banach spaces, and noncommutative analysis. Analysis of his recent publications (2018-2024) reveals a strong focus on quantization effects in high-dimensional data processing, particularly one-bit compressed sensing and covariance estimation under coarse quantization. His work bridges theoretical mathematics with practical applications in signal processing, wireless communications, and meteorological forecasting, demonstrating a consistent trajectory from foundational mathematical research to applied data science problems. Dirksen's academic career shows progression from postdoctoral work at the Hausdorff Center for Mathematics in Bonn to independent research positions. His publication record demonstrates significant contributions to the mathematics of data science, with papers appearing in top journals across mathematics, statistics, and signal processing. His research combines deep theoretical insights with practical applications, particularly in the areas of dimensionality reduction and high-dimensional statistics.

Antonios Deligiannakisمشاهده پروفایل

استاد

• Databases
• Sensor Networks
• Analytical Data Processing
• +۴ مورد دیگر

Antonios Deligiannakis is a Professor at the School of Electronic and Computer Engineering of the Technical University of Crete, specializing in database systems and distributed data processing. His academic career includes a postdoctoral position at the National and Kapodistrian University of Athens (2006-2007) and a visiting researcher role at AT&T Labs-Research (2003). His educational background includes: PhD in Computer Science, University of Maryland, USA (2005) Master's Degree in Computer Science, University of Maryland, USA (2001) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1999) Professor Deligiannakis's research spans Databases , Stream Processing , and Sensor Networks , with pioneering work in Approximate Query Evaluation for massive datasets and Complex Event Processing in distributed environments. His contributions enable efficient analytics in resource-constrained settings through techniques like synopses-based engines and windowed outlier detection. His 15 most recent publications (2020-2025) reveal a dominant focus on distributed streaming analytics, with recurring themes of cross-platform integration, federated learning, and extreme-scale interactive systems. Key innovations include the INFORE framework for interactive analytics, DAG* for IoT workflow optimization, and communication-efficient federated learning techniques—demonstrating consistent translation of theoretical advances into production-ready platforms. Scientific Awards: No specific awards were listed in the provided material. Information about advisees and research grants was not provided in available documentation, though his leadership in the Distributed Information Systems and Applications laboratory suggests active mentorship and project direction. He directs research in the Distributed Information Systems and Applications laboratory, developing systems for real-time analytics across domains including maritime surveillance, financial technology, and IoT platforms, with emphasis on scalability and fault tolerance in geo-distributed environments.

Yanrong Yangمشاهده پروفایل

دانشیار

• High Dimensional Statistics
• Large Dimensional Random Matrix Theory
• Functional Data Analysis
• +۳ مورد دیگر

Yanrong Yang is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, The Australian National University. Her research focuses on high-dimensional statistical inference, large-dimensional random matrix theory, functional data analysis, and responsible statistical learning. She has developed asymptotic theories for high-dimensional statistics and applied them to time series forecasting and panel data analysis. PhD in Statistics, Nanyang Technological University (2009-2013) MSc in Statistics, Shandong University (2006-2009) BSc in Statistics, Shandong University (2002-2006) Her research explores high-dimensional data analysis, including eigenvalue methods, functional principal component analysis, and applications to mortality forecasting and financial portfolio optimization. Recent publications examine fairness-aware models for annuity pricing, robust PCA techniques, and eigen-analysis for time series clustering. She has published extensively in top journals such as the Annals of Statistics, Journal of Econometrics, and Journal of the American Statistical Association. Her current project, Feature Learning for High-dimensional Functional Time Series (2023-2026), investigates representation learning in financial time series.

Robert Laubacherمشاهده پروفایل

پژوهشگر ارشد

• Collective Intelligence
• Artificial Intelligence
• Organizational Design
• +۳ مورد دیگر

Robert Laubacher is a Research Fellow at the MIT Center for Collective Intelligence within the Sloan School of Management , focusing on how technological innovations like generative AI and large language models are reshaping organizational practices, work structures, and social patterns. Education: B.A. in American Studies, Northwestern University M.A., Doctoral Coursework in Modern History, Harvard University His research explores: Developing ontologies for work activities inspired by biological taxonomy Enhancing human creativity through AI collaboration frameworks Building collective intelligence systems for global challenges like climate change and sustainable development Identifying foundational elements of collective intelligence Historical analysis of IT's impact on employment relationships Recent publications focus on human-AI co-creation , crowdsourced problem-solving , and organizational adaptation to digital transformation. His work has been featured in Harvard Business Review , Sloan Management Review , and ACM conference proceedings.