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Ly Fie SugiantoView profile

Associate Professor

• Accounting Information Systems
• Data Analytics
• Artificial Intelligence
• +8 more

Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.

Seth Lewis GilbertView profile

Associate Professor

• Distributed Systems
• Algorithms & Theory
• Systems & Networking
• +6 more

Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .

Justine SherryView profile

Assistant Professor

• Computer Networking
• Networked Systems
• Middleboxes
• +5 more

Justine Sherry is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University, affiliated with the College of Engineering. She holds a PhD (2016) and MS (2012) from UC Berkeley and a BS/BA (2010) from the University of Washington. Her research focuses on networked systems, including middleboxes, cloud computing, congestion control, and hardware acceleration (e.g., SmartNICs/FPGAs). Notable projects include Pigasus (open-source 100Gbps IDS), APLOMB (cloud-based middlebox scaling), and BlindBox (encrypted traffic scanning). Her academic roles include serving on the SIGCOMM CARES Committee, DARPA ISAT Study Group, and ACM CoNEXT Steering Committee. Awards include the Alfred P. Sloan Fellowship, VMware Systems Award, and IETF Applied Networking Prize. She advises over 15 students and collaborates with industry partners like Intel and VMware. Research highlights include radical shifts in datacenter architectures (SmartNIC compute control), fairness in congestion algorithms (BBR analysis), and database-proxy innovations (Tigger with eBPF). Her teaching emphasizes systems as science labs, integrating experimental design and hypothesis testing into projects. Education: PhD UC Berkeley (2016), MS UC Berkeley (2012), BS/BA University of Washington (2010) Labs/Teams: CyLab, SNAP Research Group, CMU Portugal Collaboration Grants: NSF, Intel, Google Faculty Awards

Kari IngstadView profile

Professor

• Healthcare Management
• Sociology
• Health Services Organization
• +4 more

Kari Ingstad is a Professor of Sociology at the Faculty of Nursing and Health Sciences, Nord University, and currently serves as Vice-Dean for Research. Her work bridges academia, clinical practice, and policy, focusing on healthcare organization, leadership, and innovation in Norway's health sector. She holds a PhD in Sociology from NTNU (2011) and has extensive clinical experience in municipal and specialist healthcare. Education: PhD in Sociology, NTNU, 2011 Nursing background from municipal and specialist healthcare sectors Research Interests: Workforce planning and staffing strategies in healthcare Impact of shift scheduling on employee well-being and patient safety Integration of artificial intelligence in healthcare operations Cross-sectoral collaboration and innovation in health services Recent Research Trends: Her 2024 publications emphasize strategic staffing solutions, AI-driven shift planning, and long-shift work-life balance. Key projects include the OptiCare-AI initiative (Norwegian Research Council-funded), exploring AI applications for optimizing healthcare resource allocation. Leadership & Grants: Leads the Acute Care, Innovation and Patient Safety (AKIP) research group Principal Investigator on OptiCare-AI (2023–present) Labs/Teams: Active collaborations with the AKIP group and national/international networks on healthcare workforce issues.

Kaiyang LiuView profile

Assistant Professor

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +3 more

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

• Wireless Networks
• Internet of Things
• Network Security
• +7 more

Robin Kravets is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Mobius research group. She earned her Ph.D. in Computer Science from the Georgia Institute of Technology in August 1999. Her office is located at 3114 Siebel Center for Comp Sci, and she can be contacted via email at rhk@illinois.edu or phone at (217) 244-6026. Courses Taught: CS 438 (ECE 438) - Communication Networks CS 498 WN3 (CS 498 WN4) - Wireless IoT Lab CS 591 PH2 (CS 591 PHD) - PhD Orientation Seminar CS 591 SCH - PhD Job Search Prep CS 591 WN - Wireless Networking Seminar ECE 439 (CS 439) - Wireless Networks ENG 572 - Professional Practicum Research Focus: Dr. Kravets specializes in wireless and mobile systems, with emphasis on IoT security, energy-efficient networking, and privacy-preserving protocols. Her work addresses challenges in dense network environments, vehicular communication, and low-power devices, often leveraging probabilistic models and adaptive protocols to enhance system resilience. Publication Trends: Her recent articles (2016-present) predominantly explore wireless IoT security (e.g., MAC randomization defenses, BLE optimization) and practical applications in environmental sensing/retail. Earlier work (2007-2015) established foundations in vehicular networks, disruption-tolerant communication, and energy-aware protocols. Leadership: She directs the Mobius Group, which develops solutions for mobile and IoT systems, including projects like IVP (Illinois Vehicular Project) and contributions to standards in pervasive computing.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +13 more

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Retsef LeviView profile

Professor

• Food Supply Chain Analytics
• Supply Chain Optimization
• Health Systems
• +7 more

Retsef Levi is the J. Spencer Standish (1945) Professor of Operations Management at the MIT Sloan School of Management, affiliated with the MIT Operations Research Center. He co-directs the Leaders for Global Operations (LGO) Program. His work focuses on data-driven decision models for healthcare systems, supply chain optimization, and risk management. Levi holds a PhD in Operations Research from Cornell University and has led industry collaborations with major hospitals and organizations like the FDA and Walmart Foundation. Education: PhD in Operations Research, Cornell University, 2005 Bachelor’s in Mathematics, Tel-Aviv University, 2001 Research Interests: Levi’s research addresses complex decision-making under uncertainty in healthcare, supply chains, and logistics. Key areas include food safety analytics, risk-based sampling, and predictive modeling for zoonotic diseases. He designs algorithms for inventory control, appointment scheduling, and healthcare resource allocation. Articles Overview: Recent work spans AI-driven epidemiological models, supply chain cybersecurity, and agricultural market interventions. His articles emphasize practical applications of operations research in healthcare and public health. Awards: NSF Career Grant INFORMS Optimization Prize (2008) Wagner Prize (2013) Harold W. Kuhn Award (2016) Advising & Grants: Advised 10 PhD students and 34 master’s students. Led multi-million-dollar projects like the Walmart Foundation initiative for China’s food safety. Active in hospital process optimization and FDA risk management contracts. Labs & Teams: Runs MIT’s Food Supply Chain Analytics and Sensing Initiative, collaborating with global partners on predictive risk tools and healthcare analytics.

Banu LokmanView profile

Professor

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +3 more

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Lata NarayananView profile

Professor

• Distributed computing
• Graph algorithms
• Routing algorithms
• +7 more

Dr. Lata Narayanan is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. Her research spans theoretical and applied aspects of distributed systems, with a focus on algorithms for mobile agents, communication networks, and sensor networks. Department: Computer Science and Software Engineering University: Concordia University Research Interests Lata Narayanan specializes in algorithms for mobile robots and ad hoc networks , with expertise in routing on distributed networks , parallel algorithms , and social network analysis . Her work addresses challenges in sensor network optimization, barrier coverage, and time-energy tradeoffs for evacuation systems. Article Trends Her recent publications (2021-2025) emphasize game theory for network dynamics, cloud resource allocation , and temporal graph exploration . Key themes include strategic diversity, truck-drone delivery logistics, and energy-sharing protocols for mobile agents.

• Combinatorial Optimization
• Scheduling Theory
• Operations Research
• +3 more

Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.

Dimitris KolovosView profile

Professor

• Automated and Model-Based Software Engineering
• Model-Driven Software Engineering
• GenAI for Software Engineering
• +4 more

Roles and Affiliations : Dimitris Kolovos is a Professor of Software Engineering at the University of York's Department of Computer Science. He leads the Automated Software Engineering (ASE) research group and is an Eclipse Foundation committer, leading development of the Epsilon open-source platform. His roles include research leadership, teaching, and academic service. Education : PhD in Software Engineering - University of York MSc in Software Engineering with Distinction - University of York First Class Honours Degree in Informatics - Athens University of Economics and Business Research Interests : Kolovos focuses on advancing Model-Driven Engineering (MDE), GenAI integration in software development, low-code platforms, and data analytics. His work emphasizes scalable modeling tools, education technology (e.g., MDENet platform), and industry collaboration with organizations like NASA, BAE Systems, and Siemens. Labs and Projects : He leads the Epsilon project under the Eclipse Modelling initiative, developing tools for model transformation, validation, and code generation. His research group also explores AI-driven model transformations and hybrid graphical-textual editors.

Jung Yun BaeView profile

Assistant Professor

• Robotics
• Multi-robot systems
• Coordination of Heterogeneous Robot Systems
• +4 more

Jung Yun Bae serves as Assistant Professor in Mechanical and Aerospace Engineering at Michigan Technological University with a secondary appointment in Applied Computing within the College of Computing, joining MTU in 2019 after five years as Research Professor at Korea University's Intelligent Systems and Robotics Laboratory. Her academic credentials include: PhD in Mechanical Engineering from Texas A&M University MS in Mechanical Engineering from Hongik University BS in Mechanical Engineering from Hongik University Dr. Bae's research program centers on Robotics with emphasis on Multi-robot systems, particularly Coordination of Heterogeneous Robot Teams and Vehicle Routing Problems. Her work develops operational strategies for multi-agent autonomous vehicle systems through Multi-robot System Control and Optimization techniques, extending to Autonomous Navigation and Operational Research applications. Current investigations focus on underwater robotics coordination and neuroevolution approaches for connected vehicle systems. Analysis of her recent publications reveals consistent focus on workload-balanced task allocation for heterogeneous robot teams across challenging environments. Her underwater robotics research addresses tether management and entanglement avoidance, while neuroevolution applications target autonomous vehicle control at uncontrolled intersections and hybrid powertrain optimization, bridging robotics with operations research and artificial intelligence. Prior to MTU, Dr. Bae maintained affiliation with Korea University's Intelligent Systems and Robotics Laboratory. Her current work at MTU connects with the Great Lakes Research Center context, though specific laboratory details aren't provided in available materials.

Nicole MegowView profile

Professor

• Combinatorial optimization
• Discrete optimization
• Mathematical optimization
• +11 more

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).

Gabor ViragView profile

Associate Professor

• Economic Theory
• Auction Theory
• Search Theory
• +5 more

Gabor Virag is an Associate Professor of Economic Policy Analysis at the University of Toronto, Mississauga , with a cross-appointment to the Rotman School of Management . He serves as the PhD Coordinator for the Economic Analysis and Policy area. His research focuses on market dynamics, auction theory, and search theory, with a particular interest in decentralized market interactions and information policy. Education : PhD in Economics from Princeton University, MA from Central European University, BA from Budapest University of Technology and Economics. Research Interests : Economic Theory, Industrial Organization, Game Theory, and their applications to dynamic contests, patent markets, and labor economics. Publications : His work appears in top-tier journals such as the American Economic Journal, Games and Economic Behavior, and Review of Economic Studies, emphasizing auctions with resale, innovation prizes, and wage inequality. Collaborations : Co-author of studies with scholars from Bocconi University, Claremont McKenna College, and the Hungarian Academy of Sciences.