جستجوی استاد راهنمای دانشگاهی

Avijit Khanraمشاهده پروفایل

استادیار

• Scheduling
• Stochastic Systems
• Production & Supply Chain Management
• +۲ مورد دیگر

Avijit Khanra is an Assistant Professor in the Department of Management Sciences at the Indian Institute of Technology Kanpur. He holds a PhD (FPM) in Production & Quantitative Methods from IIM Ahmedabad and a BE in Mechanical Engineering from Jadavpur University, bringing both academic rigor and industry experience to his role. His research focuses on Scheduling, Stochastic Systems, and Production & Supply Chain Management, with specific interests in Operations Research and Operations Management. His work often explores optimization problems in supply chain contexts, particularly related to the newsvendor model and job-shop scheduling. His research demonstrates a strong theoretical foundation with practical applications in manufacturing and supply chain systems. Dr. Khanra's scholarly work has been published in leading operations research journals including Operations Research Letters and the European Journal of Operational Research, reflecting his expertise in mathematical modeling and optimization techniques for complex business problems. Excellence-in-Teaching Award 2021 (by IIT Kanpur) Prof. Tirath Gupta Memorial Thesis Award 2014-15 (by IIM Ahmedabad) Industrial Finance Corporation of India Thesis Proposal Award 2012-13 (by IIM Ahmedabad) Dr. Khanra has received significant sponsored research funding from Mitsubishi Heavy Industries for projects on Large-scale Job-shop Scheduling and Machine Learning Applications in Job-shop Scheduling. Prior to his academic career, he worked as an Engineer/Manager in Manufacturing at Tata Motors Ltd from 2005-2009, providing him with valuable industry experience that informs his research and teaching approach. He is actively affiliated with the Analytics Society of India, Operational Research Society of India, and Society of Operations Management, India. He teaches courses in Applied Probability, Operations Research, and Operations Management, focusing on both theoretical foundations and practical applications of quantitative methods in business decision-making.

Ewa Deelmanمشاهده پروفایل

پژوهشگر

• Distributed Computing
• Scientific Workflow Management
• Cloud Computing
• +۴ مورد دیگر

Ewa Deelman is a Principal Investigator and Director of CI Compass at the University of Southern California. She is a leading expert in automation technologies, particularly scientific workflow management systems. Key roles: PI for CI Compass (NSF Cyberinfrastructure Center of Excellence), leader of Pegasus workflow software Research focus: Distributed computing, scientific workflows, cloud computing, and data/resource management optimization Current projects: SWARM, MATCH, Pegasus, CI Compass, Analytics4MD, PoSeiDon, CI4Resilience, FlyNet, RobustScience, PATh Funding agencies: National Science Foundation, Department of Energy, DARPA, National Institutes of Health Her work enables automated execution of large-scale scientific applications across distributed resources in domains like astronomy, bioinformatics, and gravitational-wave physics. She actively recruits PhD students and postdocs for research at the intersection of distributed computing, big data, and machine learning.

• Multi-objective Optimization
• Nonlinear Optimization
• Evolutionary Algorithms
• +۴ مورد دیگر

Lino António Antunes Fernandes Costa is an Associate Professor (with tenure) at the Department of Production and Systems, School of Engineering, University of Minho, Portugal. He conducts his research activities at the ALGORITMI R&D Centre as a member of the Systems Engineering and Operational Research (SEOR) research group. His academic credentials include a PhD in Production and Systems Engineering, an MSc in Informatics, and a DEng in Informatics and Systems Engineering. These qualifications form the foundation for his interdisciplinary research bridging engineering, computer science, and mathematical optimization. Dr. Costa's research focuses on multi-objective optimization, nonlinear optimization, evolutionary algorithms, and applied statistics. His work demonstrates significant application to real-world industrial problems, particularly in manufacturing optimization, agricultural technology (with emphasis on olive cultivation), emergency response systems, and quality control. His methodological approaches often combine traditional optimization techniques with modern machine learning and artificial intelligence methods. His publication record shows a clear evolution toward Industry 4.0 applications, with recent work emphasizing the integration of optimization techniques with smart manufacturing systems, agricultural technology, and emergency management. His research demonstrates strong interdisciplinary connections between operations research, computer science, and domain-specific applications in manufacturing and agriculture. h-index: 17 Total Publications: 126 Total Citations: 1,193 Q1/Q2 Journal Publications: 40 Dr. Costa serves as a regular reviewer for prestigious journals including IEEE Transactions on Evolutionary Computation, Evolutionary Computation, IEEE Intelligent Systems, and European Journal of Operational Research. He has contributed to over 40 international scientific events as a program committee member, including major conferences such as ACM GECCO, IEEE CEC, and EMO. His professional activities extend to project-based learning initiatives in industrial engineering education, demonstrating his commitment to both research and teaching excellence. His laboratory work centers around the ALGORITMI Centre's SEOR research group, where he collaborates on developing optimization frameworks for distributed manufacturing systems, agricultural technology applications, and emergency resource management. Current projects include applying multi-objective optimization to olive cultivation challenges and developing intelligent systems for firefighting resource allocation.

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +۶ مورد دیگر

Eranda Dragoti-Cela is an Associate Professor at the Department of Optimization and Discrete Mathematics, Graz University of Technology. She specializes in combinatorial optimization, focusing on polynomially solvable special cases of NP-hard problems like the Quadratic Assignment Problem (QAP), portfolio optimization, and algorithmic graph theory. Education: M.Sc. in Applied Mathematics, University of Tirana (1992) Ph.D. in Technical Sciences, Graz University of Technology (1995) Habilitation in Applied Mathematics (2001) Her research spans combinatorial optimization (QAP, assignment problems, location problems), portfolio optimization (risk-return models, ordinal information integration), and graph theory (edge intersection graphs, network flows). Recent work includes linearization of quadratic shortest path problems and data arrangement on trees. She supervises bachelor's and master's theses in discrete mathematics and teaches courses like Combinatorial Optimization 1 and Risk Theory and Management , emphasizing algorithmic rigor and practical applications in finance and engineering.

• Embedded Systems
• Cyber-Physical Systems
• Performance Engineering
• +۳ مورد دیگر

Jeroen Voeten is a Full Professor in the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He also holds a position as a Research Fellow at the Embedded Systems Institute in Eindhoven and is a Senior Scientist and Scientific Advisor to TNO-ESI since 2017. Academic Background: MSc in Mathematics and Computing Science (1991, TU/e) PhD in Electrical Engineering (1997, TU/e) Voeten's research focuses on formal methodologies for hardware/software system specification, design, and implementation. His work spans computer architectures, embedded systems, performance modeling, and cyber-physical systems. He is currently leading the Carm 2G project with ASML to enhance model-based engineering environments for wafer scanner control systems. His recent publications emphasize advancements in global scheduling, fault-tolerant real-time systems, and hybrid performance modeling. These studies address critical areas like latency reduction, schedulability improvements, and data age analysis in multi-rate task chains. Scientific Awards: Best Paper Award, Forum on Specification and Design Languages (FDL 2005) Best Paper Award, Property-Preserving Synthesis for Unified Control and Data-Oriented Models (2005) Voeten has contributed to 87 conference reports, 13 academic reports, 11 book chapters, and 11 journal articles, reflecting his extensive involvement in both academic and industrial research. Labs and Collaborations: He is affiliated with the Model-Based Design Lab and the High Tech Systems Center at TU/e, collaborating with institutions like TNO-ESI and industry leaders such ASML. His work aligns with the UN Sustainable Development Goals (SDGs) through applications in embedded systems and high-tech manufacturing.

• Health Systems Optimization
• Medical Decision Making
• Cancer Care
• +۶ مورد دیگر

Dr. Maryam Eghbalizarch serves as Assistant Professor in the Department of Industrial and Systems Engineering at Kennesaw State University's Southern Polytechnic College of Engineering and Engineering Technology. Her institutional affiliations include: Current: Assistant Professor, Kennesaw State University Previous: Data Scientist, The University of Texas MD Anderson Cancer Center Previous: Postdoctoral Fellow, Wayne State University (2022-2024) Previous: Assistant Professor, Alzahra University (2021-2022) Visiting Researcher, University of Pittsburgh (2016-2017) Member, Cancer Intervention and Surveillance Modeling Network (CISNET) Member, Society for Medical Decision Making (SMDM) Member, INFORMS and IISE Her educational background features a Ph.D. in Industrial Engineering from the University of Tehran (2018), with M.Sc. and B.Sc. degrees from K.N. Toosi University of Technology. As director of the Health Systems Optimization Lab, she leads research at the intersection of advanced modeling techniques and healthcare applications. Dr. Eghbalizarch's work integrates Markov decision processes , multi-objective optimization , and machine learning to solve critical problems in cancer care (ovarian/lung cancer modeling) and diabetes management . Her methodology-driven approach focuses on sequential decision-making under uncertainty to improve healthcare delivery and patient outcomes through cost-effective interventions. Her recent publications (2024-2025) demonstrate strong momentum in oncology modeling and healthcare optimization, with multiple papers in high-impact journals including American Journal of Obstetrics and Gynecology and Lung Cancer. These works reveal consistent themes in histology-specific cancer modeling, natural history validation, and diabetes treatment optimization using advanced analytics. Key recognitions include: Institute for Data Science in Oncology Fellowship at MDACC Consecutive Postdoctoral Trainee Research Awards (Wayne State, 2023-2024) National Elites Foundation Award (Iran) International Affairs Department Research Grant Dr. Eghbalizarch maintains active grant funding through CISNET and currently recruits fully funded graduate students for Fall 2026. Her mentorship approach emphasizes hands-on research experience in healthcare analytics, with students contributing to high-impact projects in cancer screening optimization and diabetes management systems. The HSOpt Lab provides a collaborative environment focused on translating engineering methodologies into practical healthcare solutions, with strong connections to cancer research networks and clinical partners. Current projects emphasize data-driven decision support systems for medical practitioners and health policy makers.

• Urban and Regional Planning
• Landscape Reclamation
• Sustainable Development
• +۴ مورد دیگر

Luis Loures is Full Professor and President of Polytechnic Institute of Portalegre, Portugal. A landscape architect and agronomic engineer by training, he holds a PhD in Planning (University of Algarve) and post-doctoral qualification in Agronomy (Universidad de Extremadura). His distinguished career includes international research positions at University of Toronto and Michigan State University. His research encompasses: Urban/regional planning and landscape reclamation Sustainable development frameworks Cross-border cooperation mechanisms Climate-smart agriculture systems Mediterranean environmental management Recent publication trends (2021-2025) show concentrated focus on: Sustainable tourism in rural border regions Agroecological soil management techniques Spatial analysis of landscape patterns Climate adaptation in agriculture Urban-periurban ecosystem services Awards include: Honoris Causa Doctorate from Universidad Tecnológica ECOTEC (Ecuador) Research leadership includes: Principal investigator for 34 grants including EU COST Actions VALORIZA Research Centre affiliation Projects spanning irrigation efficiency, circular economy, and regenerative landscapes Laboratory affiliation: VALORIZA - Research Centre for Endogenous Resource Valorization

Maha Abdelhaqمشاهده پروفایل

استادیار

• Mobile Ad Hoc Networks
• Intrusion Detection Systems
• Network Performance Analysis
• +۴ مورد دیگر

Maha Abdelhaq is an Assistant Professor in the Department of Computer Science at Princess Nourah bint Abdulrahman University. Her research spans mobile ad hoc networks, cybersecurity, and AI-driven solutions for IoT and 6G systems. She has authored over 100 publications, with recent work focusing on adaptive learning frameworks, anomaly detection, and quantum-enhanced privacy mechanisms. Her core research interests include: Network performance analysis and intrusion detection in dynamic environments Machine learning applications for IoT security and edge computing 6G network optimization using quantum and federated learning techniques Recent publications (2023-2025) reveal a strong emphasis on AI/ML-driven methodologies, with trends in: Real-time anomaly detection for cyber-physical systems Resource-aware federated learning for medical IoT Hybrid quantum-classical approaches for vehicular network security No awards, lab affiliations, or supervised students are documented. Collaborative projects frequently address emerging challenges in decentralized network security and metaverse applications.

Justine Sherryمشاهده پروفایل

دانشیار

• Networked Systems
• Middleboxes
• Congestion Control Algorithms
• +۵ مورد دیگر

Justine Sherry is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University (CMU). Her research focuses on networked systems across software and hardware layers, particularly middleboxes, congestion control, FPGA-based acceleration, and Internet fairness. Affiliations: Computer Science Department (CSD) , CyLab , SNAP Lab . Academic Rank: Associate Professor Her recent work explores radical shifts in datacenter architectures via SmartNICs, hardware-software co-design for network functions, and database proxy innovation using eBPF. She leads research on Internet fairness , denial-of-service mitigation , and high-speed intrusion detection through systems like Pigasus. Key scientific contributions span 15 recent publications at venues like SIGCOMM, OSDI, and VLDB, addressing topics such as: Mathematical fairness in congestion control KERNEL bypass strategies in FPGA SmartNICs BBR algorithmic unfairness Wireless latency optimization (Zhuge) Heterogeneous hardware performance analysis Open-source security infrastructure Major awards include: Sloan Research Fellowship VMWare Systems Award NSF grants Intel Outstanding Researcher Award Early Career Diamond from UW Engineering She mentors students in systems research, including Ranysha Ware , Nirav Atre , and Hugo Sadok , while collaborating with institutions like Google, Facebook, and DARPA.

Weina Wangمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +۷ مورد دیگر

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .

Rodrigo Fonsecaمشاهده پروفایل

دانشیار

• distributed systems
• networking
• operating systems
• +۵ مورد دیگر

Rodrigo Fonseca is an Associate Professor with Tenure in the Computer Science Department at Brown University's School of Engineering. He received his PhD from UC Berkeley in 2008 under Ion Stoica, focusing on execution tracing for distributed systems. His educational background includes a PhD in Computer Science from UC Berkeley (2008). Fonseca's research focuses on distributed systems, networking, and operating systems, with particular interest in understanding complex system behavior. His work spans distributed tracing and debugging (X-Trace, Pivot Tracing), network management in datacenters (2DFQ, Planck), power management in mobile systems (Quanto, Application Modes), and software-defined networking (Participatory Networking, Simon). His research integrates theoretical insights with practical implementations that have influenced both academic and industrial systems. His publication record shows a strong focus on distributed system instrumentation, with recurring themes of causal tracing, resource management, and network monitoring. The most recent publications demonstrate continued innovation in data-aware indexing structures, in-network packet analysis, and context propagation techniques for distributed systems. Fonseca has received significant recognition including an NSDI Test of Time Award for X-Trace, an NSF CAREER Award, and a Best Paper Award at SOSP for Pivot Tracing. His work has been consistently published in top-tier venues including SIGCOMM, NSDI, SOSP, and OSDI. He has mentored numerous graduate students, with five PhD graduates as of 2019 who have gone on to positions at Google, Microsoft, Apple, and MPI-SWS. His research has been supported by funding from the National Science Foundation, Google, Intel, and Microsoft Research, where he also spent visiting periods. He is actively involved in the research community, having served on program committees for major conferences and co-organizing workshops including HotCloud and the New England Networking and Systems Day. In 2019-2020, he served as PC co-chair for HotNets and General Chair for SoCC'2020.

• Bioinformatics
• High Performance Computing
• Intelligent Scheduling
• +۴ مورد دیگر

Peter Kulczycki is a researcher at the Research Center Hagenberg Bioinformatics, University of Applied Sciences Hagenberg. He serves as a Researcher specializing in bioinformatics and high-performance computing with expertise in developing computational solutions for biological research challenges. His research interests span multiple domains: Bioinformatics and computational biology High Performance Computing and parallel processing Intelligent scheduling algorithms Resource management systems DNA analysis and primer design Web-based bioinformatics education Dr. Kulczycki's work focuses on bridging computational science and biological research through efficient algorithms and systems for processing complex biological data. He has made significant contributions to scheduling systems that optimize heterogeneous computing resources for bioinformatics applications, with research progression from foundational bioinformatics resources to sophisticated scheduling systems that predict job execution times. His publication record shows consistent focus on computational efficiency in bioinformatics through innovative scheduling approaches, with notable work on heterogeneous multiplatform hardware, DNA primer calculation using GPGPUs, and web-based bioinformatics education frameworks. Dr. Kulczycki served as Principal Investigator for the Bioinformatics Resource Facility Hagenberg project (2009-2012) under the COIN Cooperation & Innovation program. He has been active in academic discourse, delivering invited talks on Evolution and Genetics and the Birth of Life in 2007. His research collaborations include G. Lirk, H. Brandstätter-Müller, B. Parsapour, and A. Hölzlwimmer on various bioinformatics projects.

• Artificial Intelligence
• Artificial Neural Networks
• Information Systems
• +۳ مورد دیگر

Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at

• Agricultural Robotics
• Precision Agriculture
• Technology Adoption
• +۳ مورد دیگر

Tseganesh Wubale Tamirat serves as Assistant Professor in the Department of Food and Resource Economics at the University of Copenhagen, Denmark. Her research bridges agricultural technology, economics, and social science with field-based investigations across Europe and Ethiopia. Her expertise centers on agricultural robotics adoption dynamics, precision farming economics, and sustainable technology implementation. Key interests include ethical implications of farm automation, socioeconomic barriers to innovation, and comparative analysis of smallholder versus industrial farming systems. She examines how cultural, economic, and policy factors shape technology uptake through multi-stakeholder frameworks. Recent publications (2018-2024) reveal a concentrated focus on field robotics, with 50% of works analyzing European farmer perspectives on robot deployment. Cross-cutting themes include data governance challenges, labor displacement concerns, and optimal integration of automation within crop rotations. Her research demonstrates strong interdisciplinary alignment between agricultural engineering, resource economics, and sustainability science.

Karim Tamssaouetمشاهده پروفایل

دانشیار

• Scheduling
• Inventory-Transportation Integration
• Operations Research
• +۳ مورد دیگر

Karim Tamssaouet is an Associate Professor at BI Norwegian Business School's Department of Accounting, Auditing and Business Economics. His academic trajectory includes positions as Assistant Professor at BI (2020-2024), Postdoctoral Researcher at École des Mines de Saint-Étienne (2019-2020), and R&D Engineer at STMicroelectronics (2016-2019). He holds a PhD from École des Mines de Saint-Étienne, with additional graduate degrees from Université Paris Dauphine and Ecole Nationale Polytechnique. His research specializes in optimization methodologies for industrial challenges, with core interests in: Scheduling algorithms for job-shop and flexible manufacturing systems Integrated inventory-transportation modeling Metaheuristic design (scatter search, neighborhood structures) Dynamic lot-sizing with logistics constraints Bayesian network learning for decision optimization Publication analysis reveals consistent focus on operational efficiency in manufacturing and supply chains, with recent expansions into machine learning applications for optimization. Methodological contributions include novel neighborhood frameworks for scheduling problems and multi-objective approaches for semiconductor production. He currently teaches Business Optimization and Supply Chain Analytics, bridging theoretical operations research with business applications. No research lab affiliations, scientific awards, or supervised students are documented.