Find Academic Supervisors

Johannes FottnerView profile

Professor

• Technical Logistics
• Material Flow Optimization
• RFID Technology
• +5 more

Prof. Dr.-Ing. Johannes Fottner is a full professor of Technical Logistics at the Technical University of Munich (TUM), affiliated with both the TUM School of Engineering and Design and the TUM School of Management. He holds a doctorate in mechanical engineering from TUM (2002) and has extensive industry experience, including leadership roles at Swisslog Group (2002–2008) and MIAS Group (2008–2016). His academic work focuses on material flow optimization, RFID technology integration, and practical applications for SMEs. He leads the Logistics Innovation Center (liz) and RFID Application Center Munich (RFID-AZM) at TUM. Research interests include: Control and optimization of material flow processes using RFID Logistics planning with digital tools Human factors in logistics automation Sustainable logistics systems and circular economy AI-driven decision-making in supply chains His articles emphasize cutting-edge topics like AI integration in healthcare logistics, digital twins for long-lasting systems, and AMR communication protocols. Awards include the 2003 Rudolf Schmidt-Burkhardt Memorial Prize. He actively bridges academic research with industrial practice, particularly in SME contexts. Current projects explore self-optimizing supply chains and predictive analytics for logistics KPIs. Key contributions also include: Development of modular warehouse allocation algorithms Standardization of screw conveyor designs across global industries Advocacy for system-level thinking in construction automation Led TUM's 2024 Fachtagung Schüttgutfördertechnik conference and collaborates internationally on robotics in construction and recycling. Active in professional societies like VDI, serving as deputy chairman of its Production and Logistics Society.

Adam AbdinView profile

Researcher

• Urban Mobility
• Critical Infrastructure Resilience
• Energy Systems Optimization
• +3 more

Adam Abdin is a researcher specializing in interdisciplinary systems optimization, with a focus on urban mobility, critical infrastructure resilience, and space engineering. His work bridges operations research, AI applications, and climate adaptation strategies. He collaborates extensively with institutions on projects involving pandemic response, energy systems, and autonomous technologies. Key research areas include predictive maintenance frameworks, climate change exposure modeling, and on-orbit servicing mission optimization. Developed methodologies for coordinating traffic-power systems and enhancing AI-driven risk management in space missions. Recent research emphasizes strategic planning for pandemic control and optimizing testing strategies, as well as designing resilient energy systems against extreme weather events. His publications highlight contributions to electric vehicle rate design, cyber-risk mitigation in space AI, and comprehensive frameworks for critical infrastructure interdependency.

Anton BravermanView profile

Associate Professor

• Stochastic Modeling
• Applied Probability
• Queueing Theory
• +3 more

Anton Braverman is an Associate Professor of Operations at the Kellogg School of Management, Northwestern University, serving since 2017. His research focuses on stochastic modeling, applied probability, and their applications in ridesharing systems and healthcare operations. He holds a PhD in Operations Research from Cornell University (2017), an MS in Operations Research from Cornell (2015), and a BS in Mathematics and Statistics from the University of Toronto (2012). Braverman's research explores advanced analytical techniques for stochastic systems, including diffusion approximations, queueing networks, and steady-state analysis. His work bridges theoretical probability with practical applications in operations management and engineering systems. Notable contributions include studies on the Join-the-Shortest-Queue model in heavy traffic regimes and optimal routing strategies for ridesharing platforms. He has received the 2017 Best Publication Award from the Applied Probability Society of INFORMS. His teaching includes advanced courses such as Queueing Networks: Models, Algorithms and Emerging Applications and Stochastic Processes I , emphasizing stochastic analysis and dynamic programming techniques. Key Awards: 2017 Best Publication Award (INFORMS Applied Probability Society) Research Focus: Stochastic processes, queueing theory, applied probability applications Teaching: Specializes in stochastic modeling and operations management courses

Zhenghui ShaView profile

Assistant Professor

• System Science
• Design Science
• Artificial Intelligence for Design
• +6 more

Zhenghui Sha is an Assistant Professor in the Walker Department of Mechanical Engineering at the University of Texas at Austin, holding the Walker Scholar title since 2024. He previously served as Assistant Professor at the University of Arkansas (2017-2020) and held a postdoctoral fellowship at Northwestern University (2016). His research focuses on advancing decision-centric science for complex systems engineering, integrating AI, swarm manufacturing, and human-machine interaction. Key affiliations include the Advanced Manufacturing and Design Area, CAMDI, and the Oden Institute. Dr. Sha’s research spans design theory, AI-driven engineering, and sociotechnical systems such as transportation networks. Notable contributions include work on generative design frameworks, multi-robot additive manufacturing, and network-based customer preference modeling. His work has been recognized with awards including the 2022 YEA from ASME and the 2017 Best Dissertation Award. He teaches courses on machine elements, design methodology, and data-driven decision-making. His lab, SiDi Lab, explores interdisciplinary applications of system science and design innovation. Dr. Sha actively contributes to academic leadership roles in ASME committees and has authored over 100 publications.

• Intelligent Transport Systems (ITS)
• Transportation Engineering
• Urban Systems
• +4 more

Dr. Fangce Guo is a Senior Lecturer in the Department of Civil and Environmental Engineering at Imperial College London, part of the Faculty of Engineering. She is affiliated with the Urban Systems Lab and the Centre for Transport Engineering and Modelling. Her research bridges pedagogical strategies and transportation systems, focusing on peer learning's impact on interdisciplinary self-efficacy and technical areas like short-term traffic forecasting and ITS applications. Education includes a PhD in Intelligent Transport Systems (Imperial College London), MSc in Communications and Signal Processing, and a Master in Education (both from Imperial College London). She also holds a BS in Electronic/Information Engineering from Dalian University of Technology. Her technical research emphasizes sensor data analysis, traffic state estimation, and car park management within ITS. Notable projects include the Wellcome Trust-funded urban health initiative to reduce health inequalities and the Innovate UK-backed oneTRANSPORT project, developing IoT platforms for predictive transport analytics. Awards: Fellowship of the Higher Education Academy (2022). Teaching Qualifications: Postgraduate Certificate (2021) and Diploma (2022) in University Learning and Teaching. Grants: Wellcome Trust (Urban Health Project), Innovate UK (oneTRANSPORT). Her work integrates machine learning and traffic engineering to address urban mobility challenges, with a focus on autonomous vehicles, energy-efficient systems, and real-time traffic management.

Xinfan LinView profile

Associate Professor

• Dynamic System Modeling
• Machine Learning
• Battery Management Systems
• +5 more

Xinfan Lin is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis, part of the College of Engineering. His research focuses on dynamic system modeling, diagnostics, and control, with a particular emphasis on machine learning, data analytics, and control-integrated design optimization. His work addresses applications in intelligent battery management systems, electric vehicles, unmanned aerial systems (UAS), electric-vertical-takeoff and landing (eVTOL) aircraft, and spacecraft. He leads the Lin Research Lab, which combines multi-physical domain knowledge, control theories, and machine learning to advance transportation and aerospace technologies. Lin has received the NSF CAREER Award and was elected as an IEEE Senior Member. His research interests include battery electrochemical dynamics, system-level estimation and control, and energy systems optimization. He has contributed to advancing methodologies for battery health monitoring, energy-efficient UAV mission planning, and hybrid physics-based machine learning models. Key applications of his work include improving battery management for electric vehicles, optimizing multirotor drone efficiency, and enhancing aerospace systems through integrated system modeling. His recent publications highlight advancements in energy-optimal trajectory planning, reinforcement learning for battery diagnostics, and lightweight electrochemical modeling techniques.

Sylvia RatnasamyView profile

Professor

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

Sylvia Ratnasamy is a Professor of Computer Science at the University of California, Berkeley, specializing in networked systems design. She holds affiliations with the International Computer Science Institute (ICSI), the Networked Systems Lab (NETSYS), and the Software Principles for Advanced Networking (SPAN) Center. Her research focuses on scalable network architectures, middlebox virtualization, data center networking, and distributed systems. Education: PhD in Computer Science from UC Berkeley (2002), Bachelor's in Computer Engineering from the University of Pune, India (1997). Research interests include networked systems, software-defined networking, middlebox architectures, and performance optimization. Notable contributions include the design of NetBricks, E2 framework for NFV, and foundational work on data-centric storage (e.g., OpenDHT). Recent work explores cloud-augmented autonomous driving, cellular network architectures, and hardware-accelerated scheduling. Major awards include the ACM Grace Murray Hopper Award (2014), ACM SIGCOMM Test-of-Time Award (2011), and Sloan Research Fellowship (2012). She has advised numerous projects funded by NSF, DARPA, and industry collaborations. Labs/Teams: Co-leads the SPAN Center for networking research. Active in academic service, including SIGCOMM Technical Steering Committee and program committees for top conferences like NSDI, SOSP, and HotNets.

Haiying ShenView profile

Associate Professor

• Distributed Systems and Networks
• Cloud/Edge Computing
• Distributed Machine Learning
• +3 more

Haiying Shen serves as Associate Professor in the Department of Computer Science at the University of Virginia, where she joined in 2016 after faculty positions at Clemson University (2009-2016) and the University of Arkansas (2006-2009). Her research bridges theoretical and practical aspects of large-scale distributed systems with applications across computing domains. Education: B.S. in Computer Science and Engineering from Tongji University (2000) M.S. in Computer Engineering from Wayne State University (2004) Ph.D. in Computer Engineering from Wayne State University (2006) Professor Shen's research focuses on optimizing resource utilization in distributed environments through innovative approaches to scheduling, virtualization, and security. Her work in cloud/edge computing addresses critical challenges in machine learning deployment and cyber-physical system integration, particularly for smart city infrastructure. Recent projects demonstrate strong interdisciplinary connections between networking theory, machine learning, and real-world urban applications. Publication trends from 2014-2020 reveal consistent contributions to top-tier venues in distributed systems, with emphasis on resource management for machine learning workloads, privacy in cyber-physical systems, and sustainable smart infrastructure. Her work frequently appears in IEEE INFOCOM, ACM CoNEXT, and IEEE/ACM Transactions on Networking. Scientific Awards: TCSC Mid-career Award (2015) IBM Faculty Award (2015) NSF CAREER Award (2013) Sigma Xi Clemson Chapter Young Investigator of the Year Award (2013) Microsoft Faculty Fellowship Award (2010) Award for Faculty Excellence of Clemson University (2010) Undergraduate Mentor Thesis Award of University of Arkansas (2009) Professor Shen has secured significant research funding as Principal Investigator, including NSF grants #1404981 and #1254006 focusing on adaptive computing platforms and distributed data sharing systems, plus Microsoft Research funding. Her mentorship excellence was formally recognized with the University of Arkansas Undergraduate Mentor Thesis Award. She serves as Associate Editor for IEEE/ACM Transactions on Networking and IEEE Networking Letters, and has held leadership roles in major conferences as program co-chair. Her research lab in Rice Hall 532 at UVA drives innovation in distributed systems through rigorous theoretical frameworks and practical implementations for real-world cyber-physical applications.

• Network Optimization
• Combinatorial Optimization
• Logistics
• +3 more

Francesca Guerriero is Full Professor of Operations Research at the Department of Mechanical, Energy and Management Engineering , University of Calabria, Italy. She serves as Dean since 2021, Vice President of AIRO (Italian Operations Research Society), and Director of the LOGICA Laboratory. PhD in Systems Engineering and Informatics, University of Calabria (1997) Graduate in Management Engineering (1993), summa cum laude Her research spans Network Optimization , Combinatorial Optimization , Logistics , Revenue Management , and Big Data Optimization , with focus on large-scale problems in transportation, energy, and healthcare. Recent publications highlight integration of quantum computing and deep reinforcement learning in logistics. She supervises PhD projects on topics like Green Logistics , Distributed Optimization , and Smart City Applications , collaborating with institutions like MIT and Amazon. Her work includes EU/Italian Ministry-funded projects on Smart Cities , Healthcare Systems , and Biomedical Applications .

Elnaz IrannezhadView profile

Senior Lecturer

• Transport Engineering
• Logistics Engineering
• Automated Vehicles
• +10 more

Dr. Elnaz Irannezhad (also known as "Elli") is a Senior Lecturer at the School of Civil and Environmental Engineering, University of New South Wales (UNSW Sydney). She leads research at the Research Centre for Integrated Transport Innovation and bridges transport engineering with blockchain technology, automated vehicles, and decarbonisation. PhD in Civil Engineering from the University of Queensland (2014-2017) Postdoctoral Research Fellow at UQ Business School (2017-2020) Principal Engineer at Australian Road Research Board (2020-2022) Her research spans cross-disciplinary domains including: Behavioral and agent-based freight transport modelling Logistics 4.0 and blockchain applications Automated heavy vehicles and hydrogen fuel cell technology Smart supply chains and IoT integration Circular economy in logistics Decision support systems for transport networks Recent publications focus on blockchain platforms like LogisitcChain for supply chain optimization, machine learning for GPS-based truck behavior analysis, and behavioral econometric studies of maritime transport demand. She also contributes to human rights discourse as Associate of UNSW's Australian Human Rights Institute and Equity, Diversity, and Inclusion (EDI) Officer for the School of Civil Engineering. Teaching activities include: CVEN9421: Transport Logistics Engineering ENGG1400: Infrastructure Systems Engineering CVEN3402: Transport Engineering and Environmental Sustainability She co-leads the JINA Program for refugee background female students and co-chairs the virtual Bridging Transport Researchers (BTR) conference to reduce academic travel emissions.

• Environmental Economics
• Behavioral Economics
• Transportation Modeling
• +7 more

Dr. C. Anna Spurlock is a Researcher at the University of California at Berkeley , working within the Sustainable Energy and Environmental Systems Department at Lawrence Berkeley National Laboratory. As a Deputy Department Head and head of the Berkeley Lab Sustainable Transportation Initiative, she leads projects like the DOE-funded BEAM CORE and the Geo-Economic Multi-Modal Systems (GEMS) Model. Her work bridges behavioral economics with advanced data science to analyze consumer decision-making in transportation and energy efficiency policy. Education: PhD in Agricultural and Resource Economics, University of California at Berkeley (2013) MS in Agricultural and Resource Economics, University of California at Berkeley (2009) BA in Anthropology, University of California at Santa Cruz (2002) Research interests span transportation equity , machine learning for mobility modeling , and energy justice frameworks . Her publications focus on autonomous vehicles, fleet turnover, and the intersection of life events with mobility patterns. Articles address topics like probe vehicle data analysis , CO2 emissions reduction , and gender disparities in transportation . Scientific Awards: 2021 Spot: Team Workforce Development Award 2018 Director’s Award for Exceptional Achievement: Diversity 2017 Early Career Development LDRD Award 2017 CUCSA Kevin McCauley Memorial Outstanding Staff Award She has advised research teams on projects such as the WholeTraveler initiative under DOE SMART Mobility and founded the Behavior Analytics research initiative at LBNL. Her work includes collaborations with DOE programs , data-driven policy evaluation , and advocacy for diversity and inclusion in academic institutions.

Saumya BhatnagarView profile

Researcher

• Artificial Intelligence
• Machine Learning
• High Performance Computing
• +3 more

Saumya Bhatnagar is a Researcher affiliated with the School of Computing and Engineering and the Centre for Autonomous and Intelligent Systems at the University of Huddersfield. Their work focuses on Artificial Intelligence applications in urban traffic systems, machine learning, and high-performance computing. Key areas of expertise include traffic signal optimization, autonomous systems, and data-driven mobility solutions. Research interests prominently feature AI planning for urban challenges, real-time traffic control, and sensor data fusion. Recent publications emphasize applying AI techniques to solve transportation problems such as autonomous bus routing and traffic data consistency assessment. The work contributes to UN Sustainable Development Goals related to smart cities and sustainable infrastructure. Active in presenting at international conferences, Bhatnagar has discussed topics including Waze data insights, MaaS governance in Beijing, and customized bus demand prediction. Their research bridges theoretical AI advancements with practical urban mobility solutions, leveraging interdisciplinary approaches to address 21st century urban challenges.

• Sustainable Operations Management
• Supply Chain Management
• Reusable Packaging Systems
• +3 more

Mahyar Taheri is an Assistant Professor of Operations Management at NEOMA Business School. Holding a PhD in Logistics and Management from Kuhne Logistics University alongside B.Sc. and M.Sc. degrees in Industrial Engineering, his research focuses on sustainable operations and supply chain management with particular emphasis on reusable packaging systems in consumer goods and food delivery industries. Primary research domain: Interplay between supply chain collaboration, market competition, and consumer preferences affecting sustainability performance Contribution to plastic packaging waste reduction through reusable packaging initiatives Key publication trends show expertise in: Supply chain network optimization Bi-objective logistics modeling Time-dependent demand management

Christofer SundströmView profile

Associate Professor

• Vehicular Systems
• Electric Vehicles
• Heavy-Duty Vehicle Electrification
• +7 more

Christofer Sundström is an Associate Professor and Docent at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Vehicular Systems (FS) research group within the Faculty of Science and Engineering. His research is part of the university’s strategic focus area on Transport and Mobility, where he contributes to advancing intelligent transportation systems through modeling and optimization. His research interests lie at the intersection of vehicular electrification, control systems, and energy management. Key areas include the electrification of heavy-duty vehicles, smart charging integration with renewable energy, optimal route and speed planning, and model predictive control for hybrid and electric powertrains. His work emphasizes practical applications in collaboration with industry partners like Scania and Ragn-Sells. The recent publications highlight a strong trend in applying advanced control theory to real-world transportation challenges, particularly in optimizing energy use, extending battery life, and improving grid compatibility for electric fleets. His work bridges theoretical control algorithms with industrial implementation in automotive systems. Scientific Awards: No awards listed in the provided text. Advising and Grants: While no specific students or grants are listed, Dr. Sundström leads research in critical areas of vehicular electrification and intelligent transport systems, likely involving supervision of graduate students and participation in externally funded research projects, particularly through collaborations with Scania and Ragn-Sells. Labs and Teams: He is an active member of the Vehicular Systems (FS) research group at Linköping University, which focuses on modeling, simulation, and control of vehicle systems. This team is central to the university’s high-profile research initiative in Transport and Mobility, working on next-generation transportation solutions.

Nils-Hassan QuttinehView profile

Academic

• Optimization
• Operations Research
• Mathematical Modeling
• +7 more

Nils-Hassan Quttineh is a Senior Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) within the Faculty of Science and Engineering. His research focuses on optimization, mathematical modeling, and operational research with applications in logistics, industrial software testing, and sustainable energy systems. His primary research interests lie in optimization , particularly metaheuristics , Lagrangian relaxation , and discrete and bi-objective optimization . He applies these methods to real-world challenges such as vehicle routing, test case prioritization in software engineering, and sustainable placement of biogas plants. His work bridges theoretical advances with practical implementation, especially in industrial and environmental contexts. The recent publications show a strong trend in developing and evaluating heuristic and bounding methods for complex optimization problems, particularly in logistics and sustainability. His collaborative work with Torbjörn Larsson and others demonstrates consistent contributions to methodological rigor in operational research. Applications span industrial software engineering and circular economy initiatives, notably through involvement with the Biogas Research Center. He is actively involved in research through the Biogas Research Center (BRC) , where his optimization expertise supports sustainable energy projects, particularly in site selection and resource efficiency. This reflects a growing interdisciplinary effort to apply mathematical methods to environmental challenges.