Find Academic Supervisors

• Open Innovation
• Technology Transfer
• Food Industry Innovation
• +4 more

Barbara Bigliardi is an Associate Professor at the Department of Engineering and Architecture, University of Parma, with national scientific qualification for full professor in Economic-Management Engineering (SSD ING-IND/35). She serves as President of the Management Engineering Program at University of Parma and Director of Bachelor's/Master's programs at University of San Marino, co-leading double-degree initiatives between the two institutions. Over 100 publications (57 SCOPUS-indexed) with H-index=20 Editorial roles: Cambridge Scholars Publishing (2019), MDPI Sustainability, Sci, European Journal of Innovation Management Key research areas: Open Innovation, Technology Transfer, Food Industry Innovation, Industry 4.0, Supply Chain Sustainability Recent Publications (2024-2025) demonstrate leadership in: Industry 4.0 integration with circular economy AI applications in public administration and healthcare Digitalization of food supply chains Green startup resource orchestration Simulation-based optimization in remanufacturing Sustainable additive manufacturing Scientific Recognition : 2005 Emerald Highly Commended Award 2013 Most Cited Paper in Trends in Food Science & Technology 2019 Highly Cited Paper in Review of Policy Research Research Leadership includes: National Observatory on Start-ups (President since 2021) National Observatory on Reputation (Vice President since 2019) INAIL-funded mobile risk assessment systems (2018-2020) INAF space technology transfer projects (2018-present) Academic Contributions : Supervised over 300 theses Deputy Coordinator of Industrial Engineering Doctoral Program Director of Management Engineering Programs (Parma & San Marino) Founder of academic spin-offs: Sistemi per il marketing di contenuto S.r.l. (2016-present), Univenture SrL (2006-2010)

Liu YeView profile

Professor

• Sustainable Environmental Engineering
• Greenhouse Gas Mitigation
• Biological Wastewater Treatment
• +5 more

Liu Ye is a Professor at The University of Queensland (UQ) within the School of Chemical Engineering. She leads the Greenhouse Gas (GHG) research program at UQ Urban Water Engineering and has secured over AU$10M in competitive research funding. Her work focuses on achieving net-zero emissions through innovations in urban wastewater systems, with collaborations spanning 15+ water utilities, governments, and technology firms like Suez and Veolia. Research Areas: Greenhouse gas mitigation, sludge minimization, biogas production, advanced biological nutrient removal, online process control, and resource recovery from wastes. Key Awards: Research Innovation Award (Australia Water Association), UQ Foundation Research Excellence Award, EAIT Faculty Teaching Excellence Award, and Fellow of the Royal Society of Chemistry (RSC). Teaching: Courses include CHEE2020 (Process Equipment and Control), CHEE2501 (Environmental Systems Engineering I), CHEE4012 (Industrial Wastewater Management), and thesis supervision. Leadership: Associate Editor for Environmental Science: Water Research and Technology , member of IWA Strategic Council, and contributor to industry peak bodies like WSAA and WaterRA. Recent Articles: Her 2025 publications highlight strategies for balancing energy recovery with GHG emissions, systematic frameworks for N2O quantification, and advancements in phosphorus removal. Earlier works (2024–2022) explore electrochemical iron applications, ferric salt enhancements, and hybrid modeling techniques combining mechanistic and deep learning approaches. Scientific Impact: Her research has driven a 35% reduction in N2O emissions at an Adelaide treatment plant, saving $50M annually, and pioneered free nitrous acid (FNA) technology for biofilm control and sludge reduction.

• Transportation planning
• Dynamic traffic assignment
• Traffic estimation and prediction
• +5 more

Xuesong Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment , Arizona State University (ASU). He leads the ASU Transportation+AI Lab and develops open-source tools like DTALite, NEXTA, and OSM2GMNS with over 100,000 downloads. His research focuses on multimodal transportation planning , dynamic traffic assignment , and rail scheduling with methodological contributions to traffic flow theory and operations research . Dr. Zhou's research bridges transportation system operations , computer applications for ITS , and logistics optimization . His work on differentiable programming reformulations and state-space-time network modeling has advanced real-time traffic prediction and multi-echelon facility scheduling . Scientific awards include: 2022 Elsevier Multimodal Transportation Best Article Award 2018 Transportation Research Part C Best Associate Editor Award 2012 INFORMS Railway Applications Section Best Paper Award He has advised 9 PhD students and 6 postdoctoral researchers to completion, with mentees now at institutions like Georgia Institute of Technology and Michigan State University. Current projects include NSF CONNECT and DOE Argonne collaborations on multi-scale traffic simulation and smart campus cyberinfrastructure .

• Maritime Risk Analysis
• Autonomous Ship Systems
• Safety Engineering
• +3 more

Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics , Norwegian University of Science and Technology (NTNU). His work focuses on integrating advanced methodologies for safety and risk control in autonomous maritime systems. He has held academic positions since at least 2014, with a consistent record of research collaboration and publication. Research Areas: Maritime risk analysis, autonomous ship systems, safety engineering, dynamic positioning systems, systems-theoretic process analysis (STPA) Key Publications: 15 most recent articles cover topics like trajectory prediction for autonomous vessels, hybrid power systems safety, machine learning in risk assessment, and dynamic positioning system reliability His publications (2014-2025) emphasize safety-critical systems in marine environments. Common themes include: Application of STPA for hazard analysis in autonomous shipping Development of risk-informed control systems Integration of machine learning with engineering risk assessment Comparative studies of different ship autonomy levels As a supervisor, Rokseth has guided master's students including Ane Joramo Stokke and Ludvig Vik Løite. His work has been presented at international conferences such as the European STAMP Workshop, International Conference on Conceptual Modeling, and the International Seminar on Safety and Security of Autonomous Vessels.

Mathias UnberathView profile

Associate Professor

• Computer-assisted medicine
• Computer vision
• Artificial intelligence and machine learning
• +4 more

Mathias Unberath is the John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University, with secondary appointments in Ophthalmology and Otolaryngology—Head and Neck Surgery at the School of Medicine. He is a core faculty member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare, and affiliate faculty at the Institute for Assured Autonomy and Data Science and AI Institute. Education: PhD in Computer Science from Friedrich-Alexander University of Erlangen-Nürnberg (2017), MSc in Optical Technologies (2014), BSc in Physics (2012) His research focuses on computer-assisted medicine, integrating computer vision, machine learning, and medical robotics to develop human-centered solutions through mixed reality and embodied technologies. His work addresses surgical phase recognition, explainable AI, and digital twin representations for clinical workflows. Unberath's 15 most recent publications demonstrate expertise in surgical AI (7/15), medical imaging (12/15), and mixed reality (8/15), with specific subfields including segmentation frameworks (3 papers), cognitive load estimation (4 papers), and surgical robotics (5 papers). NSF CAREER Award NIH NIBIB Trailblazer R21 Google Research Scholar Award Inaugural DSAI Junior Faculty Award IPCAI 2025 Best Paper Award He teaches graduate courses in machine learning, AI system design, and interpretable machine learning. His group, the ARCADE Lab, develops technologies for computer-assisted interventions, emphasizing robustness, explainability, and human-AI collaboration in clinical settings.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Andrea Del PreteView profile

Associate Professor

• Robot Control
• Reinforcement Learning
• Trajectory Optimization
• +7 more

Andrea Del Prete is an Associate Professor in the Industrial Engineering Department at the University of Trento (Italy) since 2022. His research focuses on robot control, reinforcement learning, trajectory optimization, and numerical algorithms for dynamic systems. He leads the Interdepartmental Robotics Lab (IDRA) and has previously held roles as a tenure-track assistant professor at the University of Trento (2019-2021), a research scientist at the Max-Planck Institute for Intelligent Systems (2018), and an associated researcher at LAAS-CNRS (2014-2017) working with the HRP-2 humanoid robot. Earlier, he conducted PhD and post-doc research at the Italian Institute of Technology (2010-2013) on iCub robot control. PhD in Robotics (2013) - Italian Institute of Technology MEng in Computer Engineering (2009) - University of Bologna BSc in Computer Engineering (2006) - University of Bologna Dr. Del Prete specializes in merging learning and model-based techniques for safe robot control, particularly in legged systems. His work bridges trajectory optimization (TO) with reinforcement learning (RL) to overcome local minima challenges (CACTO/CACTO-SL algorithms) and develops robust controllers for humanoid and quadrupedal robots in unstructured environments. He explores viability kernels in MPC, safety certificates, and bi-level optimization for co-designing hardware/control policies. Key application areas include mountain rescue robotics (ALPINE platform), aerial maneuver recovery, and energy-efficient legged locomotion. His recent publications (2023-2025) emphasize numerical optimization algorithms, multi-contact locomotion, and hybrid control frameworks. Topics span from analytical integral optimization (2025) to climbing robots for mountain operations (2025), demonstrating a trajectory from theoretical algorithm development to real-world robotic applications. Research keywords include robotics, numerical optimization, and machine learning, with sub-fields like MPC for dynamic systems, humanoid control, and terrain adaptation. As an educator, he teaches advanced courses on: Optimization and Learning for Robot Control (48-hour master's course) Optimization-based Control of Legged Robots (12-hour PhD course) Task-Space Inverse Dynamics (3-hour PhD course) Current PhD advisees include Mohammad Hasan Yeganegi (generalization bounds for imitation learning), Pietro Noah Crestaz (numerically-efficient RL), Veronica Campana (ergodic control for defect detection), Elisa Alboni (data-efficient model-based RL), and Gianni Lunardi (MPC for legged locomotion).

• Human Factors Engineering
• Automated Driving
• Teleoperated Driving
• +5 more

Niklas Grabbe is a postdoctoral researcher at the Chair of Ergonomics, Technical University of Munich (TUM), where he leads the research group on "Automated Driving and Mobility Systems" and is establishing a new working group focused on "Modelling complex socio-technical systems" with emphasis on Resilience Engineering and the Functional Resonance Analysis Method (FRAM). His research centers on human factors in automated and teleoperated driving, mobility systems, and behavior modeling. He applies systems engineering principles to analyze safety and usability, particularly using FRAM to study performance variability and resilience in socio-technical systems. His work addresses critical challenges in urban automated driving, teleoperation, and multi-driver interactions through rigorous quantitative modeling and field studies. Analysis of Grabbe's publications reveals a dominant trend in applying FRAM to driving scenarios, with recurring themes of safety enhancement, human-automation interaction, and usability evaluation. His interdisciplinary work bridges transportation psychology, safety engineering, and human-computer interaction, consistently emphasizing systems thinking to solve complex problems in automated vehicle technology. Grabbe contributes to academic instruction through courses like "Modelling Complex Sociotechnical Systems" (Winter 2025/26). He operates within TUM's Chair of Ergonomics infrastructure, utilizing specialized labs including driving simulators and ergonomic mockups to support experimental research in human factors and automated systems.

Anuj PathaniaView profile

Assistant Professor

• Low Power Design
• Sustainable Systems
• Electronic Design Automation
• +4 more

Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.

Craig KnoblockView profile

Research Professor

• Knowledge Graphs
• Data Semantics
• Schema Alignment
• +5 more

Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.

• Artificial Intelligence
• Decision-Making Systems
• Fuzzy Logic
• +5 more

Núria Agell Jané is a Full Professor at ESADE Business School , Universitat Ramon Llull, specializing in Artificial Intelligence and Decision-Making Systems. She leads the JUICE (Judgements and Decisions in the Market Place) research group and the ESADE D3 - Institute for Data-Driven Decisions . Doctorate in Applied Mathematics (Qualitative Reasoning Modelling), UPC-BarcelonaTech Bachelor's in Mathematics, University of Barcelona Her research focuses on Artificial Intelligence , Decision-Making Systems , and Fuzzy Logic , with applications in Business, Marketing, and Sustainability. Recent publications emphasize Hesitant Fuzzy Linguistic Term Sets , Consensus Modeling , and AI in Sustainable Development . She coordinates multiple publicly and privately funded projects applying AI to Business and Marketing challenges. As PhD Programme Director (2005-2013) and current Department Director of Operations, Innovation and Data Sciences , she has shaped academic and research strategies at ESADE. Her work spans collaborations with institutions like LAAS-CNRS (France) and University of Edinburgh Business School , with over 40 journal publications and 50 conference contributions. She has directly supervised 11 PhD students in AI and Decision Sciences.

Kai HobergView profile

Professor

• Supply Chain Analytics
• Inventory Modeling
• Digital Transformation in Supply Chains
• +7 more

Prof. Dr. Kai Hoberg is a Professor of Supply Chain and Operations Strategy at Kühne Logistics University (KLU) since 2017, where he also served as Department Head of the Operations and Technology Department from 2017 to 2023. Prior to joining KLU as an Associate Professor in 2012, he was an Assistant Professor at the University of Cologne (2010–2012) and a strategy consultant at Booz & Company (2006–2010). His research interests span supply chain analytics and technology integration inventory modeling for intermittent demand digital transformation in operations management additive manufacturing in after-sales services human-machine interaction in forecasting pharmaceutical supply chain challenges IoT-enabled vendor-managed inventory behavioral aspects of operations . Recent publications highlight empirical studies leveraging machine learning for semiconductor order fulfillment, typologies for additive manufacturing adoption, and process mining applications in SCM. His work frequently combines theoretical modeling with real-world validation, including partnerships with firms in food manufacturing, postal services, and medical devices. He earned a PhD in Supply Chain Management from Münster University (2006) and a Diplom in Industrial Engineering from Paderborn University and Monash University. He has held visiting scholar roles at institutions like Cornell, NUS, Oxford, and Stellenbosch.

Edwin RomeijnView profile

Professor

• Optimization Theory
• Radiation Therapy Treatment Planning
• Supply Chain Management
• +2 more

Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.

Jason HartlineView profile

Professor

• Mechanism Design
• Auction Theory
• Algorithmic Game Theory
• +4 more

Jason Hartline is a Professor of Computer Science at the McCormick School of Engineering, Northwestern University, with a courtesy appointment in Managerial Economics & Decision Sciences. His research bridges computer science and economics, focusing on mechanism design, auction theory, and approximation algorithms. Ph.D. in Computer Science from the University of Washington (2003) Postdoctoral Fellow at Carnegie Mellon University (2003-2004) Researcher at Microsoft Research (2004-2007) His work develops methodologies to analyze and design economic systems using computational theory, particularly in auction mechanisms and non-truthful settings. Key contributions include the textbook Mechanism Design and Approximation and frameworks for Bayesian and prior-independent mechanism design. Recent publications (2018-2023) span topics like non-truthful mechanism learning, multi-dimensional agent modeling, and computational law. Collaborations include researchers from Harvard, Microsoft, and institutions across economics and theoretical computer science. Grants include multiple NSF awards (CCF, ECCS, HDR TRIPODS) for projects in data economics, machine learning integration, and peer grading systems. Former advisees hold academic positions at Stanford, Yale, and Penn State.

Maria MushtaqView profile

Associate Professor

• Embedded System Security
• Microarchitectural Security
• Artificial Intelligence Applications in Security
• +2 more

Maria Mushtaq is an Associate Professor at Telecom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Secure and Safe Hardware (SSH) Lab . She received her PhD in Information Security from the University of South Brittany, France (2019) and completed 2 years of postdoctoral research at LIRMM, University of Montpellier under the CNRS excellence post-doc grant. Research Focus: Microarchitectural vulnerability assessment, runtime mitigation against side/covert-channel attacks, cryptanalysis, OS-based security primitives, and hardware-software interface security Technical Expertise: Cache timing attacks, transient execution attacks (Spectre/Meltdown), Hardware Performance Counter analysis, gem5 simulation Her recent work involves RISC-V security analysis using gem5 simulations and machine learning for attack detection. She serves as Guest Editor for the Journal of Applied Sciences special issue on Side Channel Attacks in Embedded Systems and has been on the Program Committee for the European Test Symposium (2020-2021). 2021 HiPEAC Collaboration Grant recipient Organized IP Paris Winter School on Microarchitectural Security (2022) Active in international conferences as panelist and keynote speaker