جست‌وجوی استاد و پژوهشگر

Roberto Santiago Vassoloمشاهده پروفایل

استاد

• Strategic Management
• Business Policy
• Strategic Leadership
• +۳ مورد دیگر

Roberto Santiago Vassolo is a tenured Professor of Business Policy at IAE Business School-Universidad Austral. He directs the Doctoral Program in Business Administration and the Strategic Leadership program at IAE Business School, and serves as co-editor-in-chief of Long-Range Planning, the world's oldest business strategy journal published by Elsevier. Professor Vassolo's educational background includes: PhD in Strategic Management from Purdue University Postgraduate studies in Economics from Universidad Di Tella Bachelor of Economics from Universidad Católica Argentina His research focuses on strategic management in highly uncertain environments, with particular emphasis on the impact of macroeconomic volatility on businesses and strategic leadership. Professor Vassolo has made significant contributions to understanding how organizations navigate complex economic conditions and develop effective leadership strategies in challenging contexts, especially relevant for emerging markets. Professor Vassolo has published extensively in top-tier academic journals including the Academy of Management Journal, Journal of Management Studies, Journal of Management, Organization Science, Organization Studies, Research Policy, Strategic Management Journal, and Harvard Business Review. His influential books include "Strategy as Leadership" (Stanford University Press), "Strategic Management in Emerging Countries" (Granica), and "The Business of the Divide" (Galerna). His scientific contributions have been recognized through his editorial role at Long-Range Planning and his active participation in major academic conferences like the annual meeting of the Academy of Management Research. With over 20 years of experience in strategic consulting, Professor Vassolo has worked with Argentine companies, multilatinas, and multinational firms across various industrial sectors. This practical experience complements his academic work, providing valuable real-world insights that inform his research and teaching. Professor Vassolo maintains an active email correspondence at rvassolo@iae.edu.ar and continues to be a leading voice in business strategy research, particularly in the context of emerging economies and volatile economic environments.

• Applied Microeconomic Theory
• Game Theory
• Information Economics
• +۳ مورد دیگر

Manuel Foerster is an Assistant Professor at the Center for Mathematical Economics, Bielefeld University. His research focuses on applied microeconomic theory and game theory, particularly in information economics , social and economic networks , industrial organization , and political economy . Education: Ph.D. in Economics from University of Paris 1 and University of Louvain Awards: DFG Grant (FO 1272/2-1) for a project on 'Information, Fake News, and Social Networks' Contact: Office: UHG B4-129, Bielefeld University; Email: manuel.foerster@uni-bielefeld.de His current research explores strategic communication, fake news, and social networks. Recent working papers analyze narrative transmission under model uncertainty , influencer marketing dynamics , and media bias in digital news . The DFG-funded project investigates disinformation mechanisms in uncertain environments. Scientific Awards : DFG Grant for 'Information, Fake News, and Social Networks' (2020) His methodological work on perfect price discrimination , soft evidence manipulation , and ambiguous belief distributions contributes to understanding information economics and strategic behavior in digital markets.

Ioannis Konstantarasمشاهده پروفایل

دانشیار

• Operations Research
• Inventory Control
• Reverse Logistics
• +۴ مورد دیگر

Ioannis Konstantaras is an Associate Professor at the Department of Business Administration, University of Macedonia, Thessaloniki, Greece, holding this position since July 2016. He contributes to the Master in International Business, Master in Business Administration, and Master in Business Analytics and Data Science programs. His academic qualifications include: B.Sc. in Mathematics from Aristotelian University of Thessaloniki (1999) M.Sc. in Statistics & Operations Research from University of Ioannina (2001) Ph.D. in Operations Research from University of Ioannina (2006) Konstantaras specializes in mathematical modeling and optimization of inventory systems for supply and reverse logistics chains. His research addresses production planning, deteriorating items, imperfect quality products, and recovery processes through operations research methodologies. He develops analytical models for optimizing decision-making in complex logistics environments. His publication record (2010-2014) shows concentrated research on inventory models for deteriorating items, lot-sizing in recovery systems, and pricing strategies for reverse logistics. Key contributions include extensions to Economic Order Quantity models, optimization of two-warehouse systems, and integration of inspection learning effects in quality control. He serves as Co-Editor for the International Journal of Systems Science: Operations & Logistics since January 2014 and has reviewed for numerous international journals. Konstantaras led the research program "Logistics Management: Quantitative Methods for Inventory Management" funded by the ARCHIMEDES program under EPAYAEK II. No publicly available information lists his doctoral advisees.

Yiwei Lyuمشاهده پروفایل

استادیار

• Safe Control
• Verification
• Behavior Planning
• +۶ مورد دیگر

Yiwei Lyu serves as Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University, joining the faculty in August 2025 after completing her Ph.D. at Carnegie Mellon University. Her research program bridges control theory, machine learning, and social science to develop safe, socially-compliant autonomous systems. Her educational background includes: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University (2025) B.S. in Electronic Information Engineering, The Chinese University of Hong Kong, Shenzhen (2019) Professor Lyu's work centers on risk-aware safe control for multi-agent systems, with emphasis on verifiable safety guarantees in human-robot interaction. She pioneers methods integrating control barrier functions with social preference modeling to enable robots that navigate complex environments while respecting human norms. Her research spans autonomous driving, multi-robot coordination, and human behavior prediction, consistently addressing the critical challenge of safety assurance in uncertain real-world scenarios. Analysis of her 15 most recent publications reveals a cohesive trajectory: evolving from foundational safe control (2021-2022) to sophisticated risk-aware frameworks (2023-2024) that incorporate social compliance and learning. Her work consistently targets high-impact applications in autonomous vehicles while maintaining theoretical rigor, with 7 papers receiving best paper awards or nominations since 2021. Her exceptional contributions have been recognized through: MIT EECS Rising Star (2024) - top global early-career honor IEEE/ACM Human-Robot Interaction Pioneer designation (2024) Qualcomm Innovation Fellowship (2023) Multiple best paper awards at ICRA, IROS, and AAMAS venues Professor Lyu actively builds her research group, having welcomed her first PhD student in August 2025. Her competitive fellowship funding and rapid publication record indicate strong grant support potential. She serves on editorial boards (ICRA 2026 Associate Editor) and professional committees (Junior Co-chair, Multi-Robot Systems Technical Committee), demonstrating growing leadership in the robotics community. As a new faculty member, Professor Lyu combines fresh perspective with proven research excellence, offering students unique opportunities to shape the future of safe autonomy at the intersection of engineering and social sciences.

Ilhan Akbasمشاهده پروفایل

دانشیار

• Complex Systems
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Ilhan Akbas is an Associate Professor in the Electrical Engineering and Computer Science Department within the College of Engineering at Embry-Riddle Aeronautical University. He serves as Director of the Flexible & Intelligent Complex Systems (FICS) Research Group, Co-director of the Wireless Devices and Electromagnetics (WIDE) Lab, and holds leadership positions in the Center for Aerospace Resilient Systems (CARS) and the Advanced Air Mobility Research and Innovation Lab (AAMRIL). With over 20 years of experience in machine learning, complex systems, and modeling & simulation, Dr. Akbas's research focuses on validation and verification of autonomous systems, particularly in aerospace applications. His work develops methods for decision-making in high-dimensional, probabilistic scenarios with applications in autonomous vehicles, cybersecurity for unmanned aerial systems, and cyber-physical systems operating in dynamic/uncertain environments. Dr. Akbas's research has secured funding through grants from the National Science Foundation, Federal Aviation Administration, U.S. Air Force, Office of Naval Research, and industry partners. His publication record includes more than 30 journal and 80 conference papers in IEEE, ACM and AIAA venues, with recent work focusing on AI validation frameworks, autonomous vehicle safety testing, and cybersecurity for unmanned systems. His scientific contributions are recognized through his status as a Senior Member of IEEE and membership in several professional organizations including ACM, AIAA, SAE, the International Alliance for Mobility Testing and Standardization, the Complex Systems Society, and the Cyber Safety Commercial Aviation Team. Dr. Akbas actively mentors students through research projects and teaches courses including Computer Science Capstone Design, Digital Communications, and graduate thesis supervision. His research group utilizes advanced tools including Python, Julia, Rust, simulation environments like AirSim and CARLA, and hardware platforms including NVIDIA Jetson for edge computing applications.

• Cognitive Psychology
• Behavioral Decision Science
• Heuristics and Biases
• +۲ مورد دیگر

Professor Thorsten Pachur is a leading cognitive scientist holding the Chair of Behavioral Research Methods at Technical University of Munich's School of Management. His research bridges experimental psychology and decision theory, focusing on the cognitive architecture of human judgment under real-world constraints. Core research domains include: Heuristics and biases in uncertain environments Metacognitive processes in hindsight distortion Computational modeling of decision strategies Foundations of intelligence in biological and artificial systems As a key investigator in the DFG Cluster of Excellence 'Science of Intelligence' (EXC 2002), he contributes to TUM's flagship initiative uniting 50+ research groups. His past DFG projects have systematically tested when intuitive judgments succeed or fail compared to analytical approaches, with applications in risk communication and AI design. Professor Pachur's methodological rigor in behavioral experimentation informs both theoretical models of cognition and practical interventions for improving decision quality. His position as Chair underscores his leadership in advancing behavioral research infrastructure at TUM.

Fabrice Lamarcheمشاهده پروفایل

دانشیار

• Virtual Humans
• Crowd Simulation
• Motion Planning
• +۵ مورد دیگر

Fabrice Lamarche serves as an Associate Professor at Université de Rennes 1 within the ESIR School of Engineering, while maintaining dual affiliation with the MimeTIC research team at IRISA / INRIA Rennes. His academic career spans uninterrupted service since 2004, evolving from Assistant Professor roles at IFSIC (2003-2009) to current positions at ESIR. As co-founder of Golaem (2009), he bridges academic research with commercial application in crowd simulation technology. His institutional journey includes sequential membership in SIAMES (2004-2006), Bunraku (2007-2011), and MimeTIC (2011-present) research teams at INRIA. Lamarche earned his PhD in Computer Sciences from Université de Rennes 1 in 2003 with thesis work on virtual human autonomy. His educational foundation includes a Master of Computer Sciences specializing in Computer Graphics and AI (1999-2000), a Master of Engineering from INSA de Rennes (1997-2000), and a Technical degree from IUT de Limoges (1995-1997). His research program centers on virtual human behavior modeling with emphasis on decision-making systems, path planning under environmental constraints, and crowd simulation architectures. Key innovations include TopoPlan for human-scale navigation and frameworks integrating high-level task scheduling with low-level motion planning. This work addresses fundamental challenges in creating autonomous virtual characters capable of navigating complex 3D environments while exhibiting realistic behaviors, with applications spanning virtual reality, gaming, and simulation-based training systems. Publication analysis reveals consistent output from 2001-2014, evolving from foundational behavioral animation (2001-2004) to sophisticated crowd simulation systems (2013-2014). A notable trajectory shows increasing integration of cognitive modeling with motion planning, alongside exploration of Brain-Computer Interfaces for virtual navigation. Recent work demonstrates particular strength in semantic decomposition of urban environments and time-space constrained task scheduling. His scientific contributions have earned significant recognition: Rennes city medal (2009) for research excellence Second prize at Deutsch Telekom Awards (FMX 2008) for TopoPlan/MKM integration As an active researcher and educator, Lamarche advises students through Université de Rennes 1 while leveraging INRIA resources and Golaem industry partnerships. His publication record indicates sustained grant funding, particularly through INRIA channels, with collaborative projects extending to neuroscience applications via Brain-Computer Interface research. The MimeTIC team affiliation provides infrastructure for multimodal interaction research in complex virtual environments. Lamarche's laboratory work through MimeTIC focuses on developing practical implementations of virtual human autonomy systems. His research group maintains strong industry connections via Golaem, which commercializes crowd simulation technology. Current efforts emphasize semantic understanding of virtual urban spaces and robust path planning under dynamic constraints, building on foundational work in topological navigation and behavioral decision systems.

• Machine Learning
• Theoretical Machine Learning
• Graph Theory
• +۴ مورد دیگر

Ulrike von Luxburg is Professor of Machine Learning Theory at the University of Tübingen's Wilhelm-Schickard-Institute for Computer Science, serving as spokesperson for the Cluster of Excellence 'Machine Learning: New Perspectives for Science' (EXC 2064) and leading multiple DFG-funded research initiatives. Her research centers on theoretical machine learning with emphasis on graph-based methods, statistical analysis of neighborhood structures, multi-scale graph decomposition, and explainable AI frameworks. She develops rigorous mathematical foundations for learning algorithms while exploring applications in neuroscience and uncertain environments. Her scientific recognition includes: Communicator Award 2024 (DFG) Current research leadership encompasses: Theoretical guarantees for explainable machine learning (DFG research grant) EXC 2064: Machine Learning: New Perspectives for Science (Cluster of Excellence) Forecasting and modeling in uncertain environments (Collaborative Research Center) Previous projects include the Heisenberg Professorship 'Statistical analysis of neighborhood graphs', 'Multi-scale analysis of graphs', and neuroscience-focused 'Analysis and modeling of neuronal interactions in the brain'. As head of the Chair of Machine Learning Theory, she directs research on fundamental algorithmic principles, fostering collaborations across computer science, statistics, and life sciences while maintaining active grant funding for theoretical innovation.

Loïc Adamمشاهده پروفایل

دانشیار

• Incremental Preference Elicitation
• Multicriteria Decision Making
• Decision Theory under Risk and Uncertainty
• +۸ مورد دیگر

Loïc Adam serves as an Associate Professor in Data Engineering at ISAE-ENSMA since 2024, concurrently holding a lecturer position in Digital Information. Previously, he was an ATER at Université de Technologie de Compiègne (2023-2024) where he taught operational research and data analysis courses. He completed his PhD in Computer Science at UTC in 2023 and currently serves on ENSMA's improvement council and scientific expertise commission (2025-2027). His research spans decision theory under risk and uncertainty, with core expertise in incremental preference elicitation, multicriteria decision-making, and uncertainty modeling through possibility theory and belief functions. He actively investigates information fusion, machine learning applications, and game-theoretic approaches to preference learning, focusing on resolving inconsistent preferences and developing robust decision support frameworks under severe uncertainty. Adam's publication record (2020-2024) reveals consistent contributions at the intersection of artificial intelligence and decision science, particularly in preference modeling and uncertainty quantification. His work demonstrates methodological innovation in applying possibility theory to inconsistency resolution and developing imprecise probabilistic models for label ranking, with publications spanning top venues in AI, fuzzy systems, and decision theory. Scientific Awards: No major awards or fellowships were documented in the source material. He currently supervises M2 intern Aicha AIT HAMMOUCH on auto-detection of erroneous models through personalized data elicitation in uncertain environments. As a member of LIAS Laboratory's Data Engineering team, he collaborates across ENSIP (University of Poitiers) and ISAE-ENSMA sites, contributing to interdisciplinary projects that bridge theoretical decision science with real-world engineering applications in control systems and real-time data processing.

Đorđe Žikelićمشاهده پروفایل

استادیار

• Formal Methods
• Program Analysis and Verification
• Trustworthy AI
• +۵ مورد دیگر

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.