Prof. Dr. Mirko Kremer is a Professor of Supply Chain Management at the Frankfurt School of Finance & Management since July 2014. Prior to this, he was an Assistant Professor at Pennsylvania State University (2008-2014) and held visiting roles at London Business School, Kellogg School of Management, and INSEAD. He earned a Ph.D. in Business Administration from the University of Mannheim (2008), with prior studies in Business Administration at Mannheim (1999-2003) and Economics at Ruhr-Universität Bochum (1997-1999). Research Focus: Micro-behavioral foundations of supply chains, inventory management, sales forecasting, and service systems. Editorial Roles: Department Editor for Production and Operations Management , Associate Editor for Operations Research . Teaching: Courses in Operations Management, Supply Chain Strategy, and quantitative decision modeling at Frankfurt School, INSEAD, London Business School, and Kellogg School of Management. Research Trends: His work bridges behavioral economics with supply chain operations, emphasizing how human decision-making (e.g., managerial biases, consumer behavior) impacts inventory systems, pricing strategies, and forecasting accuracy. Recent articles focus on diagnostic errors under congestion, strategic sourcing biases, and dynamic pricing experiments.
Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.
Eva Ascarza is a Professor of Business Administration in the Marketing Unit at Harvard Business School (HBS) . She co-founded the Customer Intelligence Lab at HBS's D 3 Institute, focusing on responsible and effective customer data utilization. Research Interests include: Customer retention and churn analysis Algorithmic bias in marketing AI Field experimentation (A/B testing) for targeting optimization Customer lifetime value (CLV) modeling Dynamic personalization strategies Scientific Awards and Recognitions: 2023 Weitz-Winer-O'Dell Award (winner) 2022 Paul E. Green Award (PNAS publication) 2020 Marketing Science Institute (MSI) Scholar 2019 Erin Anderson Award for Emerging Female Scholar 2018 Paul E. Green Award (JMR publication) 2014 Frank M. Bass Outstanding Dissertation Award Her articles demonstrate cutting-edge applications of statistical modeling , Bayesian methods , and fair AI frameworks in modern marketing challenges.
Dr. Livia Pancotto is a Senior Lecturer in Banking at the Department of Accounting and Finance, University of Strathclyde. She serves as Director of the MSc in International Banking and Finance and Deputy Director of the Responsible Business Institute (ReBI). She holds a PhD and MSc from Bangor University and degrees from Sapienza University of Rome. Her research focuses on European banking regulation, financial stability, and gender diversity's impact on climate finance. She chairs the International Accounting & Finance Doctoral Symposium and edits The European Journal of Finance. Awards include the 2014 Best Banking and Finance Master’s dissertation. She leads research on negative interest rates, non-performing loans, and green lending through EU-funded projects. Education: PhD Banking & Finance (Bangor), MSc (Sapienza), BSc (Sapienza) Research explores empirical banking dynamics, regulatory frameworks, and sustainable finance. Key topics include EU banking union evolution, climate change mitigation via gender-diverse bank boards, and market reactions to policy shocks. Recent work analyzes negative interest rate impacts on bank efficiency and Italy's NPL crisis. She has secured £153k in research funding (2023-2025). Grants: Research Excellence Awards (Strathclyde SRSS) Led workshops on Responsible Business and AI, presented at FSB Financial Innovation Network, and peer-reviewed for top journals. Supervises doctoral candidates through IAFDS. Active in editorial and advisory roles enhancing academic rigor in finance disciplines.
Jan Grimell is an Associate Professor and Senior Lecturer at the Department of Sociology, Umeå University. His research focuses on military chaplaincy, veterans' mental health, and identity formation, combining sociological and religious perspectives. He is affiliated with the research groups Sociology of Organizations, Sociology of Religion, and Sociology of Work. Key research themes include Moral Injury, PTSD, and the transition challenges veterans face when leaving military service. His work explores existential and spiritual dimensions of health, identity reconstruction, and the role of chaplaincy in armed forces and healthcare settings. Recent projects include studies on Ukrainian military chaplaincy during the Russia-Ukraine war, the impact of moral injury among healthcare providers during the pandemic, and the long-term identity struggles of Swedish veterans. His publications span journals like Frontiers in Sociology , Journal of Pastoral Care & Counseling , and Health and Social Care Chaplaincy , with a focus on interdisciplinary approaches linking social psychology, medical sociology, and religious studies.
Soroush Saghafian is an Associate Professor of Public Policy at Harvard Kennedy School, specializing in applying operations research and machine learning to address public health challenges. He leads the Public Impact Analytics Science Lab (PIAS-Lab), focusing on analytics-driven solutions for societal problems. His research spans healthcare delivery optimization, emergency department efficiency, and public health policy. Notable collaborations include Massachusetts General Hospital and Harvard's Center for Health Decision Science. Awards include the INFORMS MSOM Responsible Research Award and the Pierskalla Award for healthcare research. Recent work includes studies on hospital closures' impacts, predictive analytics for bipolar disorder using Fitbit data, and policy implications of race in disease risk models. He teaches courses on machine learning and big data for public impact.
Cong Shi, also known as Alex Shi, is a Professor of Management at the Miami Herbert Business School, University of Miami, since 2025. Previously, he served as Associate Professor at the University of Michigan (2019-2023) and Assistant Professor there (2012-2019). His academic journey began with a B.Sc. in Mathematics (First Class Honors) from the National University of Singapore (2007) and a Ph.D. in Operations Research from MIT (2012) under Professor Retsef Levi. Education : MIT (Ph.D.), NUS (B.Sc.) Current Role : Professor, Management, Miami Herbert Business School Prior Roles : Associate Professor (Tenured), University of Michigan; Assistant Professor, University of Michigan His research spans Revenue Management, Supply Chain Management, Healthcare Operations, Human-Robot Interaction, and Data-Driven Optimization. Recent publications focus on fairness-constrained inventory, sequential pricing, and trust-aware robotics. He has received prestigious awards including the Senior Research Award (2025) and Amazon Research Award (2021), alongside multiple INFORMS recognitions. The 15 most recent articles highlight advancements in inventory control with fairness constraints, sequential pricing algorithms, and trust propagation models in robotics. His work bridges theoretical rigor with practical applications in supply chains and human-robot collaboration. Scientific Awards : Senior Research Award, Miami Herbert Business School, 2025 Amazon Research Award, 2021 INFORMS Meritorious Service Awards (2018, 2019, 2021, 2023) IOE Graduate Course Professor of the Year, University of Michigan, 2019 He has advised 10 PhD students, many now in academia (e.g., UC Berkeley, Penn State) or tech roles (Meta, Amazon). Grants include NSF funding as PI and Co-PI.
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
**Dr. JIN Chen** is an **Associate Professor** in the **Department of Information Systems and Analytics** at the **National University of Singapore (NUS) School of Computing**. He holds a PhD in Industrial Engineering and Management Science from Northwestern University (U.S.) and joined NUS in 2018 after postdoctoral work at the Wharton School, University of Pennsylvania. His research focuses on **online platforms**, **information systems operations**, and **digital transformation**, with a particular interest in addressing challenges like fake information dissemination and platform competition. His work bridges **operations management** and **information systems**, emphasizing practical applications in e-commerce and fintech. **Education**: B.Sc. (Shanghai Jiao Tong University) M.Sc. & Ph.D. (Northwestern University, U.S.) **Research Interests**: Platform design and competition Information bundling and pricing strategies Consumer search behavior Right-to-repair policies and environmental impacts **Awards & Recognition** (selected): Finalist, MSOM Service Management SIG Best Paper Award (2024) Nomination for NUS Young Researcher Award (2022–2024) Winner, INFORMS ENRE Young Researcher Prize (2022) Teaching Excellence Awards (2021–2023) **Key Projects**: Optimal information structure design to combat fake orders/reviews Environmental and welfare implications of right-to-repair laws Strategic bundling and pricing in multi-service platforms
Olga Papaemmanouil is a Professor of Computer Science at Brandeis University and Senior Associate Provost for Academic Affairs and Curriculum. She is affiliated with the Michtom School of Computer Science and the Volen National Center for Complex Systems. Ph.D. in Computer Science, Brown University (2008) M.S. in Information Systems, University of Economics and Business, Athens (2001) B.S. in Computer Science and Informatics, University of Patras, Greece (1999) Her research focuses on data management, integrating machine learning with cloud databases, query optimization, and performance prediction. She has pioneered techniques for interactive data exploration, reinforcement learning in query scheduling, and economic models for database provisioning. Recent publications highlight her work on learned query optimizers , deep reinforcement learning for database operations, and platform-independent neuroscience data interfaces . NSF Career Award (2013) Amazon Research Award (2019) Huawei Innovation Research Awards (2017, 2018) SIGMOD Best Demonstration Award (2015) Paris Kanellakis Fellowship (2002) She has secured multiple NSF grants and developed systems like Neo (learned query optimizer) and XCloud (performance management for cloud data services). Her work bridges database systems and machine learning for scalable data analytics.
Andreas Eklund is an Assistant Professor in the Marketing Department at the University of Wisconsin-La Crosse (UWL). He holds a Ph.D. in Marketing from Linnaeus University (Sweden), specializing in consumer psychology, and a Master’s degree from Lund University (Sweden). His research focuses on sensory marketing, branding, and service-dominant logic, exploring how sensory cues influence consumer behavior and brand experiences. He has taught courses on branding, consumer behavior, and marketing strategy at universities in Sweden, Norway, and the U.S. Education: Ph.D. in Marketing (Consumer Psychology), Linnaeus University, Sweden M.Sc. in Marketing, Lund University, Sweden Research Interests: Sensory marketing and its intersection with branding Multi-sensory consumer experiences in automotive and retail environments Experiential marketing and value proposition design Congruency theories in brand-consumer relationships Teaching: Recent courses include MKT351 (Principles of Marketing), MKT362 (Consumer Behavior), and MBA755 (Advanced Marketing) Focuses on experiential learning and real-world marketing applications Professional Background: Prior career as a chef informs his sensory marketing research Experience in Scandinavian universities before joining UWL Personal Interests: Outdoor activities (disc golf, ice skating) Swedish salt licorice enthusiast Manchester United football fan
Dr. Edward Wei is a Research Fellow at the University of Sydney Business School's Institute of Transport and Logistics Studies (ITLS), where he has been since 2018. His work focuses on transport modeling, urban mobility, and policy analysis, with an emphasis on post-pandemic commuting patterns and sustainable transport initiatives. He holds a PhD from the University of Technology Sydney (UTS), an MBus from Queensland University of Technology (QUT), and a BEc from UIBE in China. Dr. Wei's research explores the intersection of work arrangements (e.g., hybrid work, flexi-time) and their impacts on transportation demand, urban planning, and environmental sustainability. His recent projects include developing state-wide transport models, analyzing micro-mobility adoption, and assessing the role of non-traditional mobility service providers in MaaS frameworks. Key areas of expertise include discrete choice modeling, transport demand forecasting, and policy evaluation. His publications span over 30 peer-reviewed articles, with recent contributions emphasizing post-pandemic commuting behavior, zero-emission truck transitions, and sustainable university travel strategies. His academic contributions bridge theoretical transport economics with practical policy applications, addressing challenges such as car tolling regimes, public transport patronage optimization, and the integration of remote work patterns into urban planning frameworks.
Professor Eugene O'Brien serves as Professor of Civil Engineering within the School of Civil Engineering at University College Dublin's College of Engineering and Architecture. His research focuses on critical structural assessment methodologies for long-span bridges, with particular expertise in traffic load modeling and bridge safety evaluation. His research interests center on structural engineering challenges related to bridge infrastructure, specifically traffic load assessment for long-span bridges , structural health monitoring systems , and sustainable infrastructure management . Professor O'Brien pioneered camera-based monitoring techniques to overcome limitations of traditional Weigh-in-Motion sensors during congested traffic conditions, enabling more accurate safety assessments of aging bridge infrastructure. His work addresses the critical gap in quantifying traffic loading on bridges with spans up to 2 kilometers, where conventional methods fail during stop-and-go traffic scenarios. Analysis of his 15 most recent publications reveals a consistent research trajectory focused on probabilistic modeling of traffic loads, with increasing sophistication in handling extreme events and long-term infrastructure performance. His work spans fundamental statistical methods for load effect prediction, practical applications in real-world bridge assessments, and environmental considerations regarding infrastructure carbon footprints. The research demonstrates strong methodological evolution from basic traffic modeling to comprehensive lifetime assessment frameworks incorporating sustainability metrics. As co-founder and director of Roughan O'Donovan's subsidiary Innovative Solutions (ROD-IS), Professor O'Brien has translated research into practice through significant projects including the Malahide Railway Viaduct assessment (2009), EU-funded bridge lifespan simulation tools (2011), vibration reduction systems for bridge cables, and structural assessments for major international projects including the Ting Kau Bridge in Hong Kong, the Forth Road Bridge in Scotland, and the Chacao Channel Bridge in Chile. His consultancy work demonstrates direct application of academic research to critical infrastructure challenges worldwide. Professor O'Brien's research group operates at the intersection of structural engineering and sustainable infrastructure management, with particular emphasis on extending bridge service life through accurate safety assessment. Their work on the Chacao Channel Bridge demonstrates practical implementation of traffic monitoring systems using toll data to manage truck loads, while their environmental impact analysis shows how accurate safety assessments reduce unnecessary bridge replacements, thereby lowering the carbon footprint of transportation infrastructure through extended service life of carbon-intensive materials like concrete and steel.
Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).
Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.
