Marshall L. Fisher is the UPS Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research focuses on retailing and supply chain management, with notable work in dynamic pricing, omnichannel operations, and workforce optimization. Academic Affiliation: Wharton School, University of Pennsylvania Research Themes: Retail innovation, supply chain responsiveness, labor economics Recent Trends: Real-time pricing algorithms, inventory-demand interactions, digital transformation Scientific Recognition: INFORMS Kimball Medal (2007) POMS Fellow (2005) INFORMS Fellow (2002) NAE Election (1994) Academic Contributions: Pioneering work on Lagrangian relaxation in integer programming (1981), Edelman Prize-winning liquid gas optimization, and groundbreaking research on retail staffing and training effectiveness. His teaching includes courses on global supply chain management and AI in business.
Masayuki Goto is a Professor in the Department of Industrial Systems Engineering, School of Creative Science and Engineering at Waseda University, Japan, where he has served since 2011. He earned his Doctor of Engineering from Waseda University and leads research integrating statistical science, machine learning, information theory and management engineering to solve business-analytics, marketing, AI ethics and industrial optimisation problems. Education: Doctor of Engineering, Waseda University Research Interests: His work spans data science, machine learning, business analytics, statistical learning theory, generative AI, deep neural networks, natural language processing, network analysis and information theory, with recent emphasis on trustworthy AI and synthetic data generation. Publication Trends: Over 2024-2025 his group has published extensively on deep learning for tabular data, vision-language models, recommender systems, causal inference and ethical AI, demonstrating a shift toward generative-AI-driven business analytics and interpretable models. Scientific Awards: Best Paper Award, CIE51 2024 Outstanding Paper Award, APIEMS 2023 Best Paper Award, APIEMS 2022 Best Paper Award, 20th ANQ Congress 2022 2022 PC Conference Best Paper Award Best Paper Award, JASMIN 2021 Best Paper Award, 19th ANQ Congress 2021 Encouragement Award, AAMSA 2021 IDR User Forum 2020 Enterprise & DBSJ Special Awards Best Paper Award, APIEMS 2019 Best Paper Award, ANQ Congress 2018 World CIST'18 Best Paper Award Best Paper Award, ANQ Congress 2017 JSPS Grant Review Commendation 2016 JIMA Distinguished Research Award 2015 Best Paper Award, Journal of JIMA 2015 IPSJ National Convention Best Paper Awards (2015 & 2012) Advising & Grants: He has mentored a large cohort of graduate students evidenced by co-authorship on over 100 recent papers. He has served as PI on numerous JSPS KAKENHI grants and industry projects focused on data-driven management, AI marketing and ethical AI frameworks. Labs & Teams: He heads the Goto Laboratory within the Waseda Institute for Advanced Study, leading interdisciplinary projects on business AI, data-ethics education and industrial optimisation.
Dr. XiaoYue Cathy Liu is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah. She holds a PhD in Transportation Engineering from the University of Washington and advanced degrees in Transportation Planning and Electronics Engineering. Her research focuses on sustainable transportation systems, shared mobility, public transit optimization, managed lanes, and intelligent transportation systems. She actively serves on committees including the Transportation Research Board's Highway Capacity Quality of Service (HCQS) Committee and chairs its Technology Transfer Subcommittee. She also advises the Utah Model Advisory Committee and previously served on Salt Lake City’s Transportation Advisory Board. Dr. Liu is a licensed professional engineer in Utah. Education: PhD, Transportation Engineering, University of Washington (2013) MA, Transportation Planning & Management, Texas Southern University BS, Electronics & Electrical Engineering, Beijing Jiaotong University Graduate Certificate, Global Trade & Logistics, University of Washington (2011) Research Interests: Electric vehicle infrastructure and charging networks Smart transportation systems and agent-based modeling GIS-based asset management for transportation infrastructure Snowplowing operations optimization and winter maintenance Managed lanes and high-occupancy toll (HOT) lane analysis Public transit equity and accessibility Her recent publications emphasize data-driven approaches to transportation challenges, including EV demand forecasting, drone delivery networks, and resilience in interdependent transit systems. She collaborates on projects addressing Utah’s unique transportation needs, such as optimizing snowplow routes and integrating renewable energy into public transport systems. Professional Contributions: Board member, Utah Model Advisory Committee Former Chair, Salt Lake City Transportation Advisory Board (2013-2016) TRB Managed Lane Committee member TRB Transit Capacity & Quality of Service (TCQS) Committee member (2016-2019) Dr. Liu’s work bridges transportation engineering with computational methods, focusing on equity, sustainability, and operational efficiency. She leads research initiatives involving large-scale modeling, geospatial analytics, and interdisciplinary solutions for modern mobility challenges.
Joshua Ignatius is a Professor of Business Analytics at the Aston Business School, part of the College of Business and Social Sciences at Aston University. He focuses on research areas including supply chain analytics, prescriptive analytics, electronic commerce, and operations management. His work often addresses challenges such as information asymmetry in supply chains, user recommender systems, and logistics optimization. He is currently accepting PhD students in topics like Information Asymmetry in Supply Chains, User Recommender Systems, and Supply Chain Analytics. His research interests are centered on leveraging data-driven approaches to improve decision-making in supply chain and operational contexts. This includes studying disruption risk management, dynamic data modeling, and the integration of AI in cloud services. He also explores strategic decisions in e-commerce logistics, customer segmentation strategies, and environmental sustainability. Recent publications highlight his contributions to supply chain resilience, optimal security in cloud computing, and sustainable manufacturing processes. For instance, his 2025 work on supply chain network viability addresses disruption risks through dynamic data strategies. Another key area is the analysis of customer behavior in product upgrades, utilizing online review data to inform quality differentiation strategies. Dr. Ignatius has collaborated on projects involving platform information sharing, manufacturer encroachment, and logistics sourcing for e-commerce firms. His research often bridges theoretical frameworks with real-world applications, emphasizing practical solutions for operational challenges. He holds a strong record of supervising PhD students and guiding projects that combine academic rigor with industry relevance. His work frequently appears in leading journals such as the European Journal of Operational Research and Journal of Operations Management.
Dr. Shirley Coleman is a distinguished Professor at Newcastle University Business School, specializing in the application of statistical methods to business and industrial problems. With over two decades of academic contributions, she has established herself as a leading expert in statistics, data science, and quality management within industrial contexts. Her research interests span several interconnected domains: Statistics, Data Science, Business Analytics, Quality Management, Six Sigma methodologies, Kansei Engineering (which integrates emotional design with product development), Industrial Statistics, Design of Experiments, Predictive Maintenance, and Customer Lifetime Value analysis. Coleman's work consistently bridges theoretical statistical concepts with practical business applications across diverse sectors including healthcare, manufacturing, facilities management, and digital marketing. Analysis of her recent publications reveals a strong focus on the evolving role of statistics in the digital age, particularly examining how statistical expertise contributes to AI development, Industry 4.0 initiatives, and data-driven business transformation. Her work demonstrates increasing emphasis on customer analytics, predictive maintenance modeling, and the strategic implementation of data science in small and medium enterprises. Coleman's publications frequently address methodological challenges while maintaining strong practical relevance for industry practitioners. Throughout her career, Coleman has been actively involved with the European Network for Business and Industrial Statistics (ENBIS), contributing to the development and dissemination of statistical methods in business contexts. Her collaborative approach is evident in numerous co-authored publications across disciplines, demonstrating her ability to work effectively with researchers from diverse fields including engineering, healthcare, and business management. Her advisory work appears focused on helping organizations implement statistical thinking in business processes, with particular attention to small and medium enterprises seeking to leverage data analytics for competitive advantage. Though specific grant information isn't detailed in the available publications, her extensive industry-focused research suggests significant engagement with practical business problems and industry partnerships. Dr. Coleman has made substantial contributions to the field through her leadership in professional organizations, particularly ENBIS, where she has helped shape the discourse around industrial statistics and their business applications. Her work on Kansei Engineering demonstrates innovative approaches to integrating human factors with statistical methods for product development.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms
Ningyuan Cao is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame, College of Engineering. He leads the Circuit and System Intelligence Research Lab , focusing on the intersection of advanced hardware design and real-time/low-power machine learning applications. Education : Ph.D., Electrical and Electronics Engineering, Georgia Institute of Technology (2020) M.S., Electrical Engineering, Columbia University (2015) B.S., Electrical and Electronics Engineering, Shanghai Jiao Tong University (2013) His research investigates custom analog/mixed-signal circuits , digital architecture , and micro-system design for machine learning acceleration, distributed intelligence, and data-driven IC design automation. Key application domains include Internet-of-Everything, tactile internet, and mixed reality systems. Recent publications highlight work on Bayesian neural networks , privacy-preserving bio-signal encoders , transformer-based surrogate models , and compute-in-memory architectures . Technical themes span neuromorphic computing, uncertainty quantification, and hardware security.
Overview Prof. Harris Kyriakou is an Associate Professor and Chair Holder of the Media & Digital Chair at ESSEC Business School. His research focuses on leveraging artificial and collective intelligence to enhance organizational value creation, digital strategy, and data-driven decision-making. He has advised multinational firms like Airbnb, Facebook, and Yelp, and his work is supported by grants from NSF and the Spanish government. Education Ph.D. in Management Sciences (Stevens Institute of Technology, 2016) M.S. in Engineering & Technology Innovation Management (Carnegie Mellon University, 2010) B.Sc. in Digital Systems (University of Piraeus, 2007) Research Focus His research explores intersections between AI/collective intelligence, blockchain, sharing economy regulations, and platform governance. Key themes include data network effects, algorithmic regulation, and digital transformation. Recent work on ChatGPT vs. Google examines AI-driven competitive dynamics in search markets. Recognition Awarded the 2024 Case Centre Triple Award, 2022 Early Career Award (AIS), and multiple best paper awards (AoM, INFORMS). Recognized as a 40-Under-40 MBA Professor by Poets & Quants. Teaching & Leadership Co-leads the 'Algorithmic Governance in Platform Economy' thesis Teaches courses on AI, digital strategy, and IT management at ESSEC and IESE Former Assistant Professor at IESE Business School (2016–2021) Professional Contributions Serves as a European Commission advisor on digitalization, reviewer for top journals (MIS Quarterly, Academy of Management Review), and mentor for doctoral candidates.
Jeeyeon Kim is a Lecturer in the Department of Management and Marketing at La Trobe Business School, La Trobe University. Prior to this, she served as an Assistant Professor at National Sun Yat-sen University in Taiwan. Her research focuses on digital marketing, omnichannel retailing, social media/influencer marketing, and digital healthcare. She holds a PhD and MA from Yonsei University, South Korea, and has held visiting roles at Yonsei University and IE University. Her work emphasizes empirical data analysis and statistical modeling to address marketing challenges. Jeeyeon has secured multiple research grants, including AUD 82,500 from Taiwan's Ministry of Science and Technology (MOST) for projects on big data-driven marketing and digital transformation. She also coordinates grants with Yonsei University and AACSB initiatives. Her editorial roles include serving on the Asia Marketing Journal and Korean Scholars of Marketing Science boards. Her research spans topics such as virtual influencers' impact on social media, omnichannel strategies, and healthcare consumer behavior. Teaching awards include the 'Outstanding Course Teaching Award' and 'Excellent Mentor Award.' She actively reviews for journals like the Asian Pacific Journal of Marketing and Logistics and conferences like the Global Fashion Management Conference.
Inigo Flores Ituarte is a Research Professor at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He leads the Digital Design and Manufacturing (D2M) research lab, focusing on sustainable manufacturing and twin-transition strategies integrating digital and green technologies. His work emphasizes optimization-driven design, additive manufacturing innovations, and AI-driven expert systems to enhance energy efficiency and reduce environmental impacts. Key research pillars include: Pillar 1: Twin-transition in Engineering Design and Manufacturing Processes, addressing sustainable manufacturing and intelligent systems Pillar 2: Development of open D2M systems and Process-Structure-Property-Performance (PSPP) linkages in advanced materials His research explores multi-disciplinary optimization combining model-based simulations and data-driven techniques. Notable contributions include generative AI integration in CAD systems, cognitive manufacturing systems, and cost-effective process monitoring using CNN-based methods. Inigo's work emphasizes environmental sustainability, with a focus on reducing manufacturing's energy consumption (54% of global use) and CO2 emissions. He advocates for interconnected material systems, smart manufacturing processes, and AI-assisted decision-making to achieve cognitive intelligence in industrial operations. His D2M lab's overarching goal is to maximize product/process performance while improving cost-effectiveness and minimizing environmental footprints. Recent projects include railway bogie demonstrators via multi-material deposition and sensor systems leveraging IoT and ChatGPT integration.
Professor Chee Yew Wong is a leading academic in supply chain management at Leeds University Business School (LUBS), where he holds the position of Professor and serves as Director for Research & Innovation in the Analytics, Technology and Operations department. He previously held a Chair in Logistics and Supply Chain Management at Hull University Business School and has served as a visiting professor in Thailand and China. His work bridges academia and industry, with over nine years of professional experience in operations and supply chain roles across multinational corporations and SMEs. His educational background includes a PhD in Supply Chain Management from Aalborg University, Denmark; an MSc in Manufacturing Management from Linköping University, Sweden; a BEng in Mechanical Engineering from the University of Technology, Malaysia; and a PG Certificate in Higher Education from Hull University, UK. Professor Wong's research centers on intelligent, responsible, and sustainable solutions for global supply chains. Key interests include digital supply chains, supply chain analytics, green logistics, human rights in supply chains, resilience, and circular economy models. He leverages technologies such as blockchain, machine learning, and IoT to enhance transparency, integration, and performance in complex supply networks. The analysis of his recent publications reveals a strong trend toward digital transformation, sustainability, and ethical governance in supply chains. His work increasingly emphasizes data-driven decision-making, environmental and social risk assessment, and the role of technology in enabling responsible global sourcing. Many projects focus on real-world applications in industries such as healthcare, fashion, retail, and manufacturing, often through Knowledge Transfer Partnerships with industry leaders. Best Reviewer Award, Operations and Supply Chain Management Division, Academy of Management Conference, Chicago, USA (2018) Prof. Xiande Zhao's Best Paper Award, International Conference on Operations and Supply Chain Management, Kaifeng, China (2017) Finalist for the Jack Meredith Best Paper Award, Academy of Management Conference, Anaheim, USA (2016) Emerald Best Paper Award, Supply Chain Management: an International Journal (2006) Professor Wong has successfully supervised 10 PhD students and 4 post-doctoral researchers, and has examined over 15 PhD dissertations internationally. He leads multiple research grants, including projects funded by Innovate UK, UKRI, ESRC, and the British Council, focusing on digital transformation, human rights, and green supply chain innovation. His collaborations span academia, government, and industry, demonstrating a strong commitment to impactful, applied research. He is actively involved in the Centre for Operations and Supply Chain Research, the Adaptation Information Management and Technology group, and the Centre for Decision Research at LUBS. These research groups support interdisciplinary work in analytics, digital technologies, and sustainable operations, fostering innovation and knowledge exchange across sectors.
Liliane Abboud is an Assistant Professor in Marketing at the University of Surrey, affiliated with the Surrey Business School and the Surrey Institute for People-Centred Artificial Intelligence (PAI). Her roles include teaching and research in marketing, with a focus on integrating technology and ethical practices into service environments. Education: She holds a PhD, MSc, and BBA, and is a Fellow of the Higher Education Academy (FHEA). Her research interests center on customer experience technology, wellbeing in services, power dynamics, and actor engagement strategies. Recent work explores collective ethics in academia and service ecosystems' management. Teaching includes postgraduate Services Marketing and undergraduate courses like Capturing Market Insights and Research in Marketing. She supervises PhD students Tariq Alwashah and Heer Paleja, focusing on AI-driven consumer decision-making and service technology. Awards: Certified Management and Business Educator (CMBE). Research highlights include studying low-power customers' wellbeing, third-party roles in service encounters, and strategies to enhance end-user experiences in complex service ecosystems. Labs/Teams: Active contributor to the Surrey Institute for People-Centred AI (PAI), exploring ethical AI applications in marketing and service systems.
Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.
Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.
