Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.
Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.
Dr. Amin Keramati is an Assistant Professor of Supply Chain Management at Widener University’s School of Business Administration. He holds a PhD in Transportation & Logistics from North Dakota State University and previously served as a graduate research assistant at the Upper Great Plains Transportation Institute. His work includes federally funded projects like the Mountain-Plains Consortium’s MPC-550 project, focusing on highway-rail grade crossing safety. Dr. Keramati teaches courses in enterprise resource planning, decision analytics, database management, transportation/logistics, project management, and data analytics. Education: PhD in Transportation & Logistics, North Dakota State University Graduate Research Assistant at Upper Great Plains Transportation Institute Research Focus: Dr. Keramati develops mathematical, statistical, and machine learning approaches to solve complex problems in transportation, supply chain, and logistics. Key areas include data mining, big data decision support, transportation network analytics, project scheduling, smart manufacturing, and accident analysis. His interdisciplinary work bridges supply chain optimization with emerging technologies like blockchain for healthcare and clinical trials. Awards & Recognition: Student Paper Award, American Association of State Highway and Transportation Officials (2017) Grants & Projects: Lead researcher on the Mountain-Plains Consortium’s MPC-550 project, which expanded his dissertation work on safety systems for highway-rail crossings. Collaborates on federally funded transportation safety initiatives and supply chain optimization for bioethanol and pharmaceutical industries. Labs/Teams: Active in Widener’s School of Business research groups focusing on supply chain innovation and interdisciplinary projects combining logistics with emerging technologies.
Wim VAN LENT is a Full Professor of Management at IÉSEG School of Management , France. He holds an HDR in Management from IAE Paris Sorbonne and a Ph.D. in Business Administration from ESSEC Business School. His academic career includes roles as Visiting Professor at Curtin University (Australia), Griffith University (Australia), and Researcher at the University of Paris I Panthéon-Sorbonne. He coordinates the 'Strategy and Sustainability' track and is a member of the LEM research group. His research focuses on Management Strategy , Business Ethics , Innovation and Entrepreneurship , and Historical Methodology . Notable areas include corporate archives' strategic use, temporal dynamics in organizations, and ethical repair mechanisms. His work often employs historical case studies, such as the Dutch East India Company (VOC) and colonial-era corporations, to analyze organizational adaptation and institutional contradictions. Van Lent's publications span top journals like Academy of Management Review , Small Business Economics , and Business Ethics Quarterly . His recent studies explore periodization practices in organizations, entrepreneurial time narratives, and moral repair frameworks. Teaching includes international strategy and corporate social responsibility at the MSc level. His research bridges historical analysis with contemporary management challenges, emphasizing how understanding past institutional dynamics informs modern organizational behavior and ethical decision-making.
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.
David Stillwell serves as Professor of Computational Social Science at Cambridge Judge Business School and Academic Director of The Psychometrics Centre, University of Cambridge. His research leverages big data to understand human psychology and behavior, with significant contributions in personality prediction from digital footprints and personalized advertising applications. His educational background includes: BSc in Psychology from the University of Nottingham (2007) MSc in Research Methods from the University of Nottingham (2008) PhD in Decision Making from the University of Nottingham (2012) Dr. Stillwell's research centers on computational social science and psychometrics, pioneering the myPersonality Facebook application that collected data from over 6 million users. His work demonstrates computers can predict personality as accurately as spouses, reveals psychological targeting's advertising effectiveness, and establishes links between personality-matched spending and life satisfaction. He also explores linguistic honesty markers through profanity analysis and personality-based dating patterns. His recent publications (2019-2025) reveal a strong trajectory toward AI evaluation using psychometric frameworks, particularly in medical and general-purpose AI assessment. Key themes include fairness metrics in language models, crisis emotional responses through social media, and computational personality recognition - demonstrating consistent innovation at the psychology-technology intersection. He has received significant recognition: Top 10 most influential papers of 2013 by Altmetric Nieman Journalism Lab highlight of 2013 Named in 'top 30 thinkers under 30' by Pacific Standard Magazine Dr. Stillwell maintains active industry engagement through consultancy with Barclays, Hilton, and Ubisoft on projects spanning computer-adaptive testing systems to interactive experiences like Predictive World for Watch Dogs 2. His policy impact is substantial, with citations by the European Data Protection Supervisor, World Bank, and multiple national governments, leading to speaking engagements at the European Parliament and Bank of England. As Academic Director of The Psychometrics Centre, he leads a global hub for advancing psychological assessment through innovative methods including Concerto open-source software, collaborating with organizations ranging from the European Commission to major corporations on psychometric applications in people analytics and digital behavior.
Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.
Ashwin BALIGA is an Assistant Professor of Sales at IÉSEG School of Management , France, since 2022. He holds a Ph.D. in B2B Marketing from the Indian Institute of Technology, Madras (2022), a Master of Technology in Engineering Management (2015), and a Bachelor’s in Mechanical Engineering (2011). Ph.D. in B2B Marketing, Indian Institute of Technology, Madras (2022) Masters of Technology in Engineering Management, Manipal Institute of Technology (2015) Bachelors in Mechanical Engineering, National Institute of Engineering, Mysore (2011) His research focuses on B2B Service Failure & Recovery , Mental Health in B2B Sales , Technology in B2B Sales , and Buyer-Supplier Relationships . Recent work includes systematic reviews and morphological analyses of DEI in B2B selling, mental health impacts, and tech-driven sales strategies. In 2025, he received the Best Reviewer Award from the Journal of Hospitality and Tourism Management (JHTM). Additional accolades include the Sri N Kannan Prize for Best PhD Thesis in Marketing (2022), multiple gold medals for academic excellence (2015, 2011), and recognition for research contributions at NASMEI and the Sheth Foundation Doctoral Consortium. Baliga teaches Selling Techniques at the bachelor’s level and Services Marketing and Sales Management at the master’s level. He is a member of the Laboratory of Economics and Management (LEM) and has contributed to journals like the Journal of Personal Selling and Sales Management, Journal of Business Research, and the International Journal of Mineral Processing.
Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .
Dr. Sezgin Ayabakan is an Associate Professor in the Department of Management Information Systems at the Fox School of Business and Management, Temple University . He holds a PhD in Management Science from the University of Texas at Dallas, with a focus on Management Information Systems (MIS), an MS in Industrial and Systems Engineering from the University of Florida, and a BS in Industrial Engineering from Bilkent University in Turkey. His research focuses on healthcare analytics , particularly exploring how health information technology (HIT) impacts healthcare costs, quality, and utilization. Key research interests include data analytics applications in healthcare and social media, the business value of information systems, and open-source production strategies. He has examined topics such as telehealth efficacy, electronic health record (EHR) adoption challenges, hospital readmission reduction programs, and the interplay between healthcare IT and clinician well-being. Dr. Ayabakan teaches undergraduate and graduate courses in information systems, including MIS 2101: Digital Systems and SBM 3586: Diamond Peer Teachers - Internship II . His work has been published in top-tier journals like MIS Quarterly and Health Services Research and Management Epidemiology , with recent studies addressing telehealth utilization, EHR strategy impacts, and the role of organizational routines in clinician satisfaction. While no formal awards are listed, his contributions to healthcare IT research demonstrate significant academic impact. His advisory and grant activities are not detailed in the provided text, but his research often intersects with practical healthcare system challenges, suggesting potential collaborations with healthcare institutions and policy organizations.
Dr. Anna Dubiel is a Senior Lecturer in Marketing at King's Business School, King's College London. Her research focuses on the intersection of innovation and R&D management with international markets, particularly in emerging economies. She teaches a broad range of Marketing topics in postgraduate and executive programmes covering International Marketing and Digital Marketing. Dr. Dubiel obtained her PhD from WHU – Otto Beisheim School of Management, Vallendar, Germany, where she previously served as Assistant Professor of International Innovation Management. Her academic journey includes visiting researcher positions at Boston College, Judge Business School, and London Business School, as well as a Visiting College Research Associate position at Wolfson College, University of Cambridge in 2017. Her research expertise centers on Innovation Management , New Product Development , R&D Management , International Marketing , and Emerging Economies . Dr. Dubiel's work particularly examines how businesses can develop better products and services for consumers and businesses at the subsistence market ends. Her current research project focuses on 'Risk Management in Emerging Economies: A Marketing Perspective. The Context of Human-Wildlife Conflict (HWC) Insurance,' which studies how to reach buyers of complex new services in subsistence marketplaces and raise insurance literacy. Dr. Dubiel's publications reveal a consistent focus on innovation in emerging markets, with significant contributions to journals such as the Journal of Product Innovation Management, Industrial Marketing Management, Journal of Business Research, European Journal of Marketing, and International Marketing Review. Her work often explores frugal innovation, new product development strategies, and international market adaptation, demonstrating how businesses can succeed in resource-constrained environments while creating social impact. Her notable scientific achievements include: Highly Commended Paper Winner of the Journal of Indian Business Research (2014) Finalist for the Thomas P. Hustad Best Paper Award for the Journal of Product Innovation Management (2016) Academy of Marketing Teaching Excellence Award (2020) British Academy of Management Early Career Education Practice Award (2021) Finalist for the Academy of International Business Teaching Innovation Award (2021) HEA Fellowship (2019) Multiple Case Writing Competition awards from AESE Business School (2010, 2011) Dr. Dubiel has received research funding from several intramural grants as well as the British Academy/Leverhulme Trust. Her teaching methodology emphasizes practical application through managerial guest lectures and consulting projects conducted by students for real organizations. While she is not currently accepting new PhD students, her research group focuses on marketing financial services to first-time buyers in emerging markets, with evidence from Human-Wildlife Conflict (HWC) Micro-Insurance in Kenya.
Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He serves as the Founding Director of the Center for Business Analytics and Disruptive Technologies and has a secondary appointment in Information Systems. His roles include Ph.D. Program Concentration Director for Operations and Supply Chain Management. With over 240 publications, he ranks #1 globally in Information Systems Research publishing and holds a robotics patent. His research focuses on AI, blockchain, healthcare analytics, and cybersecurity, with notable contributions to digital transformation and operations management. Awards include the INFORMS ISS Distinguished Fellow and POMS Fellow. He advises major journals like Production and Operations Management and organizes conferences like POMS 2018. Media mentions span NYT, WSJ, and CBS. Education: University of Washington faculty Texas A&M University faculty Research Interests: Dr. Kumar explores AI applications, blockchain in fintech, healthcare system optimization, and supply chain analytics. He investigates cybersecurity’s operational impacts and data mining strategies. His work bridges theory and practice, addressing challenges in digital transformation and platform economies. Publications Trends: Recent work emphasizes digital platforms (e.g., blockchain adoption, telemedicine impact), organizational behavior (real-time feedback systems), and healthcare operations (personalized treatment algorithms). He frequently collaborates internationally, with studies appearing in Management Science, Information Systems Research, and Decision Sciences. Awards & Recognition: Changjiang Scholars Chair (China) ISB Visiting Professorship POMS Executive Editor Grants & Labs: Secured NIH grants for health analytics projects. Leads Temple’s analytics center fostering industry partnerships. Active in editorial roles (e.g., Production and Operations Management Journal Deputy Editor). Teaching: Offers courses in data science, optimization methods, and innovation entrepreneurship at graduate/undergraduate levels.
Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.
Professor Christian Bizer is a leading figure in web-based systems and data integration at the University of Mannheim , where he chairs Information Systems V: Web-based Systems . His research focuses on integrating data from multiple sources using large language models and LLM-based agents, with applications in product data extraction and DBpedia knowledge graph construction. He co-founded the DBpedia project and initiated the WebDataCommons initiative. Current research areas: Entity matching, schema matching, table annotation, information extraction, data discovery Key projects: WebMall benchmark, WInte.r integration framework, Schema.org analysis His work applies to e-commerce data integration and knowledge graph construction, with empirical studies on schema.org adoption. He supervises PhD students including Alexander Brinkmann and Ralph Peeters. Scientific Awards: Best Paper at iiWAS 2024 SWSA Ten-Year Award at ISWC 2019 Yahoo FREP Award 2015 Semantic Web Challenge winners Teaching includes courses on web data integration, web mining, large language models, and data mining for master's programs. He leads the DWS PhD colloquium and team projects on LLM agents for data integration.
