Aileen Nielsen is a Ph.D. Candidate at ETH Zurich's Center for Law & Economics and a Fellow in Law & Tech. She holds a J.D. from Yale Law School, a B.A. in Anthropology from Princeton University, and advanced degrees in Applied Physics (Columbia) and Comparative Human Development (University of Chicago). Her research focuses on regulatory and judicial responses to technological innovation, particularly in AI governance, data privacy, and medical technology. She has practiced law in NYC and worked in tech startups across healthcare and political organizations. Education: Ph.D. Candidate (ETH Zurich), J.D. (Yale), M.S. Applied Physics (Columbia), M.A. Comparative Human Development (Chicago), B.A. Anthropology (Princeton) Her work combines empirical and experimental methods to address challenges like algorithmic fairness, AI liability in medicine, and public perceptions of data markets. Recent findings explore regulatory frameworks for AI systems and the ethical implications of algorithmic surveillance. She teaches courses on algorithms and fairness, law & tech research, and authentication security. Publications span law journals, cybersecurity white papers, and AI ethics conferences, with a focus on balancing innovation with societal accountability. Her books Practical Fairness and Practical Time Series Analysis further bridge technical and legal domains.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.
Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.
Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.
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.
Henry Rothstein is a Professor of Risk & Regulation at King's College London's Department of Geography, School of Global Affairs. He serves as Deputy Director of the King's Centre for Risk Management. His research focuses on risk governance, regulatory transparency, and comparative policy analysis across Europe and China. He holds a BSc from Bristol University and advanced degrees from Sussex University. Key Roles: Deputy Director (King's Centre for Risk Management), Editorial Board Member (Journal of Risk Research, Health, Risk and Society) His work examines institutional factors shaping risk governance in public and private sectors, including healthcare quality regulation via big data, food safety policies, and EU regulatory coordination. Recent projects include analyzing pandemic responses across China/US/UK/Germany and evaluating NHS healthcare quality monitoring systems. Notable grants include a £1.1M ESRC-Open Research Area grant (2012). He supervises PhD students researching comparative risk governance and regulatory philosophies. Current research explores 'big data for healthcare quality regulation' and 'national political philosophies of risk management.' Labs/Teams: King's Centre for Risk Management, Risk, Hazard & Society research group.
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.
Gustavo Vulcano is an Adjunct Professor in the Department of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University, where he has been affiliated since 2002. He served as Assistant Professor (2002–2010), Associate Professor (2010–2017, tenured in 2012), and has held an adjunct role since 2017. His academic work bridges theoretical and applied operations management with strong industry engagement. Education: Ph.D. in Operations Management, Columbia University, 2003 M.Phil. in Operations Management, Columbia University, 2000 M.S. in Computer Science, University of Buenos Aires, 1997 B.S. in Computer Science, University of Buenos Aires, 1994 His research focuses on revenue and pricing analytics , retail operations , and supply chain management , particularly emphasizing customer choice modeling , data-driven optimization , and computational methods in network revenue management . He integrates stochastic modeling and behavioral insights to develop practical pricing and operational strategies. His work is deeply rooted in real-world applications across airlines, retail, and financial services. The analysis of his publications reveals a consistent trend in leveraging data-driven decision-making under uncertainty, with a focus on dynamic pricing, demand learning, and robust optimization. His articles span premier journals such as Operations Research and Management Science , reflecting a strong theoretical foundation combined with empirical and computational rigor. Key thematic areas include customer behavior modeling, network revenue management, and stochastic optimization for service industries. Scientific Awards and Leadership: Chair, INFORMS Revenue Management and Pricing Section (2016–2017) Associate Editor, Operations Research and Management Science Prof. Vulcano has advised numerous PhD and master’s students and has secured research grants through industry collaborations. His consulting projects with Delta Airlines, Sabre Holdings, Aerolíneas Argentinas, and ICBC demonstrate a strong commitment to translating academic research into practical solutions. He has taught core courses such as Operations Management , Pricing and Revenue Management , and Dynamic Programming across undergraduate, MBA, PhD, and MSBA programs, shaping future leaders in data-driven decision-making. He is actively involved in research labs and teams focused on operations analytics and pricing strategy , often collaborating with interdisciplinary groups at NYU Stern and industry partners. His ongoing editorial roles and consultancy reflect sustained engagement in advancing the field of revenue management and operations science.
Professor Byron Sharp is a Professor of Marketing Science and Director of the Ehrenberg-Bass Institute for Marketing Science at the University of South Australia. The institute is recognized as the world’s largest center for research into marketing, housing over 60 marketing scientists. He plays a pivotal role in advancing evidence-based marketing principles globally. Research Interests: Marketing Science and Empirical Generalizations Brand Growth and Customer Acquisition Advertising Effectiveness and Mental Availability Consumer Behavior and Loyalty Patterns Market Modeling (e.g., Dirichlet model) Evidence-Based Marketing Practice His recent publications reveal a consistent focus on quantifying brand performance, buyer behavior, and advertising impact. Trends in his articles emphasize the application of statistical models (e.g., NBD, Dirichlet) to understand consumer repertoires, loyalty, and the effects of advertising cessation. His work bridges academic rigor with practical marketing applications, particularly in brand strategy and media planning. Scientific Awards and Recognition: His book How Brands Grow was voted Marketing Book of the Year by AdAge readers. Recognized by Warc (2015) as one of the most influential marketing books of the past decade. Advising, Grants, and Collaborations: While specific student names are not listed, he leads a large research team and frequently collaborates with prominent scholars such as Jenni Romaniuk, John Dawes, and Robert Kennedy. His work is supported by industry partnerships and institutional funding through the Ehrenberg-Bass Institute, enabling large-scale empirical studies using real-world data. Labs and Teams: He directs the Ehrenberg-Bass Institute, which functions as a major research laboratory focused on marketing science, conducting longitudinal studies, empirical generalizations, and collaborative industry research.
Paola Passalacqua is a Professor of Environmental and Water Resources Engineering and Earth and Planetary Sciences at the University of Texas at Austin, holding the L.B. (Preach) Meaders Professorship in Engineering. She leads research at the intersection of water resources engineering, geomorphology, and hydrology, focusing on river networks, coastal restoration, and remote sensing applications. Her work addresses delta dynamics, floodplain connectivity, and community resilience to compound hazards. Dr. Passalacqua earned a PhD in Civil Engineering (2009) and MS in Water Resources from the University of Minnesota, and dual MS/BS in Environmental Engineering from the University of Genoa (2002). Her technical expertise includes hydrological connectivity, network theory, and morphodynamic modeling using LiDAR and satellite data. Research interests emphasize river delta structure/dynamics, floodplain sedimentation, and translating science into community adaptation strategies. She co-developed tools like GeoFlood for large-scale flood mapping and the pyDeltaRCM numerical delta model. Her interdisciplinary approach integrates socio-technical vulnerability analysis with environmental systems. Awards include the endowed Meaders Professorship. Current projects involve coastal Alaska infrastructure resilience, SWOT satellite data applications, and delta sustainability in the Anthropocene. She leads the Passalacqua Research Group, engaging in citizen science through initiatives like UTBiome.
Kristian J. Hammond is the Bill and Cathy Osborn Professor of Computer Science at Northwestern University's McCormick School of Engineering. He directs both the Master of Science in Artificial Intelligence Program and the Center for Advancing Safety of Machine Intelligence (CASMI). His research focuses on artificial intelligence, natural language generation, narrative generation, conversational interfaces, and ethical AI applications across domains like law, education, and journalism. Hammond co-founded Narrative Science, leveraging AI for automated journalism from data. Education: PhD, MS, and BA in Philosophy (all from Yale University). His work spans technical innovation and societal impact, with notable contributions to AI transparency, bias mitigation, and machine learning ethics. Hammond has authored influential articles on AI governance, conversational systems, and the future of work in an automated economy. His leadership roles emphasize interdisciplinary collaboration between computer science, business, and humanities. Research highlights include developing AI systems that enhance human capabilities, exploring ethical frameworks for machine intelligence, and advancing AI safety through initiatives like CASMI. He frequently engages in public discourse via TEDx talks and commentaries on AI's societal implications, emphasizing the need for human-centered technology design.
Ruoqing Zhu is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, with a primary appointment in the College of Liberal Arts & Sciences. He also serves as an inaugural member of the Carle Illinois College of Medicine, a Faculty Fellow at the National Center for Supercomputing Applications, and an affiliated researcher with the Carl R. Woese Institute for Genomic Biology and the Center for Genomic Diagnostics. His roles include PhD Program Director and Advisory Board member of Prenosis Inc. Dr. Zhu holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2013), an MA in Statistics from Bowling Green State University (2008), and dual B.S. degrees in Mathematics and Financial Engineering from Nanjing University (2006, 2005). His postdoctoral training was at Yale University’s Department of Biostatistics (2013–2015). His research focuses on developing statistical methods for decision-making in personalized medicine and reinforcement learning, addressing challenges such as model interpretability, high-dimensional data, and distributional shifts. Key areas include uncertainty quantification, causal inference, and applications in bioinformatics, nutrition, and infectious diseases. He co-teaches courses at Carle Illinois, including Data Science Project and Foundations: Molecules to Populations , and contributes to interdisciplinary initiatives like the Personalized Nutrition Initiative. His recent work emphasizes trustworthy AI in healthcare, including sepsis prediction tools, metabolomic analysis, and biomarker discovery. He is actively involved in translational research, bridging computational methods with clinical and public health applications.
Levi J Hargrove is an Associate Professor at Northwestern University, holding dual appointments in the Department of Physical Medicine and Rehabilitation at the Feinberg School of Medicine and the Department of Biomedical Engineering at the McCormick School of Engineering. He is also a Research Scientist at the Center for Bionic Medicine at Shirley Ryan AbilityLab. His work focuses on developing neural control systems for prosthetic limbs, particularly in myoelectric control and pattern recognition, aiming to create clinically viable solutions for amputees. Education: BScE in Electrical Engineering, University of New Brunswick, 2003 MScE in Electrical Engineering, University of New Brunswick, 2005 PhD in Electrical Engineering, University of New Brunswick, 2008 Research Interests: Signal processing, pattern recognition, myoelectric control of powered prostheses, and neural interfaces for bionic limbs. His lab translates research into clinical applications, such as the first thought-controlled bionic leg and Coapt LLC's pattern recognition systems for upper-limb prosthetics. Awards: 2017 American Academy of Orthotists and Prosthetists Research Award 2014 Department of Defense Outstanding Research Team Award 2015 Collaboration Award from Chicago Innovation Grants: Manages a $25 million portfolio from federal, military, and philanthropic sources. Key projects include NSF-funded research on human-robot interaction and DoD grants for prosthetic innovation. Labs: Leads the Regenstein Foundation Center for Bionic Medicine and collaborates with the Neurorehabilitation and Neural Engineering Lab. His work emphasizes translational research, bridging engineering and clinical practice.
Tridas Mukhopadhyay is the Deloitte Consulting Professor of e-Business at Carnegie Mellon University's Tepper School of Business, where he has served on the faculty since 1986. His academic journey at CMU progressed from Instructor of Information Systems (1986-1987) to Assistant Professor (1987-1993), Associate Professor (1993-1997), Professor (1998-present), and Deloitte Consulting Professor of e-Business (2000-present). He also served as Director of the MS in Electronic Commerce program from 1999-2004. Ph.D. in Computer and Information Systems, University of Michigan–Ann Arbor, 1987 M.B.A. in Computer and Information Systems, Indian Institute of Management Calcutta, 1981 B. Tech. in Electrical Engineering, Indian Institute of Technology Kharagpur, 1978 Professor Mukhopadhyay's research spans multiple critical areas in information systems and technology management. His work on strategic IT use examines how organizations derive business value from information technology investments. He has conducted extensive research on business-to-business commerce, particularly focusing on e-procurement systems, web-based marketplaces, and electronic intermediation models. His cybersecurity research investigates the economic aspects of cyber security, including liability mechanisms and patch release strategies. In software engineering, he has studied productivity, quality metrics, and offshore software development contracts. His most recent publications reveal several key trends in his research trajectory. There's a growing focus on digital platform economics, examining advertising models, virtual currency systems in gaming, and sharing economy dynamics. His work increasingly incorporates behavioral aspects, studying how users respond to personalized content and how backers exert control in crowdfunded projects. Methodologically, his research employs sophisticated analytical approaches including hierarchical Bayesian models, structural equation modeling, and natural experiment designs. CART Research Frontier Award, Carnegie Mellon, 2005 Distinguished Ph.D. Alum, Michigan Business School, 2004 Best Paper, International Conference on Information Systems, 2001 Best Paper, MIS Quarterly, 1995 Xerox Research Chair, Tepper School of Business, 1988-1989 Information Systems Society Distinguished Fellow, 2012 Professor Mukhopadhyay has served on numerous editorial boards including Information Systems Research (1994-2003), Management Science (1999-2003), and MIS Quarterly (1997-1999), demonstrating his significant contributions to the field. His consulting work with major organizations including Alcoa, Chrysler, Ford, General Motors, IBM, and governmental agencies like the United States Post Office and Pennsylvania Turnpike has provided practical insights that inform his academic research. He has been actively involved in university governance through committee service including the Business Technology Faculty Search Committee and the CMU Faculty Senate. His research has been supported through various industry partnerships and academic grants, though specific grant details aren't provided in the source material. His teaching focuses on Business Computing and Strategic IT courses, reflecting his expertise in both foundational information systems concepts and strategic applications of technology in business contexts.
