Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division
Gretchen A. Macht is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island, College of Engineering. She specializes in Human Systems Engineering, with research emphasizing team performance, sustainability, electric vehicles, solar energy systems, and voting systems. Her work bridges engineering, ergonomics, and public policy. Her research interests focus on understanding and optimizing human-centered systems. She investigates team communication and performance through fuzzy cognitive maps and entropy modeling, develops sustainable design practices for products and energy systems, and analyzes user behavior in electric vehicles and solar installations. A significant portion of her research is dedicated to election systems, where she uses simulation to improve accessibility, safety, and efficiency of in-person voting, particularly during public health crises. Her recent publications span topics such as EV charging behavior, solar installation safety, voting simulation, and team dynamics. These works reflect interdisciplinary trends combining systems engineering, data analytics, ergonomics, and sustainability science. She frequently employs simulation, naturalistic data analysis, and human factors methodologies. Dr. Macht has secured substantial grant funding from organizations including the U.S. Election Assistance Commission, National Science Foundation, MIT, and Democracy Fund. She serves as Principal Investigator (PI) or Co-PI on projects related to election systems (e.g., SMILE, URI VOTES), ergonomic redesign of protective gear, and community solar development. She leads the SIS Lab and the URI VOTES initiative, fostering collaborative research on simulation and system design. Her lab supports graduate students in conducting impactful research at the intersection of engineering and societal challenges. Notable grants include: PI, MIT: “Voting in Person - White Paper” (2023) PI, US EAC: “SMILE 2.0” (2022) PI, Democracy Fund: “URI VOTES Pocket Guide Tool” (2022) Co-PI, NSF: “Election Science: Convergence Accelerator Workshop Proposal” (2021) PI, US EAC: “SMILE: Simulation Models for an Immersive Learning Environment” (2021) She has advised several graduate students, including Mehrsa Khaleghikarahrodi, Nicholas D. Bernardo Jr., and Tim Jonas, whose research aligns closely with her focus areas in EVs, elections, and team performance.
Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.
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 .
Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.
Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).
Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Terry C. Lansdown is an Associate Professor in the School of Social Sciences and Department of Psychology at Heriot-Watt University. His expertise lies in Human Factors, particularly focusing on driver safety, workload management, and attentional distraction in transportation contexts. Prior to his academic role, he worked as a senior researcher at the Transport Research Laboratory, contributing to numerous EU-funded and government-sponsored projects. His research integrates ergonomics principles with real-world applications, addressing issues like automated driving systems, social distractions in vehicles, and occupational health in construction industries. Key research interests include: Driver behavior and road safety Human factors in automated systems Workload and attentional demands in complex tasks Health and safety in small-to-medium enterprises (SMEs) He has led studies on pandemic response strategies, such as the CHARIS project analyzing behavioral adherence during the COVID-19 crisis. Lansdown’s work frequently bridges psychological theory with practical interventions, including text messaging campaigns to improve construction worker safety and experimental designs to evaluate automation failure responses. Collaborations span academic and industrial sectors, with publications addressing topics ranging from UV exposure in outdoor workers to the cognitive impacts of in-vehicle distractions. His contributions align with UN Sustainable Development Goals related to health (SDG 3) and sustainable cities (SDG 11).
Gert Cauwenberghs is a Professor of Bioengineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He co-directs the Institute for Neural Computation and holds a visiting professorship at MIT. His research focuses on neuromorphic engineering, energy-efficient neural interfaces, and wearable biosensors. Key contributions include silicon-based adaptive neural circuits, implantable neural recording systems, and in-ear biosensing devices. Education: M.Eng. in Applied Physics (University of Brussels, 1988), M.S. and Ph.D. in Electrical Engineering (Caltech, 1989–1994). Prior roles include Professorships at Johns Hopkins University and Visiting Professor at MIT. Research Interests: Biomedical integrated circuits, neuromorphic computing, brain-machine interfaces, and energy-efficient neural systems. His work bridges neuroengineering and clinical applications, emphasizing adaptive intelligence and low-power designs. Recent Work: Development of femtojoule-efficient neural chips, high-density neural interfaces, and closed-loop wearable systems. Projects include neurobench benchmarking frameworks and RRAM-based neuromorphic hardware. Awards: NSF Career Award (1997), ONR Young Investigator (1999), PECASE (2000), IEEE Distinguished Lecturer (2003–2004). Grants & Labs: Active in NIH and DoD-funded projects, co-directs the UCSD Institute for Neural Computation. Collaborates with industry on neural interface technologies. Labs/Teams: Cauwenberghs Lab at UCSD focuses on integrated neuroengineering systems, including neural recording systems and neuromorphic computing architectures.
Jenna Wiens is an Associate Professor of Computer Science and Engineering at the University of Michigan's College of Engineering. She serves as Associate Director of the Artificial Intelligence Lab and co-Director of AI & Digital Health Innovation. Leading the MLD3 research group, her work focuses on machine learning and AI applications for healthcare data. PhD from MIT (2014) under John Guttag NSF CAREER Award recipient (2016) Humboldt Foundation Carl Friedrich von Siemens Award (2024) Her research addresses four key technical thrusts: Time-series analysis for predicting clinical outcomes Robust machine learning against spurious correlations Decision-making with causal inference and offline reinforcement learning Human-AI collaboration frameworks Notable methodological contributions include the FIDDLE preprocessing pipeline for clinical time-series data, foundational work on survival analysis, and novel approaches to model selection in healthcare reinforcement learning. Her work has led to real-world AI deployments in infection prevention and patient risk stratification. Scientific honors include: Forbes 30 Under 30 (2015) MIT Tech Review 35 Innovators Under 35 (2017) Sloan Research Fellowship in Computer Science (2020) Sarah Goddard Power Award (2023) Wiens collaborates with clinicians across disciplines, emphasizing clinician-in-the-loop AI systems and ethical implementation in healthcare workflows.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Lyle Ungar is a Professor at the Department of Computer and Information Science at the University of Pennsylvania . He is affiliated with multiple graduate groups, including Genomics and Computational Biology in the School of Medicine , Operations, Information and Decisions in the Wharton School , and Psychology in the School of Arts and Sciences . His research focuses on explainable machine learning , deep learning , and natural language processing for psychology and medical research , analyzing social media and sensor data to understand well-being, empathy, and stress. His work spans bioinformatics , applied economics , and group decision-making . Recent publications examine LLM-based tutoring , cross-cultural translation , and AI in palliative care , showing trends in reinforcement learning , mobile health , and health data analytics . He has contributed to Google Scholar , PubMed , and DBLP with over 15 papers since 2023. Scientific Awards : 2019 Alan I. Leshner Leadership Institute Public Engagement Fellow His students include Vitoria Aquino Guardieiro , Yihao Li , and co-advised researchers like Shreya Havaldar with Eric Wong. He leads projects at interdisciplinary centers such as the Annenberg Public Policy Center , Center for Cognitive Neuroscience , and Institute for Translational Medicine .
Robert Wilson is an Associate Professor in the School of Psychology at Georgia Institute of Technology. His research focuses on computational cognitive neuroscience, reinforcement learning, decision making, and their applications in psychiatry and aging. Wilson leads a lab integrating computational modeling with behavioral experiments, neuroimaging, and neurostimulation to explore the algorithms underlying human cognition. He holds a Ph.D. in Bioengineering from the University of Pennsylvania (2009). Education: Ph.D. in Bioengineering, University of Pennsylvania, 2009 Research Interests: Wilson’s work spans computational models of decision making, reinforcement learning dynamics, and the neural mechanisms of explore-exploit trade-offs. His lab investigates phishing detection cognition, navigation strategies, and perceptual decision making, with recent studies on aging-related cognitive decline and psychiatric disorders. Publications: His recent work includes studies on computational models of exploration strategies, phishing susceptibility (PEST task), and aging effects on decision-making. Articles often bridge neuroscience and AI, such as applying reinforcement learning to large language models. Awards & Grants: While no formal awards are listed, his research has been supported by grants exploring computational psychiatry and neuroimaging. Labs & Teams: Wilson directs a multidisciplinary lab at Georgia Tech, collaborating with experts in AI, neuroscience, and clinical psychology to advance computational theories of the mind-brain relationship.
Dr Alison Powell is an Associate Professor in the Department of Media and Communications at the London School of Economics and Political Science (LSE), serving as Programme Director for the MSc Media and Communications (Data and Society). Her research focuses on technology design, public sector technology, and digital futures. She is known for her book Undoing Optimization: Civic Action in Smart Cities (2021) and her participatory research method, data walking. From 2019–2023, she directed the JUST AI network, supported by the Ada Lovelace Institute and UKRI, advancing AI ethics research. Her work integrates critical theory, participatory methods, and interdisciplinary collaboration. Education: PhD in Communication from Concordia University (Canada), MA in Media Studies from Toronto Metropolitan University, and postdoctoral research at the Oxford Internet Institute. Prior to academia, she worked in the film and TV industry. Research interests include civic engagement in data-driven governance, ethical AI, and participatory urbanism. She has advised projects like Africa Just AI and Operationalising Ethics for AI, funded by Horizon Europe, AHRC, and the Open Society Foundations. Her articles address topics such as AI governance, urban light ecology, feminist research praxis, and the ethical implications of technology for marginalized communities. She contributes to blogs like Media@LSE and leads initiatives at Polis and the LSE’s Digital Futures for Children project. Grants and collaborations include work with Projects by IF, Open Rights Group, and the Human Data Interactions Network (EPSRC). She explores intersections of technology, justice, and sustainability, emphasizing equitable and inclusive innovation.
