Chang S. (CS) Nam is a Professor of Industrial and Systems Engineering at North Carolina State University, with affiliations in the UNC/NCSU Joint Department of Biomedical Engineering and the Department of Psychology. He holds editorial roles as Editor-in-Chief of Brain-Computer Interfaces journal and is a Fellow of the Human Factors and Ergonomics Society. His research focuses on advancing human-centered technologies through neuroergonomics, brain-computer interfaces (BCIs), and rehabilitation engineering, supported by grants from NSF, AFRL, AFOSR, and the Laboratory for Analytic Sciences. Education: Ph.D. in Industrial Engineering, Virginia Tech (2003) MSIE in Industrial Engineering, State University of New York at Buffalo (2000) Research Interests: His work spans neurorehabilitation, haptic interaction design, and BCI applications, aiming to bridge human cognitive and neural processes with technological systems. Key areas include adaptive human-computer interaction, EEG-based emotion recognition, and spatial augmented reality for skill training. Grants & Awards: Recipient of NSF CAREER Award (2010), Air Force Summer Faculty Fellowship (2018), and the Leland S. Kollmorgen Spirit of Innovation Award (2019) Editor of Brain-Computer Interfaces Handbook: Technological and Theoretical Advances (CRC Press) Advising & Labs: Advises students like Lingchao Mao and Priscille Koutouan Leads the Brain-Computer Interface and Neuroergonomics Lab, developing BCI systems for communication in vegetative states and neurorehabilitation
Nori Franco serves as Professor in the Department of Physics at the University of Michigan and Chief Scientist at RIKEN's Theoretical Quantum Physics Laboratory in Japan. His dual appointments reflect his significant contributions to both American and Japanese academic communities, with continuous service at Michigan since 1990 and at RIKEN since 2002. His research spans quantum information, condensed matter physics, and quantum optics, with particular focus on light-matter interactions, superconducting qubits, optomechanics, and quantum open systems. Franco's work bridges theoretical foundations with experimental implementations, especially in circuit quantum electrodynamics and quantum computing applications. Analysis of his recent publications reveals a strong emphasis on non-Hermitian quantum systems, quantum control techniques, and applications of quantum information science to fundamental physics problems. His research group consistently produces highly cited work, with publications appearing in top journals across quantum physics and condensed matter disciplines. Scientific Awards: Charles Hard Townes Medal (2024) - sole recipient for fundamental contributions to quantum optics and quantum information processing Research Doctorate Honoris Causa from University of Messina (2024) Highly Cited Researcher for eight consecutive years (2017-2024) Member of Academia Europaea (2023) Willis E. Lamb Medal (2023) for quantum electronics research Throughout his career, Franco has secured significant research funding and mentored numerous students and postdoctoral researchers. His work has received international recognition through invitations to deliver prestigious lectures including the Stanislav Ulam Lecture and Sir Nevill Mott Lecture in 2024. His research group maintains strong collaborations across multiple continents, reflecting his global impact on quantum physics. At RIKEN, Franco leads the Quantum Information Physics Theory Research Team within the Quantum Computing Center, directing cutting-edge theoretical work that complements experimental efforts in quantum computing hardware development.
Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.
Yasushi Sakurai is a Professor in the Department of Translational Datability at Osaka University's Institute of Scientific and Industrial Research, co-leading the Sakurai and Matsubara Laboratory within the Center for Industrial Science and AI. His research mission focuses on transforming society through real-time prediction of natural and social phenomena using large-scale data analytics, with emphasis on practical technological implementation. His research spans time-series big data analysis, dynamic learning systems, and real-time information provision. Key areas include tensor stream mining, EEG-based healthcare applications, cybersecurity anomaly detection, and multi-omics cancer subtyping. The lab specializes in developing deployable technologies that optimize social activities through predictive modeling of evolving data streams. Recent publications (2023-2025) reveal concentrated innovation in time-series data stream processing, with dominant themes in tensor analytics, frequency-domain forecasting, and causal modeling. His team produces high-impact work accepted at premier AI venues (ICLR, AAAI, KDD, WWW), consistently featuring oral presentations that highlight technical novelty and societal relevance. Scientific Awards: FY2024 Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (Research Category) for dynamic learning and real-time data stream analysis Professor Sakurai mentors graduate students including Naoki Chihara (DEIM2024 Outstanding Paper Award winner), Yuka Tamura (DEIM2024 Student Presentation Award winner), and Ren Fujiwara. His lab maintains active industry-academia partnerships focused on practical technology deployment, with research directly addressing real-world challenges in healthcare monitoring and cybersecurity. The Sakurai and Matsubara Laboratory operates as a dynamic research unit within Osaka University's Center for Industrial Science and AI, structured around specialized teams for tensor stream analysis, medical data mining, and network dynamics. Current projects emphasize real-time prediction systems with immediate societal applications, supported by strong industry collaboration frameworks.
Flavio P. Calmon is the Thomas D. Cabot Associate Professor of Electrical Engineering at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He holds a Ph.D. in Electrical Engineering and Computer Science from MIT, an M.Sc. from the Universidade Estadual de Campinas (Brazil), and a B.Sc. from the Universidade de Brasília (Brazil). His research focuses on the intersection of information theory, machine learning, and statistics, with applications to privacy, fairness, and trustworthy AI systems. He has received prestigious awards, including the NSF CAREER Award (2018) and the James L. Massey Award (2024). Research interests include developing theoretical foundations for fair and private machine learning, understanding algorithmic bias, and designing systems with provable guarantees. His work spans information-theoretic tools for responsible AI, distributed privacy mechanisms, and understanding the limits of fairness interventions. He has advised numerous students, including Hao Wang, Hsiang Hsu, and Lucas Monteiro Paes, many of whom now hold prominent roles in academia and industry. Calmon's research is supported by grants from NSF, Amazon, Google, and IBM. He has organized workshops on AI in Brazil and leads initiatives to broaden participation in STEM from underrepresented groups. His lab collaborates with institutions globally and emphasizes both foundational theory and practical applications of machine learning.
Minghao Qi is a Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering, West Lafayette. His research focuses on integrated photonics systems for optical communications, quantum information, and precision metrology applications. Professor Qi's work spans several critical photonics domains: Design and application of microresonator-based optical frequency combs (Kerr combs) Silicon and silicon nitride integrated photonic circuits Thin-film lithium niobate devices for nonlinear optics Quantum information processing using frequency-bin entangled photons Photonic neuromorphic computing with machine learning co-design Optical sensors and time-of-flight ranging systems Analysis of his 2022-2025 publications reveals three dominant research vectors: (1) Vernier microcombs for optical atomic clocks and RF stabilization, (2) Trident edge coupler architectures for octave-spanning nonlinear processes on lithium niobate, and (3) Physics-informed neural networks applied to photonic device design and signal processing. His recent work demonstrates strong convergence between integrated photonics, quantum technologies, and machine learning.
Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
Vishal Ahuja is an Associate Professor and Corrigan Research Professor at Southern Methodist University's Cox School of Business, with adjunct faculty status at University of Texas Southwestern Medical Center. He focuses on decision analytic tools for healthcare improvement through operations management. PhD, University of Chicago Booth School of Business MBA, University of Chicago Booth School of Business His research interests span healthcare operations, service optimization, and AI applications in clinical decision-making. He collaborates with the Department of Veterans Affairs, Parkland Hospital, and pediatric institutions to address care quality and delivery efficiency. Recent publications emphasize predictive modeling for chronic disease management, adaptive clinical trial design, and regulatory healthcare policy. Awards include the Dlin/Fischer Clinical Research Award (2021), INFORMS Pierskalla Award (2012), and 2025 AI75 recognition for Dallas-Fort Worth AI leadership. D CEO Excellence in Healthcare Award - Outstanding Healthcare Innovator (2023) NSF Game Changer Academies for Advancing Research Innovation (2022) C. Jackson Grayson Faculty Innovation Award (2022-23) At SMU, he teaches graduate courses in operations, supply chain, and service management, integrating corporate sector experience from chemical and consumer goods industries. His work has been cited by the FDA in safety labeling changes workshops.
Nati Srebro is a Professor at the Toyota Technological Institute at Chicago with a cross-appointment as a Part-Time Professor in the Department of Computer Science and Committee on Computational and Applied Mathematics at the University of Chicago. He earned his PhD from MIT in 2004 and has held previous positions including post-doctoral fellow at the University of Toronto, Visiting Scientist at IBM, and Associate Professor at the Technion. Professor Srebro's research focuses on methodological, statistical and computational aspects of Machine Learning and Optimization. His work spans foundational contributions to learning theory, matrix reconstruction, and optimization techniques. He is particularly known for introducing the use of nuclear norm for machine learning, work on wider Markov networks, and advancing our understanding of the relationship between learning and optimization. His current research interests include understanding deep learning through optimization, distributed and federated learning systems, algorithmic fairness, and practical adaptive data analysis. His publication record shows consistent contributions to core machine learning conferences and workshops, with recent work focusing on symmetric and asymmetric hashing techniques, matrix parameter learning, and optimization methods. The publications demonstrate a strong theoretical foundation with practical applications across various machine learning domains. Professor Srebro has been actively involved in several research programs including the Federated and Collaborative Learning program (Spring 2026, as Visiting Scientist and Program Organizer), Modern Paradigms in Generalization (Fall 2024), and multiple summer clusters on Deep Learning Theory and Fairness. His program participation reflects his leadership in emerging areas of machine learning research. Contact: nati@ttic.edu | (773) 834-7493 | Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637
Remko Van Hoek serves as Professor of Practice in the Department of Supply Chain Management at the Sam M. Walton College of Business, University of Arkansas. He teaches Sourcing and Procurement courses across undergraduate, master's, and doctoral programs. Prior to joining the University of Arkansas, Dr. Van Hoek held academic positions in Europe and served as a visiting professor at Cranfield School of Management, complemented by extensive industry experience as a supply chain and procurement executive at global corporations including Nike, PwC, and The Walt Disney Company. He currently serves on the Council of Supply Chain Management Professionals (CSCMP) Board of Directors and acts as executive director of the CSCMP Supply Chain Hall of Fame hosted by the Walton College. Dr. Van Hoek's research centers on digital transformation in procurement and supply chain management, with particular focus on blockchain implementation, artificial intelligence applications, and sustainable sourcing practices. His work investigates how emerging technologies reshape procurement processes, supplier relationship management, and risk mitigation strategies. He explores innovative approaches to supplier diversity programs, ethical sourcing frameworks, and the strategic evolution of procurement from cost reduction to value creation. His research consistently bridges academic theory with industry practice through case studies from major corporations, developing actionable frameworks for practitioners facing digital disruption and sustainability challenges. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in AI-driven supply chain risk prevention, blockchain implementation for transparency, and sustainable supplier engagement. His work demonstrates consistent industry collaboration, drawing from case studies at Walmart, Moet Hennessy, and Bayer to address post-pandemic resilience challenges. A significant portion examines procurement's strategic evolution beyond transactional functions, with recurring emphasis on ethical considerations in digital transformation and the practical implementation barriers for emerging technologies in global supply networks. As a Professor of Practice, Dr. Van Hoek integrates executive-level industry experience into curriculum development and student mentorship. His industry-engaged teaching model includes guest lecturer programs connecting students with supply chain practitioners, as evidenced by his co-authored work on integrating industry insights into supply chain education. While specific grant funding details are not publicly documented, his leadership in the CSCMP Supply Chain Hall of Fame demonstrates commitment to professional development and industry-academia knowledge transfer. Dr. Van Hoek directs the CSCMP Supply Chain Hall of Fame initiative, which documents transformative contributions to supply chain management through interviews with industry pioneers and historical case studies. This platform serves as both an educational resource for Walton College students and a professional development tool for supply chain practitioners globally. The Hall of Fame preserves critical industry knowledge while highlighting contemporary innovations in supply chain strategy and technology implementation.
Dalibor Radovanović is a researcher at Singidunum University , affiliated with the Faculty of Business Informatics . His work spans cybersecurity, blockchain technologies, and their applications in business and IoT systems. Education: Doctoral Dissertation (2016, Singidunum University) Master's & Basic Studies (Faculty of Business Informatics) Secondary Education: ETŠ Nikola Tesla Research Interests include: Security frameworks for IoT and blockchain integration Smart card and wireless network vulnerabilities E-governance and corporate IT audit methodologies Machine learning applications in cybersecurity Environmental performance optimization in agribusiness Publication Trends reveal a focus on blockchain (2022), cybersecurity (2009-2022), and IT governance (2010-2017). His work bridges theoretical analysis with practical implementations in Serbia's digital economy. Collaborations with scholars like Marko Šarac and Saša Adamović highlight interdisciplinary approaches to securing financial systems, educational institutions, and industrial IoT applications.
Daniel Wolpert is a Professor of Neuroscience at Columbia University , where he is also Vice-Chair of the Department of Neuroscience and a key member of the Zuckerman Mind Brain and Behavior Institute . Additionally, he holds a part-time position as Director of Research at the Department of Engineering, University of Cambridge, and is a Fellow of the Royal Society and the Academy of Medical Sciences . Education: Medical Doctor (1989), D.Phil. in Physiology from the University of Oxford (1992) Previous Positions: Lecturer at Sobell Department of Motor Neuroscience (Institute of Neurology), Professor of Engineering at University of Cambridge (2005–2018) Wolpert is a world leader in sensorimotor control , combining computational neuroscience , Bayesian inference , and robotic/virtual reality technologies to reverse-engineer how the brain generates movements. His work emphasizes the brain's role in reducing sensorimotor uncertainty through predictive modeling and has implications for understanding disorders like autism and Parkinson’s disease. His awards include: Royal Society Ferrier Medal (2020) Minerva Foundation Golden Brain Award (2010) Royal Society Francis Crick Prize Lecture (2005) Daniel Wolpert actively contributes to public science communication, including a 2011 TED Talk on the computational role of the brain in movement, and leads the Wolpert Lab at Columbia, which investigates the neural basis of decision-making , motor learning , and reinforcement learning in both healthy and clinical populations.
Manish Verma is Professor of Operations Management and Associate Dean, Graduate Studies at the DeGroote School of Business, McMaster University. His academic journey began with an MBA and PhD in Business Administration with Operations Management/Management Science specialization from Desautels Faculty of Management at McGill University. Dr. Verma's research focuses on multimodal transportation of dangerous goods, risk assessment, network design and planning in transportation, humanitarian logistics, green supply chain management, and disruption/resilience in transportation systems. His current research engagements center on safety and security issues in freight transportation and humanitarian logistics, funded by NSERC and SSHRC grants. He has been frequently approached by media to comment on railroad accidents involving dangerous goods. An analysis of his recent publications reveals a strong emphasis on hazardous materials transportation risk management, with significant contributions to rail-truck intermodal systems, hazmat risk modeling using value-at-risk methodologies, and emergency response planning for transportation networks. His work bridges theoretical operations research with practical transportation safety applications. $245K research grant for rail safety research from Government of Canada As an educator, Dr. Verma has taught courses including Predictive Analytics for Managers, Network Design Issues in Freight Transportation, and Management Science Research Issues. His scholarly impact is evidenced by publications in leading journals such as Transportation Research Part E, European Journal of Operational Research, and Safety Science. He actively contributes to real-world transportation safety through media commentary and research that informs policy decisions regarding dangerous goods transportation.
Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.
