Cong Liu is an Associate Professor of Computer Science at The University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He joined UT Dallas in 2012 as an Assistant Professor and was promoted to his current rank. His research focuses on real-time and embedded systems, cyber-physical systems, and energy-efficient heterogeneous computing. Liu earned his Ph.D. in Computer Science from the University of North Carolina at Chapel Hill (2013), M.S. from Auburn University (2007), and B.E. from Wuhan University of Technology (2005). His research interests include real-time operating systems, cluster/cloud computing, and autonomous systems. He received the NSF CAREER Award in 2017 to develop algorithmic solutions for real-time data processing in autonomous vehicles and robotics. His work emphasizes GPU-accelerated embedded systems that enable autonomous decision-making in resource-constrained environments. Liu has held roles such as TPC member for IEEE RTAS and reviewer for multiple journals/conferences, including IEEE Transactions on Computers and Journal of Parallel and Distributed Computing. His publications address critical challenges in real-time scheduling, heterogeneous computing, and energy efficiency. He leads research on data-induced challenges in embedded systems, aiming to make autonomous driving systems predictable and controllable. Liu’s contributions bridge theoretical foundations with practical implementations in automotive and robotics domains.
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.
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.
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.
Satish C. Boregowda is a Senior Lecturer at the School of Mechanical Engineering, Purdue University in West Lafayette, Indiana. His work focuses on thermodynamics-based analysis of human physiological systems, energy systems engineering, and renewable energy integration. He is affiliated with Purdue's Mechanical Engineering department and maintains an office in POTR 322A. Education & Professional Background : While specific educational details are not provided, his long-term research contributions since 1992 indicate advanced expertise in thermodynamics, biomedical engineering, and energy systems. His career spans over three decades with continuous publication activity. Research Interests : Dr. Boregowda’s core research combines thermodynamics with human physiology, developing metrics like the Objective Stress Index (OSI) to quantify stress responses. His work also addresses energy security through renewable integration, entropy analysis in biological systems, and thermal comfort modeling. He applies constructal theory, fractional calculus, and finite element methods to model human thermal regulation and environmental interactions. Publications Trends : His articles (1992–2025) show sustained focus on: 1) Thermodynamic modeling of human stress and thermal comfort, 2) Renewable energy grid integration strategies, and 3) Advanced computational methods for physiological systems. Recent works emphasize decarbonization pathways and energy policy implications. Grants & Advising : No specific grants or advisees are listed in the provided data. His research likely involves collaborations with aerospace and environmental engineering groups given his work on thermal systems in microgravity and HVAC applications. Labs & Teams : While no specific lab affiliations are mentioned, his research aligns with Purdue’s mechanical engineering initiatives in renewable energy, biomedical engineering, and thermal systems design.
Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.
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.
Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.
Chasalevris Athanasios is an Assistant Professor at the School of Mechanical Engineering, National Technical University of Athens (NTUA), specializing in Rotordynamics and Tribology. His research focuses on nonlinear dynamics of machines, active bearings, and turbomachinery design. He holds a Ph.D. in Rotordynamics from the University of Patras (2009) and a Diploma in Mechanical & Aeronautical Engineering (2004). Research Interests: Machine Dynamics, Turbomachinery, Active Bearings, Bifurcation Analysis, Crack Detection. Teaching: Dynamics of Rotating Machines, Kinematics and Dynamics of Mechanisms, Machine Elements I. Collaborations: KIT (Germany), RPI (USA), MTU Aero Engines (Germany), and Cleveland State University (USA). Awards include the Humboldt Foundation Fellowship (2010), GE's 'Beyond and Ahead' Award (2017), and recognition as a Top 2% Scientist (2023). He has authored over 40 publications and supervised numerous graduate students. Current roles include Academic Advisor at DOATAP and Associate Editor for journals like ASME Journal of Tribology.
Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.
Yana Suchy is a Professor in Clinical Psychology and Neuropsychology at the University of Utah. She leads the ConVExA Lab and Executive Lab, focusing on executive functions and their role in daily living. Her research spans neuropsychological assessment, aging, and cognitive vulnerabilities in neurodegenerative conditions. Education: Ph.D. in Psychology (University of Wisconsin-Milwaukee, 1998), Postdoctoral fellowship in Clinical Neuropsychology (Evanston Hospital, 1998-2000) Labs: ConVExA Lab, Executive Lab Contact: Office 1301b BEHS, Phone 801-585-0796, Email yana.suchy@psych.utah.edu Her research explores executive functions as a stable yet fluctuating trait that determines daily behavior in healthy aging and clinical populations (e.g., dementia, brain injury). She developed the Contextually Valid Executive Assessment (ConVExA) model to address gaps in ecological validity, emphasizing how task complexity and environmental factors influence functional outcomes. Recent publications analyze executive function testing (D-KEFS), emotion regulation, diabetes management, and intra-individual variability. She mentors graduate students in advanced research methods and clinical applications. Her work highlights the interplay between executive functioning, sleep quality, pain, and self-regulation in older adults, with implications for personalized medicine and neuropsychological assessment.
Sibel Alumur Alev is an Associate Professor and Associate Chair of Graduate Studies at the University of Waterloo. Her research focuses on logistics network design, hub location optimization, and sustainable transportation systems. She actively contributes to the fields of operations research and supply chain management, with a strong emphasis on addressing uncertainty in network design and strategic infrastructure planning. Her work spans applications in autonomous mobility systems, electric vehicle charging infrastructure, healthcare logistics, and pandemic response. She has published extensively on hub-and-spoke network models, reverse logistics for environmental sustainability, and multi-period resource allocation strategies. Notable areas of interest include the integration of stochastic and robust optimization methodologies into real-world logistics challenges. Dr. Alev’s research also bridges academic and industrial needs, addressing practical problems such as optimal testing center locations during pandemics and strategic freight hub expansions. Her contributions have been featured in peer-reviewed journals and conference proceedings, reflecting her commitment to advancing both theoretical and applied aspects of logistics and operations research.
LEE Mong Li is a Professor of Computer Science at the National University of Singapore (NUS) and serves as Director of the NUS Centre for Trusted Internet and Community. She holds a Ph.D., M.Sc., and B.Sc. (First Class Honours) in Computer Science from NUS, where she was awarded the IEEE Singapore Information Technology Gold Medal as the top Computer Science student in 1989. Her academic career includes a visiting fellowship at the University of Wisconsin-Madison (1999) and consultancy with QUIQ USA (2000). Her research spans Data Management, Spatio-temporal Databases, Biomedical Informatics, and Retinal Image Analysis . She has pioneered work in data cleaning, data fusion, and analysis of semistructured data, with applications in social media analytics and healthcare. Her recent publications demonstrate strong interdisciplinary focus, particularly in AI-driven medical diagnostics including diabetic retinopathy screening and chronic kidney disease detection from retinal images. She co-authored foundational books on 'Designing Semi-structured Database' and 'Temporal and Spatio-Temporal Data Mining'. Her 150+ publications in major database conferences and journals reflect leadership in both theoretical and applied research. Recent work shows significant emphasis on Medical AI applications (retinal analysis, kidney disease prediction) Temporal fact verification systems Misinformation detection in multimodal environments Privacy challenges in large language models Key honors include: Singapore's President Technology Award (2014) for co-inventing an AI system screening eye conditions IEEE Singapore Information Technology Gold Medal (1989) She actively contributes to government-funded multidisciplinary projects building practical deployable systems. Her leadership extends to program committees of prestigious database conferences and directing the NUS Centre for Trusted Internet and Community. She teaches BT5110 Data Management and Warehousing and has co-developed an AI system for diabetic retinopathy screening deployed in Singapore's national teleophthalmology program.
Robert D. Gregg, IV is a Professor of Mechanical Engineering, Robotics, and Electrical & Computer Engineering at the University of Michigan. He serves as Associate Director for Graduate Education at Michigan Robotics and directs the Locomotor Control Systems Laboratory. His research focuses on control systems for wearable robots, prosthetics, and orthotics, emphasizing biomimetic principles and nonlinear control theory. Gregg holds a PhD from the University of Illinois at Urbana-Champaign (2010) and prior academic roles at the University of Texas at Dallas and Northwestern University. Education: PhD, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2010 MS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2007 BS, Electrical Engineering and Computer Sciences, University of California, Berkeley, 2006 Research Interests: Gregg’s work spans control mechanisms for bipedal locomotion, wearable robotics, nonlinear control theory, and rehabilitation engineering. His lab develops high-performance control systems for prosthetic legs and exoskeletons to enhance mobility for individuals with disabilities. Key areas include energy-efficient control strategies, adaptive impedance systems, and biomechanical modeling of human movement. Grant & Award Highlights: $3M NIH R01 Grant (2023): Modeling and control of agile powered prosthetic legs for varied activities. $1.7M NIH R01 Grant (2021): Modular powered orthoses for broad patient populations. NSF CAREER Award (2017) NIH New Innovator Award (2013) Advising & Labs: Gregg mentors PhD students in robotics and biomechanics, emphasizing independent research and collaborative team environments. His lab supports over 15 researchers and has produced notable alumni like Dr. Cara Welker (University of Colorado Boulder faculty). The lab’s work is supported by NIH, NSF, and industry partnerships. Recent Contributions: Recent work includes phase-variable control for stair climbing, energy shaping methods for exoskeletons, and open-source robotic leg platforms. Gregg also chairs major robotics conferences (e.g., IROS 2023) and advises on clinical translation of wearable robotics.
Jianjun (Jan) Shi is the Carolyn J. Stewart Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and holds a joint appointment with the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He previously served as the G. Lawton and Louise G. Johnson Chair Professor of Engineering at the University of Michigan. His research focuses on system informatics and control for manufacturing and service systems, with notable contributions to quality improvement, cyber-physical systems, and data-driven methodologies. B.S. & M.S. in Electrical Engineering, Beijing Institute of Technology (1984–1987) Ph.D. in Mechanical Engineering, University of Michigan (1992) Dr. Shi’s research interests include process modeling, control systems, and quality engineering. He pioneered methodologies for in-process quality improvement and developed advanced frameworks for high-dimensional data analysis in manufacturing. His work integrates statistical methods, machine learning, and system informatics to enhance operational efficiency and product quality. He has published over 150 peer-reviewed papers and secured $19 million+ in research grants from NSF, DOE, and industry partners. His lab, the System Informatics and Control Group, collaborates with automotive, aerospace, and pharmaceutical sectors. Shi leads initiatives such as the Quality Science Center at the Chinese Academy of Sciences and serves on editorial boards of journals like IIE Transactions and ASME Transactions . Recipient of the IIE Albert G. Holzman Distinguished Educator Award (2011) Fellow of INFORMS, ASME, and IIE Academician of the International Academy for Quality Shi advises 26 Ph.D. graduates, many of whom hold faculty positions or leadership roles in industry. His research group’s innovations have been implemented in global manufacturing systems, yielding significant economic impacts. Current work includes 4D printing, cyber-physical system resilience, and federated learning for industrial data.
