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LEONG Tze YunView profile

Professor

• Responsible AI
• Dynamic Decision-Making
• Neurocognitive Modeling
• +3 more

LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.

Vesna TerzicView profile

Professor

• Seismic Performance Assessment
• Infrastructure Resilience
• Structural Dynamics
• +2 more

Vesna Terzic is a Professor in the Department of Civil Engineering and Construction Engineering Management at California State University Long Beach's College of Engineering. Her research specializes in seismic performance assessment, infrastructure resilience, and probabilistic risk analysis of structures. Education includes a PhD in Structural Engineering from UC Berkeley, MS in Earthquake Engineering from Ss. Cyril and Methodius University, and BS in Structural Engineering from University of Belgrade. Research focuses on developing advanced computational frameworks for evaluating post-earthquake functionality of buildings and bridges. Her work integrates performance-based engineering with resilience quantification, emphasizing practical applications for seismic design and retrofitting. Recent publications demonstrate consistent focus on functional recovery modeling, structural health monitoring, and optimization of protective systems. Research trends show progression from component-level analysis to regional recovery simulation frameworks. Major awards: ACI Chester Paul Siess Award for Excellence in Structural Research (2017) Popert Fellowship, UC Berkeley (2009) Professional service includes membership on FEMA P-58 project committees and contributions to OpenSees development. Current projects investigate soil-structure interaction effects on tall buildings and recovery-based design methodologies.

Yannis PaschalidisView profile

Professor

• Optimization
• Control Systems
• Stochastic Systems
• +8 more

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

• Optical Networks
• Data Center Networks
• Optical Interconnects
• +7 more

Dr. Khurram Aziz is a Senior Instructor in the Faculty of Computer Science at Dalhousie University , Halifax, Canada. He is actively engaged in teaching and research, with a focus on optical networks, data center interconnects, and network performance modeling. Education: PhD in Electrical Engineering, Vienna University of Technology, Austria (2008) MSc in Electrical Engineering, National University of Singapore (2003) BSc (Hons) in Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan (1998) His research interests include optical packet and burst switched networks , optical interconnects for data centers , analytical modeling and simulation , and network routing and switching . He has contributed extensively to the design and performance evaluation of scalable optical switches and hybrid switching systems. The recent publications reflect a strong trend in data center optical networks , focusing on performance, blocking probability, signal degradation, and architectural classification. His work bridges theoretical modeling with practical simulation frameworks, such as CloudNetSim++ in OMNeT++, contributing to cloud and high-capacity network research. Dr. Aziz has no listed scientific awards in the provided text. He teaches several core computer science courses including CSCI 2141: Intro to Database Systems , CSCI 3171: Network Computing , CSCI 3132: Object Orientation and Generic Programming , and CSCI 3120: Operating Systems . There is no mention of graduate student supervision or external research grants. He has co-authored book chapters in major handbooks on data centers and switched systems. Dr. Aziz has not listed any formal lab or research team affiliations in the provided content.

Steven I-Jy ChienView profile

Professor

• Transportation Engineering
• Urban Rail Transit
• Sustainable Transportation Systems
• +4 more

Steven I-Jy Chien serves as Professor and Director of the Transportation Program within the Department of Civil and Environmental Engineering at the New Jersey Institute of Technology (NJIT). His career spans academia, government, and industry with significant contributions to transportation engineering research and practice. His educational credentials include: Ph.D. in Civil and Environmental Engineering from University of Maryland-College Park (1995) M.S. in Civil and Environmental Engineering from University of Maryland-College Park (1991) B.S. in Civil Engineering from Tamkang University (1983) Professor Chien's research focuses on transportation system resilience, sustainable transit operations, and advanced traffic modeling. His work addresses critical challenges in urban rail networks, demand-responsive transit services, and energy-efficient transportation systems. He employs sophisticated computational methods including genetic algorithms, machine learning, and Bayesian approaches to solve complex transportation problems involving network vulnerability, passenger behavior, and infrastructure optimization under uncertainty. Analysis of his recent publications reveals strong emphasis on transportation resilience against cascading failures, sustainable operations for electric transit systems, and pandemic-responsive aviation scheduling. His research spans diverse geographic contexts including major Chinese metropolitan areas and U.S. transportation networks, demonstrating global applicability of his methodologies. His professional recognition includes: 2024 Keynote Speaker at Union-Tech Lecture, National Taiwan Ocean University 2022 Keynote Speaker at International Conference of Chinese Institute of Transportation 2022 Best Poster Award from New Jersey Department of Transportation Professor Chien has secured research funding from federal agencies including the Federal Highway Administration (FHWA) where he contributed to CORSIM traffic simulation software development. His industry experience with China Engineering Consultants Inc. and Information Dynamic Inc. informs his practical approach to transportation challenges. As Transportation Program Director, he oversees curriculum development and industry partnerships that prepare students for transportation sector careers. He maintains active collaboration with transportation agencies globally, particularly through his leadership in the Transportation Program at NJIT which serves as a hub for innovation in urban mobility solutions.

CHAN Mun ChoonView profile

Professor

• Systems & Networking
• Computer Networking
• Mobile Computing
• +3 more

CHAN Mun Choon is a Professor at the School of Computing, National University of Singapore (NUS) , where he directs the NUS-NCS Joint Laboratory for Cyber Security . He previously worked at Bell Labs (1997-2003) and holds a PhD from Columbia University (1997). His research spans systems and networking with specific interests in mobile computing, software-defined networking, and cyber-physical systems . PhD, Electrical Engineering (1997), Columbia University M.Phil., Electrical Engineering (1993), Columbia University MS, Electrical Engineering (1993), Columbia University BS, Computer & Electrical Engineering (1990), Purdue University His recent work focuses on 5G network architecture , data center fault debugging , and energy-efficient mobile sensing . He has published over 100 papers and holds 7 US patents , including cache-based compaction techniques with 210+ citations. His projects include fronthaul slicing for 5G, network-wide packet history frameworks, and participatory indoor localization. Scientific recognition includes: Best Paper Awards: IEEE ICNP 2019, ACM SOSR 2019, ICDCN 2016 Best Demo: IPSN 2016 Distinguished Member, INFOCOM TPC (2016, 2020, 2021) He serves as Vice-Dean, Graduate Studies and Vice-Dean, Academic Affairs at NUS Computing, and has graduated 21 PhD students . His lab develops solutions for network security , latency-sensitive applications , and mobile sensing .

Mario BergesView profile

Associate Professor

• Smart Infrastructure
• Structural Health Monitoring
• Non-Intrusive Load Monitoring (NILM)
• +4 more

Mario Berges is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. He holds leadership roles as Co-Director of the IBM Smart Infrastructure Analytics Lab and Director of the Intelligent Infrastructure Research Lab (INFERLab). His work focuses on applying information/communication technologies to enhance the operational efficiency and resilience of built environments amid evolving resource constraints and climate changes. Education: PhD in Civil & Environmental Engineering from CMU (2010). Research Interests: Berges' research integrates smart infrastructure systems, energy efficiency, and machine learning. Key areas include non-intrusive load monitoring (NILM), structural health monitoring of pipelines, building automation systems, and urban heat risk modeling. He develops data-driven frameworks for energy disaggregation, sensor placement optimization, and real-time infrastructure diagnostics. Awards: Recognized with the 2010 FIATECH Outstanding Early Career Researcher Award and 2015 Dean’s Early Career Fellowship from CMU. Grants & Labs: Leads INFERLab, collaborating with IBM on smart infrastructure projects. His work spans academic-industry partnerships focused on building analytics, smart grid technologies, and sensor networks. Future Directions: Expanding research into AI-driven energy systems, resilient urban infrastructure, and cross-disciplinary solutions for climate adaptation.

Nate FosterView profile

Professor

• Programming Languages
• Networking
• Databases
• +5 more

Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.

Alexandre JacquillatView profile

Associate Professor

• Data-driven decision-making
• Stochastic optimization
• Integer optimization
• +6 more

Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.

Jiyuan TuView profile

Professor

• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• Thermal Sciences
• +4 more

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).

Samir DaniView profile

Professor

• Supply Chain Management
• Logistics
• Risk and Resilience in Supply Chains
• +5 more

Samir Dani is a Professor of Operations and Supply Chain Management at Keele University’s Keele Business School. He holds Chartered Manager status and is a Fellow of multiple professional bodies, including the Higher Education Academy and Chartered Institute of Logistics and Transport. Prior to academia, he worked in India’s automotive industry. His research focuses on supply chain resilience, sustainability, food logistics, and Industry 4.0 technologies like AI and Blockchain. Affiliations: Keele University (current), University of Huddersfield (2014–2019), Loughborough University (2005–2008) Leadership: Former Head of Logistics, Marketing, Hospitality, and Analytics Department at University of Huddersfield Industry Engagement: £5M Leeds City Region LEP Supply Chain Program, UK-India trade advisory roles Research Interests: Supply chain risk and resilience Food supply chain sustainability AI, IoT, and Blockchain applications Industry 4.0 transformations Recent Article Trends: Focus on climate action impacts, blockchain trust mechanisms, and SME net-zero strategies. His work bridges academic theory with industry challenges, emphasizing technology adoption and sustainability. Awards: Prix des Associations (2015), UK Top 100 Logistics Professional (2017/18) Grants: £800K+ from EPSRC, IMCRC, and KTP Advisory Roles: Editorial Board member of Supply Chain Management: An International Journal, Academic Review Board member of International Journal of Operations and Production Management.

Alexandre Bouchard-CôtéView profile

Professor

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

Michael O'BoyleView profile

Professor

• Heterogeneous Code discovery and Optimisation
• Neural Machine Translation and Neural Synthesis
• Deep Neural Network system stack
• +7 more

Michael O'Boyle is a Professor at the University of Edinburgh's School of Informatics, where he serves as Director of the ARM Research Centre of Excellence and the EPSRC Centre for Doctoral Training in Pervasive Parallelism. Holding an EPSRC Established Career Research Fellowship, he leads pioneering work in compiler technology for heterogeneous architectures, bridging theoretical advances with practical high-performance computing applications. Professor O'Boyle's research spans multiple cutting-edge areas including heterogeneous code discovery and optimization, neural machine translation for program synthesis, deep neural network system stack optimization, software-defined hardware, and compiler/architecture co-design. His approach integrates constraint analysis, program synthesis, and machine learning to address complex challenges in high-performance computing across diverse hardware platforms. His recent publications reveal a strong trend toward integrating machine learning with traditional compiler techniques, particularly in neural program synthesis, tensor optimization, and architecture-aware compilation. This work represents a paradigm shift in compiler design, moving from rule-based systems to learning-based approaches that can automatically adapt to diverse hardware targets. IEEE/ACM CGO 2025 Distinguished Paper Award for 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code' IEEE/ACM CGO 2024 Test of Time Award ACM GPCE 2023 Best Paper Award for 'C2TACO: Lifting Tensor Code to TACOM' ACM ASPLOS 2021 Distinguished Paper Award IEEE HPCA 2021 Best Paper Award for 'Prodigy: Improving the Memory Latency of Data-Indirect Irregular Workloads' Professor O'Boyle has successfully mentored numerous PhD students who have secured prominent positions in academia (including at Cambridge, Edinburgh, Leeds, and McGill) and industry (including Meta, NVIDIA, Qualcomm, Huawei, and Microsoft). His research is supported by significant funding from EPSRC, ARM, and European projects including Bonseyes and Transmuter, demonstrating strong international recognition and industry impact. He leads the influential Compiler and Architecture Design (CArD) Group at the University of Edinburgh and is a founder of the HiPEAC Network of Excellence, which has grown into a major European initiative connecting researchers and practitioners in high-performance and embedded computing.

Jian HuangView profile

Associate Professor

• Computer Architecture
• Memory and Storage Systems
• AI Infrastructure
• +5 more

Jian Huang is an Associate Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), holding a 100% appointment since August 2024. He is also an affiliated Associate Professor in Computer Science and serves as the Y. T. Lo Faculty Fellow in ECE. Huang leads the Systems Platform Research Group and received his Ph.D. in Computer Science from Georgia Institute of Technology in August 2017, where his dissertation earned the College of Computing Dissertation Award. His research focuses on Computer Architecture, Memory and Storage Systems, AI Infrastructure, Distributed Systems, and Systems Security . Huang has pioneered work in sustainable AI infrastructures, modular data centers, neural processing unit virtualization, and ransomware-aware storage systems. His research has been featured in top-tier conferences including ISCA, MICRO, ASPLOS, OSDI, SOSP, and USENIX ATC, with over 50 publications and multiple patents issued worldwide. Huang's research has significant industry impact, with some work transferred into products and featured in popular media outlets including ACM CACM, TechXplore, and Science Daily. His work on non-volatile memory and storage systems has received extensive coverage, with some projects generating over 50 media reports. Among his notable achievements are the ACM SIGMICRO Early Career Award, NSF CAREER Award, IEEE Micro Top Picks (three times), USENIX Best Paper Award, and the MICRO Best Paper Runner Up in 2024. He has secured over $4.01M in grants ($11.12M with collaborators) from NSF, DARPA, Army Research Office, and industry partners. ACM SIGMICRO Early Career Award NSF CAREER Award IEEE Micro Top Picks (three times) USENIX Best Paper Award MICRO Best Paper Runner Up (2024) Google Faculty Research Award NetApp Faculty Fellowship Award Huang actively contributes to the academic community as program co-chair of NVMW'23 and organizer of HotInfra'23. He currently advises 12 Ph.D. students, 1 master student, and multiple undergraduates. His teaching innovations include developing ECE522: Emerging Memory/Storage System and revising ECE511: Computer Architecture with cutting-edge content.

Fahui WangView profile

Professor

• Spatially-Integrated Social Sciences
• Geographic Information Systems (GIS)
• Public Policy and Planning
• +3 more

Fahui Wang is the Cyril & Tutta Vetter Alumni Professor in the Department of Geography & Anthropology at Louisiana State University (LSU). He holds a B.S. in Geography from Peking University (1988), an M.A. in Economics (1993), and a Ph.D. in City & Regional Planning (1995), both from The Ohio State University. His research focuses on spatially-integrated social sciences, public policy, and GIS applications in healthcare, urban planning, and public safety. He has authored/co-authored multiple influential books and peer-reviewed articles, including works on computational methods in GIS and healthcare accessibility analysis. Professor Wang’s work has been supported by grants from the National Institutes of Health, National Science Foundation, and U.S. Departments of Housing and Urban Development and Justice. His accolades include LSU’s Distinguished Research Master Award (2023) and CPGIS Distinguished Scholar (2023). He teaches advanced courses in GIS, urban geography, and quantitative methods, and leads research initiatives in the LSU Geoscience department. His lab focuses on geospatial technologies for addressing societal challenges in healthcare access, urban planning, and crime analysis.