Negin Enayaty Ahangar is an Associate Professor of Instruction in the Department of Operations Management at the Jonsson School of Management (JSOM), University of Texas at Dallas. Her research focuses on network optimization, interdiction, logistics, and statistical quality control. She holds a PhD in Operations Management from the University of Arkansas (2018), an MS (2014) from the same institution, and a BS in Industrial Engineering from Sharif University of Technology (2011). Her educational background includes advanced studies in operations research and industrial engineering. She teaches courses such as Quantitative Business Analysis, Managerial Methods in Decision Making Under Uncertainty, and Advanced Statistics for Data Science. Research interests emphasize solving complex optimization problems in infrastructure systems, facility layout design, and network interdependencies. Her work combines algorithmic design with practical applications in logistics and supply chain management. Recent publications address rail yard routing, multi-floor facility layouts, and multi-layered network modeling. No scientific awards or grants are explicitly mentioned in the provided information. She is affiliated with the Institute for Operations Research and the Management Sciences (INFORMS).
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
Anne Fischer, M.Sc., is a researcher at the Chair of Material Handling, Material Flow, and Logistics at the Technical University of Munich (TUM). Her work focuses on digital twins, construction automation, and resource scheduling in heavy civil engineering. She is based in Garching near Munich and collaborates with institutions like UC Berkeley and Stanford University. Research Interests: Digital Twin frameworks, simulation-based optimization, BIM integration, activity recognition in construction, and sustainable logistics systems. Collaboration: Serves as a contact person for international exchanges with U.S. institutions. Publication Trends: Her recent articles (2024–2021) address construction automation, digital twin applications, and variability management in civil engineering projects. Key Projects: Engaged in initiatives like Bauen 4.0 , MiProcess2Twin , and SiteRoute , which focus on digitalization and automation in construction. Location: Boltzmannstraße 15, Garching bei München (Room: 5505.EG.501).
Associate Professor Brendan Mulhern (University of Technology Sydney) is a health economist and outcomes researcher at the Centre for Health Economics Research and Evaluation (CHERE). He leads the Cancer Australia-funded Cancer Quality of Life Expert Support Team (CQUEST) and joined UTS in 2015, completing his PhD in 2020. BSc (Hons) Psychology, University of Wolverhampton (UK) MRes Psychology, University of Birmingham (UK) PhD Health Economics, University of Technology Sydney (Australia) His research focuses on developing and validating preference-based quality-of-life measures (EQ-5D, SF-6D) using advanced psychometric techniques like item response theory and discrete choice experiments. He specializes in condition-specific instruments for dementia, epilepsy, and diabetes, and has contributed to valuation methods including time trade-off approaches. His recent work examines context effects in palliative care valuation, cross-cultural value set development (e.g., Ghana), and improving respondent engagement in DCE studies. Key projects include: Developing the EQ-5D-5L Ghanaian value set Testing EQ-5D respiratory bolt-ons in Australia Extending QALY framework through instrument combination Validating care recipient burden scales He has received grants from EuroQol Research Foundation, Cancer Australia, and MRFF for methodological advancements in quality-of-life measurement. His work informs health technology assessment and resource allocation decisions globally.
Professor Sally McClean is an academic at Ulster University's School of Computing , holding the title of Professor of Mathematics . Her research spans computational mathematics, machine learning, and process mining, with applications in healthcare, IoT, and smart cities. Focus areas include Process Mining , Activity Recognition , and Lead Toxicity Prediction Funded by organizations like The Royal Society and Invest Northern Ireland Her work contributes to the UN Sustainable Development Goals in health and technology domains. Recent publications analyze smart home IoT systems, emergency department logistics, and federated process mining privacy frameworks. Awarded Honorary award (2021) for heartbeat rhythm analysis Received TM Forum Catalyst Industry Contribution (2022)
Yukiko Hashida serves as Associate Professor in the Department of Agricultural and Applied Economics at the University of Georgia's College of Agricultural & Environmental Sciences. Her research addresses critical environmental challenges through economic analysis of climate adaptation and natural resource management. Education Ph.D. in Applied Economics, Oregon State University (2017) Research Focus Dr. Hashida specializes in environmental and natural resource economics , with core expertise in climate change adaptation (particularly coastal systems and disaster response), land use dynamics , and valuation of natural capital . Her work examines economic incentives for managed retreat from vulnerable coastlines, wildlife habitat conservation , and policy design for natural disaster resilience . She integrates ecological data with economic modeling to assess how households and landowners respond to environmental risks under climate uncertainty. Publication Trends Analysis of her 2015-2025 publications reveals consistent focus on empirical evaluation of climate adaptation policies, especially property buyout programs and ecosystem-based solutions like wetland conservation. Recent work increasingly addresses the interconnection between adaptation and mitigation in forest and coastal ecosystems, using advanced methods including discrete-choice modeling and integrated ecological-economic frameworks. A hallmark of her research is rigorous valuation of ecosystem services to inform conservation and development trade-offs. Professional Activities No information regarding current graduate students, grant funding, or laboratory facilities was available in the provided sources.
Nathaniel Hupert, MD, MPH is an Associate Professor of Population Health Sciences and Associate Professor of Medicine at Weill Cornell Medical College, Cornell University, and an Associate Attending Physician at NewYork-Presbyterian Hospital. A practicing internist and internationally recognized public-health modeler, he directs his research toward healthcare-process optimization and emergency-response logistics for both routine care and large-scale crises. Education A.B., Harvard College (1988) M.D., Harvard Medical School (1994) M.P.H., Harvard School of Public Health (2000) Research Focus Dr. Hupert’s scholarship integrates process mining , discrete-event simulation , and data-driven decision science to strengthen preparedness for bioterrorism, pandemic influenza, COVID-19, and anthrax events. His models of mass antibiotic dispensing (BERM Point-of-Dispensing staffing model) and hospital surge capacity (AHRQ Surge Model) have been downloaded and applied by public-health agencies worldwide. Current work extends to heterologous vaccination strategies, social determinants of COVID-19 transmission, and equity in child mortality. Scientific Awards & Honors Rotary Foundation Scholarship, University of Otago (1989) Rose Seegal Essay Prize, Harvard Medical School (1994) Outstanding Volunteer Service, University of Pittsburgh Medical School (1997) Pforzheimer Public Service Award, Harvard School of Public Health (1999–2000) Most Outstanding Abstract, AcademyHealth Annual Research Meeting (2003) Grants & Leadership Roles Principal Investigator on multiple federally funded projects including Managing Epidemics by Managing Mobility (NIAID, 2022-2025) and sub-awards from the National Institute of Allergy and Infectious Diseases. He founded and led the CDC Preparedness Modeling Unit (2008-2010), served on the DHHS Anthrax Modeling Working Group (2003-2009), and currently acts as Policy Lead for the Oxford-based COVID-19 International Modeling Consortium (CoMo). Laboratory & Collaborative Networks Dr. Hupert heads interdisciplinary teams that bridge Weill Cornell, the NewYork-Presbyterian healthcare system, and global partners such as the CoMo consortium. These collaborations translate simulation insights into operational tools for hospitals, public-health departments, and federal agencies.
Sandra D. Eksioglu is a Professor at the University of Arkansas and holds the Hefley Professorship in Logistics and Entrepreneurship . She is affiliated with the College of Engineering and the Department of Industrial Engineering . Ph.D. in Industrial and Systems Engineering, University of Florida (2002) M.S. in Economics and Management Sciences, Mediterranean Agronomic Institute of Chania (1996) B.S. in Business Administration, University of Tirana (1994) Her research focuses on Operations Research , Network Optimization , and Algorithmic Development , with applications in Energy Systems , Healthcare , and Transportation . She has published extensively on stochastic supply chain models, biomass logistics, and healthcare inventory management. Recent publications highlight her work in bioenergy systems , vaccine distribution , telehealth analytics , and stochastic optimization for infrastructure planning . Her methodological expertise spans multi-stage programming , discrete event simulation , and machine learning . Scientific Awards Fellow of IISE (2022) College of Engineering Imhoff Teaching Award (2021) NSF CAREER Award (2011) Best Application Paper, IISE Transactions (2019, 2018)
Toni Milun serves as a lecturer in mathematics and statistics at Algebra University of Applied Sciences, where he bridges theoretical concepts with practical business applications. His academic foundation includes a 1999 degree from the Faculty of Science and Mathematics at the University of Zagreb and a 2012 postgraduate specialist degree in Statistical Methods for Economic Analysis and Forecasting from the Faculty of Economics in Zagreb. He is currently completing his doctoral research in Economics at the Faculty of Economics in Rijeka. His research spans Mathematics Education, Financial Mathematics, and Business Statistics, with a distinctive focus on making complex quantitative concepts accessible through innovative pedagogical approaches. Milun's work consistently connects mathematical theory to real-world economic scenarios, particularly in SME financing, investment analysis, and statistical literacy for decision-makers. His publications reveal a strong emphasis on practical applications in Croatian business contexts and educational settings. Analysis of his 15 most recent publications shows dominant themes in financial mathematics (35%), business statistics (30%), and adult education (25%), with recurring subfields including percentage calculations in commerce, regression modeling of socioeconomic factors, and maritime/IT-specific mathematical applications. His research demonstrates consistent engagement with Croatian economic data and educational challenges. His notable recognition includes: Pride of Croatia award for special contribution to education Milun actively disseminates knowledge through public-facing initiatives rather than formal academic advising. He has co-authored a high school mathematics textbook and provides consulting services in applied mathematics and statistics, focusing on business problem-solving. His educational outreach generates significant impact through digital platforms and traditional media. He leads the www.tonimilun.com educational portal and television productions ('Školski sat' and 'Financijalac'), directing teams of educators, video producers, and subject-matter experts to create accessible mathematical and financial literacy content for diverse audiences across Croatia.
Farhad Ansari is a UIC Distinguished Professor and Christopher B. and Susan S. Burke Professor of Civil Engineering at the University of Illinois at Chicago. He is a leading specialist in monitoring the structural status of bridges, dams, buildings and tunnels, with expertise in fiber optic sensor technology for structural health monitoring systems. His work spans infrastructure monitoring projects worldwide, including assessments of the Brooklyn Bridge and monitoring systems for bridges in China. Professor Ansari received his Ph.D. in Civil Engineering from the University of Illinois at Chicago (1983), M.S. in Civil Engineering from the University of Colorado (1979), and B.S. in Civil Engineering from the University of Illinois at Urbana-Champaign (1976). Professor Ansari's research focuses on the application of optical fiber sensors for structural health monitoring of civil infrastructure. His expertise includes forensic investigations of bridges, development of monitoring systems using both discrete (FBG) and distributed sensors (BOTDA, Rayleigh, MZ), and nondestructive testing methodologies. He has consulted on structural monitoring systems for bridges worldwide, including New York's Brooklyn Bridge, Lingotto Bridge in Turin, Italy, Manhattan Bridge, and numerous bridges across the United States and China. His recent publications demonstrate a strong focus on distributed fiber optic sensing technologies for bridge monitoring, with particular emphasis on crack detection, deflection monitoring, and vehicle weight detection without traditional influence lines. His work bridges civil engineering, materials science, and optical technology to create innovative solutions for infrastructure monitoring that are fast, accurate, and affordable. Professor Ansari has received numerous honors including the Aftab Mufti Medal for achievements in civil structural health monitoring (2018), Presidential Fellow of the University of Illinois System (2019-2020), and UIC Distinguished Professor designation (2015). He also received the Nova Award from the Construction Innovation Forum in 1997. As Editor-in-Chief of the Journal of Civil Structural Health Monitoring published by Springer, Professor Ansari has shaped the discourse in his field. He has served on numerous panels including as a Panel member for the Research Assessment Exercise (RAE) in Hong Kong and as Oversight Panel member for the IDEA Program of the National Cooperative Highway Research Program. His laboratory at UIC develops strategies for forensic analysis of bridges and other infrastructure, producing graduates with unique expertise in sensors, NDT, and structural health monitoring. Professor Ansari's Structural Health Monitoring Laboratory specializes in developing monitoring systems for civil infrastructure. The lab's capabilities are unique due to their combination of structural engineering expertise with specialized knowledge in fiber optic sensors. They work on the complete lifecycle of monitoring systems from design and installation to real-time data acquisition and analysis.
Dr. Prateek Bansal is a Presidential Young Assistant Professor at the National University of Singapore (NUS), leading the Behavioural Cognitive Science (BeCoS) lab. His research focuses on developing AI-driven methodologies to analyze mobility behavior and urban systems. He holds a PhD from Cornell University and has held fellowships at Imperial College London and visiting roles at multiple institutions. His expertise spans transportation engineering, econometrics, and causal inference. Dr. Bansal is an elected board member of the International Association of Travel Behaviour Research and serves on editorial boards of top journals like Transportation Research Part B . He has received prestigious awards including the Presidential Young Professorship (2021) and Leverhulme Trust Fellowship (2020). His lab investigates individual-level decision-making models and system-level urban planning frameworks. Education: PhD, Transportation Engineering (Cornell University, 2016-2019); MS, Transportation Engineering (UT Austin, 2013-2015); BTech, Civil Engineering (IIT Delhi, 2008-2013). Research interests include neurophysiological data modeling, activity-based urban systems, and causal inference for infrastructure planning. Notable contributions include studies on electric vehicle adoption, ride-sourcing demand estimation, and ethical decisions in autonomous systems. His work integrates machine learning with traditional transportation models to address contemporary challenges like urban congestion and sustainable mobility. Professional activities include organizing conferences, delivering keynotes (e.g., 2023 Summer School of Behavior Modeling), and advising on policy issues such as carsharing and road safety. The BeCoS lab collaborates globally, leveraging interdisciplinary approaches to advance transportation science.
Professor Uri Gal is a Professor of Business Information Systems at the University of Sydney Business School. His research focuses on the ethical and organizational impacts of digital technologies, particularly algorithmic decision-making, surveillance technologies, and social media. He holds a PhD from Case Western Reserve University and has authored numerous publications in top-tier journals like European Journal of Information Systems and Information and Management . His research interests include the ethical implications of technology, the relationship between people and technology, and workplace transformations driven by algorithmic systems. Professor Gal has conducted studies on AI in education, social media marketing for universities, and privacy concerns in IoT and surveillance technologies. His work frequently addresses societal challenges such as misinformation, algorithmic bias, and data governance. Professor Gal is actively engaged in media commentary, discussing topics like data privacy, AI ethics, and digital surveillance with outlets such as Quartz , The Conversation , and Australian Financial Review . He has also contributed to grants focusing on enterprise social platforms and digital transformation, including the Australian Digital Transformation Lab (ADTL). Education: PhD in Information Systems, Case Western Reserve University Grants: Includes 'Evidence-based Management for Enterprise Social Platform Success' (2016) and ADTL initiatives Labs/Teams: Australian Digital Transformation Lab
Yu Yao is a Lecturer in Machine Learning at the School of Computer Science, The University of Sydney. He joined in December 2023 and focuses on developing robust and interpretable machine learning systems. His research emphasizes robustness to data noise, adaptable ML systems, and disentangled representation learning. Yao holds a PhD from The University of Sydney under Professors Tongliang Liu and Dacheng Tao, followed by postdoctoral positions at Mohamed bin Zayed University of Artificial Intelligence and Carnegie Mellon University. Education: PhD in Computer Science (University of Sydney), postdoctoral research at MBZUAI and CMU. Research interests include causal inference in ML, multimodal learning, and label noise mitigation. He has published extensively in top venues like ICML, NeurIPS, and ICLR, and served as an Area Chair for AJCAI 2023, NeurIPS 2025, and ICLR 2025. Awards: Outstanding Reviewer (NeurIPS 2023, ICLR 2023), University of Sydney Research Excellence Prize (2019) Teaching: Advanced Machine Learning (USYD), Guest Lectures on noisy label learning (MBZUAI, China University of Petroleum) Service: Action Editor for TMLR, Area Chair for ICML/ICLR/NeurIPS, reviewer for top journals and conferences His lab focuses on trustworthy AI, with ongoing projects on causal mechanisms in robust learning and interpretable multimodal systems. Current advisees include PhD candidates Ruojing Dong and Jiyang Zheng (co-advised with Prof. Liu), and master's student Kai Lian.
Charul Rajput is a Research Fellow at Aalto University's Department of Mathematics and Systems Analysis, School of Science. Their research focuses on Information Theory, Coding Theory, Discrete Mathematics, and Algebra, with a particular emphasis on caching systems and network optimization. Recent work includes advancements in hierarchical coded caching, hotplug models, and error probability analysis in communication channels. Publications span topics like function-correcting codes, private information retrieval, and locally recoverable codes. Rajput's research also intersects with systems analysis, addressing challenges in distributed storage and network efficiency. Research interests include the theoretical foundations of coding and information theory, with applications to modern communication systems. Key contributions address the design of efficient caching schemes and error-correcting codes for high-performance networks. No scientific awards or grants are explicitly mentioned in the provided texts. Rajput is affiliated with the Algebra and Discrete Mathematics research group at Aalto University, contributing to interdisciplinary projects that bridge pure mathematics and practical network systems.
Ted Ralphs is a Professor of Industrial and Systems Engineering at Lehigh University’s Rossin College of Engineering. He serves as co-founder and director of the Computational Optimization Research at Lehigh (COR@L) Laboratory, and chairs the INFORMS Computing Society. His research focuses on large-scale computation and optimization, bridging theoretical and practical applications through high-performance computing and mathematical techniques. Ralphs holds a Ph.D. in Operations Research from Cornell University and advanced degrees in Mathematics and Applied Mathematics from Carnegie Mellon University. His expertise spans Supply Chain Management, Grid Computing, Mathematical Optimization, Financial Engineering, and Algorithm Development. He teaches courses in computational methods, discrete optimization, financial optimization, and algorithms in systems engineering. Ralphs has received notable honors including the 2021 Rossin College Outstanding Doctoral Student Advising Award and election as an INFORMS Fellow in 2023. His research contributions include advances in bilevel optimization, decomposition methods, and open-source optimization software (e.g., COIN-OR’s Cbc solver). He has led collaborative projects such as a Naval grant-funded initiative with the University of Pittsburgh on bilevel optimization. Ralphs’ work emphasizes scalable algorithms and their real-world applicability in energy markets, logistics, and combinatorial problems.
