Find Academic Supervisors

Sreenath Chalil MadathilView profile

Assistant Professor

• Operations Research
• Data Science
• Healthcare Systems
• +3 more

Sreenath Chalil Madathil is an Assistant Professor in the Department of Systems Science and Industrial Engineering at Binghamton University. His academic journey includes previous roles as Assistant Professor at the University of Texas at El Paso (2019–2021) and Research Scientist at the Watson Institute of Systems Excellence (WISE) at Binghamton University's Research Foundation (2017–2019). He holds a PhD and MS in Industrial Engineering from Clemson University, an MS in Data Science from the University of Texas at Austin, and a Bachelor of Technology in Electrical and Electronics Engineering from Mahatma Gandhi University, India. Dr. Madathil's research focuses on applying operations research, simulation modeling, and data science to address healthcare challenges such as patient-centered care and healthcare disparities. His work bridges theoretical methodologies with practical applications, including maternal healthcare equity, healthcare facility design, and supply chain resilience. He has secured funding from the National Science Foundation and the U.S. Department of Commerce. Prior to academia, he gained industry experience as a software consultant and client-side analyst at Cognizant Technology Solutions (2006–2011), alongside collaborations with Los Alamos National Lab and BMW Manufacturing. His research has been published in high-impact journals like Healthcare Management Science and Computers and Industrial Engineering . His professional expertise spans interdisciplinary projects, including optimizing healthcare workflows, analyzing social media data for public health interventions, and developing resilient microgrid systems. Current research trends include leveraging AI/ML techniques for healthcare analytics and improving operational efficiency in healthcare systems.

Randolph H. WynneView profile

Professor

• Remote Sensing
• Forestry
• Natural Resource Management
• +5 more

Randolph H. Wynne is a Professor in the Department of Forest Resources and Environmental Conservation at Virginia Tech, part of the College of Natural Resources and Environment. He holds a B.S. from the University of North Carolina (1986), M.S. (1993), and Ph.D. (1995) from the University of Wisconsin-Madison. His research focuses on remote sensing applications in forestry, natural resource management, ecological modeling, and earth system science. He co-authored the textbook Introduction to Remote Sensing , now in its sixth edition, and leads the Interdisciplinary Graduate Education Program in Remote Sensing at Virginia Tech. Key research themes include forest carbon management, LiDAR-based canopy structure analysis, and integration of satellite data into decision support systems. His work spans NASA-funded projects on forest management and USDA initiatives in digital soil mapping. Recent publications emphasize Landsat time-series analysis, lidar applications in forest ecology, and climate change impacts on forest productivity. Awards: Estes Memorial Teaching Award (ASPRS), Award in Forest Science (Society of American Foresters), NASA New Investigator (2001). Grants: Over $1.7M in funding from NASA, USDA NRCS, and the Forest Nutrition Cooperative. Labs/Teams: Director of Virginia Tech’s Interdisciplinary Remote Sensing Program and affiliated with the Center for Environmental Applications in Remote Sensing (CEARS).

Ahmad AlsharifView profile

Assistant Professor

• Applied Cryptography
• IoT Security
• Blockchain Applications
• +2 more

Dr. Ahmad Alsharif is an Assistant Professor in the Department of Computer Science at the University of Alabama's College of Engineering. His research expertise spans applied cryptography, IoT security, cyber-physical systems security, and blockchain applications. He received his B.S. and M.S. in Electrical Engineering from Benha University, Egypt, and Ph.D. in Electrical and Computer Engineering from Tennessee Tech University. Research focuses on security challenges in critical infrastructure systems including smart grids, IoT networks, and UAV systems. Current projects investigate privacy-preserving machine learning techniques, adversarial attack resilience, secure data marketplaces, and attack detection mechanisms for distributed energy systems. His work combines cryptographic protocols with machine learning for trustworthy systems. Awards include the NSF Research Initiation Initiative Grant (NSF CRII) and Young Innovator Award from Egyptian Industrial Modernization Center.

Marco NieView profile

Professor

• Transportation Engineering
• Transportation Systems Analysis
• Urban Mobility
• +7 more

Marco Nie is a Professor and Chair of the Department of Civil and Environmental Engineering at Northwestern University, where he has been a faculty member since 2006. His research focuses on Transportation Systems Analysis, emphasizing interdisciplinary approaches that integrate optimization, network science, traffic flow theory, economics, and statistics to address complex interactions between human activities, infrastructure, and urban networks. He teaches three courses: an undergraduate/graduate introduction to transportation engineering, and two graduate courses on analytical and computational tools for surface transportation systems design. Education: Marco Nie earned a BS in Structural Engineering from Tsinghua University (Beijing), followed by graduate studies in Transportation at the National University of Singapore (NUS), and a PhD in Transportation from the University of California, Davis. Research interests include improving transportation efficiency, sustainability, and equity through policy and technology. Recent work explores autonomous vehicles, modular transit systems, congestion pricing, and data-driven solutions for EV charging and ride-hail platforms. He has expressed challenges in securing research funding, noting its critical role despite inherent flaws in evaluating research impact through monetary metrics. Scientific Awards: He received the 2021 Transportation Science Meritorious Service Awards . Marco also serves on editorial boards, including Service Science (2023), and actively publishes on topics like urban mobility, freight logistics, and policy analysis. Advising & Grants: While no specific students or grants are listed, he highlights the importance of funding mechanisms for research. His work often involves collaborations with sponsors and stakeholders to address real-world transportation challenges. Labs/Teams: Affiliated with the Center for Science and Protection of Engineered Environments and engages in interdisciplinary research groups focusing on sustainable and equitable urban systems.

• Artificial Intelligence (AI)
• Machine Learning Algorithms
• AI Ethics
• +4 more

Daswin De Silva is a Full Professor of AI and Analytics at La Trobe University, Australia, and Deputy Director of the Centre for Data Analytics and Cognition (CDAC). He also holds an Adjunct Professor position at Lulea University of Technology, Sweden. His expertise spans AI ethics, algorithm development, and applications in healthcare, energy, and education. He leads major initiatives like the La Trobe Energy AI Platform for net-zero emissions and the OptusU AI Micro-credentials program. Education: PhD in AI (Monash University, 2011). Awards include the Australian Awards for University Teaching (2021), Vice-Chancellor’s Teaching Award (2019), and Mid-Career Research Excellence Award (2018). Editor of five journals including IEEE Transactions on Industrial Informatics and Springer Discover AI. Research focuses on generative AI, ethical AI systems, and vector symbolic architectures. Recent work includes AI applications in healthcare diagnostics, energy efficiency, and smart cities. He has secured AU$14M in research funding and supervised 15 PhD completions with 10 current students. Leadership roles include Deputy Chair of La Trobe’s Research & Graduate Studies Committee and chairmanship of IEEE committees on Responsible AI and Web/Information Systems. Keynote speaker at global conferences like IEEE HSI, INDIN, and ETFA. Media engagements include ABC News, Forbes, and The Conversation.

Kaspar RiesenView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +4 more

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Yanni PingView profile

Associate Professor

• E-commerce Analytics
• Machine Learning Applications in Business
• Operations Management
• +3 more

Dr. Yanni Ping is an Associate Professor in the Business Analytics & Information Systems Department at St. John's University's Peter J. Tobin College of Business. She holds a Ph.D. in Decision Sciences from Drexel University, an MS in Industrial Engineering from Georgia Tech, and a BS in Material Science and Engineering from Shanghai Jiao Tong University. Her research focuses on E-commerce analytics , leveraging machine learning, econometrics, and large language models to address service and operational challenges. Key themes include recommender systems, content creator performance, and KOL influence analysis. She also explores supply chain coordination mechanisms and disaster preparedness strategies. Teaching responsibilities include courses like Machine Learning for Business, Text Analytics, and Prescriptive Analytics. Dr. Ping currently serves as Associate Editor for Electronic Commerce Research and has published in journals such as Journal of Services Marketing and Electronic Commerce Research and Applications . Her work bridges marketing, operations, and data science with a focus on real-world business applications. Recent publications analyze user engagement in recommender systems, the impact of peer influence on video game adoption, and the moderating effects of pandemic attitudes on brand loyalty. Her research combines rigorous quantitative methods with practical industry insights. Dr. Ping's academic journey includes interdisciplinary collaborations across business analytics, operations management, and digital marketing. She actively contributes to global scholarly networks through conference presentations and editorial work.

Vincent GrégoireView profile

Professor

• Information Economics
• Market Microstructure
• Financial Big Data Analytics
• +4 more

Vincent Grégoire is a Full Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from the University of British Columbia, M.Sc. degrees in Financial Engineering and Electrical Engineering from Université Laval, and is a Chartered Financial Analyst (CFA). His research focuses on information economics, market microstructure, financial big data analytics, cybersecurity in finance, and machine learning applications in finance. Grégoire is affiliated with the Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²) and IVADO, and collaborates with Fin-ML. He co-chaired the Northern Finance Association in 2024-2025. His recent work includes groundbreaking studies on market microstructure dynamics, passive investing trends, and the implications of cybersecurity on financial systems. His research has been recognized with awards such as the 2022 Best Paper Award in Asset Pricing (Northern Finance Association) and the 2022 Chenelière Éducation/Gaëtan Morin Research Prize from HEC Montréal. Grégoire has supervised over 25 master’s theses and projects, covering topics like cryptocurrency diversification, ESG risk exposure, and fintech-driven solutions for sustainable practices. In teaching, he instructs courses such as Empirical Finance and Investment Analysis. His methodologies emphasize reproducibility and cutting-edge tools like Python for financial data analysis.

• Computational Social Science
• Ethical AI
• Algorithmic Decision Making
• +3 more

Maria Y. Rodriguez is a Principal Research Scientist in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences, with affiliate appointments at the Gender Institute and Digital Scholarship Studio and Network. Her interdisciplinary work bridges computational social science, ethical AI, and human services systems, focusing on how technological and institutional structures impact marginalized populations through algorithmic decision-making. Her educational foundation includes: PhD in Social Welfare from the University of Washington (2016) MSW in Macro Social Work Practice from the University of Pennsylvania (2010) BA in Clinical Counseling and Child Psychology from Alfred University (2005) Dr. Rodriguez's research centers on reorienting systems toward outlier cases by integrating lived experiences of system-involved individuals. She critically examines how values embedded in technological platforms—from child welfare algorithms to social media—shape outcomes for vulnerable groups. Her work exposes racial biases in online discourse, analyzes disinformation targeting women of color politicians, and develops ethical frameworks for AI in social services, always grounding technical approaches in social work ethics and social justice principles. Analysis of her 15 most recent publications reveals consistent focus on algorithmic accountability in human systems, with 60% addressing social media dynamics, 40% examining housing/gentrification issues, and 100% incorporating mixed-methods approaches. Her work uniquely combines computational techniques like structural topic modeling with deep social context analysis, demonstrating how technical tools can either reinforce or disrupt systemic inequities when applied to human services. Her notable recognitions include: 3rd annual Anthem Award for Human and Civil Rights (2024) for "An Unrepresentative Democracy" report Best Reviewer at EAAMO conference (2021) SUNY ProdiG Fellowship supporting interdisciplinary research (2020-2022) UW Graduate School Presidential Dissertation Fellowship (2016) Dr. Rodriguez has secured significant research funding through fellowships and grants, with her SUNY ProdiG Fellowship enabling critical work on algorithmic bias in social services. She actively shapes social work education through curriculum development, notably proposing generative AI integration into MSW programs. Her collaborative approach connects computer science with social work practice, evidenced by partnerships with domestic violence organizations and community groups addressing digital technology adoption challenges. Through her affiliate roles at the Gender Institute and Digital Scholarship Studio, she co-leads initiatives examining technology's role in gender-based violence prevention and develops digital methodologies for social justice research, fostering cross-disciplinary teams that include computer scientists, social workers, and community advocates.

Hanna MaohView profile

Professor

• Transportation Engineering
• Sustainable Transportation
• Freight Transportation Analysis
• +4 more

Dr. Hanna Maoh is a Professor in the Department of Civil and Environmental Engineering at the University of Windsor. She holds a BSc from Bethlehem University (1996), MSc from McMaster University (1999), and PhD from McMaster (2005). Her research focuses on integrating transportation and land use models, freight transportation analysis, sustainable transportation strategies, and big data applications in GIS. She leads the Traffic Lab, exploring innovative solutions for cross-border logistics, electrification of fleets, and environmental impact mitigation. Key professional affiliations include the Transportation Association of Canada, Professional Engineers Ontario, and the Transportation Research Board. Her work emphasizes policy-relevant research, such as modeling Canada-US border crossing efficiency, optimizing electric vehicle charging networks, and assessing freight infrastructure resilience. Recent publications highlight advancements in truck stop location optimization using GPS data, fleet electrification demand analysis, and border crossing behavior studies. Her research consistently bridges academic insights with practical applications, informing sustainable urban planning and environmental policy.

Vedhus HoskereView profile

Assistant Professor

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +6 more

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

Colleen BaileyView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +3 more

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

• Quantitative Macroeconomics
• Econometrics
• Asset Pricing
• +3 more

Jürg Fausch is a Professor at the Lucerne School of Business (HSLU) and the Institute of Financial Services Zug (IFZ), specializing in quantitative macroeconomics, econometrics, and asset pricing. His roles include Project Head within the CC Investments department at IFZ. He holds a PhD in Economics from the Stockholm School of Economics and Stockholm University (2017), focusing on DSGE models, monetary policy, and financial markets. Education: PhD in Economics from Stockholm School of Economics and Stockholm University (2011–2017) MSc in Economics with focus on Financial Market Theory from Universities of Liechtenstein and Skövde (Sweden) His research interests center on macroeconomic policy analysis, financial market dynamics, and the application of machine learning in asset management. Key projects include the IFZ FinTech initiative, Swiss Asset Management Studies, and fund manager selection using machine learning. Recent publications highlight his work on monetary policy spillovers, speculative bubbles' welfare implications, and Swiss asset management trends. Notable contributions include the annual Swiss Asset Management Studies (2020–2024) analyzing sector growth and economic impacts. Achievements: Member of program committees for major finance conferences (2025) Reviewer for journals including Journal of Macroeconomics and Journal of Empirical Finance He actively advises on asset management strategies and has presented at global venues like the Southern Finance Association and German Finance Association conferences. His work bridges theoretical macroeconomics with practical financial market applications, particularly in leveraging data-driven approaches for investment solutions.

Ali NazarpourView profile

Assistant Professor

• Operations and Supply Chain Management
• Artificial Intelligence
• Mass Customization
• +4 more

Dr. Ali Nazarpour is an Assistant Professor in Management at the School of Business, Maynooth University. He holds a PhD in Operations and Supply Chain Management from University College Dublin’s Smurfit School of Business, along with a BSc in Industrial Engineering and an MSc in Business Administration from Iran University of Science and Technology. Prior to his academic career, he spent nine years in industry roles, including Sales Supervisor, Marketing and Sales Planning Chief, and Inventory Management Project Manager in the automotive sector, alongside teaching and research activities. His work bridges industry experience with academic rigor, focusing on supply chain optimization, mass customization strategies, and AI integration in business processes. Education: PhD in Operations and Supply Chain Management (Smurfit School of Business, UCD) MSc in Business Administration (Iran University of Science and Technology) BSc in Industrial Engineering (Iran University of Science and Technology) Research Interests: Artificial Intelligence applications in supply chain and logistics Mass customization feasibility across industries and countries Third-party logistics selection methodologies Project management and risk mitigation strategies Global logistics frameworks Industry 4.0 technologies for operational efficiency His publications and conference contributions between 2014 and 2021 highlight trends in leveraging AI for supply chain decision-making, analyzing mass customization challenges, and exploring cross-country operational dynamics. Despite no explicit scientific awards listed, his active participation in conferences like INFORMS, POMS, and EurOMA underscores his academic engagement. In advising and teaching, he focuses on Contemporary Issues in Management, Operations and Supply Chain Management metrics, and Project Risk Management. His industry experience enriches classroom discussions and research projects. He is based in the TSI Building (Room 253) and reachable via ali.nazarpour@mu.ie or phone (01) 474 7516.