Find Academic Supervisors

Wei AiView profile

Assistant Professor

• Data Science for Social Good
• Machine Learning
• Causal Inference
• +4 more

Wei Ai is an Assistant Professor at the University of Maryland, affiliated with the College of Information (INFO) and the Institute for Advanced Computer Studies (UMIACS). His research focuses on data science for social good (DSSG), integrating machine learning, causal inference, and experimental design to address societal challenges in education, virtual collaboration, and quantum computing. He leads the Center for Educational Data Science and Innovation (EDSI) and has secured grants from the NSF, Gates Foundation, and Walton Family Foundation for projects like M-Powering Teachers and classroom quality assessment tools. Education: PhD in Information from the University of Michigan (advised by Qiaozhu Mei). Previous academic roles include teaching at the University of Michigan and Peking University in courses like Data Mining and Information Retrieval. Research Interests: Machine Learning and Causal Inference, AI for Education, Virtual Teams and Social Identity, Large Language Models for Social Applications. He has published in venues such as PNAS, Management Science, ACL, and the Web Conference. Grants: Major awards include a NSF grant on middle-grade math instruction analysis (with Min Sun) and a Gates Foundation grant for classroom dataset development (with Jing Liu). Labs/Teams: CLIP Lab member, collaborating on interdisciplinary projects with UMIACS and the Joint Quantum Institute. Prospective students: Open to mentoring PhD students through INFO and Computer Science programs. Actively supervises current students in education technology and quantum computing domains.

Xin FangView profile

Assistant Professor

• Power System Optimization
• Cyber-Physical Systems
• Renewable Energy Integration
• +3 more

Xin Fang is an Assistant Professor in the Electrical & Computer Engineering Department at Mississippi State University, affiliated with the Bagley College of Engineering. His research focuses on power system optimization with uncertainty, cyber-physical dynamic modeling, and renewable energy integration. He holds a Ph.D. from the University of Tennessee, Knoxville, an M.S. from China Electric Power Research Institute, and a B.S. from Huazhong University of Science and Technology. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee, Knoxville, 2016 M.S., Power System Automation, China Electric Power Research Institute, 2012 B.S., Power System Automation, Huazhong University of Science and Technology, 2009 His research interests emphasize cyber-physical systems integration, grid resiliency through multi-timescale modeling, and equitable energy distribution. Recent work addresses challenges in high-renewable grid operations, including dynamic co-simulation frameworks for electricity-transportation networks. He explores innovative solutions for frequency stability, voltage control, and market mechanisms for emerging energy systems. Publications highlight advancements in hybrid power plant optimization, distributed energy resource coordination, and equitable load-shedding methodologies. His work bridges theoretical modeling with practical applications in grid security and renewable integration.

Brian RossView profile

Professor

• Artificial Intelligence
• Computational Intelligence
• Genetic Programming
• +10 more

Brian Ross is a Professor at the Department of Computer Science, Brock University. He holds a BSc (Hon) from the University of Manitoba, MSc from the University of British Columbia, and PhD in Artificial Intelligence from the University of Edinburgh. His research focuses on evolutionary computation , particularly genetic programming , with applications in computational biology , evolutionary design , and computational aesthetics . Education: BSc Computer Science (Hon), University of Manitoba MSc Computer Science, University of British Columbia PhD Artificial Intelligence, University of Edinburgh Research Interests: Computational Intelligence: genetic programming, evolutionary algorithms, multi-objective optimization Evolutionary Design: computational aesthetics, 2D/3D modeling, energy-efficient architecture Applications: computational biology, stochastic process algebra, GPU-based image analysis Article Trends: His recent work combines deep learning with evolutionary algorithms to enhance diversity in agent behavior and design optimization. Earlier studies focused on stochastic modeling of biological networks and procedural texture generation . Collaborations span computational aesthetics, passive solar architecture, and non-photorealistic rendering techniques. Students & Collaborations: He has supervised diverse projects including evolved intelligent agents , passive solar building design , and evolutionary art systems . Notable students include Marshall Joseph (2023), Tyler Cowan (2021), and Sheikh Faishal Basher (2021).

Lauri GoldkindView profile

Professor

• Artificial Intelligence in Social Work
• Data Science for Social Services
• Ethical Use of Technology in Human Services
• +3 more

Lauri Goldkind is a Professor at the Graduate School of Social Service, Fordham University, located at Fordham Westchester. Their work focuses on the intersection of artificial intelligence (AI), technology, and social work, with an emphasis on ethical integration, data justice, and the impact of AI on human services organizations. Education details are not explicitly provided in the available text, but as a Professor in Social Service, it is typical to hold a doctoral degree, likely in Social Work or a related field. Goldkind’s research interests revolve around the ethical and practical implications of AI in social work and human services. Key areas include the responsible integration of AI technologies, data science applications in nonprofit sectors, tele-mental health practices, and promoting social justice through technology. Their work also addresses challenges such as bias in AI, data privacy, and the role of technology in advocacy. Recent publications explore topics such as AI’s impact on clinical social work, ethical considerations in AI implementation, and the use of large language models in mental health. Goldkind also examines the intersection of technology and social justice, including data labor in human services and the platform economy’s influence on social work. No information on scientific awards, advising students, or grants is provided in the available text. Their work often emphasizes interdisciplinary collaboration and the adaptation of social work practices to digital realities, including platform-based advocacy and data-driven decision-making. Labs or teams are not explicitly mentioned in the provided materials, but their research aligns with Fordham’s commitment to social justice and innovation in human services.

Kandethody M. RamachandranView profile

Professor

• Machine Learning
• Big Data
• Cyber Security
• +4 more

Kandethody M. Ramachandran is a Professor in the Department of Mathematics & Statistics at the University of South Florida's College of Arts and Sciences. With a Ph.D. in Applied Mathematics from Brown University (1987), his research spans machine learning, cybersecurity, stochastic systems, and bioinformatics. He has directed numerous doctoral students and secured over $3.4M in research grants. Research interests include: Big data analytics and streaming data processing Game theory applications in cybersecurity and electricity markets Stochastic modeling in bioinformatics and finance Honors include the National Academy of Inventors membership and Teaching Incentive Program Award. Administrative roles include: Director of Graduate Admissions (Statistics, 2003-2015) Associate Director of Statistics Program (2010-2015)

Ian DrydenView profile

Professor

• Statistical Shape Analysis
• Object-Oriented Data Analysis
• Functional Data Analysis
• +3 more

Ian Dryden is a Professor of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a Ph.D. from the University of Leeds (1989) and has held academic positions at Florida International University, University of Nottingham, University of Chicago, and multiple stints at USC. His research focuses on statistical methodology for highly-structured data, including shape analysis, functional data analysis, and object-oriented data analysis. He has authored over 150 publications and two influential books: Statistical Shape Analysis (2016) and Object-Oriented Data Analysis (2022). Dr. Dryden’s work spans applications in medical imaging, corpus linguistics, environmental science, and Bayesian spatial modeling. Recent projects include using remote sensing for peatland monitoring and Bayesian hierarchical models for urban deprivation analysis in Tanzania. He has secured over $20M in grants as PI/Co-I and is an elected Fellow of the Institute of Mathematical Statistics. His research outputs emphasize methodological innovation, with key contributions to shape trajectory analysis, manifold-valued data regression, and Riemannian manifold smoothing splines. Collaborative work includes interdisciplinary projects in genetics, pharmacology, and environmental science, reflecting his broad statistical expertise and impact across disciplines.

Yinghui WeiView profile

Associate Professor

• Statistics
• Biostatistics
• Public Health
• +4 more

Yinghui Wei is the Associate Head of School (Resources) and Associate Professor of Statistics at the University of Plymouth's School of Engineering, Computing and Mathematics. Her research focuses on statistical methodology applied to medicine, clinical trials, observational studies, and evidence synthesis, with expertise in big data analytics using NHS records and national databases. She leads impactful health data science initiatives, including the MSc Health Data Science and Statistics program, and has secured significant research funding (e.g., £3M NIHR INSIGHT grant). Her awards include the Vice-Chancellor's Award for Equality, Diversity & Inclusion (2024) and leadership in REF2021 impact case studies. She chairs equality committees and has held roles at institutions like the MRC Clinical Trials Unit and MRC Biostatistics Unit. Education: PhD and MSc in Statistics from The University of Manchester (2008, 2004). Research interests span Bayesian inference, clinical prediction models, infectious disease epidemiology, and survival analysis. Notable projects include MRC-funded work on long-term effects of COVID-19 and methodological contributions to meta-analysis of time-to-event outcomes via Stata's ipdfc module. Grants and collaborations include £970K ESRC funding for coastal community research, Royal Academy of Engineering diversity programs, and international partnerships like the Yunnan-Plymouth Joint Doctoral Training Programme. Supervised over 10 PhD students across health data science, biosensors, and kidney transplantation outcomes. Active in professional societies, including the Royal Statistical Society and Royal Society International Exchanges Committee.

Armin ZareView profile

Assistant Professor

• Control Theory
• Fluid Mechanics
• Stochastic Dynamical Modeling
• +3 more

Armin Zare is an Assistant Professor at the University of Texas at Dallas in the Department of Mechanical Engineering , affiliated with the Center for Wind Energy . He obtained his PhD in Electrical Engineering from the University of Minnesota (2016) and previously held a postdoctoral position at the University of Southern California. Education : PhD (2016), MS (2016) in Electrical Engineering from University of Minnesota; BS (2010) in Electrical Engineering from Sharif University of Technology Research Interests : Modeling, dynamics, and control of large-scale and distributed systems; optimization; dynamics and control of complex fluid flows; renewable energy generation. His work focuses on stochastic modeling of turbulent flows and wind farm dynamics using control theory. Recent Publications highlight advancements in wind farm turbulence modeling, stochastic receptivity analysis, and covariance completion algorithms, reflecting his expertise at the intersection of control systems and fluid dynamics. Scientific Awards Young Investigator Program Award, Air Force Office of Scientific Research (2023) Doctoral Dissertation Fellowship, University of Minnesota (2015) Best Student Paper Award Finalist, American Control Conference (2014) Advisees include PhD students working on topics like wall turbulence modeling, wind farm forecasting, and stochastic dynamical systems, with several former students having completed theses on wind turbine wake modeling and fluid flow uncertainties.

Vincenzo CarnevaleView profile

Associate Professor

• Statistical Physics
• Machine Learning
• Protein Structure-Function Relations
• +3 more

Vincenzo Carnevale is an Associate Professor in the Department of Biology at Temple University's College of Science and Technology. He holds a B.Sc. and M.Sc. in Physics from the University of Pisa and a Ph.D. from SISSA in Trieste, Italy. His research group focuses on statistical physics and machine learning to study sequence-structure-function relations in proteins, epistasis in molecular evolution, and self-organization in biomolecules. Key themes include how interactions drive collective phenomena and emergent behaviors in biological systems, with a long-term goal of understanding electrical signaling in excitable cells. Recent work spans computational approaches in medical applications (e.g., AI in obstetrics-gynecology diagnostics), lipid-ion channel interactions, and genotype imputation algorithms. His methods include molecular dynamics simulations, Monte Carlo techniques, and deep learning frameworks. Carnevale has no listed scientific awards, but his work emphasizes interdisciplinary collaborations at the interface of physics, biology, and computer science. He contributes to programs like Molecular and Cellular Biology and maintains a lab focused on theoretical/computational biology.

Paul MillerView profile

Professor

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +5 more

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.

• Control Systems
• Model Predictive Control
• Data-Driven Methods
• +3 more

Shuhao Yan is a Researcher and Post Doc Researcher at the University of Stuttgart, affiliated with the Department of Mathematics/IMNG within Faculty 08. His research focuses on advanced control systems, particularly in data-driven methods for uncertain systems, stochastic model predictive control (MPC), and grid-forming control in power electronics. He leads the SimTech Project Network 4-3 (II) titled 'Data-Integrated Control Systems Design with Guarantees,' emphasizing robustness and real-world applicability. His research interests revolve around integrating data with traditional control theory to address challenges in uncertain dynamic systems. Key areas include robust MPC, stochastic optimization, and the application of machine learning in control systems. He has contributed to advancements in grid stability analysis, voltage source converter control, and adaptive control strategies under intermittent data conditions. Recent publications highlight trends in learning-based control (e.g., homothetic tube MPC), stochastic MPC with probabilistic constraints, and power grid stability analysis for renewable energy integration. His work bridges theoretical foundations with practical implementations in energy systems and automation. Shuhao Yan holds a PhD and currently conducts research within the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling. His projects emphasize interdisciplinary collaboration between control theory, applied mathematics, and electrical engineering.

Lukas EinkemmerView profile

Professor

• Numerical Analysis
• Plasma Physics
• Computational Mathematics
• +3 more

Prof. Lukas Einkemmer is a faculty member at the University of Innsbruck, holding a position in the Institute of Mathematics. He specializes in numerical analysis, plasma physics, and high-performance computing. His work focuses on developing advanced numerical methods for solving complex kinetic equations and PDEs, with applications in plasma simulation and computational fluid dynamics. Education: He earned a PhD in applied mathematics (2014) and MSc in physics (2013) from the University of Innsbruck, alongside BSc in applied mathematics (2010). He completed research stays at UC Merced and holds notable academic awards, including the SciCADE New Talent Award (2015) and participation in the Heidelberg Laureate Forum (2013). Research & Teaching: His research includes exponential integrators, dynamical low-rank methods, and semi-Lagrangian discontinuous Galerkin schemes. He teaches numerical methods, PDEs, and computational courses at both undergraduate and graduate levels. He also leads training programs in parallel computing (OpenMP/MPI) at the University’s Research Center for High-Performance Computing. Publications & Grants: Over 70 peer-reviewed articles in journals like J. Comput. Phys. and SIAM J. Sci. Comput. , focusing on numerical algorithms and their applications. He has secured grants from FWF and other agencies, advancing methods for plasma physics and kinetic theory. Awards & Recognition: Multiple honors, including the Oberwolfach Leibniz Graduate Student award (2014) and sustained scholarship support for academic excellence.

Ricardo Melo CzeksterView profile

Lecturer

• Cyber Threat Intelligence
• Cyber-Physical Systems
• Risk Assessment
• +3 more

Ricardo Melo Czekster is a Lecturer in Software Engineering & Cybersecurity at Aston University's School of Computer Science and Digital Technologies. His research develops methods for securing critical infrastructure through formal modeling and threat intelligence. Education: PhD in Performance Evaluation, Pontifical Catholic University of Rio Grande do Sul (2010) MSc in Analytical and Environmental Chemistry, University of Benin (2009) BSc in Industrial Chemistry, Ambrose Alli University (2004) Research Focus: Czekster specializes in cybersecurity for cyber-physical systems, particularly smart grids and active buildings. His work develops STIX-based threat intelligence frameworks, attack path modeling with BDMPathfinder, and dynamic risk assessment methods. Recent projects address security in medical IoT and transportation systems. Teaching: Leads modules on Secure Programming, Security Governance, and Secure Software Delivery.

• Heuristic search
• Program synthesis
• Deep learning for sound

Vadim Bulitko is a Professor in the Faculty of Science at the University of Alberta, affiliated with the Department of Computing Science. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. Research Interests: His work spans heuristic search, program synthesis, and deep learning applications for sound. Recent projects focus on AI-driven puzzle generation, pathfinding in dynamic game environments, and neural network classification. Publications: His 15 most recent articles explore AI advancements in puzzle design, video-game navigation, and bioacoustic classification, emphasizing heuristic optimization and explainable AI techniques.

Amir RastpourView profile

Associate Professor

• Operations Research
• Health Care Sector
• Stochastic Models
• +5 more

Dr. Amir Rastpour is an Associate Professor of Operations Management at the Faculty of Business and Information Technology (FBIT), Ontario Tech University. He holds a PhD in Operations Management from the University of Alberta School of Business (2015), followed by a postdoctoral fellowship at the Ivey Business School, Western University (2017). His research focuses on applying operations research methodologies to healthcare challenges, including queuing theory, stochastic models, and big data analytics for optimizing healthcare operations. Specific interests include demand prediction for medical facilities, emergency medical services optimization, and quasi-birth-death (QBD) process analysis in healthcare systems. Education: PhD in Operations Management, University of Alberta School of Business (2015) Postdoctoral Fellow, Ivey Business School, Western University (2017) Visiting PhD Student, University of Chicago Booth School of Business (2014) Research Interests: Dr. Rastpour’s work emphasizes healthcare applications, including demand forecasting for heart attack treatment facilities, emergency medical services optimization, and QBD process modeling. His methods include queuing theory, count regression models, and empirical analysis to address both strategic and operational challenges in healthcare delivery. Recent work explores machine learning for predicting patient wait times and dynamic programming for ambulance fleet management. Awards: Nominated for Ontario Tech University Student Choice Teaching Award (2019) INFORMS Health Applications Society Finalist for Best Student Paper (2015) CORS Best Student Paper in Open Category (2015) University of Alberta Scholarships and Awards (2009–2014) Teaching and Advising: Dr. Rastpour teaches statistics and operations management courses, emphasizing hands-on data analysis and decision-making skills. His teaching style includes real-world examples and interactive sessions to enhance student learning. He collaborates on grants focused on healthcare operations and supply chain resilience. Labs and Teams: He is affiliated with the Business Analytics & AI Research Group at FBIT, which bridges computational methods with business challenges in marketing and operations management. His work contributes to initiatives like AI in healthcare collaboration with Lakeridge Health.