Find Academic Supervisors

Antonio De Rosa is an Associate Professor in the Department of Decision Sciences at Bocconi University, Italy. Previously, he held positions at the University of Maryland, College Park (2020–2024), and the Courant Institute of Mathematical Sciences, New York University (2017–2020). He earned his Ph.D. in Mathematics from the University of Zurich in 2017 under Camillo De Lellis and Guido De Philippis. Education: Ph.D. in Mathematics, University of Zurich (2017). His research spans Geometric Analysis , Partial Differential Equations , Calculus of Variations , Geometric Measure Theory , Optimal Transport , and Non-convex Optimization . Recent work focuses on anisotropic geometric variational problems, including existence, regularity, and uniqueness of anisotropic minimal surfaces and CMC (constant mean curvature) surfaces. He has also contributed to interdisciplinary applications in Explainable Risk Assessment and Data Analysis . The 15 most recent articles highlight advancements in anisotropic surfaces , min-max theory , optimal transport , and mathematical programming (e.g., K-means clustering, linear programming). Key trends include the intersection of geometric measure theory with nonlinear PDEs and applications in machine learning and transportation networks . Scientific Awards and Grants: 2023 Maryland Research Excellence Carlo Ciliberto Prize (2019) ERC Starting Grant ANGEVA (101076411, 2023–2028) Air Force Office of Scientific Research (AFOSR) grant (FA9550-23-1-0123) NSF CAREER Award (DMS-2143124) NSF DMS Awards (DMS-1906451, DMS-2112311) AMS Simons Travel Grant Antonio actively supervises research and teaches courses such as Optimization and Introduction to Partial Differential Equations . His work is supported by significant funding totaling approximately €3 million.

• Geometric Analysis
• Partial Differential Equations
• Geometric Measure Theory
• +3 more

Xiaoxiao Zhou is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), affiliated with multiple centers including the Center for Outcomes and Effectiveness Research and Education (COERE), Center for Clinical and Translational Science (CCTS), and the Global Center for Craniofacial, Oral and Dental Disorders (GC-CODED). She holds a PhD in Statistics from The Chinese University of Hong Kong (2022) and completed a postdoctoral fellowship at Duke University's Department of Statistical Science. Her research focuses on causal inference, Bayesian methods, longitudinal data analysis, and survival analysis, with applications in Alzheimer’s disease, cardiovascular conditions, and neurodegenerative disorders. Dr. Zhou’s work integrates advanced statistical techniques with medical and behavioral data, including neuroimaging and latent variable modeling. Key areas include handling intercurrent events in clinical trials, causal mediation analysis, and joint modeling of longitudinal and survival outcomes. She collaborates widely with clinicians and biostatisticians to address real-world challenges in healthcare and disease progression studies. Her scholarly contributions span over a dozen peer-reviewed articles, emphasizing methodological innovations in biostatistics and their practical applications. She advises students such as Zhenying Ding and actively participates in academic committees. Outside academia, she enjoys outdoor activities like mountain hiking and weight lifting.

• Causal Inference
• Bayesian Modeling
• Biostatistics
• +7 more

Gintaras Reklaitis is the Burton and Kathryn Gedge Distinguished Professor of Chemical Engineering at Purdue University's Davidson School of Chemical Engineering. He joined Purdue in 1970 and holds a B.S. from Illinois Institute of Technology (1965) and M.S./Ph.D. from Stanford University (1969). His research focuses on applying computing and systems technology to optimize processing systems, particularly in pharmaceutical manufacturing and integrated energy systems. Key areas include batch process design, enterprise-wide planning, and robust scheduling under uncertainty. Professor Reklaitis has been recognized with prestigious awards, including National Academy of Engineering membership (2007) and the Professional Achievement Award from Illinois Institute of Technology (2006). He has co-advised multiple graduate students, including Megha Das, Zachary Hillman, Shrivatsa Korde, and Dalton Yu. His work appears in journals like Computers & Chemical Engineering and Journal of Pharmaceutical Sciences . Research highlights include developing frameworks for real-time quality assurance in pharmaceutical manufacturing, integrating data management systems, and advancing continuous manufacturing technologies. His contributions also span editorial roles, including Editor-in-chief of Computers & Chemical Engineering (1986–2008). Current projects emphasize digital design tools, techno-economic analysis, and sensor-driven process monitoring.

• Computational Process Engineering
• Pharmaceutical Manufacturing
• Process Optimization and Planning
• +3 more

Prashant Mhaskar is a Professor in the Department of Chemical Engineering at McMaster University. His research focuses on model predictive control (MPC), fault detection and isolation (FDI), and data-driven modeling of chemical and bioprocess systems. He has established a four-year partnership with Sartorius to advance biomanufacturing technologies for antibody and virus-based treatments. Key research areas include: Batch and continuous process control Hybrid modeling integrating first-principles and data Stochastic and nonlinear control systems Bioreactor optimization for monoclonal antibody production Safe-parking frameworks for fault-tolerant control Economic model predictive control for industrial processes Recent work emphasizes practical implementations using recurrent neural networks, autoencoders, and physics-informed neural networks to address plant-model mismatch and sparse data challenges.

• Model Predictive Control
• Process Systems Engineering
• Fault Detection and Isolation
• +6 more

Filip Rindler is a Professor of Mathematics at the University of Warwick, based at the Mathematics Institute. His research focuses on the intersection of Applied and Theoretical Mathematics, particularly oscillation and concentration phenomena in Material Science, such as elasto-plasticity and dislocation dynamics. He has held significant grants, including ERC Starting and Consolidator Grants, and has supervised numerous PhD students and postdocs. Rindler is an editor for journals like Mathematical Models and Methods in Applied Sciences (M3AS) and Revista Matemática Iberoamericana. He earned his DPhil from the University of Oxford and a Diplom from TU Berlin. Education: 2009–2011: DPhil (PhD) in Mathematics, University of Oxford 2004–2008: Diplom in Mathematics (with distinction), TU Berlin Research Interests: Rindler’s work spans PDEs, Geometric Measure Theory, and the Mathematics of Material Science. He investigates singularities in nonlinear PDEs, particularly those arising in elasto-plasticity and dislocation mechanics. His research combines geometric and analytic techniques to understand concentration and oscillation effects, with applications to material deformation and fracture mechanics. Grants & Awards: Over £4 million in funding, including ERC grants, Lloyds Register Foundation grants, and the 2025 Blavatnik UK Awards for Young Scientists. His work has been recognized with the Whitehead Prize (2018) and multiple research fellowships. Advising & Labs: Leads the ERC-funded SINGULARITY and CONCENTRATE projects, supervising postdocs and PhD students in topics like dislocation dynamics and shape optimization. His team explores applications in data science and anomaly detection in streaming data. Labs/Teams: Principal Investigator of the ERC SINGULARITY and CONCENTRATE projects, collaborating with institutions globally on topics such as dislocation-driven plasticity and geometric measure theory.

• Partial Differential Equations (PDEs)
• Geometric Measure Theory
• Mathematics of Material Science
• +5 more

Anna Klemettinen is a University Lecturer at Aalto University's College of Engineering within the Department of Chemical and Metallurgical Engineering. Her academic work focuses on sustainable metallurgical processes and battery recycling technologies. Institution: Aalto University Academic Title: University Lecturer Key Research Areas: Battery recycling, circular economy, sustainable metallurgy, critical raw materials recovery Anna's research explores the integration of pyrometallurgical and hydrometallurgical approaches for recovering valuable metals from end-of-life lithium-ion batteries. She develops innovative methodologies for process optimization using multi-dimensional sustainability indicators and statistical entropy analysis. Her recent publications (2021-2025) demonstrate expertise in: Lithium-ion battery recycling technologies NdFeB magnet rare earth recovery Slag cleaning process optimization Integration of flotation and pyrometallurgy Circularity metrics in metallurgical systems Graphite valorization from battery waste

• Battery Recycling
• Sustainable Metallurgy
• Circular Economy
• +4 more

Dr. Di Wu is an Associate Professor in the Department of Biostatistics at the University of North Carolina (UNC) Gillings School of Global Public Health and holds a joint appointment in the UNC School of Dentistry. Her research focuses on developing statistical bioinformatics methods to analyze genomic, microbiome, and clinical data, with applications in cancer research, drug repurposing, and oral health. She has pioneered methods for gene set testing, longitudinal microbiome analysis, and integrative omics approaches. PhD in Bioinformatics, University of Melbourne (2011) MS in Biostatistics, Case Western Reserve University (2006) BS in Biotechnology, Shanghai Jiao Tong University (1998) Dr. Wu’s research interests include genomic data integration for causal inference, microbiome-driven disease prediction, and leveraging electronic health records (EHR) for public health informatics. Her recent projects emphasize causal mediation analysis and interpretable machine learning models to uncover relationships between genomic, microbiome, and metabolomic data. She has contributed to studies on early childhood caries, periodontitis, and the impact of clonal hematopoiesis on bone health. Her work has been recognized through the 2011 Early Career Overseas Fellowship from Australia’s National Health and Medical Research Council. She actively serves on dissertation committees for graduate students and has mentored researchers in biostatistics and dental informatics. Advising: Sarah Kay Youny Lee, Tanjit S. Taggar, Doug Wilson Service: Referee for Biometrics , Nucleic Acids Research , and other journals Dr. Wu leads the Statistical Bioinformatics Group, collaborating on interdisciplinary projects in oral health, cancer genomics, and computational biology. Her lab develops open-source tools like ZOE 2.0 for early childhood oral health studies and LP-Micro for microbiome-based disease prediction.

• Biostatistics
• Bioinformatics
• Genomics
• +6 more

Dr. Mulugeta Gebregziabher is a Professor and Vice Chair for Academic Programs at the Medical University of South Carolina (MUSC) , with dual appointments at the MUSC Global Health Institute and College of Graduate Studies . He is a Research Health Scientist and Methodology Core Leader at the Charleston VA Medical Center's HEROIC Innovation Center , focusing on health disparities, longitudinal data analysis, and health services research for conditions like diabetes, stroke, and HIV/AIDS. His methodological expertise includes: Longitudinal data analysis Zero-inflated data modeling Joint modeling of multiple outcomes Missing data techniques Large dataset analysis Clinical collaborations span diabetes, chronic kidney disease, cardiovascular disease, lung cancer, and global health. He has contributed to over 50 funded NIH/VA grants and 200+ peer-reviewed publications, developing novel statistical methods for health services research. Current teaching includes advanced biostatistics courses and summer institute workshops on Bayesian analysis and epidemiological statistics. Scientific leadership roles: Fellow, American Statistical Association President, American Statistical Association SC Chapter Editorial board member, British Medical Journal-Heart Founding member, Statistical Society of Ethiopians in North America Board member, Worldwide Fistula Fund

• Longitudinal Data Analysis
• Missing Data Analysis
• Joint Modeling
• +12 more

Thomas Adams serves as an Adjunct Professor in the Department of Chemical Engineering at McMaster University. His research spans energy systems engineering and sustainable process design, with particular expertise in solid oxide fuel cells, semicontinuous distillation processes, and techno-economic analysis of clean energy systems. Coming from a family of engineers (third generation), Dr. Adams has maintained scholarly activity for over five decades with publications extending to 2025. Dr. Adams' research focuses on energy system integration and sustainable process design , addressing critical challenges in hydrogen production, waste-to-energy conversion, and carbon management. His work demonstrates particular strength in analyzing long-term degradation effects in fuel cell technologies and developing optimization methodologies for polygeneration systems. Recent research increasingly emphasizes techno-economic and environmental assessment of emerging energy technologies, with significant contributions to standardization frameworks in this field. Analysis of Dr. Adams' publication trends reveals a strong emphasis on system integration approaches that combine different energy conversion processes for maximum efficiency. His work spans from fundamental thermodynamic analyses to practical applications in steel manufacturing, plastic recycling, and maritime energy systems. The consistent application of process systems engineering principles across diverse energy challenges represents a hallmark of his scholarly contributions. Dr. Adams contributes significantly to chemical engineering education through courses including Chemical Process Design and Simulation (CHEMENG 3G04), Energy Systems Engineering (CHEMENG 4A03/6A03), and Advanced PSE Tools and Methods (CHEMENG 740). His 2024 publication 'Teaching Process Design – Quo Vadis?' reflects his ongoing engagement with pedagogical approaches in chemical engineering education. His scholarly impact is evident through consistent publication in top chemical engineering journals including Computers and Chemical Engineering , Industrial & Engineering Chemistry Research , and AIChE Journal . With research activity spanning from 1976 to the present, Dr. Adams has adapted his expertise to address evolving industry challenges from traditional chemical processes to contemporary sustainability concerns.

• Process Design
• Energy Systems Engineering
• Sustainable Energy
• +4 more

Pieter Bruijnincx is a Full Professor in Sustainable Chemistry & Catalysis at Utrecht University's Organic Chemistry & Catalysis group within the Faculty of Science. He also serves as Director of Education at the Chemistry Education Institute and coordinates research under the Pathways to Sustainability (PtS) initiative, particularly in Science for Sustainability and Circular Economy. Education MSc in Chemistry (cum laude), Utrecht University (2002), Organic Chemistry & Catalysis under Prof. Gerard van Koten PhD in Chemistry (cum laude), Utrecht University (2007), focusing on bio-inspired oxidation catalysts and non-heme iron enzyme models under Profs. Bert Klein Gebbink, Gerard van Koten, and Bert Weckhuysen His research centers on designing catalysts and conversion routes for sustainable chemicals, materials, and fuels. Key areas include lignocellulosic biomass valorization, CO2 utilization, and waste feedstock conversion using heterogeneous/homogeneous catalysts. He emphasizes structural characterization of biomass feeds and explores hybrid catalytic concepts like Pickering Emulsions and Single Atom Catalysts. Recent publications highlight advancements in electrochemical catalysis, lignin depolymerization, and biobased polymer recycling. His work is funded by NWO, EU, and industrial partners, with projects in public-private partnerships (Catchbio, BPM) and Marie Curie ITN networks. Scientific Awards NWO Vidi Award (2014) NWO VENI Grant (2010) NWO Rubicon Scholarship (2007) Member of The Young Academy (KNAW) (2016) Bruijnincx's research bridges homogeneous and heterogeneous catalysis, focusing on structure-activity relationships, catalyst stability, and mechanistic studies. His team collaborates nationally and internationally, integrating nanomaterials science and chemometrics for real-time process monitoring.

• Biobased Chemicals and Fuels
• Biomass Conversion
• Sustainability
• +5 more

Shaoning Han is a Presidential Young Professor (Assistant Professor) in the Department of Mathematics and the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS). He previously held a postdoctoral position at the University of Southern California (USC), working under Prof. Jong-Shi Pang. His research focuses on mathematical optimization, including mixed-integer nonlinear programming, nonsmooth optimization, and their applications in operations research and data science. Han earned his Ph.D. in Industrial Engineering from USC under Prof. Andrés Gómez and a B.S. in Mathematics from the University of Science and Technology of China (USTC). His work emphasizes advancing optimization methodologies, particularly for problems involving complementarity constraints, indicator variables, and sparsity. Recent contributions include progressive MIP methods for indefinite QPs, real-time solutions for banded matrix problems, and convexification techniques for nonconvex optimization. Han is actively recruiting PhD students interested in mathematical optimization. Research Highlights: His articles span theoretical advances (e.g., epi-stationarity analysis, parametric variational inequalities) and applied frameworks (e.g., robust SVMs, binarized neural networks). Key themes include polynomial-time solvability of MRFs, convex relaxations for low-rank functions, and sparse signal estimation. Awards: Third Place, INFORMS JFIG Paper Competition (2023) Journal of Global Optimization Best Paper Honorable Mention (2023) Teaching: Courses include MA5243 (Advanced Mathematical Programming) and MA3252 (Linear and Network Optimization). Labs/Teams: Active member of IORA, NUS’s Smart Nation Research Cluster.

• Mathematical Optimization
• Mixed-Integer Nonlinear Programming
• Nonsmooth Optimization
• +3 more

Gianni Bosi is a Full Professor at the Department of Economic, Business, Mathematical and Statistical Sciences (DEAMS Bruno de Finetti) of the University of Trieste. His research focuses on mathematical methods in economics and finance, particularly utility theory, order theory, and multi-utility representations. He has contributed to foundational work on continuous and semicontinuous representations of preorders, topological structures in utility theory, and optimization techniques. Key projects include the FRA 2018 project 'UMMMM' on uncertainly modeling and the 'CLASS4.0' cluster for data-driven solutions in the sea economy sector. His recent publications address advanced topics like ad hoc continuity of interval orders, net-compact topologies, and cinema-based mathematics education. No awards or student advisees are explicitly mentioned.

• Mathematical Methods in Economics
• Finance
• Actuarial Sciences
• +5 more

Dr. Kaveh Kiani is a faculty member at the University of Salford's School of Science, Engineering & Environment, with a primary affiliation to the Informatics Research Centre. His academic profile demonstrates expertise spanning both theoretical statistics and practical AI applications across multiple industries. Doctoral degree in Applied Statistics Master's degree in Data Science Postgraduate Certificate in Academic Practice (PGCAP) Fellow of the Higher Education Academy (FHEA) Dr. Kiani's research focuses on computer vision, big data analytics, generative AI applications in education, and survival analysis. His work bridges statistical theory with practical implementations, particularly evident in medical AI applications for respiratory disease detection, skin cancer diagnosis, and prostate cancer research. His industry experience informs his approach to solving real-world problems in healthcare, construction sustainability, and financial services. His recent publications (2023-2025) demonstrate strong activity in applying deep learning techniques to medical imaging and developing novel frameworks for industry-specific challenges. The research shows a clear trajectory toward increasingly sophisticated AI applications with practical impact. Fellow of the Higher Education Academy (FHEA) awarded by Advance HE Endorsed by The Royal Academy of Engineering as a Global Talent in Data Science Dr. Kiani actively supervises PhD students and teaches across multiple postgraduate programs including MSc Data Science, MSc Artificial Intelligence, and MSc Internet of Things with Data Science. His teaching modules cover Big Data Tools and Techniques and Applied Statistics and Data Visualisation, reflecting his dual expertise in both theoretical foundations and practical applications of data science.

• Computer Vision
• Big Data Analytics
• Generative AI in Education
• +2 more

Hidde Schönberger is a mathematical researcher at Katholische Universität Eichstätt-Ingolstadt, affiliated with the Faculty of Mathematics and Geography within the Department of Mathematics. He works as part of the Chair of Analysis research team, focusing on advanced theoretical mathematics with applications to complex systems. His primary research interests center around Calculus of Variations , Partial Differential Equations , Nonlocality , Inverse Problems , and Fractional Derivatives . Schönberger's work bridges theoretical mathematics with practical applications, particularly in developing mathematical frameworks for nonlocal phenomena and fractional calculus. His research has significant implications for modeling complex physical systems where traditional local models are insufficient. Analysis of his publication record from 2019-2024 reveals a consistent focus on nonlocal mathematical structures, with increasing specialization in fractional calculus and variational methods. His work demonstrates strong international collaboration, particularly with researchers from Utrecht University and other European institutions. The publications show progression from foundational work on variational obstacle problems to sophisticated analyses of nonlocal gradients and fractional models. Graduate School of Natural Sciences Best Master's Thesis Award 2021-2022, Utrecht University (Sept. 2022) Young Talent Incentive Award from the Royal Dutch Society of Sciences, Koninklijke Hollandsche Maatschappij der Wetenschappen (Nov. 2017) Schönberger has established himself as a promising researcher in mathematical analysis through his productive publication record and recognition from academic institutions. His work continues to contribute to the theoretical foundations of nonlocal mathematical models, with potential applications across physics, engineering, and data science.

• Calculus of Variations
• Partial Differential Equations
• Nonlocality
• +2 more

Emanuela Dreassi is a Full Professor of Statistics at the University of Florence, where she currently serves as Director of the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) from November 1, 2024, to October 31, 2028. She is affiliated with the School of Economics and Management and has held various institutional roles including Director of the Bachelor's Program in Statistics (2014-2022) and Vice-President of the School of Economics and Management (2019-2022). Her research spans hierarchical Bayesian models and spatial statistics, with methodological advancements in specifying and estimating models for multilevel data, missing data, and latent variable models. She has made significant contributions to robust analysis in small area estimation, compatibility of conditional distributions, semicontinuous data modeling, Bayesian predictive inference, and knockoffs construction. Her work bridges theoretical statistics with practical applications in epidemiology, environmental studies, and medical research. Dreassi's recent publications (2020-2025) reveal a strong focus on methodological innovations in Bayesian statistics, spatial analysis, and knockoff filters for variable selection. She has published extensively in top statistical journals while also collaborating on interdisciplinary research in medical fields including plastic surgery and epidemiology. Her work demonstrates a consistent trajectory of advancing statistical methodology while maintaining strong connections to real-world applications across multiple domains. As an active referee for numerous prestigious journals including Biometrics, Journal of the Royal Statistical Society, and Statistics in Medicine, Dreassi contributes significantly to the scholarly community. She has coordinated research units for PRIN and HORIZON2020 projects and participated in numerous national and international conferences as organizer, scientific committee member, and session chair.

• Hierarchical Bayesian models
• Spatial statistics
• Multilevel data analysis
• +7 more