Find Academic Supervisors

David A. MillsView profile

Academic

• Microbiology
• Food Science
• Gut Health
• +3 more

David A. Mills is a Distinguished Professor in the Department of Food Science and Technology and Viticulture & Enology at the University of California, Davis, holding the Peter J. Shields Chair in Dairy Food Science. His research focuses on the molecular biology and ecology of beneficial bacteria in gut health and fermented foods, particularly the role of human milk oligosaccharides (HMOs) in infant nutrition and microbial symbiosis. He leads a lab translating microbiome science into commercial innovations, such as probiotic development and food biotechnology. Dr. Mills has mentored over 30 graduate students and postdoctoral researchers, generating over 210 publications. His work spans glycomics, microbiome analysis, and food processing technologies. Notable contributions include seminal studies on lactic acid bacteria and bifidobacterial genomics, and the creation of the Davis Food Glycopedia. Awards include the American Academy of Microbiology Fellowship (2015), Clarivate Analytics' Highly Cited Researcher designation (2018–2024), and the UC Davis Distinguished Professor title (2024). His research has launched multiple startups and influenced global food science and public health initiatives. Research themes emphasize interdisciplinary approaches to understanding microbiome-host interactions, pre/probiotic efficacy, and sustainable food systems. Current projects investigate dietary carbohydrates' impact on health, microbial fermentation dynamics, and engineered plant systems for producing HMO analogs.

Juan M TaboasView profile

Associate Professor

• Regenerative Medicine
• Biomedical Engineering
• Tissue Engineering
• +4 more

Dr. Juan M Taboas is an Associate Professor at the University of Pittsburgh, affiliated with the School of Dental Medicine (Department of Oral and Craniofacial Sciences), Swanson School of Engineering (Department of Biomedical Engineering), and the Clinical and Translational Science Institute. His research focuses on regenerative therapies for skeletal and craniofacial tissues, particularly in pediatric populations and underserved communities. Education: B.S. Mechanical Engineering (University of Miami, 1993), M.S. Biomechanical Engineering (Stanford, 1995), dual M.S./Ph.D. Biomedical Engineering (University of Michigan, 2003/2004). Postdoctoral training at NIST and NIAMS/NIH. Research Interests include physeal regeneration for growth plate injuries, dental pulp regeneration via injectable biomaterials, and microtissue fabrication for drug screening. His lab holds patents for regenerative therapies and microfluidic culture systems. He collaborates widely, leading multi-site NIH/DoD grants. Professional Activities: Senior Member of the National Academy of Inventors, reviewer for NIH/DoD grants, and journal manuscripts (e.g., Acta Biomaterialia, Journal of Dental Research). Serves on the University of Pittsburgh Senate. Article Trends span biomaterial design, immunomodulation, and mechanistic studies of bone/cartilage regeneration. Recent work emphasizes pediatric applications and translational therapies for skeletal disorders.

Edwin BeggsView profile

Professor

• Algebra
• Differential Geometry
• Noncommutative Geometry
• +4 more

Dr Edwin Beggs is a Reader in Mathematics within the School of Mathematics and Computer Science at Swansea University, located at the Computational Foundry on Bay Campus. His research spans Algebra, Differential Geometry, Noncommutative Geometry, Theoretical Physics, and the computability of physical systems. He co-authored the influential 2020 Springer book Quantum Riemannian Geometry , contributing to the Grundlehren series. His work bridges mathematical formalism with physical applications, focusing on quantum structures, geometric frameworks, and computational models interfacing with physical systems. Research interests include noncommutative differential operators, quantum geodesic flows, and the interplay between algebraic topology and physics. Notable contributions address quantum gravity models, soliton dynamics, and the theoretical limits of measurement and computation in physical systems. He is actively involved in postgraduate supervision and has explored computational paradigms using physical oracles, such as the Wheatstone bridge and kinematic systems, to redefine computational boundaries. His interdisciplinary approach is reflected in publications spanning quantum geometry, analogue-digital computation models, and the mathematical foundations of measurement theory. Collaborations with Shahn Majid highlight his role in advancing noncommutative geometry's applications to modern physics.

Giuseppe VinciView profile

Assistant Professor

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +8 more

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

Arul JayaramanView profile

Professor

• Molecular systems biotechnology
• Systems biology of cytokine signaling
• Inter-kingdom signaling
• +7 more

Dr. Arul Jayaraman is the Executive Associate Dean of the Texas A&M University College of Engineering and a Professor in the Artie McFerrin Department of Chemical Engineering. He holds the Ray B. Nesbitt Endowed Chair and is a Presidential Impact Fellow. His academic roles include serving as Associate Agency Director of the Texas A&M Engineering Experiment Station and affiliation with Biomedical Engineering. Education: Ph.D. in Chemical Engineering from University of California, Irvine (1998), M.S. from Tufts University (1994), and B.S. from Birla Institute of Technology & Science (1987). Research focuses on molecular systems biotechnology, particularly investigating microbiota-host interactions in inflammatory diseases and colorectal cancer using systems biology and microfluidic models. Key areas include inter-kingdom signaling between bacteria and human cells, AhR receptor modulation by dietary metabolites, and development of drug screening platforms. His work bridges engineering, microbiology, and medicine with applications in precision health. Recent publications highlight advances in microbiota metabolomics, microbial corrosion modeling, and cancer microenvironment engineering. Over 20 awards include the NSF CAREER Award (2009) and multiple teaching honors recognizing his educational impact. His leadership roles drive interdisciplinary initiatives and infrastructure development in engineering education. Research teams collaborate on NIH-funded projects exploring gut microbiome influences on immunity and chronic diseases.

Nate VeldtView profile

Assistant Professor

• Graph algorithms
• Network science
• Matrix computations
• +3 more

Nate Veldt is an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on combinatorial algorithms and computational methods for data analysis, particularly in graph and network modeling. He holds a Ph.D. in Mathematics with a concentration in Computational Science from Purdue University (2019), an M.S. in Mathematics from Purdue (2017), and a B.S. in Mathematics from Wheaton College (2013). Research Interests: - Graph algorithms and network science - Matrix computations - Computational optimization - Data science and machine learning applications in network analysis. His work bridges theoretical computer science, discrete mathematics, and practical data science challenges. Notable Awards: - AFOSR Young Investigator Program Award (2025) - Dimitris N. Chorafas Foundation Award (2019) - NSF East Asia and Pacific Summer Institute Award (2016) Recent Grants and Roles: - Course development grant for a new 'Computational Methods for Network Science' course (TAMIDS, 2025) - Co-chair of SIAM Symposium on Algorithm Engineering and Experiments (ALENEX26, 2026) - Secretary of SIAM Activity Group on Applied and Computational Discrete Algorithms (since 2025) Research Contributions: Develops algorithms for hypergraph clustering, cut improvement, and network analysis. His work emphasizes practical scalability and theoretical guarantees in combinatorial optimization problems. Active in interdisciplinary collaborations, including with Lawrence Livermore National Laboratory and the University of Utah.

Rachel WatsonView profile

Professor

• Skin Ageing
• Extracellular Matrix Repair
• Asian Skin
• +6 more

Professor Rachel Watson is an Executive Director at A*STAR Skin Research Labs (SRL) and the Skin Research Institute of Singapore (SRIS). She holds adjunct roles as Chief Scientist at the National Skin Centre and Adjunct Professor at Lee Kong Chian School of Medicine, Nanyang Technological University. Her research focuses on skin aging mechanisms, extracellular matrix repair, and ethnic skin differences. With a BSc (Hons) and PhD from the University of Sheffield, her work addresses intrinsic/extrinsic aging, Asian skin biology, and therapeutic development. She leads collaborative programs involving 18+ researchers across dermatology, bioinformatics, and genomics. Research emphasizes menopause effects on skin biomechanics, UV-induced inflammation, and dietary interventions like omega-3 fatty acids. Methodologies include clinical trials, transcriptomics, proteomics, and advanced imaging. Her lab team comprises 9 staff scientists and research officers. Key collaborations include Johns Hopkins Dermatology, University of Manchester, and multiple A*STAR institutes. Publications span 1997–2024, focusing on retinoid therapies, photoprotection strategies, and ethnic skin variability. Her work bridges fundamental research with clinical applications, addressing Singapore's aging population challenges and multi-ethnic healthcare needs.

Augusto GerolinView profile

Assistant Professor

• Optimal Transport Theory
• Mathematical Physics
• Theoretical and Computational Chemistry
• +4 more

Augusto Gerolin is an Assistant Professor jointly appointed in the Departments of Mathematics and Statistics and Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a Tier II Canada Research Chair in Artificial Intelligence at the Interface of Chemistry and Mathematics. His research focuses on Optimal Transport Theory, Mathematical Physics, Theoretical and Computational Chemistry, and Machine Learning. Gerolin’s work bridges quantum chemistry, mathematical analysis, and computational methods, with applications in Density Functional Theory and quantum information science. He obtained his PhD from the University of Pisa and was a Marie Skłodowska-Curie fellow at Vrije Universiteit Amsterdam. He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and leads a research group exploring AI-driven solutions in chemistry and mathematics. His current projects include developing optimal transport frameworks for quantum systems, advancing machine learning algorithms in scientific computing, and fostering collaborations across disciplines through initiatives like the OQMG Network. Gerolin’s research has led to advancements in multi-marginal optimal transport, entropy-regularized methods, and the strong-interaction limit of density functional theory. His group actively collaborates with institutions worldwide, including the Fields Institute, IPAM, and the University of Genoa. He has supervised numerous PhD, Master’s, and undergraduate students, many of whom have contributed to cutting-edge studies in computational chemistry, quantum algorithms, and mathematical analysis. Key awards include the Canada Research Chair designation, and his work has been supported by grants from NSERC, MITACS, and the University of Ottawa. Gerolin is also committed to diversity in science, endorsing principles such as the Diversity Axioms, and advocates for academic solidarity with researchers affected by global conflicts.

Ioannis KyriakouView profile

Professor

• Actuarial Science
• Quantitative Finance
• Financial Mathematics
• +13 more

Professor Ioannis Kyriakou is a leading academic in actuarial finance and quantitative methods at Bayes Business School, City St George's, University of London, where he serves as Professor of Actuarial Finance and Director of the MSc in Actuarial Science and MSc in Actuarial Management. He holds a visiting professorship at the University of Eastern Piedmont and has previously served as affiliate faculty at the Cyprus International Institute of Management. His research spans actuarial science, derivatives pricing, risk management, computational finance, and machine learning applications in finance and insurance. His research interests focus on quantitative finance , stochastic modeling , numerical methods , and machine learning , particularly in the context of derivative pricing , pension product design , energy and commodity markets , and investor sentiment . He has developed advanced computational techniques such as moment-based approximations and transform methods for financial modeling. His work integrates simulation, optimization, and data-driven approaches to solve complex financial and actuarial problems. The recent publications reflect a strong trend toward interdisciplinary research, combining machine learning with financial economics , energy efficiency forecasting , mutual fund performance , and climate risk . His work frequently appears in top journals like Operations Research , Journal of Financial and Quantitative Analysis , and European Journal of Operational Research , showcasing expertise in both theoretical and applied finance. European Journal of Operational Research (2020) Editors' Award for Excellence in Reviewing Cass Business School (2014) Prize for Excellence in Teaching and Learning Dimitris N. Chorafas Foundation (2009) Prize for outstanding PhD research work EPSRC Doctoral Training Award (2008) He supervises several PhD students in areas such as derivatives pricing, machine learning in actuarial science, and pension optimization. He has received research support through editorial leadership, consultancy (e.g., with Lloyd’s Treasury), and academic collaboration. He is actively involved in organizing conferences and workshops, including the Finance and Business Analytics Conference. He is affiliated with research groups focusing on financial modeling , actuarial computation , and machine learning in insurance , contributing to both academic and industry-facing initiatives. His co-authored book Machine Learning in Insurance (MDPI, 2020) highlights his leadership in bridging data science with actuarial applications.

Svend BirkelundView profile

Professor

• Molecular Bacterial Pathogenesis
• Medical Microbiology
• Immunology
• +12 more

Svend Birkelund is a Professor and Head of Research Group in the Department of Health Science and Technology at Aalborg University, Faculty of Medicine. His academic work is centered on medical microbiology and immunology, with a strong emphasis on molecular bacterial pathogenesis. Doctor of Medical Science (DMSc), Aarhus University, 1992 PhD, University of Aarhus, 1989 MD, University of Copenhagen, 1983 His research focuses on how pathogenic bacteria such as Chlamydia trachomatis and Klebsiella pneumoniae interact with the human immune system, particularly the complement system and innate immune responses. His recent work explores serum resistance mechanisms, leukocyte migration via aquaporins, and biomarker discovery in inflammatory joint diseases. He contributes to UN Sustainable Development Goals related to health and well-being. Analysis of his recent publications reveals a strong trend in immunology and microbiology, with increasing use of proteomics and systems biology approaches. His work spans fundamental host-pathogen interactions, clinical immunology, and translational applications in diagnostics and therapy. He has contributed to significant research projects, including: Elimination of microorganisms by the innate immune system (Obel Family Foundation) Biomarkers in Osteoarthritis Interleukin-1 in inflammatory diseases Analysis of citrullinated peptides in autoimmune triggers His datasets on proteomic analysis of synovial fluid and complement deposition on Chlamydia are publicly available. He has supervised multiple PhD students and is frequently engaged in public discourse on antibiotic-resistant bacteria through media appearances. He is actively involved in interdisciplinary collaborations and leads a research group focused on advancing understanding of infectious and inflammatory diseases through molecular and immunological approaches.

• Algorithms
• Computational Geometry
• Discrete Mathematics
• +8 more

Mordecai J Golin is a Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST), School of Engineering. His research lies at the intersection of theoretical computer science, algorithms, and discrete mathematics, with strong applications in information theory and computational geometry. His research interests include algorithms , computational geometry , data structures , dynamic programming , coding theory , and combinatorics . He has made significant contributions to the design and analysis of optimal search trees, prefix-free coding, and minmax regret optimization in dynamic flow networks. His work often combines probabilistic analysis with algorithmic efficiency. The recent publications highlight a sustained focus on optimization problems in graphs and trees, particularly in dynamic flow networks for applications like evacuation modeling, and in data compression via advanced Huffman and AIFV coding techniques. The research spans from theoretical foundations to algorithmic innovation, with recurring themes of efficiency, robustness, and structural analysis. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants : While specific students and grants are not listed, Dr. Golin has an extensive record of collaborative research with colleagues at HKUST and internationally, suggesting active supervision and project leadership. His frequent publications in top-tier venues indicate sustained funding and research activity. Labs and Teams : Though not explicitly named, his work is likely conducted within theoretical computer science or algorithms research groups at HKUST, possibly associated with centers focusing on discrete mathematics or information sciences.

Carina CurtoView profile

Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Applied Algebra
• +4 more

Carina Curto is a Professor of Applied Mathematics and Brain Science at Brown University, affiliated with the Division of Applied Mathematics and the Carney Institute for Brain Science. She holds a B.A. in Physics from Harvard University (2000) and a Ph.D. in Mathematics from Duke University (2005). Her research focuses on theoretical and computational neuroscience, applying algebra, topology, and dynamical systems to study neural networks and neural coding. She has held faculty positions at Penn State (2014–2024) and the University of Nebraska-Lincoln (2009–2014), and was a postdoctoral researcher at Rutgers and NYU. Research Interests : Neural network theory, topology in neuroscience, algebraic structures of neural codes, and their applications to understanding brain circuits and cognition. Her work bridges mathematical theory and experimental neuroscience, with emphasis on threshold-linear networks, combinatorial coding, and circuit dynamics. Grants & Awards : NIH R01, NSF grants (DMS-1951165, DMS-1516881), Simons Fellowship (2021–22), Penn State Faculty Scholar Medal (2020), Sloan Research Fellowship (2011–15). Labs & Collaborations : Leads the Curto Lab for Mathematical Neuroscience at Brown, focusing on interdisciplinary research connecting network dynamics to brain function. Collaborators include Anda Degeratu, Chad Giusti, and Katherine Morrison. Her lab includes postdocs, PhD students, and affiliate members working on projects like CTLN dynamics and clique topology.

• Network Analysis
• Random Graph Theory
• High-Dimensional Statistics
• +5 more

Can M. Le is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of statistics, network science, and high-dimensional data analysis, with a focus on theoretical and applied aspects of network modeling and inference. Ph.D. in Statistics, University of Michigan, Ann Arbor His research interests include network analysis, random graph theory, community detection, high-dimensional statistical inference, and regularization of network data. He develops methods for analyzing noisy, complex network structures and has contributed significantly to spectral methods and low-rank approximations in network science. His work bridges theoretical statistics with practical applications in social and biological networks. The recent publications demonstrate a strong trend in modeling and inference for network-linked data, with emphasis on robustness, adaptivity, and concentration properties of random graphs. His work combines deep probabilistic analysis with statistical methodology, particularly in community detection and network estimation under noise and heterogeneity. His research is supported by the National Science Foundation (NSF) grant DMS-2015134, indicating active funding and ongoing contributions to the field. While no formal list of advisees is provided, his collaborative work with leading statisticians such as Elizaveta Levina and Roman Vershynin suggests an active research group and mentoring role. He has no listed scientific awards in the provided text. However, his consistent publication record in top journals (JASA, JRSSB, Annals of Statistics, JMLR) underscores his scholarly impact. Dr. Le's work is closely tied to theoretical and applied statistical research on networks, likely involving a research lab or team focused on network data science, though specific lab names or team structures are not mentioned in the text.

Emma LejeuneView profile

Assistant Professor

• computational mechanics
• biomechanics
• applied mechanics
• +5 more

Emma Lejeune is an Assistant Professor of Mechanical Engineering at Boston University, affiliated with the Synthetic Biology and Tissue Engineering & Mechanobiology research groups. Her office is located at 730 Commonwealth Ave., EMA 209, and she can be reached at elejeune@bu.edu . She leads the Lejeune Lab , focusing on computational mechanics applied to biological systems. Education: Ph.D., Stanford University Research interests center on leveraging computational mechanics to study multiscale phenomena in biological systems, particularly integrating data-driven and physics-based models. Key areas include heterogeneous soft tissue mechanics, biomechanics, and machine learning applications in mechanics. Her work emphasizes open science, with contributions to benchmark datasets like the Mechanical MNIST collections and open-source software tools such as SarcGraph and MicroBundleCompute . Notable awards include the American Heart Association Career Development Award and the David R. Dalton Career Development Professorship . Her research is supported by grants from the NSF, Office of Naval Research, and Boston University’s Dean’s Catalyst Award. Lab activities include hosting Closer Look Journal Club and contributing to open-access datasets. Current projects explore mechanics of cardiac microtissues, fracture simulations, and machine learning in material science.

Jeffrey B. SchmidtView profile

Associate Professor

• New Product Development
• Marketing Strategy
• Leadership
• +3 more

Jeffrey B. Schmidt is an Associate Professor of Marketing & Supply Chain at the Michael F. Price College of Business, University of Oklahoma. He joined OU in 2005 and also holds adjunct professor roles at the Drucker School of Claremont Graduate University and Oakland University. His research focuses on new product development, marketing strategy, leadership, and work-life interface. He has received numerous teaching awards, including the Alumni Teaching Award (2008, 2009) and the Weinstein Excellence Award (2002). Education Ph.D. in Business Administration, Eli Broad Graduate School of Management, Michigan State University M.B.A., Oakland University B.S. in Packaging (with Honors), Michigan State University Research Interests Dr. Schmidt specializes in new product development processes, marketing strategy, and the psychological dynamics of decision-making in business contexts. His work explores proactive behaviors, leadership, and the balance between work and personal life. Key themes include regret anticipation in project management and cross-functional team collaboration. Awards & Recognition Sheth Foundation Best Paper Award (2002) AMA Best Paper Awards (2003, 2006, 1994) Commerce Council Outstanding Teaching Award (2002) Multiple Delta Sigma Pi awards for teaching impact Advising & Grants While no formal student advisees are listed, his impactful teaching and research contributions reflect extensive mentorship. His work has been supported by grants leading to publications in top-tier journals like Journal of Product Innovation Management and Journal of the Academy of Marketing Science . Labs & Teams He collaborates with interdisciplinary teams in new product development and marketing strategy, though specific lab affiliations are not detailed.