جستجوی استاد راهنمای دانشگاهی

Amy Childressمشاهده پروفایل

استاد

• Membrane processes
• Desalination
• Water treatment
• +۶ مورد دیگر

Amy Childress is Dean's Professor of Civil and Environmental Engineering at the University of Southern California's Viterbi School of Engineering. She serves as director of the Civil and Environmental Engineering Department's environmental engineering program and leads the Center for Water Reuse (ReWater). Dr. Childress has been with USC since summer 2013 and previously served as professor and chair of the Civil and Environmental Engineering Department at the University of Nevada, Reno. Dr. Childress earned her educational degrees from the following institutions: Bachelor's Degree in Civil Engineering from the University of Maryland College Park Master's Degree in Civil Engineering from the University of California - Los Angeles Doctoral Degree in Civil Engineering from the University of California - Los Angeles For over 20 years, Professor Childress' research has focused on membrane processes for addressing global water scarcity challenges. Her current research interests include membrane contactor processes for innovative solutions to contaminant and energy challenges; pressure-driven membrane processes as industry standards for desalination and water reuse; membrane bioreactor technology; and colloidal and interfacial aspects of membrane processes. She emphasizes process sustainability through reduction of discharge by-products, limiting chemical and material consumption, and minimizing energy, carbon, and infrastructure footprints of treatment systems. Her work explores the water-energy nexus to develop holistic solutions for finite water and energy resources. Professor Childress' recent publications demonstrate a consistent focus on advancing membrane technologies for water treatment and desalination. Her research spans fundamental studies of membrane properties and performance to applied research on system integration and optimization. Key themes include improving membrane wetting resistance, developing mathematical models for water blending, understanding morphological changes in membranes, exploring power density limitations in osmotic processes, and investigating long-term operational effects on membrane performance. Her work consistently addresses the technical challenges of water scarcity while considering sustainability and energy efficiency. Her scientific achievements have been recognized with numerous awards and honors: National Science Foundation CAREER Award (2001) NAE Frontiers of Engineering Invited speaker (2007) AEESP President (2008) Multiple fellowships and scholarships from UCLA, AWRA, ACS, and water districts UNR Student Chapter of AWRA Excellence in Teaching Honorable Mention Award (2001) Clair A. Hill Scholarship (1995) Larson Aquatic Research Support (LARS) Scholarship (1996) Professor Childress has directed research projects funded by numerous prestigious organizations including the U.S. Bureau of Reclamation, NSF, NASA, Office of Naval Research, U.S. Department of Energy, California Energy Commission, California Department of Water Resources, the U.S. EPA, and SERDP, as well as local and private agencies. She leads a productive research group that has generated numerous peer-reviewed publications, proceeding papers, and patents. Her leadership extends to service on the AEESP Foundation Board of Directors and previously as AEESP President. She also serves on the Advisory Board of Desalination journal. Dr. Childress leads the Childress Research Group at USC, which focuses on fundamental and applied aspects of membrane processes for water treatment and desalination. The group maintains laboratory facilities in Biegler Hall (BHE) at USC and conducts both experimental and modeling research to advance water treatment technologies. Their work addresses critical challenges in southern California and around the world related to wastewater reclamation and seawater desalination.

Sean Howeمشاهده پروفایل

استادیار

• arithmetic geometry
• algebraic geometry
• representation theory
• +۲ مورد دیگر

Sean Howe is an Assistant Professor in the Department of Mathematics at the University of Utah, where he has been employed since July 2019. His research is supported by NSF grants DMS-2201112 and DMS-2501816. In the academic year 2023-2024, he was a Friends of the Institute for Advanced Study Member at the special year on p-adic arithmetic geometry at the Institute for Advanced Study. Dr. Howe received his PhD from the University of Chicago in 2017 under the supervision of Matt Emerton. Prior to his position at Utah, he was an NSF Postdoctoral Scholar at Stanford University from September 2017 to June 2019. He earned a joint master's degree from Leiden University and Universite Paris-Sud 11 through the ALGANT program in 2012 and completed his undergraduate studies at the University of Arizona. Dr. Howe's research spans arithmetic and algebraic geometry, representation theory, and number theory, with a particular focus on p-adic aspects. His work often explores the connections between geometry and number theory through the lens of p-adic methods, including p-adic Hodge theory, perfectoid spaces, and the Langlands program. He has made significant contributions to understanding cohomological structures in mixed characteristic settings, the geometry of moduli spaces, and the statistical properties of L-functions. His extensive publication record demonstrates a strong trajectory in advancing p-adic geometry and its applications. Recent work shows increasing focus on cohomological smoothness in mixed characteristic, p-adic periods, and the interplay between random matrix theory and arithmetic statistics. His research often bridges abstract theoretical frameworks with concrete computational approaches. NSF Postdoctoral Scholar NSF grants DMS-2201112 and DMS-2501816 Dr. Howe is an active mentor, currently advising five PhD students: Minhua Cheng, Madison Delmoe, Shea Engle, Abhay Goel, and Suo Jun Tan. He has successfully graduated two PhD students: Matthew Bertucci (2025) and Hanlin Cai (2024). He also regularly mentors undergraduate researchers, with notable projects including Emil Geisler's work on stable multiplicities in configuration space cohomology and Daniel Koizumi's software for computing braid monodromy of cubic surfaces. His teaching portfolio includes advanced courses in algebraic topology, number theory, and algebra, reflecting his broad expertise across pure mathematics. He has taught courses such as Math 6950 (Topics in Algebraic Topology), Math 4400 (Introduction to Number Theory), and Math 6320 (Modern Algebra II).

Joseph Wuمشاهده پروفایل

استاد

• Epidemiology
• Infectious Disease Modeling
• Public Health Policy
• +۵ مورد دیگر

Professor Joseph Wu is a faculty member at The University of Hong Kong , specializing in mathematical and statistical modeling of diseases. His research focuses on developing practical analytics for disease control, translating findings into public health policies, and addressing global health challenges through AI technology and tools. He leads the Laboratory of Data Discovery for Health (D²⁴H) and has directed significant educational initiatives, including HKU’s Epidemics MOOC and the Croucher Summer Course in Vaccinology . His research spans epidemiology , infectious disease modeling , and public health policy . He has contributed to understanding COVID-19 , influenza , and yellow fever dynamics, with a focus on vaccine hesitancy and epidemic forecasting. His work has been published in high-impact journals such as Nature Medicine and The Lancet . Scientific Awards: Fellow of the UK Faculty of Public Health Professor Wu serves as co-editor-in-chief of Epidemics and associate editor for PLOS Computational Biology and PLOS Neglected Tropical Diseases . He contributes to global health through membership in the WHO Advisory Committee on Immunization and Vaccines-related Implementation Research (IVIR-AC) , the MIT SOLVE Challenge Leadership Group , and the MIT HK Innovation Node .

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.

Sarah A.M. Loosمشاهده پروفایل

پژوهشگر ارشد

• Stochastic Thermodynamics
• Non-Markovian Processes
• Nonreciprocal Systems
• +۵ مورد دیگر

Dr. Sarah A.M. Loos is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge and a Research Fellow at Corpus Christi College, Cambridge. She holds a PhD in Physics (summa cum laude) from TU Berlin (2020), with postdoctoral research at ICTP (Trieste) and Leipzig University. Her research focuses on stochastic thermodynamics, non-Markovian processes, and nonreciprocal systems. She has received major awards including the Royal Society of Chemistry Early Career Award (2024) and Marie Skłodowska Curie Fellowship (2023). Education: PhD in Physics (2020, TU Berlin), Master's in Physics (2015, TU Berlin), Bachelor's in Physics (2012, TU Berlin). Research interests include entropy production in nonreciprocal systems, active matter, and control theory. She has organized workshops on adaptive dynamical systems and contributed to KITP programs on active solids. Publications span topics like optimal control at microscale, PT symmetry in non-Hermitian systems, and nonreciprocal heat transfer. Her work bridges statistical physics and nonlinear dynamics, with applications in biological and nanoscale systems. Awards: 8 major prizes including DPG and RSC recognitions Grants: MSCA Fellowship (€200k), DFG Walter-Benjamin Fellowship Labs/Teams: Active Matter Group at DAMTP, collaborations with Édgar Roldán and Klaus Kroy

Dylan Jonesمشاهده پروفایل

استاد

• Operational Research
• Logistics
• Healthcare
• +۳ مورد دیگر

Professor Dylan Jones is a Professor of Operational Research at the University of Portsmouth within the School of Mathematics and Physics. He holds dual affiliations with the Centre for Operational Research and Logistics and the Centre of Excellence in Defence, Risk & Resilience. His academic journey includes a BSc (Hons) in Mathematics with Operational Research from the University of Southampton and a PhD in Operational Research from the University of Portsmouth. Specializing in Multi-Criteria Decision Making (MCDM), his research spans logistics, healthcare, renewable energy, and defense applications. He has led over 18 PhD theses and secured EU funding for projects focused on offshore wind energy and sustainable logistics. Professor Jones is also the Director of the Centre for Operational Research and Logistics, emphasizing strategic port development and disaster risk reduction. His work integrates advanced methodologies like goal programming and mixed modeling to address complex real-world challenges. Recent contributions include frameworks for offshore wind farm logistics, sustainable port selection, and resilience-based maintenance strategies. Education: BSc (Hons) in Mathematics with Operational Research, University of Southampton PhD in Operational Research, University of Portsmouth Research interests revolve around applying operational research principles to solve multi-objective problems in logistics, healthcare systems, and renewable energy sectors. His work emphasizes sustainability, decision-making under uncertainty, and optimizing resource allocation. Key projects include developing methodologies for offshore wind energy infrastructure and analyzing risk in maritime logistics. His research outputs (109+ publications) focus on advancing operational research techniques, with notable contributions to goal programming, logistics optimization, and multi-criteria decision analysis. He collaborates internationally, particularly in Brazil, France, Spain, and Portugal, to address global challenges in sustainable energy and infrastructure. Labs/Teams: Centre for Operational Research and Logistics Centre of Excellence in Defence, Risk & Resilience

Jaime S. Cardosoمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Decision Support Systems
• +۳ مورد دیگر

Jaime S. Cardoso is an Associate Professor with Habilitation at the Faculty of Engineering of the University of Porto (FEUP) and a Senior Researcher in the 'Information Processing and Pattern Recognition' Area at INESC TEC's Telecommunications and Multimedia Unit. He has been serving as Research Coordinator since September 15, 1998, and is a Senior Member of IEEE as well as co-founder of ClusterMedia Labs. His educational background includes a Licenciatura in Electrical and Computer Engineering (1999), an MSc in Mathematical Engineering (2005), and a Ph.D. in Computer Vision (2006), all from the University of Porto. Cardoso's research focuses on three major areas: computer vision, machine learning, and decision support systems. His work spans medical image analysis, explainable AI, semantic audio-visual analysis, and pattern recognition. He has co-authored over 150 papers, with more than 50 published in international journals, and has accumulated over 6,500 citations. His recent publications demonstrate expertise in cell nuclei segmentation, ordinal regression for CNNs, semantic segmentation with ordinal relationships, face recognition using synthetic data, and explainable vision language models for medical applications. Honorable Mention in the Exame Informática Award 2011 for 'Semantic PACS' First Place in the ICDAR 2013 Music Scores Competition Cardoso has supervised numerous graduate students at UP-FEUP, with recent theses focusing on multimodal explanations, autonomous driving, medical diagnosis, and explainable AI. His research group works at the intersection of computer vision, machine learning, and practical applications in healthcare and autonomous systems.

• Finite Model Theory
• Graph Structure Theory
• Computational Complexity
• +۳ مورد دیگر

Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.

• Evolutionary Biology
• Mathematical Biology
• Theoretical Evolutionary Theory
• +۴ مورد دیگر

Alan Grafen is an Honorary Fellow at Jesus College, University of Oxford, and holds Tutorial Fellow roles at St John’s College in Quantitative Biology. His primary affiliation is the University of Oxford’s Faculty of Biology and Medicine. He is a leading evolutionary biologist specializing in mathematical and logical models of evolutionary processes. Research Interests: Grafen’s work focuses on evolutionary theory, particularly the formalization of Darwinism, inclusive fitness, signal evolution, and sexual/kin selection. He leads the Formal Darwinism Project to mathematically formalize natural selection’s role in evolution. His models include the mathematical underpinning of Zahavi’s handicap principle and advancements in reproductive value theory. Key Contributions: Published influential works like Modern Statistics for the Life Sciences (2002) and co-authored analyses of Richard Dawkins’ evolutionary ideas. His recent articles (2018–2024) explore natural vs. sexual selection distinctions, kin recognition stability, and extensions to Fisher’s fundamental theorem. Awards and Grants: Not explicitly listed, but his work has shaped evolutionary theory. His research spans theoretical models in Nature -level journals and interdisciplinary collaborations with statisticians and biologists. Labs/Teams: Directs the Formal Darwinism Project and collaborates with evolutionary theorists globally. His work bridges mathematics and empirical biology, influencing both academic and popular science discourse.

Jennifer Lewisمشاهده پروفایل

استاد

• Bioengineering
• Materials Science
• Soft Matter
• +۷ مورد دیگر

Jennifer Lewis is the Hansjorg Wyss Professor of Biologically Inspired Engineering and Jianming Yu Professor of Arts and Sciences at Harvard University's Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). Her research focuses on bioengineering, materials science, and advanced manufacturing, with emphasis on 3D-printed functional materials, organoids, and soft robotics. She leads the Lewis Research Group, which develops biomimetic technologies for regenerative medicine, energy systems, and robotics. Lewis holds appointments in SEAS, the Department of Chemistry and Chemical Biology, and the Wyss Institute for Biologically Inspired Engineering. Her research areas include applied mathematics, fluid mechanics, soft matter physics, and bioengineering applications such as kidney organoid models, vascularized tissues, and programmable materials. Notable innovations include kidney organoid-on-chip systems for drug testing, 3D-printed liquid crystal elastomers, and bioprinted cardiac tissues. Lewis was awarded the 2025 James Prize in Science and Technology Integration for pioneering interdisciplinary research. Her lab's projects span organ building blocks, immune-response modeling in transplanted tissues, and acoustophoretic printing techniques for high-resolution bioprinting. Collaborations include the NIH Somatic Cell Genome Editing Program and industry partnerships for bioprosthetic valve research. She advises on grants totaling over $20M and mentors a multidisciplinary team of postdocs and graduate students in materials science, biomedical engineering, and mechanical engineering. Lewis' lab facilities include the Pierce Hall lab (Cambridge) and Allston SEAS campus, with state-of-the-art 3D printing systems, microfluidics platforms, and bioreactors for organoid culture. Current projects aim to engineer functional human tissues for therapeutic applications and develop smart materials with programmable mechanical/chemical responses.

• Stochastic Processes
• Financial Mathematics
• Systemic Risk
• +۶ مورد دیگر

Jean-Pierre Fouque is a Professor in the Department of Statistics and Applied Probability (PSTAT) at the University of California, Santa Barbara. His research focuses on stochastic processes, financial mathematics, systemic risk, and reinforcement learning, with a particular emphasis on mean field games and multi-scale stochastic models. He explores applications in portfolio optimization, risk management, and algorithmic finance. His work combines theoretical advancements in stochastic analysis with practical applications in economics and finance. Notable contributions include developing models for systemic risk in financial networks, analyzing reinforcement learning algorithms in mean-field frameworks, and studying stochastic volatility effects in derivatives pricing. Recent research trends include integrating deep learning techniques for systemic risk quantification, advancing multi-scale asymptotic methods for portfolio optimization, and investigating strategic interactions in financial systems using game-theoretic approaches. His publications frequently address topics such as stochastic volatility calibration, optimal investment strategies under uncertainty, and the dynamics of financial markets under stress scenarios. Dr. Fouque has contributed to foundational textbooks and edited volumes on systemic risk and mean field games. His interdisciplinary work bridges probability theory, mathematical finance, and computational methods, impacting both academic research and practical risk management practices.

Jesse D. Jenkinsمشاهده پروفایل

دانشیار

• Energy Systems Modeling
• Deep Decarbonization
• Low-Carbon Energy Technologies
• +۳ مورد دیگر

Jesse D. Jenkins is an Associate Professor of Mechanical and Aerospace Engineering and the Andlinger Center for Energy and the Environment at Princeton University. His joint appointment bridges the School of Engineering and Applied Science and the Andlinger Center, focusing on macro-scale energy systems engineering. He holds a Ph.D. in Engineering Systems from MIT (2018) and completed a postdoctoral fellowship at Harvard Kennedy School. Research Interests: Energy systems modeling, deep decarbonization pathways, low-carbon technologies, and energy policy. He leads the Princeton ZERO Lab, which develops optimization-based models to evaluate clean energy transitions and inform policy. Key Awards: Howard B. Wentz Jr. Junior Faculty Award, Princeton Engineering Teaching Excellence Award, TIME100 Next (2024), and recognition in ENR's 2022 Top 25 Newsmakers for leadership in climate policy analysis. Professional Contributions: Serves on advisory boards for Eavor Technologies, Rondo Energy, and Dig Energy. Co-hosts the podcast Shift Key on energy transition strategies. Labs/Teams: Directs the ZERO Lab and co-leads the REPEAT Project, analyzing U.S. decarbonization pathways and federal policy impacts.

Kaighin McCollمشاهده پروفایل

دانشیار

• Terrestrial Water Cycle
• Climate Science
• Hydrology
• +۵ مورد دیگر

Kaighin McColl is an Associate Professor at Harvard University with joint appointments in the Department of Earth and Planetary Sciences and the School of Engineering and Applied Sciences. His research focuses on the terrestrial water cycle and its interactions with weather and climate over land. PhD from MIT (2017) with NSF Graduate Research Fellowship Bachelor's degrees in environmental engineering and applied mathematics from University of Melbourne (2009) Research interests include: Water limitation effects on evapotranspiration Surface energy balance dynamics Atmospheric boundary layer behavior Convective precipitation mechanisms Applications to storm, drought, and heatwave forecasting Scientific contributions span 15 recent articles analyzing soil moisture dynamics, climate engineering impacts, and land-atmosphere interactions. His work has practical implications for wildfire prediction and climate adaptation strategies. Scientific Awards: Sloan Research Fellowship in Earth System Science NSF CAREER award Kavli Fellow by the National Academy of Sciences McColl advises graduate students including Tara Gallagher, Aidan Matthews, and Mariya Pershyna. His lab emphasizes international collaboration and interdisciplinary research requiring physics, mathematics, and climate science expertise. Teaching responsibilities include two different undergraduate course teaching fellowships during PhD training.

• AgriFood Supply Chain Logistics
• Supply Chain Management
• Logistics Network Design
• +۴ مورد دیگر

Prof. Jack van der Vorst is the Personal Professor of AgriFood Supply Chain Logistics at Wageningen University's Operations Research and Logistics Group. Previously serving as a member of the Board of Directors of Wageningen University & Research (until 2024) and General Director of the Social Sciences Group, he leads over 1000 personnel across three institutes. His advisory roles include the Topteam AgriFood (Science Captain), Top consortium for Knowledge and Innovation (TKI), The Sustainability Consortium (TSC), and Florensis BV's Supervisory Board. With 20+ PhD supervisions and 200+ publications, his research focuses on innovative logistics concepts in AgriFood systems, including supply chain resilience, sustainability, and system innovation. His work integrates modeling frameworks with practical industry applications, emphasizing perishable products, horizontal collaboration, and environmental efficiency. Research interests span AgriFood System Design, Supply Chain Strategy, and Performance Management. His recent studies address challenges like postharvest loss reduction in developing countries, CO2 emission minimization in cold chains, and circular economy implementation in mushroom supply chains. Methodologically, he employs multi-criteria decision models, optimization techniques, and simulation to address complex logistics problems. Publications highlight themes such as horizontal collaboration success factors, vulnerability assessment frameworks, and green supply chain design. His work bridges academic rigor with industry needs, often collaborating with policymakers and international organizations to promote sustainable agri-food systems.

Christiana Mavroyiakoumouمشاهده پروفایل

استادیار

• Fluid Dynamics
• Fluid-Structure Interactions
• Vortex Dynamics
• +۶ مورد دیگر

Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.