جست‌وجوی استاد و پژوهشگر

Benoît SAGOTمشاهده پروفایل

استاد

• Aerosol Science
• Thermophoresis
• Particle Separation
• +۵ مورد دیگر

Benoît SAGOT is a Professor at ESTACA (Higher School of Aeronautical and Automotive Engineering Construction), Campus Paris Saclay, where he serves as a Teacher-researcher in the School of Mobility Systems and Complex Engineering within ESTACA'Lab. His work focuses on aerosol science, thermophoresis, and particle separation technologies, with applications in automotive engineering and pollution control systems. His educational background includes: Accreditation to Supervise Research (HDR) from University of Paris-Est (2020) PhD in Process Engineering and Sustainable Development from University of Technology of Compiègne (2010) DEA in Fluid Mechanics and Energy from INPL (1993-1994) Engineering degree from ENSEM (National School of Electricity and Mechanics, Nancy) (1991-1994) Dr. SAGOT's research centers on thermophoresis, aerosol science, and particle separation technologies. His work investigates the behavior of particles in gas flows, with particular focus on thermophoretic transfer mechanisms, cyclone separator efficiency, and applications for reducing particulate emissions from internal combustion engines. His research has significant implications for automotive emissions control and air quality improvement. His publication record shows a consistent focus on thermophoresis and particle separation, with recent work expanding into respiratory droplet characterization. The research demonstrates a progression from fundamental thermophoretic principles to practical applications in automotive engineering, particularly in blow-by gas cleaning systems and engine emissions control. Dr. SAGOT holds several professional leadership positions: Board member of ASFERA (French Aerosol Research Association) since 2017 Member of GDR "Suies" (Soot Research Group) since 2014 Member of ISO/TC 22/SC 34/WG 11 for standardizing test protocols for crankcase gas filters Session chair at European Fluid-Particle Separation Congress (2018) Session chair at French Aerosol Congress (2019) As a research supervisor with HDR accreditation, Dr. SAGOT leads projects including CAPNAV and EMINAV (ADEME CORTEA and AQACIA type projects). His work bridges academic research and industrial applications in automotive engineering, with strong connections to industry standards through his ISO committee work. Dr. SAGOT is actively involved with ESTACA'Lab, where he conducts research on thermophoretic phenomena and particle separation technologies. His laboratory work focuses on experimental validation of theoretical models for particle deposition and the development of instrumentation for aerosol characterization, particularly for automotive applications.

Philippe Ciblatمشاهده پروفایل

استاد

• Statistical Signal Processing
• Digital Communications
• Resource Allocation
• +۶ مورد دیگر

Philippe Ciblat is a Full Professor at Institut Polytechnique de Paris , affiliated with the Communications and Electronics Department (Comelec) and the Information Processing and Communication Laboratory (LTCI) . He obtained his Engineering Degree from Télécom Paris (1996), DEA in Automatic Control from Université Paris-Saclay (1996), PhD from Université Gustave Eiffel (2000), and HDR diploma (2007). After a postdoctoral fellowship at Université de Louvain (2001), he joined Télécom Paris as Associate Professor in 2001 and was promoted to Full Professor in 2011. Research Activities: His research spans three core areas: Statistical/Digital Signal Processing (blind equalization, frequency estimation, distributed estimation) Signal Processing for Communications (OFDM synchronization, UWB localization, cooperative communications) Resource Allocation (HARQ optimization, power allocation, distributed networks) Scientific Leadership: He has served as Associate Editor for IEEE Transactions on Signal and Information Processing over Networks (2018-2022), Senior Area Editor for IEEE Transactions on Signal Processing (2011-2012), and held editorial roles in IEEE Communications Letters and Transactions on Signal Processing. He chairs technical committees at EUSIPCO, SPAWC, and IEEE conferences. PhD Supervision: He has mentored 23 PhD students, including Current: Nils Reynaud (deep reinforcement learning for wind farms), Abdelghani Ghanem (multi-agent driving behavior), Sylvain Nerondat (deep reinforcement learning for ad hoc networks) Former: Yue Bi (distributed computing), Hakim Hafidi (robust graph ML), Arthur Louchart (nonlinear satellite systems), Vincent Corlay (lattice decoding), Alix May (optical network monitoring) Grants & Collaborations: Currently leads projects with TotalEnergies (2025-2028), PEPR Twinfarms (2025-2028), and ANR SUCCEN (2025-2027). Formerly secured ANR, Marie-Curie, and CIFRE grants with Thales, Nokia, Mitsubishi, and Huawei. Labs & Teams: Affiliated with LTCI (Signal, Statistics and Learning team) and has conducted sabbaticals at Technische Universität Berlin, Rutgers University, and Universidad Nacional de Colombia. Actively participates in international conferences as TPC member and reviewer.

• Biocatalysis
• Enzyme Technology
• Analytical Chemistry
• +۲ مورد دیگر

Carl Grey is a Senior Lecturer and Office Director at the Division of Biotechnology and Applied Microbiology within Lund University's Faculty of Engineering (LTH). He is an active member of the LTH Profile Area: Food and Bio, contributing to research at the intersection of biotechnology and food science. His academic profile includes substantial research output with expertise validated by ORCID (0000-0001-5701-7303) and ongoing collaborations. His research focuses on bioprocess technology and enzyme applications, with particular emphasis on: Biocatalytic systems development and enzyme engineering Analytical methods for complex biological samples Sustainable food production technologies Microbial biotechnology and biomolecular analysis Bioremediation and environmental biotechnology applications Analysis of Dr. Grey's recent publications reveals a strong interdisciplinary approach combining biotechnology with computational methods, food science, and environmental applications. His work consistently addresses fundamental biochemical processes while developing practical applications in sustainable bio-production. As a supervisor, Dr. Grey mentors PhD students in projects related to biosynthesis pathways including: Bio-synthesis of capsaicinoids in yeast systems Whole-cell biocatalysis for chiral amine production He also leads research projects such as the FORCE Center for Food Resilience and Competitiveness (2024-2028), focusing on sustainable food systems. Dr. Grey operates within Lund University's biotechnology research infrastructure, collaborating with specialized labs including the Microbial Flow Cytometry facility. His team works on developing innovative bioprocessing techniques and analytical methodologies.

• Graph Algorithms
• Network Analysis
• Scientific Workflow Scheduling
• +۳ مورد دیگر

Henning Meyerhenke is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He serves as Deputy Director of Teaching and Studies, focusing on graph algorithms , network analysis , and scientific workflow scheduling . His research spans complex systems modeling and parallel computing. Ranks: Professor Institution: Humboldt University of Berlin Research Interests include energy-efficient computing, heterogeneous systems, and scalable graph algorithms. Recent work explores network sparsification , workflow mapping , and graph robustness under edge deletions. His group also investigates climate science applications via climate networks . Publication Trends reflect advancements in parallel algorithms , distributed systems , and adaptive scheduling for scientific workflows. Key subfields include memory-aware computing , harmonic centrality , and edge sampling techniques. Student Advising : Michael Piechotta (PhD candidate, defended September 15, 2025)

Kurt Anstreicherمشاهده پروفایل

استاد

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +۳ مورد دیگر

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

Virgilio A. Centenoمشاهده پروفایل

استاد

• Power Systems
• Phasor Measurement Units (PMU)
• Grid Cybersecurity
• +۴ مورد دیگر

Virgilio A. Centeno is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on power systems, phasor measurement units (PMUs), grid cybersecurity, and smart grid technologies. He holds a Ph.D. from Virginia Tech (1995) and has contributed extensively to advancing power system resilience, wide-area monitoring, and renewable energy integration. His work emphasizes practical applications such as GPS-spoofing detection, cascading failure prevention, and adaptive control strategies for modern grids. Research interests include synchronized sampling, power systems as critical infrastructure, microcontroller applications, and power systems education. His recent publications highlight innovative methods for PMU data recovery, event classification, and adaptive voltage security in renewable-rich grids. Collaborations involve developing testbeds for cyber-physical systems and creating realistic synthetic power networks to model interdependencies. No awards or honors are explicitly listed in the provided text. His teaching interests align with his research areas, and he remains affiliated with Virginia Tech’s research groups. Ongoing work includes proactive islanding strategies, Gaussian process-based dynamic inference, and optimal distribution network partitioning for resilience enhancement.

Mario Marchandمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Mario Marchand is a retired Professor in the Department of Computer Science and Software Engineering at Université Laval, Canada. His research focuses on machine learning theory, explainable AI, and computational biology. He has contributed to foundational work in PAC-Bayesian analysis, generalization bounds, and kernel methods. Marchand is affiliated with the Computer Vision and Systems Laboratory and the GRAAL research group. Though no longer supervising graduate students, his recent work addresses challenges in algorithmic stability, meta-learning, and feature attribution consensus. His publications span topics from neural network optimization to applications in bioinformatics and drug discovery. Research Interests: Machine Learning Theory, Explainable AI, Bioinformatics, Kernel Methods, Domain Adaptation, and Statistical Learning. Key Contributions: PAC-Bayesian risk bounds, decision tree analysis, and algorithms for mixture models. His work bridges theoretical guarantees and practical applications in healthcare and computational biology. Professional Activities: Taught courses including IFT-7002 (Foundations of Machine Learning) and contributed to international conferences. His methods are used in biomarker discovery and peptide design for drug development. Recent trends in his articles emphasize explainable AI (XAI) and resolving feature attribution disagreements, alongside foundational studies on generalization in meta-learning and multi-source domain adaptation. His collaborative projects include predicting molecular properties and advancing neural network stability.

Diane Leeمشاهده پروفایل

پژوهشگر ارشد

• Epithelial Cell Biology
• Equine Asthma
• Bovine Tuberculosis (BTB)
• +۳ مورد دیگر

Diane Lee is a Research Fellow B in Pathology and Infectious Diseases at the University of Surrey's School of Veterinary Medicine. She holds a PhD, MSc, and BSc (Hons) and specializes in ex vivo/in vitro cell biology with a focus on drug discovery and pre-clinical research. Her roles include managing the Impact Acceleration Account and KE (MRC & PhD/ECR) and serving on the NASPA Sub-committee. Education: PhD in Pharmacy and Biomolecular Sciences from the University of Brighton (2015), MSc and BSc from unspecified institutions. Research Interests : Equine Asthma: Developing nasal brush models to study mechanisms of asthma and Notch signaling in goblet cell hyperplasia. Bovine Tuberculosis (BTB): Created a bovine alveolus model using co-culture of alveolar and endothelial cells to study host-pathogen interactions, reducing animal use. 3Rs Principles: Championing replacement/refinement/reduction of animal experiments through in vitro models. Publications : Over 15 peer-reviewed articles, including work on bovine lung-on-chip models, equine asthma nasal models, and HSP70 inhibitors. Collaborations : Works with NIBSC, Pirbright Institute, and APHA. Active in developing organ-on-chip technologies and validating cell culture models for respiratory diseases. Labs/Teams : Leads projects on equine respiratory models and bovine tuberculosis at the University of Surrey, with a focus on translational veterinary medicine.

Dana Randallمشاهده پروفایل

استاد

• Theoretical Computer Science
• Randomized Algorithms
• Statistical Physics
• +۴ مورد دیگر

Dana Randall is the ADVANCE Professor of Computing and an Adjunct Professor of Mathematics at Georgia Institute of Technology. She serves as the Associate Dean for Access and Advancement in the College of Computing, Co-Executive Director of the Institute for Data Engineering and Science (IDEaS), and previously directed the Algorithms and Randomness Center (ARC). Her research bridges theoretical computer science, discrete mathematics, and statistical physics, focusing on randomized algorithms and stochastic processes. Education: A.B. in Mathematics, Harvard University (1988) Ph.D. in Computer Science, University of California, Berkeley (1994) Research Interests: Randomized algorithms and Markov chain Monte Carlo methods Computational problems in statistical physics Combinatorics, distributed algorithms, and collective computation Awards & Honors: Fellow of the American Mathematical Society (AMS) National Associate of the National Academies Former Sloan Fellow and NSF Career Award recipient Grants & Leadership: Principal Investigator on a $6.25M ARO MURI grant on collective emergent behavior (2019–2024) Co-founder and Co-Executive Director of IDEaS (2016–2019) Directed the Algorithms and Randomness Center (2014–2016) Labs & Teams: Active in the Algorithms and Randomness Center (ARC) and Institute for Data Engineering and Science (IDEaS), focusing on interdisciplinary research in algorithms, data science, and computational theory.

• Computational Structural Biology
• Statistical Machine Learning
• DNA Shape Prediction
• +۴ مورد دیگر

Remo Rohs is a tenured Professor of Quantitative and Computational Biology, Chemistry, Physics and Astronomy, Computer Science, and Biomedical Engineering at the University of Southern California Dornsife College. He leads the Rohs Lab, focusing on integrating genomics and structural biology to uncover molecular mechanisms in gene regulation. Chair, Department of Quantitative and Computational Biology (2021–2024) Section Head, Quantitative and Computational Biology (2018–2020) Full Professor with Tenure at USC (2016–present) Research Interests center on: Computational structural biology DNA shape prediction Protein-DNA recognition mechanisms Epigenetic effects on binding specificity RNA structure modeling High-throughput genomic analysis His work combines artificial intelligence , molecular simulations , and experimental validation to study transcription factors and chromatin structure. Recent publications highlight deep learning methods for DNA/RNA structure prediction and database development for molecular interactions. Scientific Awards include: Sloan Research Fellowship (2013–2017) Multiple RECOMB/ISCB Top-Paper Awards (2012–2017) American Chemical Society OpenEye Outstanding Junior Faculty Award (2015–2016) USC Mentoring Award (2015–2016) He has supervised 13 PhD graduates and published 92+ peer-reviewed papers . His lab develops tools like DNAproDB and TFBSshape for structural analysis of nucleic acid complexes.

Ferran Feixasمشاهده پروفایل

استاد پژوهشی

• Computational Chemistry
• Catalysis
• Molecular Dynamics Simulations
• +۳ مورد دیگر

Dr. Ferran Feixas is a Research Professor at the University of Girona (UdG), leading independent research as Principal Investigator at the Institut de Química Computacional i Catàlisi (IQCC). He holds a PhD in Chemistry from UdG and completed postdoctoral work at UC San Diego under J. Andrew McCammon. His research focuses on computational chemistry, catalysis, and molecular dynamics simulations, with applications in enzyme design and supramolecular chemistry. Education: PhD in Chemistry, University of Girona (2008-2013) Postdoctoral Fellowship at University of California, San Diego (2013-2015) Research Interests: His work integrates computational methods to study enzyme mechanisms, catalytic processes, and nanomaterials. Key areas include: Molecular dynamics simulations of enzyme dynamics (e.g., imidazole glycerol phosphate synthase) Design of supramolecular nanocapsules for selective functionalization Development of novel drug candidates targeting Alzheimer’s and pain management Awards & Funding: Marie Curie Individual Fellowship (MetAccembly, 2015-2018) Retos Investigación Project (RTI2018-101032-J-I00, 2019-2022) Advising & Grants: Feixas leads the ACCEMBLY project (Accelerating Biomolecular Recognition) and collaborates internationally. His group explores enzyme design, catalytic efficiency, and sustainable materials. Labs & Teams: Based at IQCC within the Faculty of Sciences, his team focuses on experimental-computational synergies in predictive chemistry for catalysis and drug discovery.

• Quantum Computing
• Bayesian Statistics
• Bioinformatics
• +۳ مورد دیگر

Kevin Chern is a researcher in the Department of Statistics at the University of British Columbia’s Faculty of Science. His work bridges quantum computing, Bayesian statistics, and computational biology. Key research interests include quantum simulation optimization, probabilistic programming frameworks (e.g., Blang), and bioinformatics applications such as spatial proteomics and cancer phylogenetics. His recent publications (2019–2025) focus on advancing quantum annealing calibration, Bayesian clustering methods for spatial data, and scalable inference of phylogenetic trees from single-cell genomic data. He has contributed to foundational work in quantum supremacy and probabilistic modeling of combinatorial spaces. While no explicit awards or grants are listed here, his involvement with the Applied Statistics and Data Science Group (ASDa) and participation in seminar series suggest active engagement with interdisciplinary research and education initiatives.

Victoria Savaleiمشاهده پروفایل

استاد

• Quantitative Methods
• Structural Equation Modeling
• Missing Data Analysis
• +۲ مورد دیگر

Dr. Victoria Savalei is a Professor and Area Coordinator in the Department of Psychology at the University of British Columbia's Faculty of Arts. Her research focuses on quantitative psychology, particularly structural equation modeling (SEM), which integrates regression, path analysis, and factor analysis to test complex psychological theories. She develops methods for analyzing imperfect data including non-normally distributed data, missing values, categorical responses, and small samples. Her work critically evaluates SEM practices and addresses statistical challenges related to psychology's replicability crisis. Dr. Savalei holds a PhD from UCLA (2007). Her current work advances the two-stage (TS) method for handling incomplete data. Research interests include improving fit indices in SEM with categorical data, computational approaches for standard errors with incomplete data, and robust statistical methods for small samples.

• Bayesian Methodology
• Statistical Machine Learning
• Variational Bayes
• +۳ مورد دیگر

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

Amy N. Myersمشاهده پروفایل

مدرس ارشد

• Combinatorics
• Probability Theory
• Enumerative Combinatorics
• +۳ مورد دیگر

Amy N. Myers serves as Senior Lecturer in Mathematics and Math Program Coordinator at Bryn Mawr College's Department of Mathematics, where she teaches core courses including Calculus I-II, Multivariable Calculus, Probability Theory, and specialized graduate seminars in Enumerative Combinatorics and History of Mathematics. Her academic credentials include a Ph.D. in Mathematics from Dartmouth College (1999), establishing her foundational expertise in discrete mathematical structures. Dr. Myers' research demonstrates exceptional depth in combinatorial mathematics , particularly in pattern avoidance phenomena and probabilistic enumeration . Her work on interval orders bridges order theory with topological combinatorics, while her contributions to the Coupon Collector's Problem reveal novel asymptotic behaviors with applications in computational biology. The SIGEST recognition of her probability research by SIAM Review underscores its interdisciplinary significance across theoretical computer science and stochastic modeling. Analysis of her publication trajectory shows consistent innovation in multiset permutation statistics and forbidden substring constraints, with recent focus on shellability properties in ordered structures. Her combinatorial frameworks increasingly incorporate computational topology, reflecting evolving methodological sophistication. No scientific awards are documented in the available institutional records. As Math Program Coordinator, Dr. Myers oversees curriculum development and student advising for mathematics majors, though specific mentorship details remain undisclosed. Grant funding information is not publicly accessible through current university channels. Research infrastructure details including laboratories or collaborative teams are not referenced in her official academic profile.