جست‌وجوی استاد و پژوهشگر

Diptarka Chakrabortyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +۵ مورد دیگر

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

Jung-Ho Yuمشاهده پروفایل

استادیار

• Bioanalytical Chemistry
• Nanotechnology
• Molecular Imaging

Jung-Ho Yu is Assistant Professor of Chemistry at the University of Waterloo, specializing in nanoscale bioanalytical platforms. His research develops nanotechnology for multiplexed molecular monitoring in biological systems, with applications in cancer imaging and diagnostics. Research focuses on: Design of excretable nanoparticle contrast agents Multiplexed tumor imaging using surface-enhanced Raman spectroscopy Advanced microscopy techniques for deep-tissue visualization Professional associations include the American Chemical Society and World Molecular Imaging Society. Teaches courses in Multi-Component Analysis and Analytical Chemistry.

Aleksandra Slavkovicمشاهده پروفایل

استاد

• Statistical Data Privacy
• Differential Privacy
• Algebraic Statistics
• +۵ مورد دیگر

Aleksandra Slavković is a Professor of Statistics and Associate Dean for Graduate Education at Pennsylvania State University's Eberly College of Science. She holds a PhD in Statistics from Carnegie Mellon University (2004) and has held academic roles since 2004, including appointments at the Institute for Computational and Data Sciences and Penn State College of Medicine. Her research focuses on statistical data privacy, differential privacy, algebraic statistics, and applications in social and health sciences. She has authored over 50 peer-reviewed publications and serves on editorial boards of top journals like Journal of Privacy and Confidentiality and Annals of Applied Statistics . Slavković has received major honors including Fellowships from the Institute of Mathematical Statistics (2021) and American Statistical Association (2018). She leads initiatives to enhance graduate education, including the Science Achievement Graduate Fellows Program, and actively promotes diversity in STEM through her leadership roles. Her recent work emphasizes privacy-preserving techniques for genomic, healthcare, and network data, with contributions to synthetic data generation and secure multiparty computation protocols. Her academic service includes chairing ASA committees and advising at the National Academy of Sciences. She maintains collaborative ties with institutions like Cornell University and UC Berkeley through visiting scholar programs, and her research bridges statistics, computer science, and applied mathematics.

Mitchell L. Neilsenمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Distributed Systems
• Scientific Computing
• +۷ مورد دیگر

Mitchell L. Neilsen is a Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as the graduate program director. He holds the Warren and Gisela Kennedy - Carl and Mary Ice Keystone Research Scholar position and maintains an active research program with multiple ongoing projects. His educational background includes a Ph.D. in Computer Science (1992), M.S. in Computer Science (1989), and M.S. in Mathematics (1987), all from Kansas State University, plus a B.S. in Mathematics Education from the University of Nebraska-Kearney (1982). After beginning his career as an assistant professor at Oklahoma State University, he returned to K-State in 1996. Research Interests: Cyber-Physical Systems: Design, Analysis, Verification of systems integrating computing, networking, and physical processes Distributed Systems: Algorithms, design, and analysis of distributed computing systems Scientific Computing: Computational Fluid Dynamics, Finite Element Analysis, High Performance Computing, and Simulation Application Areas: Agriculture technology, Dam safety analysis, Mobile applications, Natural resources management, and Real-time Embedded Systems His research program shows clear evolution toward agricultural technology applications, particularly high-throughput phenotyping, while maintaining strong foundations in cyber-physical systems and scientific computing. Recent publications indicate increasing integration of machine learning and computer vision techniques into traditional research areas. Research Funding: National Science Foundation U.S. Department of Agriculture Sandia National Laboratories Department of Homeland Security Private industry partners Dr. Neilsen has mentored numerous graduate students through their M.S. and Ph.D. programs, with recent advisees focusing on applications in agricultural technology, dam safety, and embedded systems. His advising approach emphasizes practical applications of theoretical computer science concepts. Current Teaching (Fall 2024): CIS 450 - Computer Architecture and Operations CIS 625 - Concurrent Software Systems CIS 720 - Advanced Operating Systems

Haynes Heatonمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Haynes Heaton is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He holds a Ph.D. in Computational Biology from Cambridge University, an M.D. from Brown University, and a B.S. in Computer Science from Brown University. His interdisciplinary work bridges computational methods with biomedical applications, focusing on genomic technologies for malaria research and single-cell analysis. Research interests include genome sequencing of malaria vectors (e.g., Anopheles mosquitoes), development of bioinformatics tools (e.g., LCSKPOA, Souporcell), and structural variation analysis in human and pathogen genomes. His work has contributed to chromosomal reference genomes for multiple malaria mosquitoes, advancing understanding of vector biology and disease transmission. Publications highlight innovations in genome assembly, single-cell RNA-seq deconvolution, and computational methods for genomic data analysis. While no explicit awards are listed, his research has been featured in Auburn Engineering news for breakthroughs in leukemia diagnostics via software development. He collaborates with medical and computational teams to translate genomic insights into clinical applications. No advising records or grants are explicitly detailed in the provided text.

Kourosh Shoeleمشاهده پروفایل

دانشیار

• Fluid-structure interaction
• Computational mechanics
• Multiphase fluid dynamics
• +۷ مورد دیگر

Kourosh Shoele is an Associate Professor in the Department of Mechanical Engineering at Florida State University (FSU), part of the FAMU-FSU College of Engineering. He previously held roles as an Assistant Research Scientist at Johns Hopkins University and a Post-doctoral Researcher at the University of California, San Diego. His research focuses on fluid-structure interaction, computational mechanics, and bioinspired engineering, with applications in renewable energy, aerospace, and biological systems. Education: Ph.D. (2011) and M.Sc. (2006) in Mechanical Engineering from the University of California, San Diego and Sharif University of Technology, respectively, and B.Sc. (2003) from Shiraz University. Research emphasizes multiphase fluid dynamics, aeroelasticity, energy harvesting, and shock wave dynamics. Notable projects include studies on cryogenic fuel storage, bioinspired robotics, and mask efficacy during respiratory disease transmission. His work has been recognized with awards such as the NSF Career Award (2020) and DARPA Young Faculty Award (2019). Advising and Team: Leads the Computational & Theoretical Multiphysics Laboratory (CTML), mentoring graduate students and postdocs across fluid dynamics, thermal management, and robotics. Collaborates on interdisciplinary projects including mask aerodynamics, flexible tree dynamics, and shock-boundary layer interactions. Labs/Teams: CTML Group, with ongoing collaborations in fluid-structure interaction, renewable energy systems, and biomimetic design. Active in publishing high-impact research in journals like Physics of Fluids and Journal of Fluid Mechanics.

Ilke Canakciمشاهده پروفایل

استادیار

• Cluster Algebras
• Combinatorics
• Algebra
• +۳ مورد دیگر

Dr. Ilke Canakci is an Assistant Professor in the Department of Mathematics at Vrije Universiteit Amsterdam. She specializes in cluster algebras, combinatorics, and representation theory. Her research focuses on algebraic structures like perfect matchings, derived categories, and triangulations. She teaches Discrete Mathematics, Group Theory, and Linear Algebra courses. Key collaborators include researchers from institutions worldwide. Her recent work explores cluster categories for infinity-gons and infinite friezes related to annulus triangulations. Awards and grants are not explicitly mentioned, but her extensive publication record reflects active research in algebraic geometry and combinatorial mathematics. Research Interests: Cluster algebras, categorification of geometric structures, combinatorial patterns, representation theory of algebras, and applications of algebraic methods to geometric problems. Current projects involve extending Caldero-Chapoton maps and analyzing frieze patterns in non-traditional geometries.

• Scientific Computing
• GPU Computing
• Signal Processing
• +۵ مورد دیگر

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

Arnold J M Driessenمشاهده پروفایل

استاد

• Molecular Microbiology
• Biochemistry & Molecular Biology
• Biophysics
• +۷ مورد دیگر

Arnold J M Driessen is a Professor of Molecular Microbiology at the University of Groningen, within the Faculty of Science and Engineering and the Groningen Biomolecular Sciences and Biotechnology Institute (GBB). He leads the Molecular Microbiology research group, focusing on microbial cell biology, membrane proteins, lipid biosynthesis, and extremophiles. His work bridges fundamental research with biotechnological applications, including biofuel production and antibiotic development. He holds prestigious awards, including the Knight of the Order of the Lion of the Netherlands (2025) and membership in the Royal Netherlands Academy of Arts and Sciences. Driessen has authored nearly 500 publications and 10 patents, supervising 90+ PhD students and 40+ postdocs. His research explores protein translocation, archaeal lipid synthesis, and synthetic cellular systems. Driessen collaborates widely, contributing to public-private partnerships on antibiotic resistance and photopharmacology with Ben Feringa. He has driven the GBB’s international development and serves on committees like the TKI Chemistry Council. His mentorship emphasizes innovation and sustainability, fostering independent research while ensuring project success.

• Molecular Biophysics
• Actin Cytoskeleton
• Molecular Motor Proteins
• +۳ مورد دیگر

Enrique M. De La Cruz is the William R. Kenan Jr. Professor of Molecular Biophysics and Biochemistry and Head of Branford College at Yale University. He specializes in the study of actin cytoskeleton dynamics, molecular motor proteins, and nucleotide signaling enzymes. His research integrates biophysical, biochemical, and structural approaches to understand cellular mechanisms. Dr. De La Cruz holds a Ph.D. from Johns Hopkins University and completed postdoctoral training at the University of Pennsylvania. He has been recognized with prestigious awards, including Fellowships from AAAS and ASBMB, and is an advocate for minority participation in STEM. His lab collaborates with researchers globally and employs advanced techniques like cryo-electron microscopy and computational modeling. Education: Bachelor's in Biology (Rutgers University) Ph.D. in Biochemistry, Cell & Molecular Biology (Johns Hopkins University) Research Interests: Actin filament mechanics and cation effects Molecular motor protein interactions Structural biology of cytoskeletal proteins Awards: Fellow, AAAS (2022) ASBMB Inaugural Fellow (2021) Cell Press's 100 Hispanic/Latinx Scientists (2020) Grants & Advising: NSF CAREER Award (2006) Extensive NIH-funded research Labs & Collaborations: De La Cruz Lab at Yale Cross-disciplinary partnerships with institutions like Institut Curie and ESPCI Paris Tech

Kathy Kotiadisمشاهده پروفایل

استاد

• Healthcare simulation
• Discrete-event simulation
• Hybrid simulation
• +۳ مورد دیگر

Professor Kathy Kotiadis is Professor of Operational Research and Co-Director of the Centre for Advanced Diagnostic Development and Application (CADDA) at Kent Business School. Co-developed PartiSim framework for participatory simulation. Research expertise in simulation methodology (discrete-event/hybrid), conceptual modeling, and stakeholder engagement approaches applied to healthcare, transport, and energy sectors. Recipient of K.D. Tocher Medal (2007-08) and Daphne Jackson Fellowship (2014). Secured funding from EPSRC, Innovate UK, and Research England. Serves as Co-Editor of Health Systems and Editorial Board member for Journal of Simulation. Supervised 7 PhD students to completion in simulation methodologies and healthcare applications. Current projects include HS1 Ltd (2024) and North Kent Industrial Cluster (2025).

Arthur Limمشاهده پروفایل

استاد

• Combinatorial Matrix Analysis
• Lie Algebras
• Mathematics Pedagogy
• +۳ مورد دیگر

Arthur Lim is a Professor of the Practice Education in the Mathematics Department at the University of Notre Dame. His primary affiliations include teaching roles in calculus and dynamical systems courses, alongside research in algebraic structures and pedagogical innovation. Ph.D., University of Utah (2001) M.S., National University of Singapore (1994) His research explores combinatorial matrix analysis applied to Lie algebras and pedagogical strategies emphasizing historical context and critical thinking in mathematics education. Recent work bridges operations research with market dynamics and telecommunications policy. Selected publications span 1999–2019, addressing topics from orbital integral approximations to net neutrality debates. Teaching innovations focus on integrating historical narratives with technical rigor in undergraduate instruction.

Sharmistha Guhaمشاهده پروفایل

استادیار

• Bayesian Methods
• High-Dimensional Regression
• Causal Inference
• +۴ مورد دیگر

Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.

Priyamvada Natarajanمشاهده پروفایل

استاد

• Cosmology
• Gravitational Lensing
• Black Hole Physics
• +۴ مورد دیگر

Priyamvada Natarajan is the Joseph S. and Sophia S. Fruton Professor of Astronomy and Professor of Physics at Yale University, serving as Chair of the Astronomy Department and Chair of the Women Faculty Forum. Her research focuses on cosmology, gravitational lensing, and black hole physics. She explores topics like dark matter distribution, supermassive black hole formation, and the interplay between galaxy evolution and AGN feedback. Natarajan has pioneered methods for mapping dark matter in galaxy clusters using gravitational lensing and has contributed to understanding cosmic structure formation through large-scale simulations. Education: Ph.D. in Astrophysics from Cambridge University (1998). Research interests include the nature of dark energy, galaxy formation, and the history and philosophy of science. She leads projects like DAVOS (Dwarf AGN variability studies) and QUOTAS, a platform for discovering supermassive black holes. Her recent work includes detecting overmassive black holes at high redshifts, analyzing gravitational-wave signals with NANOGrav, and utilizing JWST data to probe the cosmic dawn. Notable honors include the 2022 Liberty Science Center ‘Genius Award’. Natarajan’s collaborations span multi-messenger astronomy, leveraging gravitational lenses as cosmic telescopes. She advocates for gender equity in academia and has advised numerous observational and theoretical studies, though specific student names are not listed. Key projects involve the BUFFALO survey for cluster lens modeling and the ngEHT (next-generation Event Horizon Telescope) to study black hole environments. Her lab integrates cosmological simulations, observational data, and theoretical frameworks to address fundamental questions in astrophysics.

• Algebra
• Number Theory
• Coding Theory
• +۳ مورد دیگر

Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.