جست‌وجوی استاد و پژوهشگر

Pedro Paredesمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Spectral Graph Theory
• Pseudorandomness
• +۳ مورد دیگر

Pedro Paredes is a Lecturer in the Department of Computer Science at Princeton University . He completed his PhD in 2022 at Carnegie Mellon University under Ryan O'Donnell , following undergraduate and master's degrees from the University of Porto where he was advised by Pedro Ribeiro . He received the SEAS Excellence in Teaching Award in 2025. Education PhD, Carnegie Mellon University (2022) MSc & BSc, University of Porto (2017) Research Interests Specializes in Theoretical Computer Science , particularly Spectral Graph Theory , Pseudorandomness , Coding Theory , and Quantum Information Theory Contributes to Subgraph Analysis and Network Science through collaborative works Publication Trends Focuses on Expander Graphs and their applications in Quantum Computing , with recent works on Quantum LDPC Codes and Approximate Unitary Designs Develops algorithms for Spectral Graph Operations and Graph Expansion with mathematical rigor Engages in Interdisciplinary Research connecting Time Series Analysis with network theory Awards SEAS Excellence in Teaching Award (2025) Teaching & Outreach Teaches Algorithms and Data Structures at Princeton Organizes Competitive Programming Club at Princeton Active in Computer Science Education and Math Olympiads

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +۲ مورد دیگر

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

Gheorghe Craciunمشاهده پروفایل

استاد

• Mathematical Biology
• Chemical Reaction Networks
• Dynamical Systems
• +۴ مورد دیگر

Gheorghe Craciun is a Professor in the Department of Mathematics and Department of Biomolecular Chemistry at the University of Wisconsin-Madison. His research focuses on mathematical and computational methods in biology and medicine, particularly chemical reaction networks, dynamical systems, and their applications to biochemical processes. He has organized and participated in workshops such as the Madison Workshop on Mathematics of Reaction Networks and the AIM-style Workshop on Mathematics of Reaction Networks, fostering collaborations and advancing the field. His work bridges theoretical mathematics with practical biological modeling, including studies on neurofilament transport, gene regulatory networks, and acoustic wave turbulence. Craciun's research spans diverse areas such as mass-action kinetics, graph-theoretic stability analysis, and algebraic approaches to reaction networks. He has published extensively in journals like SIAM Journal on Applied Mathematics, Bulletin of Mathematical Biology, and Journal of Mathematical Biology, often collaborating with interdisciplinary researchers. His teaching includes courses like Math 703 and involvement in the Madison Math Circle and Putnam Club.

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

Saibal Mukhopadhyayمشاهده پروفایل

استاد

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +۵ مورد دیگر

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

Yuè Liمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Population Genetics
• +۴ مورد دیگر

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

Peter Braunsteinsمشاهده پروفایل

مدرس

• Probability Theory
• Stochastic Processes
• Branching Processes
• +۳ مورد دیگر

Dr Peter Braunsteins serves as a Lecturer in Statistics within the School of Mathematics and Statistics at the University of New South Wales (UNSW Sydney), operating under the Faculty of Science. His academic appointment focuses on advancing theoretical and applied probability through rigorous mathematical research. He completed his PhD at the University of Melbourne in 2018, followed by postdoctoral positions at the University of Amsterdam and King Abdullah University of Science and Technology (KAUST) before joining UNSW. His scholarly background bridges European and Middle Eastern research institutions with Australian academia. Braunsteins' research centers on stochastic processes , with pioneering contributions in three interconnected domains: branching processes (modeling population dynamics and extinction events), random graphs (analyzing network evolution and structural properties), and spatial extremes (studying rare events in geographical contexts). His work combines deep theoretical insights with applications in epidemiology, insurance risk modeling, and network science, often employing large deviation principles and parameter estimation techniques for complex systems. Analysis of his 15 most recent publications reveals a sustained focus on branching process theory, particularly population-size-dependent models and extinction probabilities, while simultaneously developing novel frameworks for dynamic random graphs. His research demonstrates increasing interdisciplinary reach, connecting probability theory with actuarial science through adaptations of the Cramér-Lundberg model and with network science through graphon analysis. No scientific awards or major honors are documented in the available materials. His collaborative network includes prominent researchers such as Sophie Hautphenne, Frank den Hollander, and Michel Mandjes across multiple continents. Professional activities include manuscript review for leading probability journals and participation in academic seminars at UNSW, though specific advising roles or grant funding details remain undisclosed in the source material. His office is located in Room 2056 of the Anita B. Lawrence Centre at UNSW Sydney.

Thomas Britzمشاهده پروفایل

مدرس ارشد

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +۵ مورد دیگر

Dr. Thomas Britz is a Senior Lecturer at the School of Mathematics and Statistics, UNSW Sydney . He is a member of the Combinatorics Research Group and serves as Chief Editor of Parabola and Managing Editor for the Australasian Journal of Combinatorics . PhD in Mathematics (Aarhus University, 2003) Research: Discrete Mathematics, Combinatorics, Graph Theory, Matroid Theory, Coding Theory, Design Theory, and applications to renewable energy, bioinformatics, criminology, and more His research has been supported by grants including an ARC Discovery Grant and international fellowships. He has supervised numerous PhD, Master’s, and Honours students across diverse projects. Recent publications focus on harmonic Tutte polynomials, group divisible designs, and closeness centrality in graphs, reflecting his expertise in foundational and applied combinatorics. Awards : Vice-Chancellor’s Award for Student Wellbeing (2021), Science Dean's Award for Excellence in Student Wellbeing (2021), Vice-Chancellor's Award for Contributions to Student Learning (2015) He contributes to outreach activities , editorial service, and university committees, including the Education Excellence Committee and Sustainability Committee at UNSW.

Andrew Elliottمشاهده پروفایل

مدرس ارشد

• Statistics
• Data Analytics
• Modeling in Space and Time
• +۸ مورد دیگر

Dr Andrew Elliott is a Senior Lecturer in the School of Statistics at the University of Glasgow. His research bridges Statistics , Computational Statistics , and Machine Learning & AI , with applications in Social & Urban Studies and Imaging, Image Processing & Image Analysis . Education: Not explicitly detailed in the text. Dr Elliott's work focuses on modeling in space and time , synthetic data generation , and network analysis . His recent publications explore fairness constraints in AI , agent swarms , and temporal network modeling , reflecting interdisciplinary applications in urban analytics and environmental science. His publications from 2014–2024 span network science , machine learning , image analysis , and geospatial studies . Key trends include privacy auditing , counterfactual explanations , and deep learning for network time series . Dr Elliott supervises doctoral students and has advised Zhengduo Zhao and Weiyue Zheng . He actively contributes to research groups in Statistics & Data Analytics and Machine Learning . Notable collaborations include work with Mihai Cucuringu and Gesine Reinert .

Soumya Beraمشاهده پروفایل

دانشیار

• Theoretical Condensed Matter Physics
• Quantum Simulation
• Topological Phases
• +۴ مورد دیگر

Soumya Bera is an Associate Professor in the Department of Physics at the Indian Institute of Technology Bombay (IIT Bombay). His research focuses on theoretical condensed matter physics, integrating computational approaches to study quantum systems. Key research interests include quantum simulation of novel phases of matter, topological phases, many-body localization, and non-equilibrium dynamics. Current research topics emphasize quantum phase transitions, quantum criticality, and the interplay of topology, disorder, and interactions. His work often explores these themes through quantum information theory and Floquet systems. Notable contributions include studies on disorder-induced delocalization, Chern-Hopf insulators, and Floquet thermalization. Publications span topics like quantum Hall criticality, graphene disorder effects, and many-body localization dynamics. His work frequently appears in high-impact journals like Phys. Rev. B and SciPost Phys., with a focus on disordered systems and quantum critical phenomena. While no explicit awards or grants are listed here, his prolific publication record underscores sustained contributions to condensed matter theory. His research group actively investigates quantum matter under non-equilibrium conditions and topological material properties.

Daniel Romeroمشاهده پروفایل

استاد

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +۵ مورد دیگر

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Sheldon Howard Jacobsonمشاهده پروفایل

استاد

• Operations Research
• Aviation Security
• Optimization
• +۳ مورد دیگر

Sheldon Howard Jacobson is a Founder Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds cross-appointments in Electrical and Computer Engineering, Industrial and Enterprise Systems Engineering, Biomedical and Translational Sciences, Mathematics, and Statistics. His research focuses on operations research, optimization, and security systems, with notable contributions to aviation security, pediatric vaccines, and homeland security. Jacobson has been recognized with prestigious awards including the AAAS Fellowship (2019), George E. Kimball Medal (2020), and Guggenheim Fellowship (2003). His work bridges theoretical and applied domains, addressing real-world challenges in public health, policy, and technology. He has contributed to media discussions on topics like pandemic impacts and coronavirus safety measures. In research, Jacobson emphasizes interdisciplinary approaches, combining mathematical modeling with policy analysis. His recent work includes optimizing political redistricting and analyzing viral transmission risks in aviation. Collaborations span multiple disciplines, reflecting his role as a bridge between academia and practical problem-solving.

Jayadev S. Athreyaمشاهده پروفایل

دانشیار

• Dynamical Systems
• Geometric Topology
• Number Theory
• +۴ مورد دیگر

Jayadev S. Athreya is an Associate Professor in the Department of Mathematics at the University of Washington, where he also serves as Director of the Washington Experimental Mathematics Lab. His primary affiliation is with the College of Liberal Arts & Sciences. He holds dual roles as a Professor of Mathematics and Professor of the Comparative History of Ideas. Athreya co-directs the Pacific Institute for the Mathematical Sciences and is a founder of the Washington Experimental Mathematics Lab, emphasizing interdisciplinary experimental mathematics. His research interests span dynamical systems, geometric topology, number theory, and algebraic geometry. He focuses on translation surfaces, billiards dynamics, geometric flows, and probabilistic methods in geometry. His work often intersects with problems in ergodic theory, Teichmüller theory, and moduli spaces. Athreya has an extensive publication record, with recent work exploring billiard complexity in regular polygons, linear flows on translation prisms, and spectral properties of marked tori. His papers frequently address counting problems, asymptotic distribution of geometric objects, and connections between number theory and dynamical systems. He has taught advanced courses such as Quasiconformal Maps and Teichmüller Theory, Complex Analysis, and Elementary Number Theory. His pedagogical approach emphasizes hands-on exploration through the Washington Experimental Mathematics Lab, fostering collaborative research projects with undergraduates. Athreya is committed to accessible mathematics education, reflecting his alignment with Federico Ardila-Mantilla's axioms on equity and inclusivity in mathematical experiences. His work bridges theoretical research with educational outreach, advocating for mathematics as a universal, adaptable tool.

Steven Strogatzمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Complex Systems
• Applied Mathematics
• +۳ مورد دیگر

Steven Strogatz is the Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics, and a Stephen H. Weiss Presidential Fellow at Cornell University's College of Arts and Sciences, Department of Mathematics. He holds a Ph.D. from Harvard University (1986). His research focuses on nonlinear dynamics, complex systems, and applied mathematics, with notable contributions to synchronization in networks, mathematical biology, and science communication. Strogatz is renowned for his books, including Infinite Powers and The Joy of x , and his media work, such as the New York Times column and Quanta Magazine's The Joy of Why podcast. He has received prestigious awards like the National Award for Science Communication and the George Pólya Prize for Mathematical Exposition. His academic roles include teaching courses on nonlinear dynamics and supervising research. Strogatz's work bridges academia and the public, emphasizing the beauty and relevance of mathematics. His research extends to modeling social networks, epidemic dynamics, and urban mobility systems, with applications in physics, biology, and technology.

Shuangning Liمشاهده پروفایل

استادیار

• Econometrics
• Statistics
• Machine Learning
• +۵ مورد دیگر

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.