جست‌وجوی استاد و پژوهشگر

Phong Nguyenمشاهده پروفایل

استاد پژوهشی

• Cryptography
• Cryptanalysis
• Post-quantum Cryptography
• +۵ مورد دیگر

Phong Nguyen is a Research Professor at Inria (Directeur de recherche) and a part-time professor at the Computer Science Department (DI ENS) of École Normale Supérieure (ENS), PSL University in Paris. He leads the ENS Crypto Team (Inria Equipe Projet Cascade) and serves as the principal investigator for the ERC Advanced Grant PARQ (2020) focused on lattices in parallel and quantum computing. He holds a PhD (1999) and Habilitation (2007) from ENS-Lyon, with an agrégation de mathématiques (1997). His research integrates cryptography, algorithmic number theory, and lattice-based computations, emphasizing: Cryptanalysis : Deconstructing cryptographic protocols, especially lattice-based systems Post-quantum cryptography : Developing quantum-resistant solutions Lattice algorithms : Optimization of reduction, enumeration, and sieving techniques Real-world applications : Bridging theoretical constructs with practical security implementations His publications (spanning Eurocrypt, Asiacrypt, and Journal of Cryptology) demonstrate deep expertise in lattice cryptography, with recurring themes in algorithm efficiency, cryptanalysis of NTRU/GGH systems, and theoretical advancements in lattice reduction. Recent work (2024) continues this trajectory with improved BKZ analysis and hypercubic lattice optimizations. Awards include : ERC Advanced Grant (2020) for PARQ project Best Paper Award at EUROCRYPT 2006 Cor Baayen Award (2001) He advises PhD students (e.g., Henry Bambury, Leo Ducas) and interns from institutions like École Polytechnique and ENS. He directs the ENS Crypto Team and previously held leadership roles as: French Director of the Japanese-French Laboratory for Informatics (2015-2019) European Director of LIAMA (Sino-European Computer Science Lab, 2013-2015) Coordinator of ECRYPT II virtual labs (2008-2012)

Alastair Kayمشاهده پروفایل

مدرس

• Quantum Computation
• Quantum Information Theory
• Quantum Cryptography
• +۱۳ مورد دیگر

Dr. Alastair Kay is a Lecturer in the Department of Mathematics at Royal Holloway, University of London. His research focuses on theoretical quantum computation, quantum information theory, and quantum cryptography, particularly addressing challenges in quantum state transfer, error correction, and networked quantum systems. He holds a PhD from the University of Cambridge under Prof. Artur Ekert and a physics degree from Keble College, University of Oxford. His research spans topics such as Quantum state transfer protocols using engineered Hamiltonians Quantum error correction mechanisms for experimental systems Entanglement properties in graph states and spin networks Applications of quantum computing in cryptography and information theory The articles listed reflect his work in quantum information science, computational physics, and theoretical cryptography. Key trends include advancements in fault-tolerant quantum communication, optimization of spin chain dynamics, and foundational studies in quantum correlations and nonlocality. Alastair actively develops software tools like quantikz for quantum circuit diagrams and ConTeXi for LaTeX equation integration in Microsoft Office. He also emphasizes open science principles and reproducibility in quantum research through personal commentary and collaboration with his fiancée, a Panton Fellow in open research practices.

Tanushree Royمشاهده پروفایل

استادیار

• Cybersecurity
• Fault diagnostics
• Socio-technical systems
• +۴ مورد دیگر

Dr. Tanushree Roy serves as an Assistant Professor in the Department of Mechanical Engineering at Texas Tech University's Whitacre College of Engineering and is an Affiliate Faculty member at the National Wind Institute. Her research pioneers resilient human-centric smart city infrastructures through the integration of control theory, mathematical modeling, and machine learning to address critical challenges in safety, security, and resource optimization for urban systems. Her academic foundation includes: Ph.D. in Mechanical Engineering from The Pennsylvania State University (2022) M.S. in Mathematics from University of Central Florida (2015) M.E. in Electrical Engineering from Indian Institutes of Engineering Science and Technology, India (2011) B.Tech in Applied Electronics and Instrumentation from Maulana Abul Kalam Azad University of Technology, India (2009) Dr. Roy's research centers on cybersecurity , fault diagnostics , and socio-technical systems with specialized applications in smart transportation networks and battery energy storage systems. She develops innovative frameworks that merge human-centric sensing with technical measurements to combat cyberattacks and physical faults in cyber-physical-social systems, emphasizing safety-critical resilience for urban citizens. Her methodology uniquely combines model-based control with data-driven machine learning to address challenges like social data integrity, human behavior modeling, and multi-scale anomaly characterization. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in cyberattack detection for connected vehicles, thermal fault tolerance in battery systems, and socio-technical traffic modeling. Key technical approaches include Koopman operator theory for secure estimation, control barrier functions for safety certification, and redundancy-based data fusion techniques. These works consistently bridge theoretical control systems with practical smart city implementation, demonstrating strong interdisciplinary connections between transportation engineering, energy systems, and cybersecurity. No scientific awards are documented in the provided information. Dr. Roy actively mentors three PhD students—Sanchita Ghosh (since 2022), Faysal Ahamed, and Soumyoraj Mallick (both since 2024)—alongside undergraduate researcher Mercedes Hernandez. Her research is executed through the Smart Human-centric Automation Resilience (SHARE) Lab, which has secured projects including the secure autonomous mobility testbed and participates in workforce development via Texas Tech's Engineering Research Internship Experience (ERIE) program for high school students. The SHARE Lab operates at the intersection of transportation and energy systems, maintaining two primary research thrusts: resilient human-centric transportation networks and safeguarding battery energy storage infrastructure. Current projects include SUMO-based cyberattack validation for connected vehicle platoons, self-learning voltage estimation under sensor attacks, and thermal fault-tolerant battery management. The lab maintains active collaborations with national conferences (ACC, CCTA) and industry partners to advance real-world implementation of resilient smart city technologies.

• Intelligent Transportation Systems
• Traffic Control
• Machine Learning
• +۳ مورد دیگر

Dr. Maha Elouni is an Assistant Professor of Computer Science at Randolph-Macon College since 2022, with expertise in Intelligent Transportation Systems , Machine Learning , and Traffic Control . She earned her PhD in Computer Engineering (2021) and MSc in Applied Mathematics (2015) from Virginia Polytechnic Institute and State University, and her BS and ME in Computer Science Engineering from the National School of Computer Science in Tunisia. PhD : Computer Engineering, Virginia Tech, 2021 MSc : Applied Mathematics, Virginia Tech, 2015 BS/ME : Computer Science Engineering, National School of Computer Science, Tunisia, 2012 Her research focuses on Intelligent Transportation Systems , integrating machine learning and control theory to optimize traffic flow and develop adaptive controllers for urban road networks. She has contributed to studies on sliding mode control , game-theoretic decentralized systems , and connected vehicle technologies . Selected publications highlight her work on network perimeter control , dynamic freeway speed controllers , and weather-adaptive traffic systems . Recent articles explore applications of machine learning and clustering algorithms in transportation engineering. Student of the Year , Urban Mobility and Equity Center (UMEC), 2020 Best Paper Award , VEHITS Conference, 2018 Dr. Elouni teaches foundational and advanced courses, including Introduction to Computer Science , Data Structures , Object-Oriented Programming , and Android App Development . Her interdisciplinary background bridges computer engineering , mathematics , and transportation systems .

Zhuang Liuمشاهده پروفایل

استادیار

• Deep Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Zhuang Liu is an Assistant Professor of Computer Science at Princeton University, where he leads a research group focused on deep learning and computer vision. His work spans vision and language, unified by a focus on deep learning methods, representations, and architectures. Prior to joining Princeton, he was a Research Scientist at Meta AI Research (FAIR) in New York City. He received his Ph.D. from UC Berkeley and his B.E. from Tsinghua University, both in Computer Science. His educational background includes: Ph.D. in Computer Science, University of California, Berkeley, 2022 B.E. in Computer Science, Tsinghua University (Yao Class) Liu's research focuses on empirical approaches to understanding how deep learning models work and behave. He explores simple approaches to gain empirical insights into neural networks, often challenging existing beliefs in architectures, training, pruning, and datasets. His work spans multiple domains including computer vision, natural language processing, and multimodal learning. He has made significant contributions to the field, including DenseNet and ConvNeXt architectures, which have influenced modern neural network design. His recent publications reveal a strong focus on understanding and improving large language models and vision-language systems. His work examines idiosyncrasies in LLMs, pruning approaches for efficient inference, visual shortcomings of multimodal systems, and bias in large-scale visual datasets. He also investigates fundamental questions about neural network architectures, exploring the relationship between ConvNets and Transformers, and developing normalization-free transformer variants. His research consistently bridges theoretical insights with practical applications, as evidenced by his numerous conference publications and industry collaborations. His notable scientific achievements include: CVPR Best Paper Award NeurIPS'18 Compact Neural Networks Workshop Best Paper Award Professor Liu actively mentors students and postdoctoral researchers, currently advising seven Ph.D. students including Wenhao Chai, Tony Chen, Sachin Konan, Taiming Lu, Zhuorui Ye, David Yin, and Boya Zeng. He has successfully guided graduates such as Jiachen Zhu (now at Skild AI) and Mingjie Sun (now at Thinking Machines Lab). His research has practical implications for improving the efficiency, interpretability, and fairness of deep learning systems, with applications across multiple domains. His research group maintains active collaborations with industry partners including Meta FAIR, NVIDIA Research, and Adobe, and regularly hosts research interns. The group's work has established Zhuang Liu as a leading voice in empirical deep learning research, with invitations to speak at top academic institutions including Harvard, Stanford, Columbia, and Boston University.

Jiabao Heمشاهده پروفایل

پژوهشگر

• Control Theory
• System Identification
• Information Theory
• +۵ مورد دیگر

Jiabao He is a Ph.D. Student in the Division of Decision and Control Systems at KTH Royal Institute of Technology , supervised by Prof. Håkan Hjalmarsson. He holds a Master's degree in Control Engineering (2021) and a Bachelor's degree in Mechanical Engineering (2018) from Tsinghua University. Education M.S. in Control Engineering (Tsinghua University, 2021) B.S. in Mechanical Engineering (Tsinghua University, 2018) His research interests lie in systems, control theory, and information theory , with current focus on subspace identification, finite-sample analysis , and data-driven control . Past work explored fuzzy control and descriptor systems . His publications reflect expertise in subspace methods , statistical learning , and Markov parameter identification . Conference Participation includes Reglermötet 2025 (Lund) , 63rd IEEE CDC 2024 (Milan) , 32nd ERNSI Workshop 2024 (Venice) , and SYSID 2024 (Boston) . He is a reviewer for journals and conferences like IEEE Transactions on Automatic Control and IFAC Symposium on System Identification . Scientific Awards IEEE TC SIAC Student Paper Award (2025) Travel Scholarship from Karl Engvers Foundation (2024) Travel Scholarship from Björns stiftelse (2024) He has served as a Teaching Assistant for courses such as EL1020 Automatic Control and EL2520 Control Theory and Practice , and supervised Bachelor theses on Learning in Dynamical Systems (2023-2024). His work bridges theoretical control methods with practical applications in descriptor systems and space robotics.

Andrej Bogdanovمشاهده پروفایل

استاد

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۶ مورد دیگر

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

Joy Morrisمشاهده پروفایل

استاد

• Combinatorics
• Algebraic Graph Theory
• Group Theory
• +۴ مورد دیگر

Joy Morris is a Professor at the Department of Mathematics & Computer Science at the University of Lethbridge. She earned her BSc from Trent University in 1992 and her PhD from Simon Fraser University in 2000 under Brian Alspach. Her academic journey includes tenure in 2005, promotion to Associate Professor, and full Professor status in 2015. Research Focus: Interactions between group theory and graph theory, with emphasis on Cayley graphs and automorphisms Key Contributions: Solving the distinguishing number problem for various graph families, advancing DCI/CI group theory Research Overview Her work bridges abstract algebra with graph theory through Cayley graphs. She investigates automorphism groups, Hamilton cycles, and graph symmetry properties. Notable contributions include resolving the DCI property for specific groups, analyzing Praeger-Xu graphs, and studying color-preserving automorphisms. Academic Leadership As a co-author of two influential open-access textbooks ( Proofs and Concepts with Dave Morris and her own Combinatorics text), she has shaped curriculum development and mathematical education outreach programs for parents in Alberta. Her teaching portfolio includes foundational courses like Math 2000 and advanced combinatorial theory instruction. Collaborative Impact With over 30 publications since 1996, her collaborations span international researchers including C. Praeger, P. Spiga, and E. Dobson. Current projects involve hypercube distinguishing costs and automorphism group analysis of vertex-transitive digraphs. She maintains active research into graphical regular representations and graph isomorphism problems.

Robert D. Kleinbergمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +۵ مورد دیگر

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Eunji Limمشاهده پروفایل

دانشیار

• Operations Management
• Supply Chain Management
• Business Analytics
• +۲ مورد دیگر

Eunji Lim is an Associate Professor in the Department of Decision Sciences and Marketing at the Robert B. Willumstad School of Business, Adelphi University, located in Garden City, New York. She has been a faculty member at Adelphi since 2018, initially as an Assistant Professor and promoted to Associate Professor in 2023. Her prior academic appointments include Assistant Professor roles at Kean University (2013–2018) and the University of Miami (2008–2013). Education Ph.D. in Operations Research (implied by department) , Stanford University (2008) Research Interests Lim’s research lies at the intersection of operations research, statistics, and management science. She focuses on simulation-based decision making for operations and supply-chain systems, leveraging large-scale stochastic simulation to optimize complex processes. A second major theme is nonparametric function estimation under shape constraints , where she develops statistically rigorous methods to estimate convex, monotonic, or smooth functions from noisy data. These methodologies find applications in business analytics, revenue management, inventory control, and engineering design. Publication Trends Her recent articles (2013–2025) reveal a consistent emphasis on convex and isotonic regression , simulation analytics , and stochastic optimization . A clear trajectory emerges from theoretical foundations—convergence rates, consistency, and asymptotic distributions—toward practical algorithms for high-dimensional and noisy environments. Interdisciplinary impacts are evident in publications targeting both statistics and operations-research audiences, with cross-cutting keywords such as simulation optimization, shape-restricted inference, and robust estimation. Scientific Awards No specific awards or fellowships are listed in the provided materials. Advising & Grants No information on current or former PhD/Master’s students, postdocs, or funded research grants is provided. Labs & Teams No laboratory or research-group affiliations beyond her departmental appointment are mentioned.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

• Networked Systems
• Computer Networking
• Communications Engineering
• +۴ مورد دیگر

Professor Mark Handley is a Professor of Networked Systems in the Department of Computer Science at University College London. His research focuses on network architecture, protocols, and systems with a particular emphasis on low-latency networking, datacenter networks, and network security. Education: Doctor of Philosophy, University College London (1997) Bachelor of Science (Honours), University College London (1988) Professor Handley's research spans multiple areas of computer networking with a focus on practical, deployable solutions. His work addresses fundamental challenges in network architecture, including low-latency routing, congestion control, network security, and datacenter networking. He has made significant contributions to Multipath TCP, congestion control algorithms, and network security protocols. His research often bridges theoretical foundations with practical implementation, ensuring real-world applicability of his innovations. His recent publications demonstrate a continued focus on cutting-edge networking challenges, particularly in low-latency routing, datacenter networks, and network security. The trend shows increasing attention to space-based networking, in-switch processing, and novel approaches to congestion control. His work consistently addresses the fundamental tension between theoretical network design and practical deployment constraints. Scientific Awards: IEEE Internet Award (2012) Usenix NSDI Best Paper Award (2011) ACM SIGCOMM Test of Time Award (2011) Roger Needham Award (2007) Professor Handley has served on numerous program committees including ACM SIGCOMM and Usenix NSDI. His work has influenced both academic research and industry standards, with contributions to IETF RFCs including Multipath TCP and TCP encryption. He has mentored numerous students and researchers who have gone on to make significant contributions in the networking field. His research group at UCL focuses on next-generation network architectures, with particular expertise in low-latency routing, datacenter networks, and network security. The group maintains strong collaborations with industry partners to ensure practical relevance of their research.

Aravindan Vijayaraghavanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning Theory
• Data Science
• +۷ مورد دیگر

Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department. Research interests include theoretical computer science , machine learning algorithms , quantum information , and combinatorial optimization under non-worst-case paradigms. He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24). Academic Background : PhD in Computer Science from Princeton University (advisor: Moses Charikar ) Bachelor's Degree in Computer Science from Indian Institute of Technology Madras Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants Research Contributions : Developed smoothed analysis frameworks for random matrices with dependent entries Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions Advanced quantum entanglement certification algorithms for subspaces Improved weak-to-strong generalization theory with data distribution expansion properties Designed error-tolerant e-discovery protocols for legal document classification Scientific Recognition : NSF CAREER Award NSF AITF Award (CCF-1637585, CCF-2154100) Google Research Scholar Program grant Amazon Research Awards program support Simons Postdoctoral Fellowship Academic Leadership : General Chair for FOCS 2024 Co-organizer of Junior Theory Workshop and Northwestern QTW series Active in program committees for COLT , ICML , NeurIPS , and STOC conferences Teaching Portfolio : CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science) CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015) CS496: Graduate Algorithms (since 2016) CS396/496: Quantum Computation & Information (co-taught with S. Rao) CS497: Machine Learning Theory (Spring 2025 offering)

• Information Theory
• Data Privacy
• Network Coding
• +۳ مورد دیگر

Professor Parastoo Sadeghi is a distinguished academic in the School of Engineering and Information Technology at the University of New South Wales (UNSW) Canberra, where she serves as Professor of Electrical Engineering. She joined UNSW Canberra in October 2020 after spending 15 years at the Australian National University (2005-2020). Professor Sadeghi received her bachelor's and master's degrees in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1995 and 1997, respectively, and completed her Ph.D. in electrical engineering from UNSW Sydney in 2006. Professor Sadeghi's research spans several cutting-edge areas in information theory and communications, with particular emphasis on information theory , data privacy , network and index coding , wireless communications theory and systems , and spherical signal processing . Her work has resulted in over 200 refereed journal articles and conference papers, plus a book on Hilbert Space Methods in Signal Processing published by Cambridge University Press in 2013. Her recent publications demonstrate a strong focus on differential privacy mechanisms, information leakage analysis, and network coding optimization, with significant contributions to the theoretical foundations of these fields. Professor Sadeghi has held several prestigious positions in the academic community, including serving as Associate Editor for coding techniques for the IEEE Transactions on Information Theory (2016-2019), General Co-chair of the 2021 IEEE International Symposium on Information Theory in Melbourne, and as an elected member on the Board of Governors of the IEEE Information Theory Society (2019-2020). She has been a Senior Member of the IEEE since 2007 and has conducted research visits at leading institutions including the Technical University of Munich (2008) and MIT (2009, 2013, 2020). Her recent work shows increasing focus on privacy-preserving technologies and the theoretical foundations of secure communications, with numerous publications in top-tier venues. Senior Member of IEEE (since 2007) General Co-chair of 2021 IEEE International Symposium on Information Theory Associate Editor for IEEE Transactions on Information Theory (2016-2019) Elected member, IEEE Information Theory Society Board of Governors (2019-2020) Professor Sadeghi actively supervises graduate students in fundamental problems related to information theory, wireless communications, data privacy, and network coding. She offers competitive scholarships of $35,000 AUD for high-achieving PhD students with strong mathematical backgrounds. Her research group maintains strong international collaborations, as evidenced by her frequent research visits to top global institutions and numerous co-authored publications with international researchers. She continues to be an active contributor to the advancement of information theory and its applications to modern communication and privacy challenges.