جست‌وجوی استاد و پژوهشگر

Christopher Bishopمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۶ مورد دیگر

Christopher Bishop is a Microsoft Technical Fellow and Director of Microsoft Research AI for Science, concurrently serving as Honorary Professor of Computer Science at the University of Edinburgh and Fellow of Darwin College, Cambridge. His distinguished career spans theoretical physics, neural computing, and leadership in AI research. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Founding member of UK AI Council Member of Prime Minister's Council for Science and Technology (2019) Delivered Royal Institution Christmas Lectures (2008) His research focuses on probabilistic models and machine learning, with significant contributions to AI for scientific discovery. Bishop pioneered the concept of the fifth paradigm of scientific discovery , where AI transforms traditional research methodologies across natural sciences. His work bridges theoretical computer science with practical applications in fusion energy, materials science, and computational biology. Analysis of his recent publications reveals a strategic shift toward AI-driven scientific infrastructure , with emphasis on machine learning foundations that endure technological evolution. His 2024 textbook became Springer Nature's top-selling publication, demonstrating exceptional impact in both academic and industrial contexts. Deep Learning: Foundations and Concepts (2024) Pattern Recognition and Machine Learning (2006) Neural Networks for Pattern Recognition (1995) Bishop leads Microsoft's global AI for Science initiative, establishing research teams in Berlin and coordinating interdisciplinary projects that apply machine learning to climate science, fusion energy, and molecular biology. His leadership in the Prime Minister's Council shapes national AI strategy while maintaining active engagement in public science communication through lectures and media appearances.

• Network Security and Privacy
• Open-RAN Security and Privacy
• Green Networking
• +۲ مورد دیگر

Mohammad Shojafar (M'17-SM'19) is an Associate Professor at the Institute for Communication Systems within the Faculty of Engineering and Physical Sciences at the University of Surrey , UK. He has secured over £1.9M in research funding as Principal Investigator for projects like ORAN-TWIN (EPSRC), PRISENODE (MSCA-IF), TRACE-V2X (MSCA-SE), and D-XPERT (Innovate UK), among others. Previously held positions include Senior Researcher at University of Toronto and Toronto Metropolitan University, Senior Researcher at Italian universities (Telecom Italia Mobile), and Postdoc at University of Padua Key affiliations: Associate Editor for IEEE Transactions on Network and Service Management, Intelligent Transportation Systems, Green Communications and Networking, and Consumer Electronics Magazine Research Specialism: 5G/6G Security and Privacy Open-RAN Security Green Networking Adversarial Machine Learning Applied Cryptography Publication Trends: Focus on Open RAN security challenges (bearer context migration poisoning, KPI poisoning attacks), IoT/Fog security (GAN-based attacks, distributed intrusion detection), Lightweight Cryptography (multi-signature protocols, authentication schemes), and AI-driven Network Optimization (federated learning, reinforcement learning applications). Recent work addresses security in vehicular networks, smart grids, and video streaming frameworks. Scientific Recognition: Marie Curie Individual Fellowship (MSCA-GF-IF) Intel Innovator ACM Professional Member Sustainability Fellow at Institute for Sustainability IEEE Senior Member Supervision: Currently supervising 6 PhD students and has graduated 5 PhD/MSc students since 2021. Active in 5G/Open RAN security research with over 20 related publications since 2022.

Sonja Isabel Veithمشاهده پروفایل

پژوهشگر

• Science Education
• Phenomenography
• Physics Education
• +۶ مورد دیگر

Dr. Sonja Isabel Veith is a Scientific Staff member at the Institute for Special Education, Faculty of Philosophy, Leibniz University Hannover. Her work focuses on research and teaching in the fields of scientific and technical education, with a strong emphasis on phenomenography and inclusive didactics. Phenomenographic Research Science Education Physics & Computer Science Teaching Biomedical Optics Artificial Intelligence Applications Educational Background: M.Sc. in Physics (minor: Meteorology), Leibniz University Hannover B.Sc. in Physics (minor: Computer Science), Leibniz University Hannover Fellow of the International Max Planck Research School on Gravitational Wave Astronomy Dr. Veith's research explores children's perceptions of physics concepts like sound, interdisciplinary science education, and racism-critical teaching methods. She has developed innovative didactic approaches for visualizing sound and integrating computational thinking in elementary education. Her publications show a consistent focus on: Phenomenographic analysis of science concepts Physics education in elementary schools Interdisciplinary teaching methods AI applications in sensor data analysis Historical and societal contexts in science Inclusive didactic frameworks Scientific Honors: Fellow of the International Max Planck Research School on Gravitational Wave Astronomy Dr. Veith has collaborated extensively across disciplines, working with institutions such as the Albert Einstein Institute and Laser Zentrum Hannover. Her career spans multiple domains including physics, computer science, biomedical optics, and educational theory.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۷ مورد دیگر

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

Francis de Véricourtمشاهده پروفایل

استاد

• Decision Science
• Analytics
• Operations Research
• +۸ مورد دیگر

Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin, where he also holds the Joachim Faber Chair in Business and Technology. He has held faculty positions at Duke University and INSEAD and was a post-doctoral researcher at MIT, reflecting a global academic footprint across France, the USA, Germany, and Singapore. His educational background includes a PhD from Université Paris VI and an engineering degree from ENSIMAG (Grenoble Institute of Technology), establishing a strong foundation in applied mathematics and computer science. Francis's research focuses on decision science, analytics, and operations, with impactful applications in healthcare, sustainability, and human-AI interaction. He investigates how mental models—'framing'—enable individuals and organizations to transcend data and generate better alternatives for decision-making. His work emphasizes cognitive agility, translational innovation, and the role of human intuition in the age of artificial intelligence. The analysis of his recent publications reveals a consistent trajectory in understanding cognitive frameworks in decision-making, the integration of AI in human contexts, and the ethical and strategic dimensions of deep-tech innovation. His writings bridge academic rigor with practical insight, targeting both scholarly and industry audiences. ENRE Best Publication Award, INFORMS MSOM Best Publication Award, INFORMS He has been a Department Editor for Operations Research and MSOM , and his academic leadership includes establishing the Center for Decisions, Models, and Data at ESMT. He has received multiple teaching awards for his work with MBA and Executive MBA students and is deeply engaged in executive education and corporate learning solutions. His book Framers , published by Penguin Random House and listed among the Financial Times' Best Books, has amplified his influence in both academic and public spheres. Francis leads DEEP—the Institute for Deep Tech Innovation—which fosters research, education, and entrepreneurial action in areas like AI, quantum computing, and biotechnology. The DMD Center, now integrated into DEEP, explores how modeling and representation enhance decision-making beyond data. These initiatives reflect his commitment to cultivating cognitive and entrepreneurial capabilities within scientific and business communities.

• Computer Science
• Artificial Intelligence
• Cybernetics
• +۳ مورد دیگر

Dilip Sarkar serves as an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, University of Miami. His academic profile demonstrates a strong research focus across multiple domains of computer science with particular emphasis on theoretical foundations and practical applications. Dr. Sarkar's research interests include: Artificial Intelligence and Cybernetics Parallel Algorithms and Programming Combinatorics Image Processing and Compression Internet of Things Security Machine Learning Applications in Healthcare Quantum Computing Approaches His publication record from 2018-2024 reveals a researcher engaged with cutting-edge computational challenges. Recent work spans quantum tensor networks for time series analysis, security frameworks for single sign-on systems, efficient computation in belief theoretic models, and machine learning applications for traumatic brain injury recovery prediction. His research demonstrates consistent integration of theoretical computer science with practical problem-solving across diverse application domains. As a member of CCS (Center for Computational Science) at the University of Miami, Dr. Sarkar participates in interdisciplinary research initiatives that bridge computational methods with scientific discovery across multiple fields. His technical expertise spans both theoretical frameworks and practical implementations, with notable contributions to image compression techniques, particularly for medical applications, and security protocols for modern web and IoT environments.

• Mobile Generative AI
• Edge General Intelligence
• Quantum Computing and Networking
• +۳ مورد دیگر

Roles & Affiliations: Professor and President's Chair Professor in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. Member of the IEEE and IET, with roles in editorial boards of major journals like IEEE Transactions on Network Science and Engineering and IEEE Communications Surveys & Tutorials. Current positions: Editor-in-Chief of IEEE Transactions on Network Science and Engineering, Area Editor in multiple journals including IEEE Communications Surveys and Tutorials. Education: Ph.D. (2008) and M.Sc. (2005) in Electrical and Computer Engineering from the University of Manitoba, Canada. B.Eng. (1999) in Computer Engineering from King Mongkut's Institute of Technology Ladkrabang, Thailand. Research Interests: Focuses on mobile generative AI, edge general intelligence, quantum networking, incentive mechanisms, and wireless communication systems. Explores applications in 6G, IoT, UAV networks, and secure communication. Recent Articles & Trends: 15 most recent articles highlight advancements in generative AI for wireless networks, quantum computing integration, secure communication protocols, and AI-driven network optimization. Key themes include semantic communication frameworks, RIS-aided systems, and federated learning in edge networks. Awards: Over 20+ prestigious awards including IEEE Fellow (2017), IET Fellow (2022), Stuart Meyer Memorial Award (2024), and multiple best paper awards at top conferences like IEEE WCNC and IEEE ICC. Advising & Grants: Advisor to numerous students (not listed explicitly). Active in funding initiatives for AI in wireless systems, quantum networks, and low-altitude economy networking. Leads projects on GenAI for networking and 6G innovations. Labs & Teams: Research focuses on CCDS labs at NTU, collaborating on projects like GenAI for ISAC, mobile edge computing, and secure federated learning systems.

Naveed Mahmudمشاهده پروفایل

استادیار

• Quantum Computing
• Hybrid Quantum-Classical Systems
• Machine Learning Applications in Quantum Contexts
• +۳ مورد دیگر

Naveed Mahmud is an Assistant Professor at the Department of Electrical Engineering and Computer Science, Florida Institute of Technology. He specializes in quantum computing, hybrid quantum-classical systems, and reconfigurable computing architectures. His research focuses on optimizing quantum algorithms, data encoding/decoding techniques, and secure communications using quantum technologies. Research interests include quantum-classical integration, algorithm emulation on high-performance reconfigurable computers, and applications of quantum computing in pattern recognition and cryptography. Key areas of exploration are hybrid quantum-classical machine learning, quantum wavelet transforms, and securing free-space optical communications with quantum key distribution. His recent work emphasizes scalability and efficiency in quantum computing frameworks, including frameworks like QASM-to-HLS for quantum circuit acceleration, and decoherence-optimized quantum circuits. Articles highlight advancements in quantum data decoding, algorithm emulation, and secure communication systems. No scientific awards or formal advisees are listed. His profile includes links to ORCID, Google Scholar, and ResearchGate for further details on publications and collaborations.

Siamak Shahandashtiمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Privacy-Enhancing Technologies
• Applied Cryptography
• +۴ مورد دیگر

Dr. Siamak Shahandashti is a Senior Lecturer in the Department of Computer Science at the University of York, UK. He leads the Cyber Security & Privacy Research Group and is actively involved in academic governance roles including Chair of the Physical Sciences Ethics Committee and Programme Lead for the MSc Cyber Security programme. His research focuses on applied cryptography, privacy-preserving technologies, electronic voting systems, and blockchain applications. He holds a PhD in Computer Science and has contributed to over 40 peer-reviewed publications. Education: PhD in Computer Science, MSc in Telecommunications Engineering, BSc in Electrical Engineering. Professional qualifications include a PGCert in Academic Practice. Research interests span cyber security fundamentals, usable security mechanisms, and the intersection of cryptography with real-world systems. Notable projects include verifiable electronic voting systems and DoS-resilient blockchain protocols. He has led funded projects on e-voting scalability and contributed to standards reviews for ISO/IEC protocols. Award-winning educator in cyber security curriculum development, with active roles in admissions and academic ethics compliance. His work bridges theoretical cryptography with practical implementations, emphasizing user-centric security design principles. Labs/Teams: Cyber Security & Privacy Research Group (lead), Departmental Ethics Team (lead). Collaborations include institutions in Australia, France, and across Europe.

Evimaria Terziمشاهده پروفایل

استاد

• algorithmic data mining
• network analysis
• recommendation systems
• +۳ مورد دیگر

Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.

Santiago Figueiraمشاهده پروفایل

استاد

• Theoretical computer science
• Computability theory
• Algorithmic randomness
• +۸ مورد دیگر

Santiago Figueira is a Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, and a Researcher at the Institute of Computer Science (ICC), CONICET (Argentine National Council for Scientific and Technical Research) in Buenos Aires, Argentina. He leads the Logic, Language and Computability Research Group (GLyC) and co-directs the Argentinian-French Laboratory SINFIN, a collaborative initiative between Université Paris-CNRS and Universidad de Buenos Aires-CONICET. Additionally, he is a researcher in the Quantum Information, Computation, and Communication team (QuICC). Dr. Figueira's research spans theoretical computer science with a strong emphasis on computability theory, algorithmic randomness, Kolmogorov complexity, and mathematical logic. His work extends to applications in database theory, quantum information, and cognitive modeling. He has made significant contributions to modal logics, model theory, and the theory of databases, particularly in the context of data trees and XPath query languages. His interdisciplinary approach bridges formal methods with practical applications in quantum computing and human cognition. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on modal logic extensions, quantum information theory, cognitive modeling of concept learning, and database query languages. His collaborations span international institutions, particularly with French researchers through the SINFIN laboratory, and with cognitive scientists studying human memory and concept formation. Dr. Figueira has supervised numerous PhD students and postdoctoral researchers, including Gabriel Goren-Roig, Santiago Cifuentes, Edwin Pin Baque, Sergio Romano, Gabriel Senno, and Sergio Abriola. His former postdocs and CONICET researchers include Guido Bellomo, María Emilia Descotte, and Ariel Bendersky, indicating an active and productive research group. He has developed lecture notes in Spanish on Logic and Computability (2020) and Computability Theory (2021), contributing to academic education in his field. His research group GLyC serves as a hub for theoretical computer science research in Argentina, fostering collaborations between Argentine and international researchers.

Erkao Baoمشاهده پروفایل

استادیار

• Symplectic Geometry
• Contact Geometry
• Differential Geometry
• +۳ مورد دیگر

Erkao Bao is an Assistant Professor in the School of Mathematics at the University of Minnesota, where he is a member of the differential geometry group. His office is located at Vincent Hall 356, 206 Church Street SE, Minneapolis, MN 55455, and he can be reached at bao@umn.edu. Dr. Bao specializes in Symplectic Geometry and Contact Geometry, with research focusing on Morse homology, Floer theory, and Lagrangian submanifolds. His work explores equivariant structures in geometric contexts, with recent publications addressing reflection actions via broken trajectories, coherent orientations in symplectic field theory, and immersed Lagrangian Floer cohomology. His research demonstrates a clear progression from foundational work on J-holomorphic curves and contact homology toward increasingly sophisticated treatments of equivariant structures. The integration of geometric methods with potential applications in neural networks (as seen in his 2019 paper on equivariant neural networks) shows his ability to bridge pure mathematics with contemporary computational challenges. Office: Vincent Hall 356, 206 Church Street SE, Minneapolis, MN 55455 Email: bao@umn.edu Office Hours: Typically Monday and Friday 1:00-2:00 PM, or by appointment As an educator, Dr. Bao teaches undergraduate courses including Math 2374 (CSE Multivariable Calculus) and Math 5378 (Differential Geometry), as well as graduate courses Math 8301-8302 and Math 8365 (Manifold and Topology), covering topics from vector calculus to advanced differential geometry and topological methods.

• Entrepreneurship Theory
• Strategic Management
• Economic Calculation
• +۶ مورد دیگر

Steven Phelan is a Professor of Entrepreneurship at Kennesaw State University , College of Business . With a career spanning 30+ years, he has taught MBA students globally, led entrepreneurship centers, and contributed to research on entrepreneurial theory, strategic management, and economic systems. His work explores intersections of AI ethics , institutional change , and competitive advantage . B.Sc. (Hons.) - University of Melbourne M.B.A. - Monash University Ph.D. - La Trobe University Dr. Phelan's research focuses on entrepreneurship theory, complexity science, and technological impact. His recent publications examine generative AI regulation , competitive advantage frameworks , and economic calculation debates in digital contexts. He employs agent-based modeling and multivariate analysis to investigate entrepreneurial behavior and firm dynamics. His work bridges machine learning ethics with Austrian economics , analyzing how artificial systems can replicate entrepreneurial processes. Current projects address innovation value creation , strategic landscapes , and institutional change .

Luigi Di Marinoمشاهده پروفایل

پژوهشگر

• Machine Learning in Holography
• Gauge/Gravity Correspondence
• Celestial Holography
• +۳ مورد دیگر

Luigi Di Marino is a PhD researcher in Theoretical Physics III at the University of Würzburg, Germany, working under Dominik Neuenfeld and Johanna Erdmenger since January 2025. His office is located in building B2, room 1.102, with contact email luigi.marino@uni-wuerzburg.de. His academic background includes: B.Sc. in Physics from University of Naples Federico II (2019-2022), bachelor thesis: "The Feynman’s propagator" (supervised by Massimo Taronna) M.Sc. in Theoretical Physics from University of Amsterdam (2022-2024), master thesis: "Soft modes & infrared dressing in the context of black hole evaporation" (supervised by Ana-Maria Raclariu) Di Marino's research integrates machine learning with quantum gravity frameworks, focusing on holographic dualities and infrared structures in scattering processes. His work specifically examines gauge/gravity correspondence applications, celestial holography formalisms, and quantum information aspects of black hole physics, contributing to the Theoretical Physics III group's investigations into quantum field theory-gravity dualities and non-equilibrium statistical systems. He actively participates in the Würzburg Seminar on Quantum Field Theory and Gravity within the university's research ecosystem.

• Optimization methods
• Metaheuristics
• Heuristics
• +۱۲ مورد دیگر

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.