جستجوی استاد راهنمای دانشگاهی

Akshay Narayanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• AI Planning
• Reinforcement Learning
• +۶ مورد دیگر

Akshay Narayan is a Senior Lecturer (Educator Track) at the School of Computing, National University of Singapore (NUS), where he teaches senior undergraduate and graduate-level courses in AI Planning and Decision Making, as well as introductory and intermediate-level Software Engineering courses. Education: Ph.D. in Computer Science from National University of Singapore (completed in 2020) M.Tech. in Information Technology from International Institute of Information Technology Bangalore, India B.E. in Computer Science & Engineering from Visveswaraya Technological University, India Research Interests: Dr. Narayan's research spans multiple domains within computer science with a primary focus on artificial intelligence and its applications. His current research centers on transfer learning in reinforcement learning, multi-agent decision making, and AI planning. He has also made significant contributions to cloud computing research, particularly in areas such as smart metering, chargeback systems, power-aware cloud metering, and workload analysis for virtual machine sizing. His work bridges theoretical foundations with practical applications, addressing real-world challenges in computing systems. He has recently expanded his research to include technology in education, exploring how AI can be integrated into teaching and learning processes. Publication Trends: Dr. Narayan's publication record demonstrates a clear evolution from foundational work in cloud computing to more recent explorations in reinforcement learning and AI education. His early work focused on practical applications in cloud systems, including smart metering and QoS monitoring. More recently, his research has shifted toward AI planning, decision making, and the educational applications of AI. This progression shows his ability to adapt to emerging fields while maintaining a strong foundation in systems research. Awards and Recognition: Teaching and Mentoring: Dr. Narayan teaches a variety of courses at NUS including CS2113 Software Engineering & Object-Oriented Programming, CS3219 Software Engineering Principles and Patterns, CS3268 Responsible AI: From Algorithms to Impact, and IT5100F Industry Readiness: Data Analytics and AI in Practice. He has also taught CS4246/CS5446 AI Planning and Decision Making. His teaching approach integrates his research expertise with practical applications, providing students with both theoretical foundations and hands-on experience. He has taught these courses across multiple academic years from AY-2013/14 through AY-2020/21. Research Groups and Collaborations: Dr. Narayan has collaborated with researchers across multiple institutions, including work with Prof. Tze Yun Leong at NUS (his PhD advisor), Shrisha Rao, Zhuoru Li, and others. His research has often involved interdisciplinary collaborations that bridge theoretical computer science with practical system implementations.

Virginia Youngمشاهده پروفایل

استاد

• Actuarial Mathematics
• Financial Mathematics
• Insurance Decision Making
• +۳ مورد دیگر

Virginia Young is the Cecil J. and Ethel M. Nesbitt Professor of Actuarial Mathematics at the University of Michigan's Department of Mathematics, within the College of Literature, Science, and the Arts. She holds a Ph.D. from the University of Virginia (1984). Her research focuses on actuarial and financial mathematics, particularly decision-making processes for individuals and insurance companies in financial and insurance contexts. This includes topics like optimal reporting strategies, reinsurance mechanisms, and risk management under uncertainty. Her work addresses modern challenges such as defined contribution pension plans and strategic insurance product design. Key research areas include stochastic control theory, game-theoretic models in insurance markets, and optimization under model ambiguity. She explores how insurers and individuals make decisions under risk, with applications to annuities, reinsurance chains, and lifetime financial planning. Recent studies investigate Stackelberg games in reinsurance, optimal deductible insurance, and minimizing lifetime ruin probabilities through strategic annuitization. Virginia Young has no listed scientific awards in the provided texts. She advises no formally documented students, though her role likely involves mentoring within the Mathematics Department. Her work contributes to both theoretical advancements and practical applications in actuarial science and financial risk management.

Linan Chenمشاهده پروفایل

دانشیار

• Probability Theory
• Analysis
• Geometry
• +۴ مورد دیگر

Linan Chen is an Associate Professor in the Department of Mathematics and Statistics at McGill University since 2014, following a postdoctoral position at the same institution (2011–2014). He holds a Ph.D. from MIT (2011, supervised by Daniel Stroock) and a B.Sc. from Tsinghua University (2006). His research focuses on probability theory and its intersections with analysis and geometry, including partial differential equations, functional analysis, Gaussian measures, and random geometry. He is affiliated with the Probability Lab of the Centre de Recherches Mathématiques (CRM) and the CNRS-Unite Mixte Internationale (CNRS-UMI) since 2014. Chen teaches advanced probability courses such as Honours Probability (Math 356) and Advanced Probability Theory I/II (Math 587/589), alongside special topics courses like Topics in Geometry and Topology (Math 599). He has advised students including Leila Sloman, Reinhold Willcox, Ulysse Blau, and Olivier Nadeau-Chamard through independent study programs. His research explores cutting-edge topics in probability, including Gaussian free fields, degenerate diffusion equations, and asymptotic properties of geometric stochastic structures. Recent work addresses high-dimensional phenomena, stochastic processes in geometry, and applications in mathematical physics. Chen’s contributions span theoretical advancements and methodological innovations, with publications in journals such as the Journal of Theoretical Probability, Annales Henri Poincaré, and SIAM Journal on Mathematical Analysis.

• Artificial Intelligence in Education
• Educational Technology
• Online Learning Systems
• +۳ مورد دیگر

David Lefevre is a Professor of Practice at the Department of Management and Entrepreneurship, Imperial College Business School. His research focuses on AI applications in education, digital innovation in higher education, and tech-transfer. He co-founded the Edtech Lab in 2004, which pioneered Imperial’s online courses and Global Online MBA. The Lab has won awards including a Gold at IMS Learning Impact (2010) and Silver at QS Reimagine (2018). He also co-founded educational tech companies Epigeum (now part of Oxford University Press) and Insendi (now Study Group). He is Special Advisor on Digital Innovation at Study Group and a British Council Trustee, focusing on digital transformation. Lefevre’s work emphasizes strategic adoption of AI, online education quality, and bridging cultural barriers in learning. Education & Background: While formal education details are not provided, his career reflects deep expertise in education technology and entrepreneurship. His roles include: Professor of Practice, Imperial College Business School Co-founder & former leader of the Edtech Lab Co-founder of Epigeum and Insendi Expert in Residence, Imperial Enterprise Lab Research Interests: Lefevre’s work spans AI in education, online learning infrastructure, and cross-cultural e-learning. He advocates for data-driven strategies to improve online course design and learner engagement. His recent focus includes AI-driven feedback systems and holographic teaching methods. Key Achievements: Launched Imperial’s first online MBA program in 2015 Developed an LMS alternative using middleware Promoted adaptive learning environments for diverse learners Awards & Recognition: Gold award at IMS Learning Impact awards 2010 Silver award for Business Education at QS Reimagine Education 2018 Advisory & Grants: Advises Study Group and the British Council on digital innovation. His ventures have secured significant investments, though specific grant details are not disclosed. Labs & Collaborations: Leads the Edtech Lab and collaborates with Imperial Enterprise Lab. Active in global education networks such as QS Reimagine and the British Council.

Taylor Johnsonمشاهده پروفایل

دانشیار

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +۷ مورد دیگر

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

Richard E. Pattisمشاهده پروفایل

استاد

• Computer Science Education
• Programming Pedagogy
• Formal Languages (EBNF)
• +۳ مورد دیگر

Richard E. Pattis is a Professor of Teaching at the University of California, Irvine , affiliated with both the Department of Computer Science and the Department of Informatics within the Donald Bren School of Information and Computer Sciences . His primary role focuses on undergraduate education, emphasizing innovative teaching methods and curriculum design. He teaches courses such as ICS 33 (Intermediate Programming), ICS 193 (Tutoring in ICS), and has developed educational materials like the EBNF chapter for programming syntax. His work integrates pedagogical approaches with practical programming concepts, prioritizing student engagement and critical thinking. Pattis actively curates education-related video clips and maintains a repository of quotations relevant to learning and programming. His research interests include formal language theory in education, the application of EBNF in introductory courses, and fostering effective debugging practices. He emphasizes the importance of clear communication and problem-solving in computer science education.

Michael Wrightمشاهده پروفایل

مدرس ارشد

• Haptic Feedback
• Educational Technology
• User Interaction Design
• +۴ مورد دیگر

Michael Wright is a Senior Lecturer in the Department of Computer Science at the University of Bath. His research focuses on haptic feedback systems, educational technology, and user interaction design. He is affiliated with the Institute of Coding and has collaborated on projects involving mid-air haptics, wearable technology, and inquiry-based learning environments. His work spans interdisciplinary areas such as thermal technology for social interaction (e.g., WarmConnect), perceptual studies in haptics, and educational platforms like nQuire. Recent projects emphasize the integration of visual and tactile feedback systems to enhance user experiences. Wright's publications highlight trends in mid-air haptics research, exploring thresholds of tactile perception and multimodal feedback systems. His educational research addresses challenges in professional development through degree apprenticeships and online learning. He has supervised a doctoral thesis on gesture recognition and contributed to projects like 'Day of the Figurines,' a narrative-driven mobile game. His work bridges computational methods with human-centered design principles.

Jennifer L. Smithمشاهده پروفایل

استاد

• Phonology
• Phonetics
• Linguistic Theory
• +۵ مورد دیگر

Jennifer L. Smith is a Professor and Associate Chair in the Department of Linguistics at the University of North Carolina, Chapel Hill. Her research focuses on phonology, particularly the interplay between phonological constraints, morphosyntactic categories, and experimental methods. Key areas include lexical-category effects (e.g., noun/verb phonological privilege), loanword adaptation, blend phonology, and intonation patterns in Fukuoka Japanese. She has pioneered experimental approaches using artificial-language learning and surfeit-of-the-stimulus experiments to test hypotheses about constraint interaction and phonological universals. Smith’s work bridges formal phonology and phonetics, addressing questions like how phonetic naturalness influences phonological constraints and whether category-specific phonology reflects innate biases. Recent projects include investigating positional effects in phonology (e.g., salient positions) and the role of core-periphery structures in loanword nativization. She has collaborated extensively with researchers like Elliott Moreton and Katya Pertsova on blend phonology and experimental phonology. Her teaching includes foundational courses like Linguistics 60: How Reading Works , emphasizing interdisciplinary connections between language, cognition, and literacy. Smith maintains key resources for the field, including a widely referenced Phonetics resources page with curated materials for teaching and research. Her office is located in Smith Building 309, and she holds regular office hours via Zoom.

Joel W. Simmonsمشاهده پروفایل

دانشیار

• Political Economy
• Economic Growth
• Globalization
• +۳ مورد دیگر

Joel W. Simmons is an Associate Professor at Georgetown University, affiliated with the Edmund A. Walsh School of Foreign Service and the Department of Government. He holds a PhD from the University of Michigan. His research focuses on political economy, emphasizing economic growth, globalization, social policy, democracy, and gender dynamics. Key works include his book The Politics of Technological Progress (Cambridge University Press, 2016) and articles in top journals like World Politics and International Organization . His 2024 article on democracy and economic growth explores theoretical and empirical linkages, while recent studies address gendered economic consequences of exchange rates and corruption's impact on voter behavior. His work spans methodological innovations (e.g., detecting government violence anomalies) and policy-relevant topics like resource wealth's gendered effects. No formal awards are listed, but his publications reflect sustained academic impact. He advises no listed students but collaborates with institutions like Princeton and Rice University. His research often bridges macro-level political economy with micro-level gender and voter dynamics, contributing to debates on development, governance, and global inequalities.

Nguyen Khanhمشاهده پروفایل

استادیار

• Programming Languages
• Compiler & Runtime Support
• Memory Management
• +۵ مورد دیگر

Khanh Nguyen is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on improving scalability and efficiency in Big Data systems through compiler and runtime innovations, particularly in memory management and distributed computing. Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) M.S. in Computer Science, University of California, Irvine (2015) B.S. in Computer Science, University of California, Irvine (2012) His research interests include programming languages, compiler design, memory management, and Big Data systems. He has developed techniques such as Gerenuk for thin computation over big data, Skyway for distributed heap connectivity, and Yak , a high-performance garbage collector. His work emphasizes resource efficiency and workload scalability, particularly for machine learning and data-parallel applications. Recent publications (2021–2024) highlight advancements in adaptive memory management for warehouse-scale computers, semantics-aware swapping in disaggregated systems, and query-driven distributed tracing. Awards: Google Ph.D. Fellowship (2017) Facebook Ph.D. Fellowship Finalist (2017) His research bridges compiler/runtime systems with large-scale data processing, addressing challenges in distributed systems and far-memory utilization. He collaborates with industry and academic partners to advance practical, scalable solutions for modern data-intensive workloads.

Cristina Vatulescuمشاهده پروفایل

استاد

• Literature and Policing Practices
• Archival Theory
• Secret Police Archives
• +۷ مورد دیگر

Professor Cristina Vatulescu teaches Comparative Literature at NYU's College of Arts and Science, specializing in the intersection of literature, policing practices, and archival theory. An award-winning scholar, her book Police Aesthetics won the Heldt Prize and Outstanding Academic Title Award. She holds a Harvard PhD and completed a Harvard Society of Fellows junior fellowship. Her research examines secret police archives, Soviet-era cultural production, and intermedia genres. Current projects include Reading the Archival Revolution and Arts of Attention: A Literary Seed Bank . Fellowships: NYU Humanities (2021-2022), Goddard (2010) Research spans film pedagogy, Foucault's archives, and Iron Curtain cultural politics Publications in Kritika, Diacritics, and Law and Literature

Salvatore Ruggieriمشاهده پروفایل

استاد

• Data Mining
• Knowledge Discovery
• Discrimination Discovery and Prevention
• +۹ مورد دیگر

Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.

Geoffroy Couteauمشاهده پروفایل

پژوهشگر

• Cryptography
• Secure Multiparty Computation
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Geoffroy Couteau is a CNRS research scientist at IRIF (Institut de Recherche en Informatique Fondamentale), Université Paris Cité, where he conducts research in theoretical and applied cryptography. He obtained his PhD from École Normale Supérieure de Paris in 2017 under the supervision of David Pointcheval and Hoeteck Wee, followed by a postdoctoral position at Karlsruhe Institute of Technology (KIT) from 2017 to 2019. His primary research interests include secure multiparty computation, zero-knowledge proofs, and the theoretical foundations of cryptography, with a particular emphasis on pseudorandom correlation generators and efficiency improvements in cryptographic protocols. He has made significant contributions to fine-grained cryptography, non-interactive zero-knowledge proofs, and post-quantum secure computation. The recent publications reflect a strong trend toward foundational advances in secure computation, with increasing focus on efficiency, practicality, and connections to complexity theory and learning theory. His work often bridges theoretical hardness assumptions with practical protocol design. ERC Starting Grant (2023) for project OBELiSC (Overcoming Barriers and Efficiency Limitations in Secure Computation) Geoffroy Couteau has advised numerous PhD and master’s students, including Dung Bui, Clément Ducros, Eliana Carozza, and Ulysse Léchine. He has also hosted many visiting students and postdocs, fostering a vibrant research group. He has served on the program committees of major conferences such as EUROCRYPT, CRYPTO, TCC, and PKC. He is currently leading research in cryptography at IRIF and is involved in postdoctoral hiring for projects in advanced cryptographic primitives. He maintains a research blog and resource collection for students, including LaTeX templates, a probability cheat sheet, and curated answers to common cryptography questions.

• Space Systems Engineering
• Computer Architecture
• Embedded Systems
• +۵ مورد دیگر

Pablo Parra Espada is an Associate Professor at the Department of Automática, University of Alcalá (Spain), affiliated with the Space Research Group (SRG-UAH). He holds a PhD from the University of Alcalá (2012) titled Integración de tecnologías de desarrollo y análisis basadas en componentes bajo un enfoque multi-plataforma , supervised by Dr. Sebastián Sánchez Prieto and Dr. Óscar Rodríguez Polo. His research focuses on space systems engineering , particularly in RISC-V processor design , embedded systems , and model-driven engineering . Key areas include hardware-software co-design for satellite systems, real-time computing, and fault-tolerant architectures. He has contributed to the Solar Orbiter mission through work on the Energetic Particle Detector (EPD) and its on-board software validation. His recent work emphasizes virtualization techniques for LEON processors, FPGA-based digital beamforming , and spaceborne phased array systems . He also explores model-driven approaches for automated configuration of ground support equipment. His interdisciplinary contributions bridge computer architecture with aerospace applications. Prof. Parra Espada has published extensively on topics such as hardware performance monitoring, memory management units for satellites, and system-level verification of space software. His work combines rigorous engineering methodologies with cutting-edge technologies to address challenges in space instrumentation and embedded systems.

Fatemeh Ganjiمشاهده پروفایل

استادیار

• Hardware Security
• Cryptography
• Machine Learning
• +۳ مورد دیگر

Fatemeh Ganji is an Assistant Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), with an affiliation to the Cybersecurity program. She holds a Ph.D. in Electrical Engineering from the Technical University of Berlin (2017), where she received the BIMoS Ph.D. Award and was nominated for the ACM Dissertation Award. Prior to WPI, she served as a Post Doctoral Associate at the University of Florida (2018–2020) and at Telecom Innovation Laboratories/Technical University of Berlin (2017–2020). Her research focuses on interdisciplinary approaches in hardware security, combining machine learning and cryptography to design and evaluate security-critical hardware systems. Key areas include physically unclonable functions (PUFs), side-channel analysis, and countermeasures against tampering and counterfeiting. Her work is funded by the European Union (Horizon 2020, FP7), German BMBF, NSF, and NIST. Ganji actively contributes to the academic community as a reviewer for IEEE and ACM journals and serves on technical program committees for CHES, FPL, DATE, and SPACE conferences. Her recent projects include developing AI-driven forensic analysis for PCB tamper detection, secure multiparty computation frameworks for chiplet systems, and open-source tools for implementation security testing. Her awards include the BIMoS Ph.D. Award 2018 and recognition from the Technical University of Berlin for her doctoral work on PUF learnability. She has also pioneered methods to detect recycled integrated circuits and enhance hardware trust through reverse engineering and machine learning.