جست‌وجوی استاد و پژوهشگر

Adrian Wellerمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Huan Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Formal Verification of Machine Learning
• AI Safety
• +۴ مورد دیگر

Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.

Brandon M. Luciaمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.

Rachit Agarwalمشاهده پروفایل

دانشیار

• Systems
• Networking
• Theory of Computing
• +۶ مورد دیگر

Rachit Agarwal is an Associate Professor in the Department of Computer Science at Cornell University, with research focusing on systems, networking, and theoretical problems arising in practical systems. He leads a research group working on resource disaggregation, host architecture, secure cloud storage, and datacenter design. PhD in Computer Science, Cornell University Undergraduate, IIT Kanpur His research spans three major directions: Resource Disaggregation (with $3M NSF and Google awards), Host Architecture (exploring terabit interconnects), and PANCAKE (secure oblivious cloud storage with $1M NSF award). He also contributed to foundational work in Near-optimal Datacenter Design and Data Plane Monitoring . Awards include the Sloan Fellowship, NSF CAREER, IRTF Applied Networking Prize, and multiple best paper awards. His recent publications focus on host network architecture, congestion control, oblivious data access mechanisms, and secure cloud storage systems. He has advised multiple Ph.D. and postdoctoral researchers who now hold faculty positions at leading institutions. Sloan Research Fellowship NSF CAREER award Kavli Fellowship IRTF Applied Networking Research Prize SIGCOMM Best Student Paper Award Usenix Security Distinguished Paper Award Tau Beta Pi Professor of the Year 2025 Rachit has advised numerous students including current Cornell advisees like Midhul Vuppalapati, Shreyas Kharbanda, and Omar Eqbal. Former advisees include Saksham Agarwal (UIUC), Qizhe Cai (UVA), and Mina Tahmasbi Arashloo (University of Waterloo). His research is supported by large NSF grants and industry awards, with deployments in real-world systems and open-source contributions.

Insup Leeمشاهده پروفایل

استاد

• Cyber-physical Systems
• Real-time and Embedded Systems
• Safe Autonomy
• +۳ مورد دیگر

Insup Lee is the Cecilia Fitler Moore Professor in the Department of Computer and Information Science and Director of the PRECISE Center at the University of Pennsylvania's School of Engineering and Applied Science. He holds a secondary appointment in the Department of Electrical and Systems Engineering and the Perelman School of Medicine’s Department of Biostatistics, Epidemiology, and Informatics. IEEE TCCPS Distinguished Leadership Award (2023) Fellow of the AAAS (2022) Test of Time Award, Runtime Verification (2019) Fellow of the ACM (2017) Best Paper Awards at IEEE ICPS, ACM/IEEE ICCPS, and MEMOCODE His research focuses on cyber-physical systems , real-time and embedded systems , safe autonomy , and internet of medical things , with applications in healthcare and connected systems. He advises PhD students including Eric Lu, Kaustubh Sridhar, Sooyong Jang, and Jean Park (co-advised with Kevin Johnson). Recent publications address safety monitoring for learning-enabled systems, model-free control synthesis using reinforcement learning, and multilingual toxicity guardrails for large language models. His team collaborates with institutions like Hillrom and Penn Nursing to optimize medical device usage in clinical settings.

Kaki Ryanمشاهده پروفایل

استادیار

• Hardware Security
• Symbolic Execution
• Computer Science Education
• +۳ مورد دیگر

Kaki Ryan is a Teaching Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. She holds a Ph.D. (2025), M.S. (2021), and B.S. (2020) from UNC, with a minor in AAAD. Her research focuses on hardware security, symbolic execution, and computer science education. Education Ph.D., 2025, UNC-Chapel Hill M.S., 2021, UNC-Chapel Hill B.S., 2020, UNC-Chapel Hill (Computer Science and Mathematics) Ryan's research centers on hardware security, particularly using symbolic execution to identify bugs and vulnerabilities in hardware designs. She also emphasizes broadening participation in computing and improving pedagogical strategies in computer science education. Her recent publications (2023-2025) explore symbolic execution techniques for hardware security verification, including query caching, information flow analysis, and path explosion mitigation. These works span conferences like ASPLOS, VTS, FMCAD, and workshops such as HASP. Scientific Awards John M. Glotzer Graduate Teaching Assistant Award (2020-2021) Tanner Award for Excellence in Undergraduate Teaching (2022) As an educator, Ryan has served as Instructor of Record for COMP 435 (Computer Security Concepts) and COMP 311 (Computer Organization) in Fall 2025. She has extensive teaching experience as a Graduate Teaching Assistant, Head Teaching Assistant, and Undergraduate Teaching Assistant for courses like COMP 210, COMP 110, and COMP 435, with a focus on data structures, security concepts, and programming fundamentals.

Kenneth P. Birmanمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Machine Learning Systems
• +۵ مورد دیگر

Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.

Alessio Lomuscioمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning Verification
• Neural Network Verification
• +۵ مورد دیگر

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.

Kamalika Chaudhuriمشاهده پروفایل

استاد

• Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +۴ مورد دیگر

Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.

• Software Verification
• Formal Methods
• Programming Languages
• +۵ مورد دیگر

Jenna Wise DiVincenzo is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. She specializes in research areas such as software verification, formal methods, and programming languages, with a focus on gradual verification techniques that combine static and dynamic analysis. Her work emphasizes usability and scalability in verification tools, and she has contributed to projects like Gradual C0 and gradual null-pointer analysis. Dr. DiVincenzo earned her PhD in Software Engineering from Carnegie Mellon University (2023) and a BS in Mathematics and Computer Science from Youngstown State University (2017). She has interned at IBM Research, MIT Lincoln Laboratory, and the Software Engineering Research and Empirical Studies Lab at YSU. Her awards include the Google PhD Fellowship, NSF GRFP Fellowship, and 2022 Rising Star in EECS. Her research projects span theoretical advancements in gradual verification, empirical studies on usability, and practical tool development. She advises PhD students (e.g., Craig Liu, Conrad Zimmerman) and collaborates on initiatives like gradual verification for Rust and educational tools to teach verification concepts. Her work also explores leveraging large language models for specification generation and enhancing verification tool soundness through formal proofs.

Benjamin Carrison-Schaferمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGAs
• Electronic Design Automation
• +۷ مورد دیگر

Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.

Christopher Pottsمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Semantics
• +۵ مورد دیگر

Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.

Deian Stefanمشاهده پروفایل

دانشیار

• Security
• Programming Languages
• Systems
• +۶ مورد دیگر

Deian Stefan is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research focuses on building secure systems through interdisciplinary approaches spanning security , programming languages , and systems . Key areas include WebAssembly security , JavaScript JITs , constant-time programming , memory safety , information flow control , and program analysis tools. He co-founded Intrinsic , a web-security startup later acquired by VMWare, and contributed to standards like W3C WebAppSec and Node.js Security Working Groups. Education: PhD in Computer Science from Stanford University (2015), advised by David Mazières, John C. Mitchell, and Alejandro Russo BE and ME in Electrical Engineering from Cooper Union (2011) Research Trends: His work targets secure systems (Web frameworks, sandboxing, runtime systems), language-based security (constant-time programming, memory safety), and verification for security . Recent publications focus on mitigating speculative execution attacks, hardware-assisted sandboxing, and WebAssembly security. Awards & Recognition: Distinguished Paper Award at POPL 2021 and IEEE MICRO Top Picks 2024 Best Paper Award at CollaborateCom 2010 2010.01 Prize by Daniel J. Bernstein (DJB) Most Influential Paper Award at ICFP 2022 Teaching: He has taught graduate and undergraduate courses including Graduate Computer Security (CSE 227) , Building Secure Systems with Rust , and Programming Languages: Principles and Paradigms at UCSD since 2018. Prior teaching experience includes Stanford and Cooper Union.

Giovanni De Micheliمشاهده پروفایل

استاد

• Integrated Circuit Design
• Hardware Synthesis
• Logic Optimization
• +۵ مورد دیگر

Giovanni De Micheli is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding appointments in both the School of Computer and Communication Sciences (IC) and the School of Engineering (STI). He is affiliated with the Integrated Systems Laboratory (LSI) and serves as Scientific Director of EcoCloud. His primary office is located in INF 341 building at EPFL's Lausanne campus where he conducts research and teaching activities. Professor De Micheli's research spans multiple domains in electronic design automation and integrated circuit design. His primary interests include: Integrated Circuit Design and Hardware Synthesis Logic Optimization and Boolean Methods Nanoelectronics and 2D Electronics Quantum Computing and Emerging Technologies Low-Power Electronics and Energy-Efficient Systems Electronic Design Automation for Novel Computing Paradigms His recent publications demonstrate a strong focus on advancing logic synthesis techniques for both conventional and emerging technologies. Professor De Micheli's work bridges theoretical foundations with practical applications, particularly in adapting traditional EDA methods to novel computing paradigms like quantum computing and superconducting electronics. His research group has made significant contributions to the development of efficient algorithms for circuit optimization across multiple technology domains, with particular emphasis on area, power, and delay optimization while maintaining functional correctness. Professor De Micheli has supervised numerous doctoral students throughout his career at EPFL, mentoring over 40 PhD candidates whose research spans various aspects of electronic design automation, circuit design, and emerging technologies. His students have gone on to make significant contributions in both academia and industry. As Scientific Director of EcoCloud, Professor De Micheli leads research initiatives focused on energy-efficient computing systems and sustainable cloud infrastructure. His laboratory, the Integrated Systems Laboratory, serves as a hub for interdisciplinary research connecting computer science, electrical engineering, and materials science, with particular focus on next-generation computing technologies.

Rajeev Alurمشاهده پروفایل

استاد

• Formal Methods
• Artificial Intelligence
• Cyber-Physical Systems
• +۶ مورد دیگر

Rajeev Alur is the Zisman Family Professor in the Department of Computer and Information Science at the University of Pennsylvania, leading the School of Engineering and Applied Science. He is the Founding Director of the ASSET Center for Trustworthy AI and a member of the PRECISE Center. His research focuses on formal methods for system design, integrating AI, cyber-physical systems, and machine learning with logical reasoning to ensure safety in autonomous systems. Alur has held leadership roles in major NSF projects like ExCAPE and has directed the Embedded and Multi-Scale Systems (EMBS) program. His research interests span formal verification, temporal logics, programming abstractions, and synthesis techniques. Notable contributions include the development of Nested Words (visibly pushdown languages), streaming string transducers, and tools like AutomataTutor for education. He has advised over 60 PhD students and postdocs, many of whom now hold academic and industry leadership positions. Alur’s awards include the 2024 Knuth Prize and the 2016 Alonzo Church Award. His work on Verisig and compositional verification of neural networks has advanced safety-critical AI applications. He teaches foundational courses like CIS 2620 and develops educational tools, emphasizing both theoretical rigor and practical impact. Key projects include the ASSET Center’s focus on trustworthy AI, integration of logical specifications in reinforcement learning, and formal verification of closed-loop systems with neural components. His publications span over 350 papers, with recent work addressing neurosymbolic learning, security in large language models, and efficient neural network verification.