جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Machine Learning
• Quantum Methods
• +۳ مورد دیگر

Davi Geiger is an Associate Professor of Computer Science and Neural Science at New York University (NYU), affiliated with the Courant Institute of Mathematical Sciences. He is part of the Computer Vision Group within the College of Arts and Science and the Graduate School of Arts and Science. His research focuses on advancing computer vision and machine learning, with a recent emphasis on integrating quantum methods and group theory. His work explores topics such as stereo vision, human perception modeling, quantum entropy, and medical imaging analysis. Key research areas include quantum algorithms applied to vision problems, geometric constraints in stereo matching, and entropy-based models in physics. Notable contributions include studies on cyclopean stereo vision, spin entropy dynamics, and regularization techniques for image classification. Dr. Geiger has led projects funded by grants such as the NSF's 'Geometry-and Symmetry-Driven Computer Vision Methods for High-Throughput Automated Microscopic Imaging.' His work bridges theoretical physics and computational methods, with applications in medical imaging and developmental biology. Scientific achievements include pioneering methods for cell division detection in embryo development and developing adaptive confusion energy models for natural data distribution. His research often combines interdisciplinary approaches, leveraging insights from neuroscience and quantum computing to solve complex vision problems.

• Theory of Computation
• Fine-Grained Complexity
• Graph Theory
• +۷ مورد دیگر

Dr. Amir Abboud is a Senior Scientist at the Weizmann Institute of Science , Department of Computer Science and Applied Mathematics. His research focuses on the Theory of Computation , particularly in Fine-Grained Complexity , aiming to determine the exact computational complexity of fundamental problems. Additional interests include Graph Theory, Dynamic Data Structures, Pattern Matching, Exact Algorithms, and Distributed Computing. Education: Ph.D. in Computer Science, Stanford University M.Sc. in Computer Science, Technion B.Sc. in Computer Science, University of Haifa (via the "Etgar" program) Research Trends: Amir's work bridges theoretical computer science with practical algorithm design, emphasizing hardness of approximation, circuit complexity, and lower bounds for dynamic problems. His recent publications highlight advancements in Gomory-Hu Tree algorithms, spanner optimality, and distributed complexity. Professional Affiliations: He has served on program committees for top conferences including FOCS, STOC, ICALP, and SODA. Previously affiliated with IBM Almaden Research Center as a Research Staff Member. Contact: Located in Room 106, Jacob Ziskind Building, Weizmann Institute. Email: amir.abboud@weizmann.ac.il , Phone: +972-8-934-3618.

Oren Weimannمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Data Structures
• +۹ مورد دیگر

Oren Weimann is a Professor in the Department of Computer Science at the University of Haifa, Faculty of Natural Sciences. His research lies at the intersection of theoretical computer science, algorithm design, and data structures, with a strong focus on planar graphs, combinatorial pattern matching, and fine-grained complexity. He has published extensively in top-tier venues such as STOC, SODA, ICALP, PODC, and ESA. Education: Ph.D., Massachusetts Institute of Technology (MIT), 2005–2009. Advisor: Erik Demaine. Dissertation: "Accelerating Dynamic Programming" Postdoc, Weizmann Institute of Science, 2009–2011. Host: David Peleg M.Sc., University of Haifa, 2004–2005. Advisor: Gad Landau. Dissertation: "Using PQ trees for Comparative Genomics" B.A., Technion – Israel Institute of Technology, 1999–2002 Oren Weimann's research centers on the design and analysis of efficient algorithms, particularly for planar and structured graphs. His work explores fundamental problems such as shortest paths, distance oracles, fault tolerance, edit distance, and pattern matching. He investigates both upper and lower bounds, often pushing the limits of what is computationally feasible under fine-grained complexity assumptions. His contributions include optimal labeling schemes, compressed data structures, and breakthroughs in dynamic and distributed graph algorithms. His recent publications reveal a consistent trend in developing highly efficient algorithms for planar graphs, with a focus on distance computation, fault tolerance, and compression. Keywords across these works include planar graphs, dynamic programming, string matching, and conditional lower bounds, reflecting a deep integration of algorithmic techniques and complexity theory. He frequently collaborates with leading researchers such as Shay Mozes, Paweł Gawrychowski, and Philip Bille. Scientific Awards: Best Paper Award, CPM 2007 Best Paper Award, ICALP 2020 (mentioned in context of work) Oren Weimann has advised numerous PhD and Master’s students, including Yaseen Abd-Elhaleem, Nathan Wallheimer, Aviv Bar-natan, and Shon Feller, whose dissertations have led to publications in major conferences. He has also mentored several postdoctoral researchers such as Shay Golan, Itai Boneh, and Panagiotis Charalampopoulos. His work has been supported by competitive research grants, though specific grant titles are not listed in the text. He has served on the program committees of key conferences including SODA, ICALP, CPM, ESA, and SPIRE, demonstrating active leadership in the theoretical computer science community. He is associated with a vibrant research group focused on algorithms and data structures, likely involving collaboration with students and postdocs on projects related to graph algorithms, string processing, and complexity. While no formal lab name is mentioned, his collaborative output suggests a strong, productive research team at the University of Haifa.

• Computer Security
• Privacy
• Applied Cryptography
• +۱۲ مورد دیگر

Aggelos Kiayias is the Chair in Cyber Security and Privacy at the University of Edinburgh, where he directs the Blockchain Technology Laboratory. He also serves as Chief Scientist at Input Output, a blockchain technology company. His research focuses on computer security, privacy, applied cryptography, blockchain technologies, distributed systems, e-voting, secure multiparty protocols, and identity management. Ph.D., City University of New York B.Sc., Mathematics, University of Athens With over 200 publications, his work examines blockchain protocol design, consensus mechanisms, privacy-preserving systems, and cryptocurrency economics. Recent research explores transaction fairness, stake-based consensus, and regulatory-compliant financial systems. His studies bridge theoretical cryptography with real-world implementations, including quantum-resistant blockchain frameworks and incentive-aligned protocol designs. Scientific recognitions include the BCS Lovelace Medal (2024), ERC Starting Grant, Marie Curie Fellowship, NSF Career Award, and Royal Society of Edinburgh Fellowship. Supervised 22 Ph.D. students with impactful careers in academia and industry, including researchers at Stanford, Imperial College, and blockchain protocol architects. Current projects investigate tiered transaction fee models, privacy-preserving tokens, and decentralized reliability estimation systems. His work receives funding from European Research Council, Horizon 2020, EPSRC, NSF, and NIST.

• Algorithms
• Graph Theory
• Fine-Grained Complexity
• +۴ مورد دیگر

Virginia Vassilevska Williams is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). Her research focuses on combinatorial and graph-theoretic tools in computational domains, including shortest paths, pattern detection, fine-grained complexity, and computational social choice. Education: B.S. in Mathematics and Eng. and Applied Science from Caltech (2003), Ph.D. in Computer Science from Carnegie Mellon University (2008), postdoc at the Institute for Advanced Study (2008-2009), and Computing Innovations Fellow at UC Berkeley (2009-2011). Her work includes foundational contributions to fine-grained complexity, such as conditional lower bounds for problems in P, and algorithmic innovations in cycle detection, spanners, and approximate counting. She has also explored computational issues in social choice, including election manipulation and voting rule analysis. Recent publications highlight advancements in sparse graph algorithms, distributed cycle detection, and diameter approximation, leveraging matrix multiplication and quantum computing techniques. Her research emphasizes bridging theoretical insights with practical computational challenges. Scientific contributions include the Computing Innovations Fellowship and co-organizing key workshops like TCS For All and Simons Institute programs. She actively advises Ph.D. students and has mentored numerous graduates now at institutions like UCSD, INSAIT, and Boston University.

Andrej Risteskiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Theoretical Computer Science
• +۵ مورد دیگر

Andrej Risteski is an Associate Professor at the Machine Learning Department of Carnegie Mellon University (CMU) since 2025. He previously held the Norbert Wiener Research Fellow position jointly between the Applied Math Department and IDSS at MIT (2017–2019) after completing his PhD in Computer Science at Princeton University (2012–2017) under Sanjeev Arora . His research focuses on the intersection of machine learning, statistics, and theoretical computer science , emphasizing generative models, representation learning, and out-of-distribution generalization with applications to natural language processing and scientific domains . His recent publications explore edge embeddings in Graph Neural Networks (GNNs) , score matching efficiency , and theoretical foundations of diffusion models . Key contributions include analyzing computational bottlene.com/activities/statistical-and-computational-challenges-in-probabilistic-scientific-machine-learning-sciml/">NSF CAREER Award , DOE Computational Science Graduate Fellowship for Stephen Huan, and co-organizing the COLT workshop on Theory of AI for Scientific Computing . He advises PhD students across Machine Learning, Computer Science, and Mathematics , including Bingbin Liu (Kempner Institute Fellow) and Elan Rosenfeld (Google Research Scientist). Teaching includes Probabilistic Graphical Models and Advanced Deep Learning at CMU, plus Applied Mathematics at MIT. His work is supported by NSF, DoD , and OpenAI Superalignment grants. Education : PhD in Computer Science (Princeton), BSc in Computer Science (Princeton) Current Positions : Associate Professor, CMU Machine Learning Department Former Positions : Norbert Wiener Fellow, MIT IDSS & Applied Mathematics Research Areas : Generative Models (GANs, Diffusion Models) Representation Learning Out-of-Distribution Generalization Neural Language Models AI for Scientific Applications Sampling and Optimization Algorithms Scientific Awards : NSF CAREER Award (2023) Google Research Award (2024) Amazon Research Award (2022) OpenAI Superalignment Grant (2023) Recent Talks (2023–2025): "Architectural Nuances and Benchmark Gaps in Scientific ML" (UC Berkeley, 2025) "The Statistical Cost of Score-Based Losses" (Simons Institute Boot Camp, 2024) "Neural Networks for PDEs" (ETH Zurich, 2024) "Discernible Patterns in Transformers" (Theory of Interpretable AI, 2024) He leads a research group producing work at the interface of computational complexity and graph learning , with empirical validation on topological bottlenecks and hub node dynamics . Current projects include ICML 2025 paper on edge embeddings in GNNs and COLT 2025 workshop on AI for Scientific Computing co-organized with MIT, Duke, and ETH Zurich collaborators.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +۳ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

• Theoretical Computer Science
• Algorithms
• Complexity Theory
• +۲ مورد دیگر

Alexander Firbas serves as a Researcher at the Institute of Logic and Computation within Vienna University of Technology's Faculty of Informatics, specializing in theoretical computer science with emphasis on graph algorithms and computational complexity. His academic background includes a Diploma Engineer (Dipl.-Ing.) and Bachelor of Science (BSc), with his 2023 Diploma Thesis establishing foundational work on vertex splitting for hereditary graph properties. Current doctoral research extends into parameterized complexity and geometric graph representations. Firbas investigates structural graph properties through computational lenses, focusing on tractability boundaries for problems involving vertex splitting, cluster modifications, and geometric thickness. His methodology integrates fine-grained complexity analysis with algorithmic design for NP-hard graph problems. Recent publications demonstrate consistent exploration of graph modification paradigms, evolving from hereditary property establishment (2023) to cluster vertex splitting (2024) and geometric thickness tractability (2025), reflecting deepening specialization in parameterized approaches to graph-theoretic challenges. He contributes to the multi-year 'Parameterized Graph Drawing' project (2023–2027) while teaching Algorithmic Geometry and Graph Algorithms seminars. His advising activities include supervising master's theses within the Algorithms and Complexity research framework. As an active member of the Algorithms and Complexity research group, Firbas collaborates on theoretical projects examining computational boundaries in graph theory, with ongoing work targeting degree-constrained spanning trees and geometric embedding problems.

Taylor Erwinمشاهده پروفایل

عضو هیئت علمی

• Computational Fluid Dynamics
• Aerospace Engineering
• High-Performance Computing
• +۳ مورد دیگر

Taylor Erwin is a Research Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. They are affiliated with Oak Ridge National Laboratory and focus on computational fluid dynamics (CFD), high-order numerical methods, and finite-element solvers. Their work emphasizes turbulence modeling, shock capturing techniques, and aerodynamic simulation for aerospace applications. Research interests include the development of the COFFE solver within the HPCMP CREATE-AV Kestrel framework, high-performance computing optimizations, and mesh adaptation strategies. Erwin’s contributions span RANS and LES simulations, transition modeling, and validation against benchmark configurations. They collaborate on projects involving ultra-efficient aircraft design, slotted natural laminar flow concepts, and parallel computing scalability. Key technical trends in their articles include advancements in finite-element discretization, shock-capturing algorithms for discontinuous Galerkin methods, and the integration of turbulence models like Spalart-Allmaras. Recent work highlights the application of output-based mesh adaptation and high-order curved meshes for improving solution accuracy in complex flows. No scientific awards are explicitly listed in the provided information. Advising and graduate student supervision details are not mentioned. Erwin is involved with the COFFE project and the HPCMP framework, contributing to multi-level parallelism strategies and solver robustness in computational engineering. Affiliations include the University of Tennessee and Oak Ridge National Laboratory, with research activities centered on aerospace CFD challenges and high-fidelity simulation technologies.

• Formal Methods
• Safety-Critical Systems
• Hybrid Systems
• +۷ مورد دیگر

Zhenya Zhang is an Assistant Professor at the Intelligent Software Engineering Laboratory, Faculty of Information Science and Electrical Engineering, Kyushu University, Japan. He also serves as a Project Researcher at the National Institute of Informatics, Tokyo. His research focuses on formal methods for safety-critical complex systems , including hybrid systems (cyber-physical systems) and AI-based systems like autonomous vehicles. PhD in Informatics (2020), National Institute of Informatics/SOKENDAI Master in Computer Science (2017), Institute of Software Chinese Academy of Sciences/University of Chinese Academy of Sciences Bachelor in Software Engineering (2014), Northwestern Polytechnical University Research Interests : Quality assurance of safety-critical systems through formal methods. Specializes in verification, testing, and monitoring of hybrid systems and AI-controlled cyber-physical systems, with applications in autonomous driving validation. Key contributions include optimization-based falsification techniques and confidence measures for robustness analysis. Article Trends : Recent work emphasizes neural network verification, Monte Carlo Tree Search for falsification, and signal temporal logic monitoring. Collaborations span institutions in Japan, Singapore, and Italy, with tools like FalStar and Tumb developed for CPS testing. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (ASE'24) 2nd rank in UAV Testing Competition (SBFT'24@ICSE'24) Best Paper Award nominee (EMSOFT 2018) National Scholarship, China (2011, 2012) Grants : Principal Investigator for JST BOOST (2025-2030) and multiple JSPS/Japan Society for the Promotion of Science grants. Current projects focus on neural network verification and fine-grained monitoring of temporal logic specifications. Labs & Collaborations : Works with the ERATO Metamathematics for Systems Design Project and contributes to ARCH-COMP competitions. Tools under development include FalStar-MCTS and ForeSee for hybrid system falsification.

• Programming Languages
• Information Security
• Software Security
• +۱۲ مورد دیگر

Dr Yi Lu is a Senior Lecturer at the Queensland University of Technology (QUT), Faculty of Science, School of Computer Science. He serves as Deputy Academic Lead (HDR) for the School of Computer Science, contributing to higher degree research leadership. His academic journey includes a PhD in Computer Science from the University of New South Wales (2008) and prior roles as a Lecturer at UNSW (2007–2013) and Principal Researcher at Oracle Labs (2013–2019). PhD in Computer Science, University of New South Wales, 2008 Bachelor of Information Technology (Honours), Queensland University of Technology, 2002 Dr Lu's research lies at the intersection of programming languages and information security, with a focus on theoretical and practical aspects of software security. His work includes type systems for object synchronization, ownership types, information flow control, and the application of deep learning to vulnerability detection. He has made significant contributions to static analysis, points-to analysis, and secure multithreaded programming. His recent publications span topics such as threat hunting in industrial control systems, nontransitive security types, and multi-class vulnerability prediction using graph neural networks. These works reflect a strong trend toward combining formal methods with machine learning to enhance software security, particularly in complex and critical systems. Dr Lu has been involved in significant research initiatives, including the Cyber Security Cooperative Research Centre and the ARC Linkage Project on Java language-based security conformance. His industry experience at Oracle Labs led to patented solutions for real-world cybersecurity problems, demonstrating the practical impact of his research. He supervises research projects on fine-grained vulnerability detection, reproducible deep learning workflows, and open-source supply chain analysis. His teaching includes courses such as Principles of Software Security and Object-Oriented Design, aligning closely with his research expertise. Dr Lu is affiliated with the School of Computer Science at QUT, a research-active unit within the Faculty of Science, contributing to cybersecurity education and innovation in Australia.

Karl Bringmannمشاهده پروفایل

استاد

• Theoretical Computer Science
• Fine-Grained Complexity
• Algorithm Design
• +۵ مورد دیگر

Karl Bringmann is a Professor at Saarland University since November 2019 and is affiliated with the Max Planck Institute for Informatics, where he works in the Department of Algorithms and Complexity. He has established himself as a leading researcher in theoretical computer science, particularly in fine-grained complexity and algorithm design. His work bridges theoretical insights with practical applications in optimization problems. Bringmann's research focuses on conditional lower bounds (often based on the Strong Exponential Time Hypothesis) and algorithm design, with particular emphasis on optimization problems, string algorithms, and computational geometry. His work has significant implications for fundamental problems like Subset Sum, Knapsack, and Integer Programming, with applications ranging from scheduling to post-quantum cryptography. He develops innovative approaches combining modern algorithmic techniques, mathematical structure theory, and fine-grained complexity to design faster algorithms and establish optimality. His publication record shows a consistent trend toward developing near-optimal algorithms for fundamental problems, with significant contributions to fine-grained complexity theory. His work often establishes tight conditional lower bounds while simultaneously providing matching upper bounds, creating a comprehensive understanding of problem complexity. He has made notable advances in string algorithms (particularly edit distance), geometric problems, and optimization. ERC Starting Grant 2019: Technology Transfer between Integer Programming and Efficient Algorithms (TIPEA) EATCS Presburger Award for Young Scientists 2019 Heinz Maier-Leibnitz-Prize 2019 EATCS Distinguished Dissertation Award 2015 Google European Doctoral Fellowship 2012-2014 Bringmann leads the ERC-funded TIPEA project (2019-2024), which investigates fundamental optimization problems with the goal of developing next-generation industrial solvers. He advises several PhD students including Nick Fischer, Alejandro Cassis, and Vasileios Nakos, and has served on numerous program committees for top theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. His teaching includes advanced courses on Fine-Grained Complexity Theory and Competitive Programming.

Alex Lascaridesمشاهده پروفایل

استاد

• Theoretical Linguistics
• Formal Semantics and Pragmatics
• Natural Language Processing
• +۵ مورد دیگر

Alex Lascarides is a Professor of Semantics in the School of Informatics at the University of Edinburgh, holding the Chair in Semantics. He is affiliated with the Institute for Language, Cognition and Computation and currently on sabbatical. His research focuses on the formal semantics and pragmatics of discourse and conversation, including gesture interpretation, strategic dialogue, and interactive task learning. He co-authored Logics of Conversation , a foundational text on Segmented Discourse Representation Theory (SDRT). Research Interests: Theoretical Linguistics: Formal semantics/pragmatics, discourse coherence Natural Language Processing: Dialogue systems, gesture analysis Artificial Intelligence: Human-robot interaction, policy learning in complex games Key Projects: TAS Governance Node (UKRI, 2021–2024) – Trustworthy Autonomous Systems SDRT – Ongoing work modeling discourse interpretation ROSIE Project (2001–2005) – Robust semantic interpretation in dialogue Education: Doctorate in Linguistics/Computational Linguistics (not explicitly stated, inferred from career trajectory). Labs/Teams: Leads research on discourse semantics and embodied interaction at the University of Edinburgh. Collaborates internationally on multimodal communication and AI ethics.

• Theoretical Computer Science
• Computational Complexity
• Counting Problems
• +۱۰ مورد دیگر

Marc Roth is a Lecturer in Theoretical Computer Science at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He is also an Associate Member of the Department of Computer Science at the University of Oxford, reflecting his ongoing research collaboration with one of the leading computer science departments in the world. His research centers on computational counting problems, particularly focusing on the multivariate and exact complexity of problems that are intractable under classical computation models. He investigates motif counting in higher-order networks, which has applications in data mining, bioinformatics, genetics, and artificial intelligence. His work bridges theoretical algorithmics with practical implications, including the expressive power of graph neural networks and logical characterizations in descriptive complexity. Marc Roth's research integrates advanced algorithmic techniques such as parameterized complexity, fine-grained complexity, randomized algorithms, and approximation methods. His recent focus on higher-order structures extends traditional graph-based analysis to relational data, opening new avenues for theoretical and applied network science. While no specific awards are listed, his active supervision of PhD research and prior prestigious affiliations suggest recognition in the theoretical computer science community. He advises PhD students, as evidenced by his advertised studentship on motif counting in higher-order networks. Though no grants are explicitly mentioned, the funded PhD position indicates involvement in research funding. His academic journey includes a PhD from Saarland University under Holger Dell, a Junior Research Fellowship at Merton College, Oxford, and a Senior Research Associate role in Algorithms and Complexity Theory at Oxford’s Department of Computer Science. Marc Roth is based at the Peter Landin Building on the Mile End Campus, where he holds office hours on Fridays. His work continues to advance the foundations of computational complexity and its applications in modern data science.

Mostafizur Rahmanمشاهده پروفایل

استادیار

• Beyond CMOS Computing
• Nanoscale 3-D Integrated Circuits
• Neuromorphic Computing
• +۳ مورد دیگر

Mostafizur Rahman is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City. His research focuses on nanoscale computing paradigms, including Beyond CMOS computing with emerging devices, neuromorphic systems mimicking mammalian brain functions, and 3-D integrated circuits with advanced thermal management. He holds a Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst. His expertise spans nanoscale 3-D integrated circuits, crosstalk-based computing architectures, and experimental prototyping of novel nanodevices. Key contributions include developing Skybridge-3D-CMOS technology for fine-grained 3-D integration and exploring crosstalk computing as a foundation for reconfigurable digital logic. He investigates thermally aware circuit design, addressing challenges in transistor-level 3-D integration and scalability. Research trends in his articles emphasize scalable crosstalk computing (7nm to 180nm nodes), FPGA security through bitstream analysis, and neuromorphic systems leveraging wave interference functions. His work bridges nanoscale device physics with system-level design, targeting applications in high-density, energy-efficient computing systems. No scientific awards are explicitly documented in the provided materials. Advising and grants details are not included, but his labs focus on experimental prototyping of nanoscale fabrics and advanced 3-D IC technologies.