جست‌وجوی استاد و پژوهشگر

Vatsal Sharanمشاهده پروفایل

استادیار

• Machine Learning
• Theoretical Computer Science
• Statistics
• +۶ مورد دیگر

Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.

Gauthier Gidelمشاهده پروفایل

دانشیار

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +۵ مورد دیگر

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

Jacob Gardnerمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Computational Biology
• +۳ مورد دیگر

Jacob Gardner is an Assistant Professor in the Department of Computer & Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research bridges machine learning and scientific discovery with emphasis on computational biology and molecular design. His primary research interests include: Machine Learning Bayesian Optimization Computational Biology Molecular Design Artificial Intelligence Gaussian Processes Analysis of his 2024-2025 publications reveals a dominant focus on Bayesian optimization techniques integrated with large language models for biological applications. Key trends include therapeutic design using knowledge distillation from scientific literature, RNA splicing prediction, antibiotic development, and scalable Gaussian process methods. His work consistently addresses dimensionality challenges in molecular modeling while improving computational efficiency for high-dimensional biological data. No scientific awards were mentioned in the provided text. No information regarding student advising or research grants was provided in the source material. His research appears supported by institutional initiatives including Penn AI, Innovation in Data Engineering and Science (IDEAS), and the Data Driven Discovery Initiative (DDDI).

Jeff Zhangمشاهده پروفایل

استادیار

• Deep learning
• Computer architecture
• Embedded systems
• +۵ مورد دیگر

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

Hing-Ho Tsangمشاهده پروفایل

استاد

• Civil Engineering
• Structural Engineering
• Seismic Design
• +۷ مورد دیگر

Professor Hing-Ho Tsang is the Chair in Civil and Structural Engineering at the University of Dundee, with over a decade of academic experience in Australia and Hong Kong. His research focuses on advancing sustainable infrastructure solutions, earthquake resilience, and green technologies to enhance building and infrastructure safety against natural disasters. He holds a Chartered Professional Engineer (CPEng) certification and advises governments and industries on building codes and seismic design guidelines. Tsang chairs the Global Network for Geotechnical Seismic Isolation (GSI) and serves as the Australian National Delegate to the International Association for Earthquake Engineering (IAEE). His expertise spans geotechnical seismic isolation, recycled materials in construction, and structural dynamics, contributing to UN Sustainable Development Goals (SDGs) like resilient infrastructure, circular economy, and climate action. With over 200 publications and a career-long impact ranking in Civil Engineering, Tsang has received prestigious awards including the R W Chapman Medal and Research Impact Award. Research Focus: Seismic design, sustainable construction, bio-inspired materials, and geotechnical isolation systems. Awards: Top Cited Article (2021–2022), Teaching Excellence Award (2022), and multiple international recognitions. Leadership Roles: Editorial board member for Geosynthetics International , organizer of the 18th World Conference on Earthquake Engineering technical session. His work emphasizes equity-driven engineering solutions, fostering inclusive and disaster-resilient communities. Current projects include innovative modular building systems and AI-driven seismic response models.

• Automated Transportation Systems
• Connected Vehicles
• Electric Vehicles
• +۳ مورد دیگر

Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.

Erin Bellمشاهده پروفایل

استاد

• Structural Engineering
• Civil Engineering
• Structural Health Monitoring
• +۸ مورد دیگر

Erin Bell is a Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire . She holds a Ph.D. in Structural Engineering from Tufts University and has extensive experience in structural health monitoring, finite element modeling, and infrastructure sustainability. B.C.E., Georgia Institute of Technology M.S., Civil Engineering, Tufts University Ph.D., Structural Engineering, Tufts University Her research focuses on structural health monitoring, bridge condition assessment, and integrating AI techniques like artificial neural networks and deep reinforcement learning for infrastructure asset management. Recent work includes equitable maintenance strategies for aging bridges in flood-prone zones and tidal energy conversion for sustainable bridge monitoring systems. Key trends in her publications include the application of machine learning to structural analysis, finite element model calibration, and climate change adaptation in transportation infrastructure. She has led projects on deep reinforcement learning for bridge scour maintenance, modal-based uncertainty quantification, and multi-scale modeling of steel bridges. Grants and Collaborations : Erin Bell has secured funding from the National Science Foundation (NSF) , US Department of Energy (DOE) , and New Hampshire Department of Transportation . Notable projects include the Living Bridge initiative for tidal energy-powered smart infrastructure and statewide data exchange systems for bridge condition assessment.

Chenhao Chuمشاهده پروفایل

استاد

• RF/mm-Wave Circuits
• Power Amplification
• AI-Assisted Design Synthesis
• +۴ مورد دیگر

Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.

• Emerging memory and storage technologies
• Embedded systems
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.

Juan Alonsoمشاهده پروفایل

استاد

• Aerospace System Design
• Computational Fluid Dynamics
• Multi-Disciplinary Optimization
• +۷ مورد دیگر

Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.

Asu Ozdaglarمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• Game Theory
• +۴ مورد دیگر

Asu Ozdaglar is the EECS Department Head and MathWorks Professor at MIT, serving as Deputy Dean of Academics in the MIT Schwarzman College of Computing. Her research bridges optimization theory, machine learning, and network science with societal implications, focusing on AI ethics, data-driven decision systems, and strategic interactions in networked environments. Her technical contributions include foundational work on large-scale optimization algorithms (e.g., distributed methods, first-order methods), game-theoretic models for network systems, and federated learning frameworks. Recent work addresses critical societal challenges like misinformation dynamics, data market inefficiencies, and algorithmic fairness in AI systems. Publications from 2023-2025 highlight advancements in graphon-based network game analysis, privacy-preserving data mechanisms, and multi-agent learning dynamics. She co-leads initiatives in MIT's AI+D program, emphasizing interdisciplinary education and ethical AI development. Notable institutional roles include oversight of MIT's computing education strategy and contributions to pandemic-related research on infection control through testing optimization. Her work integrates technical rigor with policy-relevant insights, influencing both academic and real-world systems.

Bayu Jayawardhanaمشاهده پروفایل

استاد

• Mechatronics
• Nonlinear Control Systems
• Robotics
• +۵ مورد دیگر

Prof. Bayu Jayawardhana is a Full Professor in Mechatronics and Control of Nonlinear Systems at the University of Groningen, affiliated with the Faculty of Science and Engineering. He leads the Jayawardhana Group focusing on opto-mechatronics and advanced nonlinear control theories. His roles include Director of Engineering and Scientific Director of the Engineering and Technology Institute Groningen. He holds editorial positions in journals like International Journal of Robust and Nonlinear Control and European Journal of Control . Education: PhD in Control and Power Group from Imperial College London (2006), M.Eng from Nanyang Technological University (2003), and B.Eng from Institut Teknologi Bandung (2000). Research interests span opto-mechatronics for high-tech systems, nonlinear control, and systems biology. Key projects include digital twins for energy optimization, control of ocean energy systems, and modeling of cryogenic actuators for telescopes. His work integrates AI and model-based methods for high-performance systems. Notable awards include the 2016 FSE Faculty Teacher of the Year Award and the Ben Feringa Impact Award (2020). He advises on ventures like Ocean Grazer B.V. and Sencilia B.V. Teaching includes graduate courses on nonlinear control, opto-mechatronics, and fitting dynamical models to data. His research labs include the Groningen Centre for Systems and Control and the Data Science and Systems Complexity Center.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Logistics and Operations Management
• Supply Chain Resilience
• Sustainable and Circular Supply Chains
• +۹ مورد دیگر

Professor Mohamed Naim is a distinguished academic at Cardiff Business School, Cardiff University, where he holds the position of Professor in Logistics and Operations Management. He has served in several leadership roles, including former Head of the Logistics and Operations Management Section and former Deputy Dean at the Business School. He was also the founding director of the Centre for Advanced Manufacturing Systems at Cardiff (CAMSAC). He is actively engaged in high-impact research and industrial collaboration, currently leading a Knowledge Transfer Partnership (KTP) with Tower Cold Chain Solutions Ltd. and contributing to major research programs such as ASTUTE, ASTUTE2020, and AFAIR. His research interests center on the development of business systems engineering approaches to resilient supply chains, with a focus on sustainability, circularity, and flexibility in lean and agile systems. He has made significant contributions to supply chain dynamics, particularly in engineer-to-order (ETO) sectors such as construction and shipbuilding. His recent work explores digital transformation in logistics, including 5G-enabled smart ports, building information modelling (BIM), and AI-driven decision-making. The analysis of his recent publications reveals a strong trend toward interdisciplinary research, integrating operations management with healthcare (trauma systems), digital technologies, and sustainable industrial practices. His work frequently employs system dynamics, simulation, and empirical case studies to address complex supply chain challenges. Scientific Awards: Institution of Electrical Engineers' Manufacturing Division Premium (1996) Royal Academy of Engineering Global Research Award (2003) Professor Naim is actively involved in research supervision and has contributed to two Research Excellence Framework (REF2021) impact cases on innovative procurement and financially sustainable supply chains. He is a founding member of the Logistics Systems Dynamics Group (LSDG) and has led and co-directed multiple large-scale research initiatives. His work is characterized by strong industry engagement, particularly through Knowledge Transfer Partnerships and collaborations with public sector bodies like the Welsh European Funding Office (WEFO). Research Centers and Teams: Logistics and Operations Management Section, Cardiff Business School Centre for Advanced Manufacturing Systems at Cardiff (CAMSAC) Logistics Systems Dynamics Group (LSDG) ASTUTE / ASTUTE2020 / ASTUTE East programmes AFAIR project (Expert Advisor)

Vinod Vaikuntanathanمشاهده پروفایل

استاد

• Cryptography
• Fully Homomorphic Encryption
• Lattice-Based Cryptography
• +۴ مورد دیگر

Vinod Vaikuntanathan is the Ford Foundation Professor of Engineering in the MIT EECS department and a principal investigator at MIT CSAIL. He holds a BTech from IIT Madras (2003), and SM/PhD degrees from MIT (2005/2009). His research focuses on cryptography, particularly fully homomorphic encryption (FHE), lattice-based cryptography, and quantum-resistant systems. He co-founded Duality Technologies as Chief Cryptographer. **Education:** BTech in Computer Science (2003), Indian Institute of Technology Madras SM in Electrical Engineering & Computer Science (2005), MIT PhD in Computer Science (2009), MIT **Research Interests:** His work spans FHE (enabling computations on encrypted data), lattice-based cryptography (post-quantum security), and intersections with quantum computing, machine learning, and privacy. He explores applications in secure computation, algorithm design, and cryptographic protocols. **Awards:** Recipient of the Gödel Prize (2022), Simons Investigator (2023), and MacVicar Faculty Fellow (2024). His work on FHE and lattice algorithms has earned widespread acclaim in cryptography and theoretical computer science. **Teaching & Mentorship:** Advanced cryptography courses at MIT (e.g., 6.5630, 6.876J) Advised PhD students (e.g., Sergey Gorbunov, Tianren Liu) and postdocs (e.g., Nir Bitansky, Mark Zhandry) now leading positions in academia and industry **Collaborations:** Organizer of the Charles River Crypto Day and MIT Cryptography Seminar Principal investigator on grants from NSF, DARPA, and Microsoft