جست‌وجوی استاد و پژوهشگر

Vishal Sharmaمشاهده پروفایل

مدرس ارشد

• Cyber-Physical Systems (CPS)
• 5G and Beyond Security
• Internet of Things (IoT)
• +۴ مورد دیگر

Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.

Muhammad Rashedمشاهده پروفایل

استادیار

• Electronic Design Automation (EDA)
• In-Memory Computing
• Artificial Intelligence Acceleration
• +۵ مورد دیگر

Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015). Ph.D. : Computer Engineering, University of Central Florida, 2024 B.S. : Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015 His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing. The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals. Scientific Awards: UTA CARES Grant for OER Creation Research Experiences for Undergraduates (REU) Grant Alireza Seyedi Doctoral Research Innovation Endowed Scholarship David T. & Jane M. Donaldson Memorial Scholarship IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination Best Research Video Award, Design Automation Conference (DAC) Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college. His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.

Melanie Weberمشاهده پروفایل

استادیار

• Geometric Machine Learning
• Optimization on Manifolds
• Non-Euclidean Machine Learning
• +۳ مورد دیگر

Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.

Stephanie Gilمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics and Control
• Computer Science
• +۴ مورد دیگر

Stephanie Gil is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on artificial intelligence, robotics, and distributed systems, particularly addressing challenges in multi-agent coordination, resilience to adversarial attacks, and wireless communication for autonomous systems. She leads the REACT Lab, advancing research in resilient multi-robot networks and cyber-physical systems. Her work integrates machine learning, control theory, and wireless sensing to solve problems such as whale tracking via autonomous robots, proactive multi-robot routing, and decentralized exploration without explicit information exchange. She has received prestigious awards, including the DARPA Young Faculty Award (2024) and the Amazon Research Award (2021). Key research areas include resilient distributed optimization, trust-centered coordination in multi-agent systems, and leveraging wireless signals (e.g., WiFi-CSI) for sensing and bearing estimation. Her contributions span both theoretical frameworks and practical implementations, with a focus on real-world applications like autonomous rideshare routing and environmental monitoring. Dr. Gil’s research also explores trust and cybersecurity in dynamic networks, with publications on crowd vetting, malicious robot detection, and adaptive communication strategies. She collaborates on interdisciplinary projects, such as Project CETI, combining AI and robotics for ecological studies.

• Computational Linguistics
• Computer Science
• Machine Learning
• +۱ مورد دیگر

Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.

Syed Bahauddin Alamمشاهده پروفایل

استادیار

• Artificial Intelligence
• Digital Twins
• Nuclear Engineering
• +۵ مورد دیگر

Syed Bahauddin Alam is an Assistant Professor at the University of Illinois Urbana-Champaign (UIUC) in the Nuclear, Plasma & Radiological Engineering department. He holds appointments in the Grainger College of Engineering and the National Center for Supercomputing Applications (NCSA). His research focuses on AI-driven digital twins, uncertainty quantification, and cybersecurity for nuclear systems. Education: B.Sc. in Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology (BUET), 2011 MPhil in Nuclear Energy, University of Cambridge, 2013 PhD in Nuclear Engineering, University of Cambridge, 2018 Research Interests: AI and Digital Twins for Nuclear Energy Multiscale Modeling with Uncertainty Quantification Cybersecurity for Nuclear Systems Sensors and Instrumentation for Reactor Monitoring His work emphasizes explainable AI (XAI), physics-informed machine learning, and robust design optimization. Key contributions include AI-powered digital twins for nuclear systems, which received global media coverage and top 5% Altmetric scores. Awards & Honors: 2025 Dean’s Award for Excellence in Research (UIUC) 2024 Illinois Innovation Award Finalist 2022-2021 Outstanding Teaching Award (Missouri S&T) 2017 Cambridge Philosophical Society Research Studentship Award Grants & Funding: $700,000 U.S. Nuclear Regulatory Commission (NRC) Distinguished Faculty Development Award (2024) $2 million DOE grant for nuclear fuel storage solutions (2023) $500,000 NRC R&D Grant (2024) Labs & Teams: Leads the MARTIANS Lab (Machine Learning and ARTificial Intelligence for Advancing Nuclear Systems), focusing on hybrid data-physics-driven AI and explainable machine learning for nuclear engineering challenges.

George Yinمشاهده پروفایل

استاد

• Stochastic Optimization
• Stochastic Control
• Stochastic Systems Theory
• +۴ مورد دیگر

George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.

Michael Gastparمشاهده پروفایل

استاد

• Information Theory
• Signal Processing
• Communications
• +۳ مورد دیگر

Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.

Giancarlo Ferrari Trecateمشاهده پروفایل

استاد مدعو

• Automatic control
• State estimation
• System identification
• +۷ مورد دیگر

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Lucas Jansonمشاهده پروفایل

دانشیار

• High-Dimensional Inference
• Statistical Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.

Aurora Maccaroneمشاهده پروفایل

پژوهشگر ارشد

• Single-Photon Imaging
• Underwater LiDAR
• Quantum Sensing
• +۴ مورد دیگر

Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Rayan Saabمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +۵ مورد دیگر

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Fariya Sharmeenمشاهده پروفایل

دانشیار

• Urban Planning
• Travel Behavior Modeling
• Social Network Analysis
• +۵ مورد دیگر

Fariya Sharmeen is an Associate Professor of Mobility and Urban Planning at KTH Royal Institute of Technology's School of Architecture and the Built Environment (ABE), affiliated with the Digital Futures Faculty. She holds a PhD from Eindhoven University of Technology and has previously served as Assistant Professor at Radboud University, Lecturer at Bangladesh University of Engineering and Technology (BUET), and research fellow at institutions including TU Delft and Imperial College London. Her research focuses on sustainable mobility transitions, social network dynamics in travel behavior, and policy responses to emerging transport technologies like MaaS and cycling innovations. Notable honors include the 2017 Piet Rietveld Award for transport research and a 2013 Royal Geographic Society award for transport geography. Sharmeen advises doctoral and master’s students on topics such as urban transformation and mobility governance. She coordinates courses like Sustainable Mobility (FAG3187) and leads projects like Bicification and ENCom. Her work integrates quantitative methods with policy analysis, addressing challenges in both global north and south contexts.

• Machine Learning
• Generative Artificial Intelligence
• Computational Models of Human Behavior
• +۳ مورد دیگر

Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.