جست‌وجوی استاد و پژوهشگر

Kazem Kazerounianمشاهده پروفایل

استاد

• Nano-Mechanical Systems
• Robotics
• Gear Systems Dynamics
• +۴ مورد دیگر

Dr. Kazem Kazerounian is a Professor of Mechanical Engineering at the University of Connecticut, affiliated with the School of Mechanical, Aerospace, and Manufacturing Engineering. He holds B.S., M.S., and Ph.D. degrees in Mechanical Engineering from the University of Illinois at Chicago (1980–1984). His expertise spans analytical and computational kinematics/dynamics applied to bio-nano machines, robotics, gear systems, and product design with health/ergonomics focus. He is a Fellow of ASME and recipient of the ASME Lifetime Achievement Award (2006) and George Wood Award (1997). Research interests include protein-based nano-scale devices, gear system dynamics, robotic manipulation, and creativity in engineering education. He consults for OSIM International (Singapore), Pratt & Whitney, and the Singapore Productivity & Standards Board. Editorial roles include ASME Applied Mechanics Review, Journal of Medical Devices, and Journal of Mechanisms and Machine Theory. Awards: ASME Lifetime Achievement Award (2006), George Wood Award (1997), ABB Outstanding Faculty Award (1997) Consulting: OSIM International, Pratt & Whitney, Singapore Productivity & Standards Board His work bridges mechanical engineering with bio-nano systems, emphasizing interdisciplinary applications in medicine and robotics. Over 150 publications span topics like nanorobotics, orthosis design, and computational protein folding models.

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

• Neuroscience
• Artificial Intelligence
• Neuronal Modeling
• +۵ مورد دیگر

Eilif B. MULLER is a Professor in the Department of Neurosciences at Université de Montréal, Principal Investigator of the Architectures of Biological Learning Lab (ABL-Lab) at CHU Sainte-Justine Research Center, and Associate Faculty at Mila (Quebec AI Institute). His work bridges neuroscience and artificial intelligence, focusing on understanding how sensory perception is learned in the neocortex through biophysical simulations and deep learning models. He holds affiliations with IVADO (Institute for Data Valorization) and contributes to strategic initiatives like the UNIQUE Québec Center. His research integrates empirical neurophysiology with computational models, exploring dendritic processing and synaptic plasticity to inform both biological understanding and AI advancements. Teaches NSC-6044 and NSC-6045 (Neuroscience Colloquia) at Université de Montréal. Leads projects on neocortical learning mechanisms and their implications for neurodevelopmental disorders. Recipient of grants from CRSNG (Natural Sciences and Engineering Research Council), FRSQ (Health Research Fund), and institutional funding. Publications span topics in computational neuroscience, neural network modeling, and interdisciplinary AI-neuroscience research. Collaborates extensively across institutions to advance large-scale brain simulations and data-driven models.

Olivia Merkelمشاهده پروفایل

استاد

• Drug Delivery
• Pharmaceutical Technology
• Nanomedicine
• +۳ مورد دیگر

Prof. Dr. Olivia Merkel is the Chair of Drug Delivery at LMU Munich (since 2022) and holds a tenured professorship in the Faculty of Pharmacy. Her academic journey includes postdoctoral work at Philipps-Universität Marburg (2009), a PhD in Pharmaceutics (2009), and a tenure-track professorship at Wayne State University (2011–2017). She leads the Merkel Lab, focusing on advanced drug delivery systems, particularly siRNA and nanoparticle-based therapies. Research interests: Nanomedicine, targeted drug delivery, polymer engineering, and pulmonary delivery systems. Editorial roles: Molecular Pharmaceutics, Journal of Controlled Release, and European Journal of Pharmaceutics and Biopharmaceutics. Award: 2020 PHOENIX Pharma Science Award. Her lab combines experimental and computational approaches, with recent work on machine learning-driven polymer discovery and molecular dynamics simulations. Over 20 students and researchers collaborate in her group, addressing challenges in lung fibrosis treatment and T-cell targeting in asthma.

Bhavin J. Shastriمشاهده پروفایل

استادیار

• Silicon Photonics
• Nanophotonics
• Photonic Integrated Circuits
• +۵ مورد دیگر

Bhavin J. Shastri is an Assistant Professor in the Department of Physics, Engineering Physics and Astronomy at Queen's University in Canada. His research explores the physics of light for computing , pushing frontiers in information and signal processing through photonic computing and quantum/neuromorphic photonics . He is affiliated with the Centre for Nanophotonics and NUCLEUS , a pan-Canadian photonic computing program funded by NSERC CREATE, bridging artificial intelligence and quantum information . Canada Research Chair & Principal Investigator Faculty Affiliate at Vector Institute (2020-) Editorial Board Member of JPhys Photonics (2019-) Member of IEEE Photonics Society Technical Affairs Council (2019-) Visiting Researcher Scholar at Princeton University (2018-) Shastri Lab members have access to world-class shared facilities, including the Centre for Nanophotonics (CFI-Innovation Fund), Nanofabrication Kingston , the Centre for Advanced Computing , and the Digital Research Alliance of Canada . The lab takes an interdisciplinary approach combining nanophotonics with complex systems on emerging substrates. His research focuses on silicon photonics , nanophonic processors , and photonic integrated circuits with applications to deep learning , nonlinear programming , and quantum information science . His articles show consistent exploration of quantum photonic neural networks , photonic memory systems , and optical signal processing for machine learning and quantum technologies . 2020 IUPAP Young Scientist Prize in Optics 2014 Banting Postdoctoral Fellowship 2012 D. W. Ambridge Prize 2011 IEEE Photonics Society Graduate Student Fellowship 2011 NSERC Postdoctoral Fellowship Multiple Best Student Paper Awards Shastri's lab supervises Ph.D. candidates and postdoctoral fellows working on quantum photonics , neuromorphic computing , and photonic AI systems . His recent work includes photonic tensor cores for scientific computing , quantum photonic neural networks , and all-optical memory systems. Shastri Lab designs programmable nanophotonic processors with potential to outperform microelectronic processors in energy efficiency and computational speeds by seven and four orders of magnitude respectively. Their work spans from device design to system-level implementations in optical computing for machine learning and quantum information processing .

Shimeng Yuمشاهده پروفایل

استاد

• Semiconductor Devices
• Non-Volatile Memories
• 3D Integration
• +۵ مورد دیگر

Shimeng Yu is a full professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, holding the Dean’s Professorship. He earned his B.S. from Peking University (2009) and M.S./Ph.D. from Stanford University (2011/2013). His research focuses on semiconductor devices, non-volatile memories, 3D integration, and AI hardware accelerators. Yu leads SRC/DARPA JUMP 2.0 centers on memory/storage and 3D integration, with over 400 publications and 30,000+ citations (H-index 82). He serves on flagship conference committees (e.g., IEDM, VLSI) and editorial boards (IEEE EDL, JETCAS). Education: B.S., Microelectronics, Peking University (2009) M.S./Ph.D., Electrical Engineering, Stanford University (2011/2013) Research Themes: Emerging non-volatile memories for AI Monolithic 3D integration Energy-efficient computing systems His work spans device fabrication, circuit design, and system-level co-optimization. Recent projects are funded by NSF, DARPA, DOE, and industry partners (TSMC, Intel, Samsung), totaling >$17M. His lab, located at the Pettit Microelectronics Research Center, develops prototypes with cleanroom access. Awards: IEEE Fellow (2024) ACM/IEEE DAC Under-40 Innovators Award (2020) NSF CAREER Award (2016) Multiple editorship roles and distinguished lecturer appointments (IEEE EDS/CASS) Grants & Funding: Lead of two SRC/DARPA JUMP 2.0 centers Total research funding exceeds $17M

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۶ مورد دیگر

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

Juan Felipe Carrasquilla Álvarezمشاهده پروفایل

استادیار

• Condensed Matter Physics
• Quantum Computing
• Machine Learning
• +۳ مورد دیگر

Juan Felipe Carrasquilla Álvarez is an Assistant Professor in the Department of Physics at the University of Toronto. His research focuses on the intersection of condensed matter physics, quantum computing, and machine learning, emphasizing quantum many-body systems, quantum device validation, and phase identification. He holds affiliations with the Acceleration Consortium and the Centre for Quantum Information and Quantum Control at the University of Toronto, and is a Perimeter Institute Visiting Fellow. Education: PhD in Physics from SISSA (Italy), followed by postdoctoral fellowships at Georgetown University (2011-2013), the Perimeter Institute (2013-2016), and a stint as a Research Scientist at D-Wave Systems Inc. Earlier, he completed the Abdus Salam ICTP Diploma Programme (2005-2006). Research interests span quantum Monte Carlo simulations, machine learning-driven analysis of quantum systems, and applications to quantum computing validation. His work bridges theoretical physics with computational methods, addressing challenges in both classical and quantum computing paradigms. Notable contributions include developing neural network architectures for quantum state reconstruction, error mitigation in quantum simulations, and optimal control strategies for quantum thermal machines. His publications explore topics like topological order detection, shadow tomography, and hybrid quantum-classical algorithms. Awards/Fellowships: Perimeter Institute Postdoctoral Fellowship (2013-2016), Georgetown University Postdoctoral Fellowship (2011-2013), SISSA PhD Fellowship (2006-2010), and Abdus Salam ICTP Diploma Programme Fellowship (2005-2006). Advising/Grants: No formal advisee list provided. Active in interdisciplinary collaborations through affiliations with major quantum research consortia and institutions. Lab/Teams: Part of the Acceleration Consortium and the Centre for Quantum Information and Quantum Control, contributing to cutting-edge quantum computing and machine learning research.

Dana Brooksمشاهده پروفایل

استاد پژوهشی

• Biomedical Signal Processing
• Medical Imaging
• Statistical Signal Processing
• +۸ مورد دیگر

Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.

Yong-Bin Kimمشاهده پروفایل

استاد

• Integrated Circuit Design
• Nanoelectronics
• High-Speed System Integration
• +۴ مورد دیگر

Yong-Bin Kim is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, part of the College of Engineering. He has held prior positions at Intel Corp., Hewlett Packard Co., Sun Microsystems, and the University of Utah. His research focuses on integrated circuit design, nanoelectronics, bio-chip interfaces, and low-power VLSI systems. He has contributed to initiatives like the HPVLSI Lab and Microsystems and Electron Devices Lab. Education includes a B.S. in Electronic Engineering from Sogang University (Seoul, South Korea), an M.S. from the New Jersey Institute of Technology, and a Ph.D. in Computer Engineering from Colorado State University (1996). Research interests encompass high-speed low-power VLSI design, system-on-chip (SoC), physical VLSI CAD, and nanoelectronics. Specific areas include bio-sensor interface circuits, electronic neuron design, and adaptive robot controllers. Key projects involve compact power-efficient integrated circuits and high-speed transceiver design. Outstanding Paper Award, 2020 IEEE ISOCC South Korean Patent for Autonomous Impedance Calibration (2021) Best Paper Award, 2016 International SoC Design Conference Patent for Improved Receiver Circuit (2020) Patent for Method to Detect Trojan Circuits (2018) Advisees include graduate student Yixuan He. He has led research projects funded by Winchester Technology and Hynix Semiconductor, focusing on semi-self-calibration transceivers and tunable RF inductors. Labs include the HPVLSI Lab and Microsystems and Electron Devices Lab at Northeastern University, which focus on high-speed/low-power IC design and microfabrication technologies.

Giuliano Casaleمشاهده پروفایل

استاد

• Performance Engineering
• Cloud Computing
• Resource Management
• +۶ مورد دیگر

Giuliano Casale is a Professor in the Department of Computing at Imperial College London, leading the Quality of Service Research Lab (QORE). His research focuses on performance assurance, resource management, and fault-tolerance in distributed systems. He teaches courses on Probability and Statistics and Scheduling and Resource Allocation at undergraduate and Master’s levels. Casale’s work spans cloud computing, edge AI, and machine learning applications in system modeling. Key contributions include methodologies for performance engineering, anomaly detection, and automated resource management in large-scale systems. He actively participates in international conferences, delivering keynote speeches on topics such as performance evaluation and AI-driven systems. His research integrates queueing theory, machine learning, and generative models to address challenges in distributed software systems. Casale also engages in service activities like PhD admissions tutoring and collaborates on projects involving resilience planning and cloud service optimization. His lab, QORE, emphasizes practical solutions for real-world distributed systems, including edge federations and serverless architectures. Casale’s work bridges theoretical performance analysis with industrial applications, contributing to advancements in both academia and industry.

Victor Galitskiمشاهده پروفایل

استاد

• Quantum Chaos
• Theoretical Physics
• Quantum Information Science
• +۷ مورد دیگر

Victor Galitski is a Professor of Physics at the University of Maryland and a Fellow of the Joint Quantum Institute (JQI). He holds two PhDs in applied mathematics and condensed matter physics, joined UMD in 2002 as a postdoc, and became a faculty member in 2005. His research focuses on theoretical physics and quantum information science, with notable contributions to quantum chaos, superconductivity, and topological materials. He co-founded Aspen Quantum Consulting and ScienceCast.org, and serves as an Honorary Professor at Monash University and an editor for Annals of Physics . Galitski’s educational efforts include authoring ' Exploring Quantum Mechanics ' (Oxford Press) and teaching a Coursera MOOC on quantum physics (enrolled by 200,000+ students). His research interests span quantum spin glasses, hydrodynamic turbulence, Floquet topological insulators, and cavity quantum electrodynamics. Over 20 former students and postdocs now hold academic or industry leadership roles. Key scientific contributions include studies on Many-Body Quantum Chaos, quantum spin ice in Rydberg atom arrays, and the interplay of symmetry breaking in vertex models. His work frequently explores connections between quantum systems and machine learning, such as neural networks’ analogies to spin glass behavior. Galitski’s affiliations include leadership roles in the Ultra-Quantum Matter Simons Collaboration, the Institute for Robust Quantum Simulation, and the Aspen Center for Physics. He has published extensively on topics like cavity-enhanced superconductivity and quantum ergodicity, with recent work addressing universal speed limits in quantum systems and interacting anomaly effects in thermal transport.

Dukka KCمشاهده پروفایل

استاد مدعو

• Bioinformatics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications. Ph.D., Informatics, Kyoto University, 2006 M.Inf., Informatics, Kyoto University, 2003 B.Eng., Computer Science, Kyoto University, 2001 Research interests include: Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER) Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite) Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction High-throughput analysis of next-generation sequencing data Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows. Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research. The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.

Erik Luijtenمشاهده پروفایل

استاد

• Computational Materials Science
• Soft Matter Physics
• Complex Fluids
• +۷ مورد دیگر

Erik Luijten is the Associate Dean for Research and Doctoral Education at the McCormick School of Engineering, Northwestern University, where he also holds a Professorship in Materials Science and Engineering (with courtesy appointments in Engineering Sciences and Applied Mathematics, Physics and Astronomy, and Chemistry). His leadership includes overseeing research administration, doctoral programs, and global initiatives. He previously chaired the Department of Materials Science and Engineering. Educated at Utrecht University (M.Sc. Physics) and Delft University of Technology (Ph.D. Physics), Luijten specializes in computational materials science , focusing on soft matter systems like complex fluids, colloids, and active matter. His research combines advanced simulations (e.g., Monte Carlo methods) with theoretical frameworks to study self-assembly, electrokinetic phenomena, and dielectric effects. Notable contributions include accelerating simulation techniques for systems with long-range interactions and designing programmable materials. His work emphasizes practical applications , such as drug delivery via nanoparticle self-assembly, sustainable catalytic processes for plastic recycling, and dynamic hydrogel networks. Recent publications highlight innovations in active matter dynamics, nanoparticle crystal growth, and mesoporous catalytic architectures. Luijten’s awards include the NSF CAREER Award (2004) and Fellowship of the American Physical Society (2013) . He leads the Computational Soft Matter Lab , fostering interdisciplinary collaborations across engineering, physics, and chemistry. His academic service roles include the Racheff Assistant Professorship (2001–2003).

Kurt Keutzerمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.