جستجوی استاد راهنمای دانشگاهی

Sumeet Kumar Gupta is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University. His academic career spans from his current role to a prior Assistant Professorship at Pennsylvania State University (2014-2017) and an engineering position at Qualcomm Inc. (2012-2014). He holds a PhD in Electrical and Computer Engineering from Purdue University (2012), an M.S. from the same institution (2008), and a B.Tech in Electrical Engineering from IIT Delhi (2006). B.Tech, Electrical Engineering, IIT Delhi (2006) M.S., Electrical and Computer Engineering, Purdue University (2008) PhD, Electrical and Computer Engineering, Purdue University (2012) Dr. Gupta's research focuses on neuromorphic computing, low power variation-aware VLSI design in emerging nanotechnologies, device-circuit co-design, and nano-scale device modeling/simulations. His work addresses challenges in ferroelectric materials, crossbar arrays for deep neural networks, and energy-efficient AI hardware. Recent publications (2025-2024) highlight trends in: Ferroelectric HfO2/HZO thin films Compute-in-memory architectures Variability/stochasticity analysis Machine learning for device optimization Interconnect resistance/temperature effects AI hardware fault tolerance Scientific Awards & Recognitions: DARPA Young Faculty Award (2016) Early Career Professorship, Penn State (2014) 6th TSMC Outstanding Student Research Bronze Award (2012) Magoon Award (Purdue) Outstanding Teaching Assistant Award (Purdue, 2007) Intel PhD Fellowship (2009) His professional journey includes academic appointments at Purdue University (2020-present, Associate Professor) and Pennsylvania State University (2014-2017, Assistant Professor) after industry experience at Qualcomm Inc. (2012-2014). He maintains IEEE and EDS membership while publishing over 100 refereed works.

• Neuromorphic Computing
• Low Power VLSI Design
• Emerging Nanotechnologies
• +۳ مورد دیگر

Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.

• Very-Large-Scale Integration (VLSI)
• Hardware Accelerators for Artificial Intelligence
• Hardware Security
• +۷ مورد دیگر

Saumya Debray is a Professor at the Department of Computer Science , University of Arizona , with research interests in Compilers , Program Analysis and Optimization , and Programming Language Implementation . Their work bridges theoretical and applied computer science, focusing on security implications of program transformations and compiler-assisted system optimization. Education: Ph.D., The State University of New York at Stony Brook (1986). Contact: Office: GS 735 | Phone: 520-621-4527 | Email: debray@cs.arizona.edu. Research Themes: Debray’s research spans three decades, with core contributions in: JIT Compiler Analysis: Bug localization, dynamic code generation, and exploitation frameworks. Code Obfuscation & Stylometry: Evading detection through optimization-based obfuscation. Malware Analysis: Deobfuscation techniques, taint analysis, and dynamic defense modeling. Binary Rewriting: Energy optimization, code compaction, and kernel specialization. Their work intersects compiler design , cybersecurity , and system optimization , with applications in executable analysis , malware detection , and undergraduate CS education .

• Compilers
• Program Analysis
• Code Optimization
• +۵ مورد دیگر

Jay Strader is a Professor in the Department of Physics and Astronomy at Michigan State University, where he serves as Graduate Director for the astronomy PhD program and Associate Chair for astronomy. His research focuses on compact objects, particularly black holes and neutron stars in globular clusters, neutron star binaries in Fermi gamma-ray sources, and intermediate-mass black holes. He has received a Packard Fellowship for Science and Engineering and grants from NSF and NASA. His research group includes postdoc Ryan Urquhart, graduate students Thomas Do and Rebecca Kyer, and several undergraduates, with past students like Teresa Panurach (now director of NoVEL Consortium) and Samuel Swihart (NRC fellow at Naval Research Lab). Education: PhD in Astronomy, UC-Santa Cruz/Lick Observatory Awards: Packard Fellowship Collaborations: Member of Rubin Observatory's Stars, Milky Way, and Local Volume science collaboration since 2008 Previous Positions: Hubble Fellow and Menzel Fellow at Harvard-Smithsonian Center for Astrophysics (2007-2012) Program Initiatives: Co-founder of PAREDS program for early research opportunities at MSU His work has been supported by NSF and NASA grants, and he has contributed to studies on black holes in M22, hypervelocity globular clusters around M87, and transitional millisecond pulsars. His group collaborates with Laura Chomiuk and contributes to data catalogs like the M31 globular cluster velocity dispersion database.

• Compact Objects
• Black Holes
• Neutron Stars
• +۵ مورد دیگر

Imre Bartos is an Associate Professor of Physics at the University of Florida, specializing in multi-messenger astrophysics that combines gravitational waves, neutrinos, and electromagnetic signals. His research investigates extreme cosmic explosions involving black holes and neutron stars, with emphasis on merger environments in active galactic nuclei. Groundbreaking work includes demonstrating how nearby neutron star mergers influenced Solar System composition, identifying AGN disks as factories for eccentric black hole mergers, and developing techniques to probe black hole origins through gravitational wave localization. His group participates in major collaborations including LIGO, LISA, and IceCube observatories. Professor Bartos received the Sloan Fellowship and shares Breakthrough Prize honors for contributions to gravitational wave astronomy. He leads observational programs using Chandra, VLA, and Fermi telescopes while mentoring students in computational astrophysics techniques.

• Multi-messenger Astrophysics
• Black Hole Mergers
• Gravitational Waves
• +۲ مورد دیگر

Kent Yagi is an Associate Professor in the Physics Department at the University of Virginia, specializing in theoretical astrophysics, gravity, and cosmology. His research focuses on using gravitational waves from compact objects like black holes and neutron stars to probe fundamental physics, including testing General Relativity in strong-field regimes and determining the equation of state of nuclear matter. Position: Associate Professor (2023-present), previously Assistant Professor (2017-2023) Education: Ph.D. in Physics from Kyoto University (2012) Prior positions: Postdoctoral Research Scholar at Princeton University (2015-2017), Postdoctoral Research Associate at Montana State University (2012-2015) Yagi's research centers on theoretical modeling of neutron stars and gravitational wave physics. He is particularly known for discovering the 'I-Love-Q' universal relations among neutron star observables that are insensitive to the equation of state. His work enables testing strong-field gravity and probing nuclear physics through gravitational wave observations. He also investigates binary pulsar systems as precision laboratories for testing gravitational theories beyond General Relativity. His research has significant implications for multi-messenger astronomy, connecting gravitational wave observations with electromagnetic counterparts to extract fundamental physics. The field has evolved rapidly since the first gravitational wave detection in 2015, and Yagi's theoretical predictions have helped shape how we interpret these observations to test gravity and nuclear physics in extreme conditions. NSF CAREER Award (2023) Sloan Research Fellowship (2019) IUPAP Young Scientist Prize (2019) Mead Honored Faculty (2018-2019) Yagi leads an active research group at UVA with multiple graduate and undergraduate students. His group collaborates with researchers across departments, including high energy physicists, nuclear physicists, astronomers, and researchers at the National Radio Astronomy Observatory. Current research directions include multi-band gravitational wave tests of general relativity, constraining nuclear matter parameters with GW170817, and developing parameterized post-Einsteinian gravitational waveform models for various modified gravity theories. The group has received multiple student research fellowships and awards, demonstrating strong mentorship and training of the next generation of physicists.

• Theoretical Astrophysics
• Gravity and Cosmology
• Gravitational Physics
• +۶ مورد دیگر

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +۹ مورد دیگر

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

• Computer vision
• Optimization
• Medical image analysis
• +۱ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.

• Causality
• Causal Representation Learning
• Causality-inspired Machine Learning
• +۴ مورد دیگر

Manuel Linares Alegret is a Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he has been employed since September 2021. He also holds an Associate Professor position at the Polytechnic University of Catalonia (UPC) in Barcelona, Spain, since 2018. His research focuses on high-energy astrophysics with particular emphasis on neutron stars, black holes, white dwarfs, and compact objects in binary systems. Dr. Linares earned his Physics Degree from Universitat de Barcelona (1998-2004) followed by a PhD in Astronomy from Universiteit van Amsterdam (2004-2009). His subsequent career includes prestigious fellowships including Rubicon Fellow at MIT (2009-2012), IAC Fellow (2012-2017), and Marie Curie Fellow at UPC (2017-2018). His research interests primarily center on compact binary systems, particularly millisecond pulsars known as 'spiders' (including black widows and redbacks), neutron star physics, accretion flows, thermonuclear bursts, and the search for super-massive neutron stars. His work combines observational astronomy with theoretical modeling to understand extreme physics in these systems. He leads the LOVE-NEST project, which investigates compact binary millisecond pulsars to find the most massive neutron stars and understand the interaction between accretion flows, pulsar winds, and neutron star magnetospheres. An analysis of his recent publications reveals a strong focus on spider pulsar systems, with particular attention to mass measurements, orbital dynamics, irradiation effects, and the relationship between accretion and rotation-powered states. His work spans multiple observational wavelengths including optical, X-ray, and radio, often utilizing data from major telescopes and space observatories. ERC Consolidator Grant for LOVE-NEST project Marie Curie Fellow IAC Fellow Rubicon Fellow Dr. Linares has supervised numerous students at various levels, including PhD candidates, Master's students, and undergraduate research projects. He currently leads a substantial research team under the LOVE-NEST project, which has received 2M EUR in funding. His group includes multiple postdoctoral fellows and PhD candidates working on various aspects of compact object astrophysics. He teaches Observational Astrophysics (FY3215) at NTNU and has previously taught Quantum Physics and Physics I at UPC. He is the principal investigator of the LOVE-NEST (Looking for Super-Massive Neutron Stars) research group at NTNU, which focuses on compact binary millisecond pulsars. This team conducts research using multiple observational facilities worldwide and collaborates with international groups including those at the Instituto de Astrofísica de Canarias and the University of Manchester.

• High-energy astrophysics
• Compact objects in binary systems
• Neutron stars
• +۷ مورد دیگر

Jiafeng (Harvest) Xie is an Assistant Professor in the Department of Electrical and Computer Engineering at Villanova University, where he directs the Security and Cryptography (SAC) Lab. He holds a Ph.D. in Electrical Engineering from the University of Pittsburgh and has prior faculty experience at Wright State University. His research focuses on cryptographic engineering, post-quantum cryptography, hardware security, and digital design for telemetry systems. Education includes a Ph.D. from University of Pittsburgh (2013-2014), M.E. from Central South University (2007-2010), and B.E. from Yanshan University (2002-2006). He has received prestigious awards like the 2024 IEEE Philadelphia Engineer of the Year Award and the 2023 Art Ryan Award. His work spans over 66 peer-reviewed publications, with a focus on hardware acceleration for post-quantum cryptographic systems. Research interests include post-quantum cryptographic engineering, fully homomorphic encryption, fault detection methodologies, and digitalization of aeronautical telemetry systems. His grants include NSF SaTC and NIST-funded projects. Teaching includes courses like Embedded Systems and Post-Quantum Computing . Current advisees include Ph.D. students Pengzhou He, Tianyou Bao, and Yazheng Tu, along with several M.S. and undergraduate researchers. The SAC Lab collaborates with AFRL and explores novel cryptographic hardware designs, with recent breakthroughs in compact accelerators for lattice-based cryptography and approximate homomorphic encryption. His work emphasizes algorithm-architecture co-design for security and efficiency in emerging computing systems.

• Post-Quantum Cryptography
• Cryptographic Engineering
• Hardware Security
• +۵ مورد دیگر

Lionel Truquet is a Lecturer-Researcher in Statistics at ENSAI (École Nationale de la Statistique et de l'Administration Économique), where he focuses on Statistics for dependent data and Time series analysis . He serves as a Director of Research and has contributed significantly to fields like Markov chains and nonlinear dynamics . Research Interests: Time series models for ecological and economic data Statistical inference for categorical and discrete-valued processes Ergodic properties of Markov chains in random environments Mixing conditions for nonstationary processes Perturbation techniques in stochastic modeling Recent Publications: His work spans nearest neighbor sampling , multivariate autoregressive models , and mixing properties of count processes , with applications in ecology and econometrics. Key trends include nonparametric methods for high-dimensional data and stationarity analysis in time-varying systems. Scientific Awards: TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE (2021–2023) for groundbreaking work on multivariate count autoregressions.

• Statistics for dependent data
• Time series analysis
• Markov chains
• +۲ مورد دیگر

Dr Samantha Oates is a Lecturer in Astrophysics within the Department of Physics at Lancaster University's Faculty of Science and Technology. Her office is located in C035, C-Floor, Physics Building. Her research focuses on Gamma-ray Bursts (GRBs) in the gravitational wave era, investigating environments of GRB explosions, central engine mechanisms, jet structures, cosmological evolution of GRBs, and electromagnetic counterparts to gravitational wave events. She actively participates in international collaborations including Swift, LSST, STARGATE, and ENGRAVE . Her work addresses critical questions about optical/UV contaminants in gravitational wave counterpart searches and the cosmological utility of GRB correlations. Dr Oates supervises PhD students including Samuel Shilling in Observational Astrophysics. She contributes to the Astrophysics research group at Lancaster University, delivering specialized lectures and supervising projects on multi-messenger astrophysics. Recent publications demonstrate her expertise in gravitational wave follow-up campaigns, supernova classification, and nuclear transient phenomena. Her research group engages in multiwavelength observations from gamma-ray to radio wavelengths, utilizing facilities for real-time transient detection and characterization. Current projects involve analyzing data from gravitational wave events and developing methods to distinguish true counterparts from serendipitous transients.

• Gamma-ray Bursts
• Gravitational Wave Counterparts
• Multi-messenger Astrophysics
• +۳ مورد دیگر

Lionel Truquet is a Lecturer-Researcher in Statistics and Director of Research at ENSAI (École Nationale de la Statistique et de l'Analyse de l'Information). His research focuses on advanced statistical methodologies, including time series analysis, Markov chains, and ecological data modeling. He has contributed to multivariate autoregressive binary models, compact space time series models, and nonstationary count processes. Research Interests: His work emphasizes statistical theory applied to dependent data, with a strong focus on ecological applications. Key areas include ergodic properties of Markov chains, mixing properties of time series, and modeling presence-absence data. His methods address challenges in high-dimensional and nonstationary environments. Publications: Recent work includes influential contributions such as the TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE-winning paper on iterations of dependent random maps. His publications span top journals like Bernoulli, the Annals of Applied Probability, and the Journal of Time Series Analysis, reflecting his expertise in both theoretical and applied statistics. Teaching: He teaches advanced courses such as Asymptotic Statistics and Dependence at the M2 level, reflecting his commitment to training future statisticians in cutting-edge methodologies.

• Time Series Analysis
• Statistics for Dependent Data
• Markov Chains
• +۳ مورد دیگر

Prof. Dr. Michael Kramer is a Professor of Astrophysics at the University of Manchester and a Scientific Member (Managing Director) at the Max Planck Institute for Radio Astronomy. He leads the COMPACT Research Group and specializes in radio astronomical fundamental physics. University of Manchester: Professor for Astrophysics Max Planck Institute for Radio Astronomy: Managing Director, Radio Astronomical Fundamental Physics Research Interests: Dr. Kramer focuses on pulsars , neutron stars , and gravitational physics , using these as tools to test general relativity , detect gravitational waves , and study transients in the Milky Way. Recent Research Trends: His 15 most recent publications emphasize fast radio bursts (FRBs) , axion dark matter searches , black hole imaging , and pulsar timing arrays for gravitational wave detection. Studies include the M87 jet, Galactic Center magnetars, and MeerKAT telescope optimizations.

• Pulsars
• Neutron Stars
• Gravitational Physics
• +۶ مورد دیگر