جست‌وجوی استاد و پژوهشگر

Jean Laurensمشاهده پروفایل

پژوهشگر

• Neuroscience
• Spatial Navigation
• Vestibular Systems
• +۵ مورد دیگر

Jean Laurens is a Group Leader at the Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society in Frankfurt, Germany, where he heads the Laurens Lab. His research focuses on understanding how we sense our own motion and orient ourselves in three-dimensional space through neural mechanisms. His research interests include: Three-dimensional navigation and the neural basis of the 'brain compass' through head-direction cells in the limbic system Sensory signals for spatial navigation and how the brain integrates self-motion signals with visual landmarks Self-motion sensation and how the brain merges multiple sensory signals from the inner ear, vision, and proprioception with motor commands Laurens employs a multidisciplinary approach combining mathematical modeling and extracellular neuronal recordings in behaving Marmoset monkeys. His work spans computational neuroscience, systems neuroscience, and vestibular research, with significant contributions to understanding 3D orientation coding, gravity sensing in neural circuits, and spatial cognition. Recent publications demonstrate his focus on neural attractor networks, multisensory integration, and the representation of spatial orientation relative to gravity. His laboratory team includes researchers Francesca Lanzarini, Farzad Ziaie Nezhad, and Deepak Surendran, with Sogand Ghiasi managing laboratory operations. The Laurens Lab has been featured in media outlets including Süddeutsche Zeitung, with coverage of their work on balance mechanisms published in November 2020 and an article titled 'Du kannst mich Affe nennen' published on August 30, 2024.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

• Spatio-temporal Statistics
• Computational Statistics
• Environmental Informatics
• +۳ مورد دیگر

Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.

Sebastian Cioabaمشاهده پروفایل

استاد

• Spectral Graph Theory
• Algebraic Combinatorics
• Graph Theory
• +۳ مورد دیگر

Sebastian Cioaba is a Professor in the Department of Mathematical Sciences at the University of Delaware (UD), part of the College of Arts & Sciences. His research focuses on spectral graph theory, algebraic combinatorics, and their applications. He earned his Ph.D. from Queen’s University (2005) and joined UD in 2009 after postdoctoral work at UC San Diego and the University of Toronto. Cioaba has advised 8 Ph.D., 4 M.Sc., and numerous undergraduate researchers, with current advisees including John Byrne and Isabel Byrne. His work is supported by NSF, NSA, and international grants. Education - B.Sc. Mathematics & Computer Science, University of Bucharest (Undergraduate) - Ph.D. Mathematics, Queen’s University (2005) Research & Awards - 2024 College of Arts & Sciences Award - Co-editor of Discrete Mathematics and Linear Algebra and its Applications - Over 70 publications and two books: A Bridge to Advanced Mathematics (2023) and A First Course in Graph Theory and Combinatorics (2022, 2nd ed.) Teaching & Service - Organized conferences in discrete mathematics - Supervised over 25 undergraduate and high school students in research projects Advising - Current Ph.D. students: John Byrne, Isabel Byrne, Colby Sherwood - Notable past advisees include Vishal Gupta (Ph.D. 2025, Rochester) and Dheer Noal (Ph.D. 2022, Memphis postdoc)

Harpreet S. Dhillonمشاهده پروفایل

استاد

• Wireless Communications
• Communication Theory
• Stochastic Geometry
• +۸ مورد دیگر

Harpreet S. Dhillon is the W. Martin Johnson Professor of Engineering and Associate Dean for Research and Innovation at Virginia Tech's College of Engineering. He holds appointments in the Bradley Department of Electrical and Computer Engineering. His research focuses on wireless communications, stochastic geometry, machine learning, and next-generation network systems. Education: Ph.D., University of Texas at Austin (2013); M.S., Virginia Tech (2010); B.Tech., Indian Institute of Technology Guwahati (2008). Research Interests: Communication Theory, Stochastic Geometry, Machine Learning for Communication Systems, Heterogeneous Networks, IoT, and Energy Harvesting. He leads projects on vision-aided localization, LEO satellite systems, and RIS-aided networks. Key Awards: IEEE Fellow (2023), AAIA Fellow (2022), IEEE Heinrich Hertz Award (2016), and numerous early-career recognitions. His work has resulted in over 150 journal/conference publications. Advising: Supervises Ph.D. students in cutting-edge research areas like 6G localization and RIS optimization. His advisees have won awards such as the VT ECE Blackwell Award for Best Dissertation. Labs/Teams: Head of the research group focusing on communication theory and localization. Collaborates on projects funded by agencies like NSF and industry partners.

Benjamin C. Floresمشاهده پروفایل

استاد

• Radar signal processing
• Chaotic signals and systems
• High-resolution radar
• +۹ مورد دیگر

Benjamin C. Flores is a Professor of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP) , where he has built an internationally recognized career spanning advanced radar signal processing and large-scale STEM education initiatives. He directs the UT System Louis Stokes Alliance for Minority Participation (LSAMP) and the Bridge to the Doctorate Program , managing more than $40 million in funded projects aimed at increasing access and success for Hispanic and other under-represented students in STEM disciplines. Education: While specific degrees are not listed in the provided text, Dr. Flores’s faculty appointment and extensive technical expertise in radar and chaotic systems imply advanced training in electrical engineering. Research Interests: Radar & Signal Processing: high-resolution radar, inverse synthetic aperture radar (ISAR), range-Doppler processing, micro-Doppler analysis, bistatic radar, chaotic wideband signal design, neural-network-based classification of radar jamming signals. Antenna Engineering: fractal antennas, 3-D printed antenna prototyping, anechoic chamber measurements. STEM Education & Diversity: evidence-based retention strategies for non-traditional and Hispanic students, peer-led team learning, graduate mentoring, systemic change models for faculty diversity. Publication Trends: Dr. Flores’s recent articles (2021-2025) reveal two dominant thrusts—(1) cutting-edge radar/chaotic signal processing and joint radar-communication systems, and (2) rigorous, data-driven studies on broadening participation in STEM, with emphasis on mentoring, social networks, and program evaluation at Hispanic-Serving Institutions. Scientific Awards & Honors: Texas STAR Award – Texas Higher Education Coordinating Board (2005) ABET President’s Diversity Award (2006) Presidential Award for Excellence in Science, Mathematics, and Engineering Mentorship (2010) Grants & Leadership Roles: Principal Investigator & Project Director, Model Institutions for Excellence Initiative (1999-2007) Principal Investigator, UTEP PUENTES Program (US Dept. of Education, 2010-2015) Principal Investigator & Director, UT System LSAMP & Bridge to the Doctorate Program (since 2005) Laboratory & Facilities: Dr. Flores’s research group utilizes UTEP’s anechoic chamber and rapid-prototyping laboratories for antenna design and characterization, while also housing real-time radar test-beds and analog-computer platforms for chaotic oscillator experiments.

Aayush Jainمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Applied Cryptography
• +۳ مورد دیگر

Aayush Jain is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. Previously, he was a Postdoctoral Fellow at NTT Research and a PhD student at UCLA, advised by Professor Amit Sahai. His work bridges theoretical and applied cryptography with core computer science principles. Education PhD in Computer Science, University of California, Los Angeles (UCLA) Postdoctoral Fellowship, NTT Research Research Focus His research explores: foundational cryptography, indistinguishability obfuscation, functional encryption, lattice-based cryptography, secure multi-party computation, and post-quantum security. Work emphasizes rigorous theoretical frameworks with practical implications. Publication Trends Recent articles (2021-2024) demonstrate consistent focus on cryptographic primitives, obfuscation techniques, and security reductions. Dominant venues include CRYPTO, EUROCRYPT, FOCS, and STOC with emerging work in machine learning interfaces. Awards Best Paper Award at STOC 2021 for foundational contributions to indistinguishability obfuscation Advising and Collaboration Current PhD advisees: Alper Cakan, Quang Dao (co-advised), Sagnik Saha, Noah Singer (co-advised). Mentored postdocs: Mitali Bafna (2022-2023) and Rex Fernando (2022-2023). Teaches graduate courses in cryptography and theoretical tools. Leadership Leads the CMU Cryptography research group; organized the CMU Cryptography Workshop. Program committee member for FOCS, TCC, ITCS, and ICALP.

Zohreh Shamsمشاهده پروفایل

پژوهشگر

• ML Interpretability
• Explainable AI
• Knowledge Discovery
• +۵ مورد دیگر

Zohreh Shams is a Visiting Fellow at the Computer Laboratory, University of Cambridge, and Chief Scientific Officer at Leap Labs. Previously, she served as a Senior Research Associate at the University of Cambridge and held roles at Babylon Health as a Senior ML Scientist. Her research focuses on ML interpretability, explainable AI, knowledge discovery, and automated reasoning with applications in healthcare and safety-critical systems. Dr. Shams completed her PhD in Artificial Intelligence at the University of Bath, specializing in explanatory decision-making in multi-agent systems using Argumentation Theory. Her work bridges cognitive science and AI, collaborating with institutions like the University of Brighton on projects such as Accessible Reasoning with Diagrams , which explores explainable ontology reasoning systems. Her research interests include generative modeling, concept-based representations, and the integration of domain knowledge into AI systems. Notable contributions include developing frameworks like CGXplain for neural network explanations and REM for healthcare data analysis. Her publications span venues such as ECCV, AAAI, and TMLR. Shams has contributed to interdisciplinary projects, including the Integrated Cancer Medicine initiative, and maintains affiliations with Wolfson College as a former Junior Research Fellow. Her work emphasizes ethical AI practices, clinician collaboration, and the societal impact of explainable AI systems.

Garvesh Raskuttiمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +۵ مورد دیگر

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

Sonali Dasمشاهده پروفایل

مدرس

• Device Electronics
• RF and Microwave MEMS
• Solar Cells
• +۳ مورد دیگر

Sonali Das is a Lecturer in the Department of Electrical and Computer Engineering at the University of Central Florida. She earned her Ph.D. in Electrical Engineering from the Indian Institute of Engineering Science and Technology, India, in 2017. Research Interests: Her work focuses on device electronics, RF and microwave MEMS, solar cells, phototransistors, optoelectronic synaptic and neuromorphic devices, and emerging micro-nano fabrication technologies. She investigates light-trapping mechanisms in solar cells, develops biomimetic photovoltaic structures, and explores 2D material-based memristors for neuromorphic computing. Professional Activities: Das has served as a technical reviewer for the UCF Seed Funding Program (2019), instructor for Camp Connect I and II lab tours (2017–2019), and mentor for Camp Connect III programs at UCF (2019). She is an IEEE member. Scientific Awards: GAP Award, UCF Research Foundation and UCF Office of Technology Transfer (2019) Fulbright Bhaskara Advanced Solar Energy (BASE) Fellowship (2015) Teaching: She teaches courses including Semiconductor Devices (EEE 3350), Electronics I and II (EEE 3307/4309), Linear Circuits II (EEL 3123), Electromagnetic Fields (EEL 3470), Signal Analysis & Analog Communication (EEL 3552), Linear Control Systems (EEL 3657), and Digital Systems (EEE 3342).

Arian Malekiمشاهده پروفایل

دانشیار

• Compressed Sensing
• Signal Processing
• Statistical Inference
• +۶ مورد دیگر

Arian Maleki is an Associate Professor in the Department of Statistics at Columbia University, affiliated with the Faculty of Arts and Sciences and the Foundations of Data Science Center. He holds a PhD from Stanford University (2010) and previously served as a postdoctoral scholar at Rice University. His research focuses on statistical inference, signal processing, and machine learning, with particular emphasis on compressed sensing, high-dimensional statistics, and algorithm design. Key areas include noise mitigation, image reconstruction, and the theoretical analysis of algorithms for inverse problems. Recent work highlights include studies on speckle noise challenges, phase transitions in compressed sensing, and certified data removal techniques. His contributions bridge theory and application, often addressing practical computational and statistical challenges in imaging and signal processing. No scientific awards are explicitly listed. Research outputs emphasize foundational advancements in statistical methodologies and algorithmic frameworks for high-dimensional data analysis. Labs/teams: Active involvement in the Foundations of Data Science Center at Columbia University.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Karsten Urbanمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +۹ مورد دیگر

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Dan M. Ionelمشاهده پروفایل

استاد

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +۳ مورد دیگر

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Yusheng Weiمشاهده پروفایل

استادیار

• Control Systems
• Multi-Agent Systems
• Reinforcement Learning
• +۱ مورد دیگر

Yusheng Wei is an Assistant Professor in Electrical Engineering at the University of North Texas. His research specializes in control systems, multi-agent coordination, and reinforcement learning with emphasis on time-delay compensation. Recent publications focus on delay-tolerant control architectures, distributed optimization, and neural network-based synchronization. Key research trends include: 1) Event-triggered control for networked systems, 2) Reinforcement learning for delayed system stabilization, 3) Geometric/algebraic methods for signed network analysis. His work consistently addresses robustness challenges in delayed and distributed environments.