جست‌وجوی استاد و پژوهشگر

Tonya W. Stoneمشاهده پروفایل

دانشیار

• Multiscale Computational Modeling
• Molecular Dynamics
• Constitutive Modeling
• +۵ مورد دیگر

Tonya W. Stone is an Associate Professor of Mechanical Engineering, CAVS Associate Director, and holder of the Dr. Oswald Rendon-Herrero Chair at Mississippi State University (MSU). She earned her B.S., M.S., and Ph.D. in Mechanical Engineering from MSU (1995, 2006, 2009). Prior to academia, she worked at Dow Chemical Company as a Mechanical Engineer (1995–2003). Her research focuses on multiscale computational modeling of materials, including molecular dynamics, constitutive modeling, and failure analysis. She leads initiatives in brain injury modeling, powder metallurgy, and engineering education equity. Dr. Stone has held roles such as Mechanical Engineering Graduate Coordinator (2021–Present), Record Service Coordinator (2023–Present), and Associate Director for Computational Engineering Mechanics (2020–Present). She advises organizations like NSBE and MEMO, and chairs committees including the BCoE Research Advisory Council (2023–2024). Education: Ph.D., Mechanical Engineering, MSU (2009) M.S., Mechanical Engineering, MSU (2006) B.S., Mechanical Engineering, MSU (1995) Her research interests span molecular dynamics of biological systems (e.g., traumatic brain injury), material failure analysis, and powder metallurgy. She emphasizes interdisciplinary approaches and computational methods for solving complex material challenges. Notable awards include the ME Department Senior Faculty Outstanding Research Award (2018, 2022) and the Oswald Rendon-Herrero Professorship. Her work integrates academic mentorship and diversity initiatives, reflected in publications on teamwork perception in engineering programs and strategies for female/minority academics. Dr. Stone’s lab collaborations include the Center for Advanced Vehicular Systems (CAVS) and phase-field modeling projects. She balances research leadership with service to engineering education and diversity advocacy.

Rajmil Fischmanمشاهده پروفایل

استاد

• Electroacoustic Composition
• Sonic Arts
• Music Technology
• +۳ مورد دیگر

Professor Rajmil Fischman is a composer and researcher at Keele University's School of Humanities, where he founded the Digital Music Technology MA/MSc programs. His work integrates composition, sonic arts theory, and software development to create structured interactive musical experiences using gestures, spatialization, and real-time control systems. Research focuses on: Interactive immersive music systems Gesture-controlled composition Multi-channel spatial audio Algorithmic composition tools He develops open-source software including AL (algorithmic composition), MAES (gesture control), and Clouds (granular synthesis).

Chrysoula Tsogkaمشاهده پروفایل

استاد

• Numerical Methods
• Wave Propagation
• Inverse Problems
• +۳ مورد دیگر

Chrysoula Tsogka is a Professor in the Department of Applied Mathematics at the University of California, Merced (since 2019). Previously, she held academic positions at the University of Crete (2007-2019), including Professor (2014-2019) and Associate Professor (2007-2014). She also served as a Visiting Professor at Stanford University (2016-2019) and a Project Scientist at UC Merced (2017-2019). Her research focuses on numerical methods for wave propagation, inverse problems, and imaging in complex media, with applications in geophysics, radar, and structural health monitoring. Education: Ph.D. in Applied Mathematics, University Paris IX (Dauphine), France (1999) M.S. in Applied Mathematics, University Paris IX, France (1996) B.Eng. in Chemical Engineering, National Technical University of Athens, Greece (1995) Research Interests: Tsogka specializes in developing and analyzing numerical methods for direct and inverse wave propagation problems, including coherent interferometry, passive ambient noise correlation-based imaging, and sparse recovery techniques. Her work addresses challenges in imaging through complex and random media, such as turbulence and scattering effects, with applications in synthetic aperture radar (SAR), satellite imaging, and non-destructive evaluation of materials. She emphasizes robustness in imaging systems under environmental uncertainties. Grants and Awards: ERC Starting Grant (2010-2015) SIGEST Paper Award (2009) Principal Investigator (PI) on Air Force Office of Scientific Research grants (e.g., FA9550-21-0196, $284K, 2021-2024) Advising and Mentorship: Tsogka has supervised six Ph.D. students and advised numerous MSc and Ph.D. committees. Her current team includes a graduate student and four committee members. She has also mentored postdoctoral researchers and undergraduate students in her research groups. Labs and Teams: As coordinator of the Imaging and Sensing SMaRT team at UC Merced, she leads a group of 6 faculty, 2 postdocs, and 12 graduate students. The team focuses on forward/inverse imaging problems, optimization, and uncertainty quantification, with weekly seminars alternating between the Optimization and Waves groups. They collaborate on data-driven methods for remote sensing and radar applications.

Juan Peraltaمشاهده پروفایل

استاد

• Electronic Structure Theory
• Density Functional Theory
• Self-Interaction Correction
• +۱۳ مورد دیگر

Juan Peralta is a Professor in the Department of Physics at Central Michigan University (CMU), within the College of Science and Technology. He leads a computational research group focused on electronic structure, magnetic materials, and nanomaterials, and is a key member of the FLOSIC Center funded by the U.S. Department of Energy. Education: Ph.D. in Physics, University of Buenos Aires, 2002 M.Sc. in Physics, University of Buenos Aires, 1997 Postdoctoral Research, Scuseria Group, Department of Chemistry, Rice University, 2002–2007 His research centers on the development and application of computational tools based on density functional theory (DFT), with a strong emphasis on eliminating self-interaction errors through methods like FLOSIC and DOCSIC. His work spans magnetic materials, electron transport, and nanoscale systems. Peralta collaborates with researchers at UTEP, Temple, Florida, Pittsburgh, and Tel Aviv University. His recent publications reveal a consistent focus on self-interaction correction, magnetic exchange coupling, electron transport, and the electronic structure of novel materials. The articles span high-impact journals such as the Journal of Chemical Physics , Journal of Chemical Theory and Computation , Chemical Science , and Nature Communications , highlighting both methodological advances and applications in materials and molecular systems. Scientific Awards: IBM-Zener Award Peralta actively mentors graduate and undergraduate students, including Ph.D. candidates and postdoctoral researchers such as Duyen Nguyen, Yash Singh, Jeronimo, and Angel. He has secured research funding from the NSF and the Department of Energy. His group, the Peralta Lab, focuses on transforming computational challenges into scientific breakthroughs, as humorously noted by their motto: 'We transform espressos into great science.'

Özlem DEFTERLİمشاهده پروفایل

دانشیار

• Fractional Calculus
• Optimal Control Theory
• Mathematical Biology
• +۳ مورد دیگر

Dr. Özlem DEFTERLİ is an Associate Professor in the Department of Mathematics at Çankaya University, Turkey. Her academic career spans over 23 years, with continuous full-time appointments since 2002. She holds a Doctorate in Mathematics (2011) from Middle East Technical University and postdoctoral research experience at Michigan State University and Saginaw Valley State University in the US. Research Focus: Dr. DEFTERLİ specializes in fractional calculus, optimal control theory, mathematical modeling in biological systems, and computational methods. Her work bridges applied mathematics with real-world applications in fields like epidemiology (dengue fever, tumor dynamics), fluid mechanics, and gene-environment networks. Key methods include fractional differential equations, numerical schemes for nonlinear systems, and fuzzy regression models. Recent Contributions: Her 2025 paper explores fractional variational principles in nonlinear oscillators, while 2024 work analyzes pendulum dynamics with time-varying mass. Notable projects include COST Action research on fractional systems and collaborations in transnational disease control via nano-technology. Awards & Recognition: Recipient of the Wen Chen Award (2020) for fractional calculus contributions, TÜBİTAK scholarship (2021), and 20th Anniversary Service Award (2022). She has advised 3 graduate students and holds editorial roles in 24 journals including Chaos, Solitons & Fractals and Fractal and Fractional. Professional Roles: Currently serves on the Faculty Board and Management Committee at Çankaya University. Teaching responsibilities include advanced courses in scientific computation, mathematical modeling, and calculus for business/economics.

• Computer Graphics
• Image Processing
• Deep Learning
• +۸ مورد دیگر

Prof. Dr. Uluğ Bayazıt is a Professor at Istanbul Technical University's Department of Computer Engineering. His research focuses on computer graphics, image processing, deep learning, and biomedical signal analysis. Doctorate: Rensselaer Polytechnic Institute (1993) BSc: Boğaziçi University Electrical and Electronic Engineering (1991) Research interests include: Smart city solutions using computer vision 3D mesh compression algorithms Electromyography signal analysis for prosthetics Deep learning applications in agricultural monitoring Recent publications span neural computing, multimedia tools, and graphical models. Key projects include: "Eye of the City" smart city initiative Plant phenology classification via image sequences Geometry video compression techniques As Principal Investigator (PI), he leads research grants funded by BAP and TTO. He has 46 research outputs and an h-index of 126 (Scopus). Active in IEEE from 1991-2014, with expertise in signal processing and mesh geometry.

Dhawal Buariaمشاهده پروفایل

استادیار

• Fluid Dynamics
• Turbulence
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dhawal Buaria is an Assistant Professor in the Department of Mechanical Engineering at Texas Tech University . He holds a Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology , and B.Tech and M.Tech degrees in Aerospace Engineering from the Indian Institute of Technology Kanpur . Ph.D., Aerospace Engineering, Georgia Tech M.S., Aerospace Engineering, Georgia Tech B.Tech, Aerospace Engineering, IIT Kanpur M.Tech, Aerospace Engineering, IIT Kanpur His research focuses on large-scale fluid dynamics , particularly fully turbulent flows , combining theoretical frameworks , high-fidelity numerical simulations , and deep learning to uncover universal properties of turbulence, mixing, and transport phenomena. His work has been published in journals such as Nature Communications , Physical Review Letters , and PNAS . Recent publications emphasize deep learning applications in turbulence modeling, extreme event analysis , and Lagrangian-Eulerian scaling in high Reynolds number flows. His methodologies include large eddy simulation (LES) , Navier-Stokes regularization , and GPU-optimized algorithms for petascale computing. Contact: Dhawal.Buaria@ttu.edu

Shriya Srinivasanمشاهده پروفایل

استادیار

• Bioengineering
• Neural Engineering
• Neuroprosthetics
• +۴ مورد دیگر

Shriya Srinivasan serves as Assistant Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS), directing the Harvard BIONICs Lab focused on developing biohybrid organs and neural interfaces to restore sensory and motor function for neuromuscular conditions. Her work bridges surgical reconstruction, synthetic sensors, and actuators to create bidirectional neural communication systems. Her research spans critical areas in neural engineering and translational medicine: Neuroprosthetics and sensory feedback restoration Gastrointestinal neuromodulation and motility disorders Cancer neuroscience and neural interface therapies Agonist-antagonist myoneural interfaces for amputation Implantable and ingestible bioelectronic devices Closed-loop drug delivery systems Recent publications demonstrate concentrated innovation in gastrointestinal applications, including ingestible stimulators for satiety modulation and neuromuscular interfaces for motility disorders, alongside pioneering work in cancer neuroscience and chemotherapy personalization. This research consistently emphasizes clinical translation through biohybrid organ development. No scientific awards were mentioned in the provided materials. As principal investigator of the BIONICs Lab, Dr. Srinivasan oversees graduate researchers and secures grant funding for neural interface development, though specific advisees and current grants aren't detailed in the source text. Her collaborative framework actively engages with clinical and entrepreneurial partners to overcome regulatory constraints in medical device development. The Harvard BIONICs Lab operates from the Science and Engineering Complex (150 Western Ave., Boston, MA), maintaining active partnerships in gastrointestinal device innovation, cancer neuroscience therapeutics, and advanced prosthetic control systems through its focus on seamless human-device integration.

Kanat Tangwongsanمشاهده پروفایل

پژوهشگر

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ مورد دیگر

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

Robert Hewittمشاهده پروفایل

دانشیار

• Engineering Technology
• STEM Education
• Biomedical Devices
• +۳ مورد دیگر

Robert Hewitt is an Associate Professor at The University of Memphis, based in the Herff College of Engineering's Department of Engineering Technology. He holds a B.S. and M.S. in Engineering Technology from The University of Memphis, and an M.Div. from Mid-American Baptist Theological Seminar. His work bridges engineering innovation with educational outreach, focusing on biomedical devices, materials science, and STEM education strategies. He has contributed to advancements in wearable medical sensors, flexible electronics, and curriculum design. Research interests include: Development of biomedical sensors (e.g., ECG systems, acousto-bioelectronics) Advanced material characterization (aerogels, polyimide substrates) STEM education through virtual and hands-on approaches His publications reflect interdisciplinary strengths: recent work includes virtual STEM programs (2025), bioelectronic sensor design (2023), and foundational contributions to inkjet-printed capacitors (2018). Earlier research explored mechanical stretch effects on cells (2015) and cryogenic material applications (2012). While no formal advising or awards are listed, his educational background in theology suggests potential interdisciplinary interests in ethics or STEM outreach. He maintains an active lab in the Engineering Technology Building, Room 224.

Asad M. Madniمشاهده پروفایل

استاد مدعو

• Intelligent Sensors
• Wireless Sensor Networks
• MEMS
• +۷ مورد دیگر

Asad M. Madni is a Distinguished Adjunct Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA). His research focuses on intelligent sensors, wireless networks, MEMS, and signal processing, with applications in automotive safety, aerospace, and medical instrumentation. He holds over 69 patents and has authored over 200 publications, contributing to advancements in sensor technology and electronic systems. Madni's academic and professional journey includes degrees from UCLA (B.S., M.S.), California Institute of Technology (EMP), and Stanford University. He has received prestigious awards such as the IEEE Medal of Honor (2022), John Fritz Medal (2023), and induction into the National Inventors Hall of Fame (2024). His work spans academia, industry collaborations, and international recognition, including honorary fellowships from the Royal Society of Edinburgh and Royal Aeronautical Society. His research interests emphasize wearable sensors, computational sensing, and fault-tolerant systems. Recent publications highlight innovations in photonic network analyzers, autonomous sweat sampling patches, and AI-driven systems. Madni’s contributions to aerospace, defense, and biomedical engineering have significantly impacted global technological advancements.

• Graph Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +۲ مورد دیگر

Tianyi Zhang is a Researcher at the Professorship for Theoretical Computer Science, ETH Zurich, located at OAT Z 29, Andreasstrasse 5. His research focuses on advancing fundamental algorithms in graph theory, with particular expertise in dynamic graph problems, efficient spanner constructions, edge coloring optimizations, and shortest-path computations. Dr. Zhang develops both theoretical frameworks and practical implementations for complex computational challenges. His core research areas include the design of near-linear and subquadratic time algorithms for graph optimization problems, fault-tolerant network structures, streaming-optimized graph coloring, and geometric graph embeddings. Recent work emphasizes breakthroughs in Vizing's theorem implementations, dynamic set cover deamortization, and space-efficient distance oracles. Dr. Zhang's publications demonstrate consistent innovation in algorithm efficiency for planar graphs, Euclidean spaces, and dynamic network settings. His 2023-2025 articles reveal concentrated efforts on: 1) Optimizing edge coloring through multi-step Vizing chains and streaming adaptations, 2) Enhancing spanner constructions for doubling metrics and planar environments, and 3) Developing failure-resistant path algorithms with improved time/space complexity. These contributions address scalability challenges in large-scale network processing. He collaborates within the Theoretical Computer Science research group at ETH Zurich, contributing to the institution's leadership in algorithmic innovation. No information about awarded grants, supervised students, or educational background is available in the source materials.

Xu Wangمشاهده پروفایل

دانشیار

• Optical Communication
• Photonics
• Fiber Optics
• +۵ مورد دیگر

Xu Wang is an Associate Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Photonics and Quantum Sciences. His research focuses on optical communication systems, underwater wireless optical communication, fiber Bragg grating (FBG) sensing, and signal processing using machine learning. He leads efforts in secure optical communication, nanotechnology for sensing, and turbulence-resistant underwater systems. Notable projects include developing deep learning-based signal processing for UWOC and reviewing novel FBG sensing materials. His work contributes to UN Sustainable Development Goals related to industry, innovation, and infrastructure. Dr. Wang has published extensively, with over 150 peer-reviewed articles since 1999. Recent trends in his publications emphasize underwater communication systems, secure optical encryption, and interdisciplinary applications of photonics. Collaborations span international institutions, particularly in Europe and Asia. Key Research Areas: Underwater Communication, Optical Sensing, Secure Networks, Nanomaterials Recent Achievements: 2024 Review on FBG Sensing, 2023 Deep Learning in UWOC, 2021 Quantum-Enhanced Encryption Systems No scientific awards are explicitly listed, though his prolific output suggests significant recognition. His lab focuses on translating theoretical innovations into practical systems for harsh environments and secure data transmission.

• Information Theory
• Communication Systems
• Radar Systems
• +۴ مورد دیگر

Mark R. Bell is a Professor of Electrical and Computer Engineering at Purdue University, based in the Elmore Family School of Electrical and Computer Engineering. His research focuses on information theory, communication systems, radar signal processing, and free-space optical communications. He holds a BS from California State University, Long Beach (1981), an MS (1982), and a PhD (1988) from the California Institute of Technology. His work spans diverse areas including radar waveform design, signal detection in noisy environments, and interdisciplinary applications like biomedical manufacturing and disaster management. Key research interests include advanced radar systems (e.g., bistatic configurations, LFM chirp processing), communication theory (e.g., time-frequency modulation, interference management), and emerging technologies such as AAV production for biotechnology. His contributions also extend to sensor networks, smart grid monitoring, and statistical modeling of wireless interference. Recent publications highlight innovations in radar resolution enhancement, adaptive threshold detection, and cost-effective manufacturing processes. Collaborative projects include spectrum-sharing frameworks for communications and radar systems, and integration of GPR with surface imaging for civil infrastructure analysis. Prof. Bell’s work has been recognized through sustained contributions to IEEE journals and conferences. His research bridges theoretical signal processing with practical applications in defense, healthcare, and environmental monitoring.

Wolfgang Mulzerمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.