جست‌وجوی استاد و پژوهشگر

Miles Stoudenmireمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Computational Physics
• Machine Learning
• +۳ مورد دیگر

Miles Stoudenmire is a Researcher at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) , joining in 2017. He holds a Ph.D. in Physics from the University of California Santa Barbara (2010) and a B.S. in Physics and Math from the Georgia Institute of Technology (2005). His work focuses on tensor networks and their applications in quantum mechanics and machine learning. Education: Ph.D. in Physics, University of California Santa Barbara (2010) B.S. in Physics and Math, Georgia Institute of Technology (2005) Stoudenmire specializes in enhancing tensor network methods to model realistic quantum systems, including finite temperature effects and chemically accurate basis sets. He has developed the ITensor software library, which enables efficient transcription of tensor network diagrams into code for algorithm prototyping. His research bridges quantum physics and computational techniques, with applications in condensed matter physics and quantum computing. Key publication trends include advancements in density matrix renormalization group (DMRG) methods, quantum-inspired machine learning algorithms, and topological quantum simulations. Affiliations: Flatiron Institute (2017–present) University of California Irvine (postdoctoral) Perimeter Institute for Theoretical Physics (research scientist)

Tanumoy Mandalمشاهده پروفایل

استادیار

• Beyond the Standard Model theories
• Collider phenomenology
• Matter under extreme conditions
• +۲ مورد دیگر

Tanumoy Mandal is an Assistant Professor in the School of Physics at the Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, where he has been serving since February 2020. Previously, he held research positions at Uppsala University in Sweden (as Researcher and Guest Researcher), University of Delhi (as DST-INSPIRE Fellow), and Harish-Chandra Research Institute. His academic journey includes a Ph.D. from the Institute of Mathematical Sciences, Chennai, and an M.Sc. from the same institution. Dr. Mandal's research focuses on Beyond the Standard Model theories , Collider phenomenology , and Matter under extreme conditions , with particular interest in neutron stars and color superconductivity. His work bridges theoretical particle physics with experimental high-energy physics, especially focusing on LHC data analysis and interpretation. He has made significant contributions to understanding leptoquarks, vectorlike quarks, neutrino physics, and various extensions of the Standard Model. His publication record shows a clear trend toward addressing current experimental anomalies (such as muon g-2, W-mass, and B-physics anomalies) through novel theoretical frameworks including leptoquark models, U(1) extensions, and grand unification scenarios. Recent work increasingly incorporates machine learning techniques for enhanced collider searches and explores connections between high-energy physics and extreme astrophysical environments. As a research advisor, Dr. Mandal leads the PPP Group at IISER-TVM with current members including Arijit Das, Rachit Sharma, Aniket, Anirudhan, Kartik, Nayan, and Kunika. His past advisee Aniket Masaye completed his work during 2021-2022. His research has been supported through various fellowships including the DST-INSPIRE Fellowship and postdoctoral positions at prestigious institutions. Dr. Mandal maintains an active research program with 39 journal publications, 2 communicated papers, 2 arXiv preprints, and 9 conference proceedings to his credit. His work has accumulated 1231 total citations with an h-HEP index of 22, reflecting significant impact in the particle physics community. He leads the Particle Physics and Phenomenology Group (PPPG) at IISER-TVM, which focuses on theoretical and phenomenological aspects of high-energy physics and their connection to experimental data.

Alexander Korotinمشاهده پروفایل

استادیار

• Generative Modeling
• Unpaired Learning
• Optimal Transport
• +۲ مورد دیگر

Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.

• Acoustic Metamaterials
• Fibrous Materials for Sound Absorption
• Numerical Acoustics
• +۳ مورد دیگر

Yang Tao, Ph.D. , a Marie Skłodowska-Curie Postdoctoral Fellow at the Technical University of Munich (TUM), works at the Chair of Acoustics of Mobile Systems under Prof. Steffen Marburg. His research focuses on developing textile-based metamaterials for low-frequency noise absorption, bridging acoustics, material engineering, and computational modeling. Acoustic metamaterials Fibrous material characterization Numerical acoustics Multi-functional textiles Recent publications highlight his expertise in Johnson-Champoux-Allard-Lafarge models for fibrous materials, acoustic bound states, and modular multilayer systems. Yang's work addresses urban noise challenges through scalable textile manufacturing techniques. Scientific awards include the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship . His research integrates experimental and computational approaches to advance sound absorption and material design.

Carsten Schildeمشاهده پروفایل

استاد

• Particle Technology
• Process Engineering
• Digitalization
• +۶ مورد دیگر

Carsten Schilde is a Heisenberg-Professor (W2) for Digitalisation of complex systems in process and production engineering at the Institute for Particle Technology, Faculty of Mechanical Engineering, Braunschweig University of Technology. He serves as Head of Division 'Particle Simulation & Functional Structures' and leads research in digitalization of process engineering. His research focuses on the intersection of particle technology, digitalization, and process engineering. Key areas include: Particle simulation and modeling using Discrete Element Method (DEM) Integration of artificial intelligence with traditional simulation methods Battery electrode processing and characterization Nanoparticle aggregation mechanics and structure formation Digital twin development for particle processes Professor Schilde's recent publications demonstrate a strong trend toward hybrid modeling approaches that combine physics-based simulations with data-driven AI techniques. His work spans from fundamental research on particle mechanics to applied research in battery technology and pharmaceutical manufacturing processes. As an educator, he teaches courses including 'Simulationsmethoden der Partikeltechnik,' 'Mechanische Verfahrentechnik 2,' 'Digitalisierung in der Verfahrenstechnik,' and 'Hybridmodellierung und dynamische Simulation einzelner und vernetzter Feststoffprozesse.'

Theja DeSilvaمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Quantum Many-Body Systems
• Statistical Mechanics
• +۵ مورد دیگر

Theja DeSilva is an Associate Professor in the College of Science and Mathematics at Augusta University, holding appointments in both the Department of Chemistry and Physics and the Department of Physics and Biophysics. With a PhD in Physics from the University of Cincinnati (2004), DeSilva specializes in quantum many-body theory, applying statistical mechanics to biological systems, and physics education research. Research interests span Condensed matter physics of quantum spin systems and superconductors Emergent collective phenomena in biological cells Interactive teaching methodologies using Socratic engagement Non-equilibrium quantum dynamics and optical lattice simulations Their work bridges fundamental quantum theory with biological applications through statistical mechanical frameworks. DeSilva's academic contributions include: KITP Scholar Awards (2018-2019) from UC Santa Barbara ITAMP Visiting Scientist Award (2017) at Harvard University Recognition for compassionate teaching at Augusta University (2018) In education, they champion interactive engagement strategies, utilizing cooperative learning and technology integration. As a theorist, DeSilva explores how complex quantum phenomena emerge from simple particle interactions, with recent publications focusing on optical lattice dynamics, lipid membrane self-organization, and protein folding thermodynamics.

Weiguo Luمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology
• Medical Image Analysis
• +۷ مورد دیگر

Weiguo Lu, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, where he leads research in the Division of Medical Physics and Engineering. He is also the co-founder of Neural Rad LLC and a board-certified medical physicist by the American Board of Radiology with a license from the Texas Medical Board. Education: Bachelor’s and Master’s in Nuclear Physics – Peking University, China Master’s in Medical Physics – University of Wisconsin–Madison Master’s in Computer Science – University of Wisconsin–Madison Ph.D. in Medical Physics – University of Wisconsin–Madison Dr. Lu’s research focuses on the integration of artificial intelligence and machine learning into radiation oncology, particularly in medical image segmentation, adaptive radiotherapy, dose prediction, and motion management. His work leverages deep learning to improve treatment planning accuracy and efficiency, especially in complex cases involving brain metastases, breast cancer, and head and neck tumors. The recent articles highlight a strong trend toward AI-driven automation in radiotherapy, including unsupervised domain adaptation, semi-supervised segmentation, patient-specific MRI super-resolution, and end-to-end survival prediction models. These works reflect a cohesive research agenda centered on enhancing precision, personalization, and real-time adaptation in cancer treatment using cutting-edge computational methods. Scientific Awards and Certifications: American Board of Radiology Certification Texas Medical Board Medical Physics License Dr. Lu actively mentors a large team of students and researchers, as evidenced by his extensive co-authorship on publications. His lab, the MAIA Lab (Medical Artificial Intelligence and Automation), develops platforms for automated delineation, dose verification (e.g., ART2Dose), and online adaptive radiotherapy. He has contributed to clinical workflow development, such as GammaPod treatments, and has been involved in quality assurance frameworks for FLASH trials. His collaborations span across institutions and involve significant translational research from algorithm development to clinical implementation. Dr. Lu’s research is supported by institutional and clinical grants related to AI in oncology, proton therapy, and adaptive radiotherapy, though specific grant names are not detailed in the provided text. He leads a multidisciplinary team focused on pushing the boundaries of medical physics through innovation in imaging, modeling, and therapeutic delivery.

Aditya S. Pandeyمشاهده پروفایل

استاد

• Neurosurgery
• Stroke
• Brain Tumors
• +۳ مورد دیگر

Aditya S. Pandey, MD is a Professor of Neurosurgery, Radiology, and Otolaryngology-Head and Neck Surgery at the University of Michigan Medical School. He serves as Chair of the Department of Neurosurgery, Medical Director of the Comprehensive Stroke Center Surgical, and Residency Assistant Director. His affiliations include the Institute for Healthcare Policy and Innovation, Weil Institute for Critical Care Research, MM-PKUHSC Joint Institute, and AI and Digital Health Innovation. Education: MD from Case Western Reserve University Medical School (2001), BS from Washington University (1997) Research Focus: His work bridges neurosurgery, AI in medical imaging, and hemodynamic modeling. Key areas include aneurysm rupture prediction , histotripsy for brain tissue ablation , and stroke management . Publications highlight applications of machine learning in neurovascular diagnostics and innovative surgical technologies. Recent Trends: His 2025-2024 publications emphasize AI-driven neuroimaging analysis, histotripsy for hemorrhage treatment, and computational fluid dynamics in aneurysm studies. Collaborative work spans interdisciplinary domains like biomedical engineering, pharmacology, and astrophysics-inspired imaging techniques. Leadership: As Chair of the Neurosurgery Department, he leads initiatives in stroke care, neuro-oncology, and digital health innovation.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Guangyao Chen is an Assistant Professor in the Department of Computer Science within Cornell University's College of Engineering, where he leads research at the intersection of computer vision, machine learning, and artificial intelligence. His work focuses on advancing open-world visual understanding systems capable of handling unknown classes and real-world complexity. His primary research interests include: Computer Vision and Open-Set Recognition Few-Shot Learning and Cross-Domain Adaptation LLM-Visual Integration and Symbolic Reasoning Neuromorphic Computing and Spiking Neural Networks Multi-Modal Learning and Real-Time Systems Analysis of his 2021-2025 publications reveals a strategic evolution toward solving open-world perception challenges. His recent work demonstrates how large language models can unlock complex event understanding from object detectors, while his G-OSR benchmark establishes new standards for graph-based open-set recognition. Notable contributions include real-time multimodal anomaly detection frameworks, retina-inspired saliency models, and Autoagents for automatic agent generation - all addressing critical gaps in deploying AI systems in dynamic, uncontrolled environments. Though specific awards and advising details aren't documented in available sources, his prolific publication output (including 9 papers in 2025) indicates an active research program with significant community impact. His work bridges theoretical advances in representation learning with practical applications in robotics, medical imaging, and industrial systems where handling unknown classes is critical.

• Polarimetry
• Antenna Design
• High Power Microwave Systems
• +۵ مورد دیگر

Scott Tyo is an accomplished electrical engineering professor currently serving as an Adjunct Professor at UNSW Canberra's School of Engineering and Technology. He joined UNSW Canberra in 2015 as Head of the School and Professor of Electrical Engineering, following previous appointments as a professor at the University of Arizona's College of Optical Sciences (2006-2015) and the University of New Mexico (2001-2006). His academic journey began with a PhD from the University of Pennsylvania in 1997, after which he served in the US Air Force working on High Power Microwave Systems and Space-Based Remote Sensing, including military faculty service at the US Naval Postgraduate School from 1999-2001. Professor Tyo's research spans polarimetry, antenna design, and remote sensing applications. His work began with underwater imaging systems based on differential polarimetry, where he established foundational research in polarimeter optimization. His career evolved to include significant contributions to ultra wideband and high-power microwave antennas, particularly through collaborations with Dr. Carl Baum at AFRL and later with Prof. Rick Ziolkowski at Arizona on metamaterial-inspired electrically small antennas. More recently, his research has expanded into tropical cyclone analysis using satellite remote sensing techniques. His work demonstrates remarkable interdisciplinary reach, connecting optical engineering with atmospheric science. Analysis of his recent publications (2020-2025) reveals three primary research thrusts: 1) Advanced polarimetry techniques including scene-adaptive imaging systems and multi-harmonic reconstruction methods; 2) High-power microwave engineering with focus on cascaded oscillators and electrically small antenna arrays; and 3) Tropical cyclone analysis through satellite remote sensing of cloud radiative effects. These areas reflect his ability to bridge fundamental optical engineering with practical applications in defense and climate science. Professor Tyo has maintained an exceptionally active publication record spanning over 25 years, with continuous contributions through 2025. His work shows strong international collaboration, with co-authors from multiple countries and institutions. His research has practical applications in defense systems, remote sensing technologies, and climate monitoring, demonstrating both theoretical depth and real-world impact.

Sam Kriegmanمشاهده پروفایل

استادیار

• Evolutionary Robotics
• Machine Learning
• Synthetic Biology
• +۵ مورد دیگر

Sam Kriegman is an Assistant Professor of Computer Science , Chemical and Biological Engineering , and Mechanical Engineering at Northwestern University . His research focuses on evolutionary robotics , machine learning , and synthetic biology , particularly through the development of the world's first AI-designed organisms ("xenobots"). Schmidt Futures AI2050 Early Career Fellow NSF CAREER Award recipient ISAL Distinguished Early-Career Investigator Cozzarelli Prize awardee Sam's research interests span evolutionary robotics, synthetic biology, and embodied artificial intelligence, emphasizing how biological principles can inform robot design. His work explores kinematic self-replication, reconfigurable organisms, and the intersection of machine learning with cellular engineering. His most recent publications highlight advancements in AI-designed biological systems, modular robotics, and morphological co-design. These works intersect robotics , synthetic biology , and machine learning , with notable emphasis on self-replicating organisms , modular legged robots , and morphological pretraining . Scientific awards and honors include: Schmidt Futures AI2050 Early Career Fellowship NSF CAREER Award ISAL Distinguished Early-Career Investigator Award Cozzarelli Prize Sam leads the xenobot.group lab, which investigates bio-inspired robotics and evolutionary computation . He also teaches the graduate course Artificial Life , focusing on embodied AI through simulations and synthetic biological constructs.

James Traerمشاهده پروفایل

استادیار

• Ecological Inference
• Perception of Natural Scenes
• Hearing and Audiovisual Integration
• +۸ مورد دیگر

James Traer is an Assistant Professor in the Department of Psychological and Brain Sciences at the University of Iowa. His research focuses on ecological inference, perception of natural scenes, and audiovisual integration. Key Research Areas: Hearing, causal reasoning, biologically inspired technologies, and statistical signal processing. Laboratory: Laboratory for Ecological Inference Contact: james-traer@uiowa.edu | 319-467-4544 Education Ph.D. in Psychology, University of California San Diego (2011)

• Electronics
• Nonlinear Circuits
• Semiconductor Device Modeling
• +۵ مورد دیگر

Fabrizio Bonani is a Full Professor at Polytechnic University of Turin's Department of Electronics and Telecommunications (DET), affiliated with the PolitoBIOMed Lab and serving on the Internationalization Table. His research spans nonlinear circuits, semiconductor device modeling (noise/thermal/variability), quantum-inspired computation, and energy harvesting systems. Research interests include: Stability/chaos in nonlinear circuits TCAD modeling of semiconductor devices Noise analysis in oscillators Energy harvesting system optimization Quantum-inspired computational methods His recent publications focus on energy harvesting systems (7 papers), GaN HEMT modeling (3 papers), and TCAD applications (2 papers), demonstrating strong trends in renewable energy tech and semiconductor device innovation. Awards include: Ottavio Bonazzi Award (1995) IEEE Fellow (2000-present) Advises PhD students including Edoardo De Ruvo (current) and Eva Catoggio (2019-2023). Leads research groups: Microwave and Optoelectronics Group (MOG)

Eyüp Fahri Keskenlerمشاهده پروفایل

استاد

• Physics
• Condensed Matter Physics
• Semiconductors
• +۳ مورد دیگر

Eyüp Fahri Keskenler is a Professor at Bayburt University, where he holds dual roles as Acting Dean of the Faculty of Art and Design and faculty member in the Basic Education Department. His administrative responsibilities extend to chairing the Faculty Board, Faculty Board of Directors, and Department Quality Commission, while also serving on university-wide committees including the Senate, Ethics Committee, and Academic Promotion Evaluation Commission. His research focuses on semiconductor physics and materials science, with specialization in: Condensed matter systems and microelectronic applications Thin film fabrication using sol-gel and spray pyrolysis techniques Doping effects in semiconductor materials (ZnO, CdS, CZTS) Device physics of Schottky diodes and heterojunction solar cells Computational optimization methods in materials science Publication analysis reveals two primary research streams: semiconductor material development (75% of recent output) exploring doping strategies and deposition methods for optoelectronic applications, and computational studies (20%) addressing algorithm optimization. A minor historical research thread examines Turkish scientific heritage and computing history.

• Electrical and Computer Engineering
• Deep Learning
• Image Processing
• +۵ مورد دیگر

Fatema Albalooshi is an Assistant Professor in the Computer Engineering department at the University of Bahrain since 2015. She holds a Ph.D. in Electrical and Computer Engineering from the University of Dayton (2015) and an MSc in Electrical Engineering from the University of Nottingham (2009). Education: PhD, Electrical and Computer Engineering, University of Dayton (2015) MSc, Electrical Engineering, University of Nottingham (2009) BSc, Electrical Engineering, University of Nottingham (2009) Research Interests: Deep Learning Image Processing Computer Vision Biometrics Machine Olfaction Environmental Remote Sensing Peer Review Contributions: Applied Intelligence (5 reviews) Signal, Image and Video Processing (16 reviews) Electronics (1 review) Computers in Biology and Medicine (1 review) Email: falbalooshi@uob.edu.bh