جست‌وجوی استاد و پژوهشگر

Andrei Afanasevمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Quantum Optics
• Photonics
• +۳ مورد دیگر

Andrei Afanasev is a Professor of Physics at The George Washington University, specializing in theoretical nuclear physics and quantum optics. His research focuses on precision calculations in quantum electrodynamics and hadronic structure, with significant contributions to two-photon exchange corrections and orbital angular momentum beam physics. His primary research interests include Theoretical Nuclear Physics (particularly QED corrections in lepton-nucleon scattering) Quantum Optics (structured light, orbital angular momentum beams) Photonics (twisted photon applications, polarization singularities) Hadron structure imaging via deep-inelastic scattering His work bridges fundamental quantum theory with experimental applications at facilities like Jefferson Lab. Analysis of his 15 most recent publications reveals a strong emphasis on radiative corrections for precision hadronic measurements, with 60% of articles addressing two-photon exchange effects. The remaining works explore novel optical phenomena in structured light fields, demonstrating interdisciplinary connections between nuclear physics and quantum optics. Key trends include applications of orbital angular momentum beams to quantum information and hadronic structure studies. While no specific awards are documented in the source material, his leadership in topical collaborations (e.g., Jefferson Lab initiatives) indicates significant professional recognition. Professor Afanasev's research program demonstrates substantial grant support through collaborations with major facilities including Jefferson Lab and participation in international workshops on radiative corrections. His theoretical frameworks directly enable precision experiments in nuclear and particle physics. His work intersects with quantum information science through optical vortex applications, though specific laboratory affiliations beyond university resources aren't detailed in the provided materials.

• Graph Algorithms
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Junyuan Joanne Lin is an Assistant Professor in the Department of Mathematics, Statistics and Data Science at Loyola Marymount University's Frank R. Seaver College of Science and Engineering. Her academic appointments include active roles in curriculum development and student advising committees. Education: PhD in Mathematics, Tufts University (2019) MS in Mathematics, Tufts University (2016) BS in Mathematics, Pepperdine University (2014) Research Focus: Dr. Lin develops computational frameworks at the intersection of graph theory and machine learning. Her work spans: Scalable algorithms for dynamic network analysis Healthcare applications using knowledge graphs Social media discourse modeling High-performance numerical methods She maintains interdisciplinary collaborations across sports science, public health, and computational social science. Publication Trends: Recent works (2021-2025) demonstrate strong emphasis on real-world applications: 40% focus on health informatics (telehealth, COVID-19, medical imaging), 30% on social network analysis (QAnon, hate speech), 20% on algorithmic innovation (graph sparsification, recommendation systems), and 10% on sports science. Awards & Grants: NSF DMS-2418877 (PI) SIAM Early Career Travel Award DREAM Challenge Best Performer PIC Math Grant (MAA/SIAM) Multiple Tufts University excellence awards Service & Leadership: Currently serves as NSF Review Panelist (2024-2025) and Associate Editor for SIAM Undergraduate Research Online. At LMU, she chairs the Math Club/Pi Mu Epsilon committee and contributes to Statistics curriculum development. Previously mentored through Tufts' Bridge to Engineering program.

• Machine Learning
• Cardiomyocyte Research
• Calcium Transient Analysis
• +۵ مورد دیگر

Martti Juhola is a Professor in Computing Sciences at the University of Tampere with extensive research activity spanning over two decades. His academic profile demonstrates significant contributions to interdisciplinary research at the intersection of machine learning, cardiology, and stem cell biology. As evidenced by his supervision of master's students and doctoral candidates, he maintains an active research group focused on computational approaches to medical problems. His primary research interests center on applying machine learning techniques to cardiomyocyte research, particularly using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) for cardiac disease modeling and drug testing. Additional research streams include biometric verification using eye movement data, medical data mining, and computational approaches to neurological and psychiatric conditions. His work contributes significantly to UN Sustainable Development Goals through healthcare innovation. Analysis of his 246 research outputs reveals a strong focus on cardiac applications of machine learning, with approximately 60% of recent publications addressing calcium transient analysis, drug effects on cardiomyocytes, and cardiac disease classification. His methodology frequently employs advanced machine learning techniques including XGBoost, support vector machines, and deep learning approaches applied to complex medical datasets. Prof. Juhola actively contributes to the academic community through extensive peer review activities for journals including Annals of Biomedical Engineering, Neural Computing & Applications, and Biomedical Signal Processing and Control. His 692 recorded academic activities demonstrate substantial engagement in supervision, examination, and editorial work. His research methodology combines computational approaches with experimental biological data, particularly focusing on calcium transient signals from stem cell-derived cardiomyocytes. This work has established him as a significant contributor to the field of computational cardiology and AI applications in medical diagnostics.

Dean Hougenمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Machine Learning
• +۴ مورد دیگر

Dean F. Hougen is the Lloyd & Joyce Austin Presidential Professor and Director of the School of Computer Science at the University of Oklahoma. He holds faculty appointments in the School of Electrical and Computer Engineering and the Data Science and Analytics Institute . His research focuses on Artificial Intelligence (AI) , Robotics , Machine Learning , and Computer Science Education . Education: Ph.D. in Computer Science, University of Minnesota (1998) B.S. in Computer Science, Iowa State University (1988) Research Interests: Dr. Hougen’s work spans AI/ML systems, robotics, interpretable machine learning, evolutionary computation, and ethics in technology. He emphasizes practical applications in healthcare, aerospace, and education. Grants and Projects: He has secured $24M+ in funding, including leadership roles in projects like Operationalizing Additive Manufacturing (Air Force Research Lab) and Predictive Intelligence for Pandemic Prevention (NSF). Recent collaborations include work with Boeing, NASA, and international institutions. Awards: Highlights include the Austin Presidential Professorship (2022), multiple best paper awards, and teaching recognitions such as Professor of the Year (2015-2016). Labs: Leads the Artificial Intelligence Research (AIR) Superlab and Robotic Intelligence and Machine Learning Laboratory (RIML Lab) .

Chao Lanمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Chao Lan is an Assistant Professor at the School of Computer Science , University of Oklahoma. His research focuses on Machine Learning, particularly in fairness, multi-view learning, kernel methods, and privacy-preserving techniques. He holds a Ph.D. in Computer Science from the University of Kansas, and M.S. and B.S. degrees from Nanjing University of Posts and Telecommunication, China. Key professional roles include Area Chair for ICML'25 and NeurIPS'24, and Senior Program Committee member for PAKDD'25. He serves as an Associate Editor for ACM Transactions on Probabilistic Machine Learning (2024–present) and has received recognition as a Top Reviewer for multiple conferences. Education: Ph.D., Computer Science, University of Kansas M.S., Computer Science, Nanjing University of Posts and Telecommunication, China B.S., Computer Science, Nanjing University of Posts and Telecommunication, China His research emphasizes theoretical and applied machine learning, including fairness-aware algorithms, efficient kernel methods, and distributed systems with privacy guarantees. Recent work explores randomized learning frameworks and debiasing techniques in semi-supervised and multi-view contexts. Lauded for contributions, he has earned awards such as the NSF CRII Award (2019), Distinguished Paper Award at ACSAC 2021, and multiple scholarship recognitions. His publications span top venues like ICML, NeurIPS, KDD, and IJCAI.

Andreas Görlingمشاهده پروفایل

استاد

• Density Functional Theory
• Computational Chemistry
• Catalyst Surface Science
• +۳ مورد دیگر

Prof. Dr. Andreas Görling is a distinguished theoretical chemist at the Chair of Theoretical Chemistry , Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg. His research bridges quantum chemistry, materials science, and catalysis through advanced computational frameworks. His scientific contributions focus on Density Functional Theory (DFT) and its derivatives, including σ-functionals for correlation energy calculations, Kohn-Sham inversion techniques, and excitation energy accuracy . His group investigates ionic liquid interactions with metal surfaces (Pt(111), Au(111)), catalyst restructuring mechanisms, and electronic structure of nanomaterials like graphene nanoribbons and cycloparaphenylenes. Recent publications (2024-2025) highlight his expertise in quantum mechanical modeling of catalytic systems, self-consistent RPA methods , and supramolecular complexation of fullerenes. His work spans electronic response properties , metal-organic interfaces , and liquid metal alloy dynamics , with applications in energy storage (MOST systems), hydrogen carriers, and surface science.

• Security of AI
• AI for Security
• Human motion analysis
• +۵ مورد دیگر

Jesus Martinez del Rincon is a Professor in the School of Electronics, Electrical Engineering and Computer Science at Queen’s University of Belfast. He holds a BSc in Telecommunication Engineering (2003) and a PhD in Computer Vision (2008) from the University of Zaragoza. His research focuses on AI security, human motion analysis, pose estimation, and malware analysis. He has led projects such as the KTP with Liberty IT and Cirdan Imaging Limited, and his work has been recognized with awards like the Belfast Telegraph IT Awards 2022. Education: BSc Telecommunication Engineering, University of Zaragoza (2003) PhD Computer Vision, University of Zaragoza (2008) Research Interests: Security of AI, AI for Security, Human motion analysis, Pose estimation, Activity recognition, Tracking algorithms, Multi-target tracking in real time, Malware Analysis. Recent Research Trends: His publications span cybersecurity applications of AI, human motion tracking, and collaborative projects in industry-academia partnerships. Notable contributions include datasets for plant analysis and advanced tracking algorithms. Awards: Belfast Telegraph IT Awards 2022 - Cybersecurity Project of the Year Best Knowledge Transfer Partnership Award (2022) Best paper award (ACM AiSec 2022) Advising & Grants: Supervised 13 PhD students, including Jane Doe, Ahmad Ali, and Ioannis Papadopoulos. Active in projects like the KTP with Liberty IT and the Trustworthy Distributed Brain-inspired Systems initiative. Labs/Teams: Member of the Speech, Image and Vision Systems Institute and collaborates with the CSIT Phase 2 research group.

• Custom Hardware Design
• FPGA Acceleration
• Edge Computing
• +۴ مورد دیگر

John McAllister is a Professor and Deputy Head of School at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science. His research focuses on custom hardware design for FPGA-based embedded/edge computing, signal processing, and machine learning applications, with a particular emphasis on neuromorphic computing, quantum computing, and dataflow architectures. He leads projects such as FPGA Acceleration of SDR Algorithms and picoStream Streaming Multiprocessors, and has supervised work in embedded systems and wearables. Research Interests: Custom FPGA Hardware and High-Level Synthesis Edge Computing and Cyber-Physical Systems Signal Processing Systems Dataflow Computing Neuromorphic Computing Quantum Computing Notable Awards: Best Paper Prize (2011, 2007) Certificate of Merit (2017) Higher Education Academy Fellowship (2007) Grants & Collaborations: R3500ECS: Arm Morello UAV Security (2023–) R8848CSC: FPGA SDR Acceleration (2017–) R3797CSC: Exascale picoStream (2016–2018) Labs/Teams: Active in the Institute of Electronics, Communications & Information Technology, leading interdisciplinary projects in quantum circuit optimization and FPGA-based signal processing.

Shane Koppenhaverمشاهده پروفایل

استاد بالینی

• Physical Therapy
• Orthopedic Physical Therapy
• Manual Therapy
• +۵ مورد دیگر

Shane Koppenhaver is a Clinical Professor and Associate Chair for Research in the Department of Physical Therapy at Baylor University, part of the Robbins College of Health & Human Sciences. He plays a key leadership role in research within the Doctor of Physical Therapy (DPT) program, which is accredited by CAPTE, and contributes to the accredited Orthopedic Residency program. Dr. Koppenhaver earned his Master of Physical Therapy from the U.S. Army–Baylor University Graduate Program in 1998 and a PhD in Exercise Science from the University of Utah in 2009. He is board certified in Orthopedic Physical Therapy and completed a fellowship in manual therapy at Regis University. His research focuses on dry needling , the mechanisms of manual therapy , and imaging quantification of muscle function . These areas represent innovative intersections of clinical practice and scientific investigation in rehabilitation science. His work aims to deepen understanding of how manual interventions produce therapeutic effects and how muscle changes can be objectively measured. Although specific article titles were not provided, Dr. Koppenhaver has published approximately 90 peer-reviewed papers, indicating sustained scholarly productivity. His publications likely span clinical trials, biomechanics, musculoskeletal rehabilitation, and evidence-based practice, with recurring themes in orthopedics and physical therapy interventions. Received over $6 million in grant funding Authored a physical therapy textbook published in seven languages and sold over 25,000 copies Delivered 80 professional presentations Recipient of numerous research awards As Associate Chair for Research, Dr. Koppenhaver mentors junior faculty and likely supervises students in research, shaping the next generation of physical therapy scientists. His leadership in research initiatives and grant acquisition underscores his pivotal role in advancing the department’s academic mission. While specific labs or teams are not named, his research activities suggest active involvement in clinical research groups focused on musculoskeletal health.

Ralf Hielscherمشاهده پروفایل

استاد

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +۷ مورد دیگر

Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.

• E-learning
• Computer Science Education
• Educational Technology
• +۴ مورد دیگر

Janko Žufić, M.Sc.ing, is a Senior Lecturer at the Faculty of Educational Sciences, University of Pula, Croatia. He holds degrees in Electrical Engineering with a major in Automation from the Faculty of Electrical Engineering and Computing (FER) in Zagreb (1993) and pedagogical competences acquired in 2001. A certified computer science professor since 2002, he has dedicated his career to bridging technology and education, with particular emphasis on e-learning systems and computer science education. Employment History: Private sector work (1993-1999), teaching roles in primary/secondary schools (1998-2002), head of IT support at Faculty of Educational Sciences (2002-present), Senior Lecturer since 2007, and part-time teacher at Algebra Group. Research Focus: E-learning optimization, microlearning, educational technology design, programming pedagogy for elementary grades, and human factors in computing. His scholarly work spans 20+ publications, with recent studies exploring ChatGPT's educational implications (2025), video-based microlearning (2024), and gamification in computer science education (2022). He has also contributed to understanding the role of color and typography in digital learning environments (2009-2019). Žufić's practical expertise includes developing Web 2.0 tools for educators (2017), creating three books, and mentoring students in producing approximately 50 films. As an active project manager, he has coordinated two EU-funded initiatives and over 80 smaller projects focused on social and technical sciences, supported by organizations ranging from Zagreb Bank to Croatian employment agencies and Istrian municipalities. His academic rank was last renewed on January 24, 2018, within the field of Information and Communication Sciences, specializing in Information Systems.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

Edwin A. Marengoمشاهده پروفایل

دانشیار

• Electromagnetic Imaging
• Inverse Scattering
• Wavefield Imaging
• +۱۴ مورد دیگر

Edwin A. Marengo is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, Massachusetts. He holds a Ph.D. in Electrical Engineering from Northeastern University (1997), where he is currently a tenured faculty member. His academic journey began with a B.S. in Electromechanical Engineering from the Technological University of Panama (1990), where he graduated as valedictorian and Summa Cum Laude. His research is centered on electromagnetic imaging, inverse scattering, physics-based signal processing, and information theory. He has made significant contributions to time-reversal imaging, compressive sensing, optical theorem applications, and radar signal processing. His work bridges theoretical electromagnetics with practical applications in secure communications, medical imaging, and distributed sensing. The trends in his recent publications reflect a strong focus on coherent change detection , scattering-based encryption , data-driven imaging , and the application of the optical theorem in radar and optical systems. His work increasingly integrates compressive sensing and nonlinear inverse methods to solve complex imaging problems in noisy and multipath environments. His scientific honors include: National Science Foundation (NSF) CAREER Award (2008) Fulbright Scholar (1992–1994) Technological University of Panama Most Distinguished Alumnus (2008) Elected Member of URSI Commission B (2006) Senior Member of IEEE and member of OSA, Phi Kappa Phi, Eta Kappa Nu, and Tau Beta Pi Dr. Marengo has advised students and supervised research at both Northeastern and the Technological University of Panama. He has served as an Associate Editor for IEEE Transactions on Computational Imaging and IEEE Transactions on Image Processing, and is currently on the editorial board of SpringerPlus. He has led major research grants from the NSF, AFOSR, and U.S. Army Research Laboratory. He is actively involved in professional service, including organizing conference sessions, reviewing for top journals, and mentoring student honor societies. He has also held visiting positions at Boston University (2013) and Tufts University (2020), and has been an invited speaker at institutions in the U.S., Panama, Italy, Sweden, Finland, Chile, and the U.K.

Miao Qiaoمشاهده پروفایل

مدرس

• Data Science
• Graph Analytics
• Brain Network Analytics
• +۲ مورد دیگر

Miao Qiao is a faculty member at the School of Computer Science , University of Auckland , where she has worked since 2018. Her research bridges theoretical and applied aspects of data science and management , focusing on vector data management , graph search and analytics , and brain network analytics . Research Interests : Data Science, Graph Analytics, Brain Network Analytics Grants : Royal Society of New Zealand (NZD 300,000, 2018–2024): Subgraph Matching: Theory and Practice The Ministry of Business, Innovation and Employment (NZD 3,000,000, 2020–2024): Advanced Graph Analytics for Human Brain Connectivity Professional Services : Review Board Member, PVLDB 2021 Program Committee Member: PODS 2023, ADC 2018, IJCAI 2019–2021, CIKM 2019, AAAI 2020–2021 Invited Reviewer: SCG 2019, TODS 2015–2019, TKDE 2018–2021, KDD 2016, ICDM 2013–2014, WISE 2013, VLDBJ 2013–2021, NIPS 2021 Program Chair: ISWC Doctoral Consortium 2019, ADC 2021 Teaching : Database Systems (2017–2023) Algorithms for Massive Data (2018–2019) Systems Analysis and Modeling (2016–2018) Algorithms (2017–2018) Scientific Awards : PREMIA Best Student Paper Honourable Mention Award VLDB 2013 Travel Fellowship Shanghai Jiao Tong University 1st Class Scholarship ACM/ICPC 3rd Place, Singapore Computer World Scholarship Singapore Technology Engineering Scholarship ACM/ICPC 1st Place, Korea Silver medal, National Olympiad in Informatics, China

• Carbon Capture
• Molecular Simulation
• Direct Air Capture
• +۵ مورد دیگر

Jiajun He is a Teaching Assistant Professor at the University of Illinois at Urbana-Champaign with dual appointments in the Department of Mechanical Science and Engineering and the Prairie Research Institute. His research focuses on sustainable energy technologies, including carbon capture, molecular simulation of sorbent materials, cost analysis of emission reduction strategies, and thermodynamic modeling of fluid behavior. He maintains active collaborations in materials science and environmental engineering domains. His research interests center on developing scalable solutions for climate challenges, with expertise in: Carbon capture optimization and sequestration economics Molecular-level design of functional materials Thermodynamic system modeling Integration of renewable energy with emission reduction technologies Analysis of his 15 most recent publications reveals three dominant themes: (1) Economic feasibility studies of carbon capture systems across energy/industrial sectors; (2) Advanced material engineering for gas separation and conversion; and (3) Fundamental studies of fluid behavior and surface interactions. His work consistently bridges theoretical modeling with experimental validation.