جست‌وجوی استاد و پژوهشگر

Scott Reedمشاهده پروفایل

استاد

• Pharmacogenomics
• Artificial Intelligence
• Nanoscience
• +۴ مورد دیگر

Scott Reed is a Professor in the Department of Chemistry within the College of Liberal Arts and Sciences at the University of Colorado Denver. His office is located in Room 4131 of the Science Building, with mailing address at Department of Chemistry, Campus Box 194, P.O. Box 173364, Denver, CO 80217-3364. Dr. Reed earned his Ph.D. and M.S. from the University of Oregon in 2001 and 1996 respectively, and completed his undergraduate studies with a B.A. from Bard College, NY in 1994. His primary research interests span pharmacogenomics, artificial intelligence, nanoscience, chemical synthesis, and green chemistry. His work particularly focuses on developing computational tools to identify genetic variants that explain adverse drug reactions (ADRs). His research program has two main goals: creating an online tool that identifies specific ADRs and their causative drugs, and developing another tool that generates candidate genetic variants explaining specific ADRs. These tools facilitate rapid hypothesis testing about pharmacogenomics causes of harmful drug side effects. Dr. Reed's publication record shows a strong interdisciplinary approach with recent work (2022-2024) emphasizing AI/ML applications to chemical and pharmacological problems. His research spans from fundamental chemical synthesis and nanoscience to computational biology and AI-driven drug safety analysis. He is actively involved in educational initiatives including a course on "Vibe Coding," a Neural Network Tutorial available on YouTube, and the development of a CNN-in-Excel project (Tetris_CNN) that demonstrates convolutional neural networks within spreadsheet applications. He has also created a Chemistry Knowledgeable GPT to assist with molecular informatics. Dr. Reed maintains an active research laboratory with projects including the Advancing AI-Assisted Molecular Synthesis Challenge, focusing on applying artificial intelligence to chemical synthesis problems.

Husnain Rafiqمشاهده پروفایل

مدرس

• Cyber Security
• Machine Learning
• Malware Detection
• +۴ مورد دیگر

Dr. Husnain Rafiq is a Lecturer in Cyber Security at Edge Hill University, where he conducts research on machine learning applications in cybersecurity. He earned his PhD in Cyber Security from Northumbria University in December 2022, focusing on ML-based mobile malware defense. With over seven years of teaching experience, he currently supervises graduate research projects in mobile security, adversarial ML, and malware detection. Research Focus His research spans: Machine Learning for Security: Developing adversarial-aware malware detectors and evasion-resistant models Mobile Systems Protection: Specializing in Android malware detection using image processing and deep learning Cyber Defense Innovations: Creating GAN-based botnet detectors and DGA detection systems Trustworthy AI: Exploring explainable AI for fraud detection and content moderation Publication Trends Recent works demonstrate strong focus on adversarial machine learning (77% of publications) with applications in malware detection (57%), network security (35%), and trust-aware AI systems (21%). Emerging themes include explainable fraud detection, sustainable risk frameworks, and low-data regime solutions.

Simone Castagnoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Medical Informatics
• +۳ مورد دیگر

Simone Castagno is a Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), School of Physical Sciences, University of Cambridge. Their work bridges machine learning and medical applications, particularly in orthopedics and rheumatology. Research Interests: Simone focuses on interpretable machine learning for clinical prediction tools, with applications in soft tissue knee injury screening, osteoarthritis progression forecasting, and trauma surgery outcomes. Their research integrates biomedical engineering, clinical data analysis, and AI ethics in healthcare. Publication Trends: Recent articles highlight collaborations between DAMTP and medical institutions. Key themes include AI-driven predictive modeling for joint pathologies, systematic reviews of ML in osteoarthritis, and interdisciplinary studies combining computational methods with orthopedic/rheumatological clinical research. Labs & Teams: Affiliated with the Machine Learning and Artificial Intelligence Research Group at DAMTP, which specializes in data-driven solutions for biomedical challenges.

Sebastian Vollmerمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Bayesian Inference
• +۸ مورد دیگر

Sebastian Vollmer is a Professor for Application of Machine Learning at the University Kaiserslautern-Landau (RPTU) since 2021 and a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI). He has previously held tenured positions as an Associate Professor at the University of Warwick (2016–2021), a Departmental Lecturer at the University of Oxford (2014–2016), and a Postdoctoral Researcher at Oxford (2013–2014). His research bridges machine learning , Bayesian inference , and computational statistics with applications in health informatics , social good , and physics-informed anomaly detection . He has led initiatives at the Alan Turing Institute, including the Data Science for Social Good program and Health & Medical Sciences Programme . Vollmer's recent work focuses on biologically informed neural networks , narrative priming in public goods games , and knowledge retrieval from large language models . His projects include PIAD (personalized AI-based lifestyle interventions), ML-Bau-Doc+ (ML for building documentation), and TrustifAI (trustworthy AI for health). Scientific Awards : 2014 Faculty Prize, University of Warwick 2009-13 German Academy Foundation Scholarship 2009 3rd Prize, International Mathematics Competition He supervises PhD and MSc students in data science, leads research departments at DFKI, and collaborates with startups like Cera Care and Fly Notes to address public sector challenges.

Simon Geerkensمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Railway Systems
• Explainable AI
• +۳ مورد دیگر

Simon Geerkens serves as a Researcher and PhD candidate at Hochschule Düsseldorf's Faculty of Electrical Engineering & Information Technology under Prof. Dr. Braun. Currently embedded in the EU-funded safe.trAIn. consortium, he works on developing safety architectures for driverless regional trains through AI integration. His research focuses on explainability and trustworthiness of AI systems , data quality assurance , and practical AI applications in safety-critical railway environments. With expertise spanning artificial intelligence, deep learning, and data security, his work bridges theoretical machine learning with industrial railway automation requirements. Analysis of his recent publications reveals a strong emphasis on data quality tools (QI², ECS) and safety assurance pipelines for ML systems in rail transport. His research consistently addresses the challenge of implementing verifiable AI in safety-critical infrastructure, particularly through dataset validation methods and complexity assessment frameworks. Professional activities include active participation in the safe.trAIn. project consortium comprising railway industry leaders, technology suppliers, and standardization organizations working toward GoA4 automation standards.

Nathan B. Gawمشاهده پروفایل

استادیار

• Machine Learning
• Multimodal Data Fusion
• Healthcare Telemonitoring
• +۷ مورد دیگر

Dr. Nathan B. Gaw is an Assistant Professor of Data Science at the Air Force Institute of Technology (AFIT), where he develops advanced statistical machine learning algorithms for multimodal data fusion in defense, healthcare, and remote sensing applications. He holds a Ph.D. in Industrial Engineering from Arizona State University (2019) and completed postdoctoral research at both the ASU-Mayo Clinic Center for Innovative Imaging (2019-2020) and Georgia Institute of Technology's School of Industrial and Systems Engineering (2020-2021). Research Interests: His work focuses on three core areas: Feature extraction/selection using tensor decomposition and functional data analysis Spatio-temporal neural networks with attention-based ConvLSTMs Image registration/fusion through convolutional neural networks Key application domains include aviation human factors, weather remote sensing, and cybersecurity. Scientific Awards: Dr. Gaw has received 14 major scientific awards including: AFWERX Datathon Challenge (5 awards) INFORMS Principal Cup (2nd place) Harold Wolff-John Graham Award (American Academy of Neurology) Tau Beta Pi Fellowship Dean's Fellowship, ASU Professional Engagements: He serves as chair of the INFORMS Data Mining Society and council member of IISE Data Analytics Division, while maintaining IEEE membership. His 16 peer-reviewed publications (12 Q1, 4 Q2) include a U.S. patent and book chapter contributions.

Sun-Yuan Kungمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Sun-Yuan Kung is a Professor of Electrical and Computer Engineering at Princeton University specializing in high-performance learning networks and explainable AI. His research develops beyond back-propagation paradigms to create optimal neural architectures through structural learning and discriminant information metrics. His academic credentials include: Ph.D. in Electrical Engineering, Stanford University (1977) M.S. in Electrical Engineering, University of Rochester (1974) B.S. in Electrical Engineering, National Taiwan University (1971) Professor Kung pioneered Explainable Neural Networks (XNN) incorporating Internal Neuron's Learnability (INL) using internal teacher labels and discriminant information (DI) for node/layer ranking. This enables deep compression while enhancing model robustness and adaptability for real-time applications in image processing, privacy protection, and security-sensitive environments. His work directly addresses DARPA's Explainable AI (XAI) initiatives through end-user-adaptive labeling mechanisms. Recent publications (2023-2025) demonstrate sustained innovation in efficient deep learning architectures, with dominant themes in remote sensing applications (object detection, image captioning, change detection), quantum-inspired algorithms, and multimodal representation learning. Key trends include neural architecture search, model compression techniques, and reinforcement learning integration for specialized domains. His distinguished honors include: Life Fellow of IEEE (2016) IEEE Third Millennium Medal (2000) IEEE Signal Processing Society Best Paper Award (1996) IEEE Signal Processing Society Technical Achievement Award (1992) IEEE Fellow (1988) Professor Kung mentors graduate researchers including Katherine Shu-Min Li, Xiaxin Shen, and Zhuoqing Song, with funding supporting projects in deep compression, privacy-preserving ML, and XAI frameworks. His group develops foundational techniques for discriminant information-based network pruning and structural gradient methods. He leads a research team advancing the XNN framework for DARPA's Explainable AI initiatives, focusing on real-time decision support through internal neuron explainability and channel exploration for model optimization.

• Nonlinear System Identification
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Hua-Liang Wei is a Senior Lecturer at the University of Sheffield 's School of Electrical and Electronic Engineering. He leads two innovative research labs: the Dynamical Modelling, Data Mining and Decision Making (3DM) and the Digital Medicine & Computational Neuroscience (DMCN) Research Groups. Specializes in system identification for nonlinear dynamics Develops interpretable AI for healthcare applications Active in space weather and environmental forecasting His methodological expertise spans NARMAX modeling, wavelet neural networks, and multiresolution analysis. Collaborations include Sheffield Teaching Hospitals NHS Trust, multiple University of Sheffield departments (Chemistry, Oncology, Psychology), and international institutions like Beihang University. Scientific Awards include STFC and NERC grants for radiation belt modeling and environmental systems research, EU Horizon 2020 funding, EPSRC Platform grants, Royal Society support, and medical charity partnerships. Recent publications focus on hybrid wavelet-LSTM for wind power forecasting EEG analysis in epilepsy and Alzheimer's domain adaptation for fault diagnosis interpretable models for medical data covering applications from renewable energy to clinical diagnostics.

Mohammed Benaissaمشاهده پروفایل

استاد

• Hardware Cryptography
• Error-Control Coding
• Reconfigurable Hardware Design
• +۴ مورد دیگر

Mohammed Benaissa is a Professor of Information Engineering at the University of Sheffield within the School of Electrical and Electronic Engineering . As Director of Learning and Teaching (Student Experience) , he leads initiatives for academic quality and student engagement. His research bridges hardware cryptography , error-control coding , and healthcare technology , particularly in data-driven diabetes management via the WithCare+/Glucollector platform deployed in NHS trusts. Education : PhD, Dip.Ing in Electrical and Electronic Engineering Technical Leadership : Patent holder for WithCare+ (GB 2467079) His research interests span cryptosystems , residue number systems , and reconfigurable hardware , with a focus on scalable security architectures and low-power medical devices . Recent publications highlight lattice-based cryptography and AI-driven glucose prediction using NIR/MIR spectroscopy and machine learning . Scientific accolades include a best paper award for RFID security and an innovation prize for diabetes technology. He has supervised over 25 PhD students, including graduates like Abdelaal A , Al-Mbaideen A , and Khan Z U A , and serves on research funding panels and conference Technical Program Committees.

George Panoutsosمشاهده پروفایل

استاد

• Computational Intelligence
• Explainable Machine Learning
• Data-Driven Modeling
• +۷ مورد دیگر

Professor George Panoutsos is a faculty member at the University of Sheffield , affiliated with the School of Electrical and Electronic Engineering and the Department of Automatic Control and Systems Engineering (ACSE) . He was promoted to Professor of Computational Intelligence in 2019 after joining as a Lecturer in 2010. Research focus on Computational Intelligence with applications in advanced manufacturing , healthcare , energy , and infrastructure Secured over £3M in grants from EPSRC , Innovate UK , DSTL , EU Horizon 2020 , and industry partners Over 100 publications in machine learning , optimization , and decision support systems Recent research trends include: Explainable AI for neural networks and optimization algorithms Physics-informed ML in additive manufacturing and fuel cells Multi-objective optimization frameworks for industrial processes Autonomous systems in manufacturing and biomedical imaging Reinforcement learning for process control and stability Granular computing for data compression and modeling Scientific contributions : Co-PI for EPSRC MAPP Hub (£6.7M 2016-2023) Lead PI for VULCAN (Innovate UK, £334k 2019-2022) International patent in Granular Computing from PhD work Fellow of the Higher Education Academy (FHEA) Teaching activities include module leadership for ACS6402: Industry Training Programme in Advanced Manufacturing and ACS6403: Computational Intelligence . His Computational Intelligence Systems Laboratory includes researchers working on projects like NanoMan , Responsive Manufacturing , and INTEGRADDE .

• Software Engineering
• Value-Based Software Engineering
• Software Quality Assurance
• +۷ مورد دیگر

LiGuo Huang is an accomplished researcher and academic in the field of software engineering with a publication record spanning over two decades from 2003 to 2025. With 95 publications documented in the dblp database, Huang has established a significant presence in both traditional software engineering domains and emerging areas where machine learning intersects with software development practices. Huang's research has evolved from foundational work in value-based software engineering to cutting-edge applications of artificial intelligence in software analysis and maintenance. Huang's research interests encompass a broad spectrum of software engineering topics, with particular emphasis on value-based software engineering, software quality assurance, defect classification, and software process modeling. More recently, Huang has focused on applying machine learning and deep learning techniques to software engineering problems, including code summarization, vulnerability detection, and software maintenance. This evolution reflects the broader shift in the field toward data-driven approaches for software development and analysis. The publication trends reveal a consistent research trajectory with increasing publication rates in recent years, particularly in the application of machine learning to software engineering problems. Huang's work shows a strategic progression from theoretical foundations in software quality to practical applications of AI in software development. The research spans empirical studies, systematic literature reviews, and novel technical approaches to longstanding software engineering challenges, demonstrating both theoretical depth and practical relevance. Huang has collaborated extensively with researchers across multiple institutions, forming particularly strong partnerships with Jidong Ge, Bin Luo, Chuanyi Li, and Barry W. Boehm. The collaboration with Boehm in early career publications suggests mentorship that evolved into peer collaboration, while more recent work shows Huang mentoring newer researchers who now serve as primary authors on joint publications. Huang's research has practical implications for software development practices, particularly in improving software quality, enhancing developer productivity through AI-assisted tools, and providing empirical evidence for software engineering decision-making. The work bridges theoretical computer science with practical software engineering concerns, making significant contributions to both academic research and industry practice.

Kasun Wijesooriyaمشاهده پروفایل

پژوهشگر ارشد

• Wind Engineering
• Blast and Impact Effects
• Fluid-Structure Interaction
• +۳ مورد دیگر

Dr. Kasun Wijesooriya is a Research Fellow at the School of Engineering and Technology, UNSW Canberra, specializing in extreme loading events (wind, blast) and numerical finite element modelling. His research spans wind engineering, fluid-structure interaction, blast effects, sustainable construction, and AI applications in engineering. Education: PhD in Structural Engineering, University of Sydney MEng in Structural Engineering, University of Sydney MEng in Civil Engineering, University of Bristol Research Interests: Dr. Wijesooriya's work integrates computational fluid dynamics (CFD) with machine learning to predict structural responses to wind and blast loads. Recent projects focus on AI-driven blast load prediction, sustainable materials (e.g., graphene-enhanced concrete), and energy-absorbing fractal structures via additive manufacturing. Publications (Trends): His 15 most recent articles (2023–2025) emphasize machine learning applications in blast/wind engineering, experimental mechanics of novel materials, and sustainable construction techniques. Dominant themes include explainable AI for structural safety, graphene-based composites, and fractal geometry in energy absorption. Grants: Fatigue Testing of Armour Grade Materials (2025, $140,000) High-Speed Camera for Resilience & Energy Infrastructure (2025, $235,000) Scanning Pressure Measurement System (2024, $142,000) Teaching: Courses include ZEIT4600/4601 (Civil Design Practice), ZEIT2603 (Civil Engineering Materials), and ZEIT3600 (Structural Analysis) from 2021–2024.

Frederick Klauschenمشاهده پروفایل

استاد

• Precision Medicine
• Bioinformatics
• Artificial Intelligence in Pathology
• +۷ مورد دیگر

Frederick Klauschen is a Professor and Director at the Institute of Pathology, Ludwig Maximilian University of Munich , focusing on integrating multi-omics data (genomics, epigenetics, transcriptomics, proteomics) with explainable artificial intelligence (AI) to decode tumor complexity. His lab collaborates with institutions like TU Berlin and Max Delbrück Center for Molecular Medicine (MDC) Berlin . Research Highlights Digital Pathology & AI: Automated cell nucleus detection, explainable AI for histological heatmaps Proteomics: Mass spectrometry for signaling pathway analysis DNA Methylation: Epigenetic tumor classification via array and bisulfite methods Single-Cell Analysis: Genomic/transcriptomic profiling of individual cells Multiplex Immunofluorescence: Spatial tumor cell interaction mapping Article Trends : His recent work emphasizes AI-driven multimodal data integration (clinical, imaging, molecular) to predict cancer outcomes, with a focus on explainable foundation models and single-cell resolution of tumor heterogeneity. Key collaborations include Prof. Klaus-Robert Müller (TU Berlin) and Dr. Philipp Mertins (MDC Berlin). Students & Research Associates Yauheniya Zhdanovich (Bioinformatics PhD) Edgar Chimal (Research Associate) Emma Payá Cipalla (Research Associate) Maximilian Leitheiser (Research Associate) Christoph Geisenberger (MD, M.Sc.) Philipp Keyl (MD, PhD) Oliver Buchstab (MD, M.Sc.)

Xipeng Shenمشاهده پروفایل

استاد

• Programming Systems
• Machine Learning
• High-Performance Computing
• +۱۲ مورد دیگر

Xipeng Shen is a Professor in the Department of Computer Science at North Carolina State University and Director of the High-Performance Intelligent Computing (HiPIC) lab. He joined NC State in 2014 as part of the Chancellor's Faculty Excellence Program after serving as the Adina Allen Term Distinguished Associate Professor at the College of William and Mary. Shen's research spans programming systems and machine learning, with a focus on enabling extreme-scale, data-intensive, and intelligent computing through innovations in compilers, runtime systems, and ML algorithms. His work has significantly influenced the development of heterogeneous computing and modern AI systems. His research areas include High-Performance Machine Learning & Real-Time AI, Heterogeneous Massively Parallel Computing, and Foundations of Programming Systems & Languages. His recent publications demonstrate strong trends in optimizing AI deployment across various platforms including mobile devices, serverless environments, and edge computing systems, with particular emphasis on efficiency, power consumption, and real-time performance. His research increasingly integrates compiler techniques with machine learning approaches to solve complex optimization problems. ACM Distinguished Member (2018) University Faculty Scholar (2017-18) ACM Distinguished Speaker (2016-2019) IEEE Senior Member (2016) IBM CAS Faculty Research Fellow (2010-2016) Google Faculty Research Award (2015) DOE Early CAREER Award (2011) NSF CAREER Award (2010) Shen has successfully advised 15 PhD students who have gone on to positions at top universities and companies including UC Santa Cruz, UMass Amherst, Google, Meta, and Huawei. His research has been funded by numerous grants from NSF, DOE, and NIH totaling over $4 million. He co-founded CoCoPIE Inc. to commercialize his research on efficient AI deployment and serves as a consultant to major technology companies including Intel, Microsoft, Huawei, Cisco, and Meta. He leads the PICTure research group and teaches courses including Compiler Construction (CSC412/512), Code Optimization for Scalar and Parallel Programs (CSC766), and Real-time AI and Machine Learning Systems (CSC591/791).

Arpit Guptaمشاهده پروفایل

دانشیار

• Network Security
• Machine Learning for Networking
• Digital Equity
• +۵ مورد دیگر

Arpit Gupta is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he co-directs the Systems and Networking Lab (SNL). He also serves as a Faculty Scientist at Lawrence Berkeley National Laboratory and holds the Marjorie & Charles Benton Opportunity Fund Fellowship at the Benton Institute. His research focuses on two main areas: developing production-ready machine learning systems for self-driving networks that ensure secure and performant connectivity with limited infrastructure, and enabling data-driven policymaking to address digital inequity through better broadband measurement and analysis. His work bridges networking, security, and analytics to solve real-world problems at scale. Gupta's research has resulted in several influential systems including Trustee (for ML interpretability in networks), BQT (for broadband plan analysis), PINOT (programmable data collection), netUnicorn (network data collection platform), and netFound (network foundation models). His team develops practical solutions that have been deployed in production environments, including at Tencent. NSF CAREER Award (2025) Google Research Scholar Award (2025) Google ML and Systems Junior Faculty Award (2025) IETF/IRTF Applied Networking Prize (2025, 2023) SIGCOMM Doctoral Dissertation Award (2024) Best Paper Honorable Mention, ACM CCS (2022) As a mentor, he has advised multiple award-winning students including Udit Paul who received the SIGCOMM Doctoral Dissertation Award. His research is supported by substantial funding from NSF (including a $700k CAREER award), DoE, Google, Verizon, Cisco, and state agencies including the California Public Utility Commission.