جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Software Testing
• Safety Critical Systems
• +۷ مورد دیگر

Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.

• Signal Processing
• Statistical Signal Processing
• System Identification
• +۵ مورد دیگر

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.

Bo Wuمشاهده پروفایل

پژوهشگر

• Computer Vision
• Multimodal Learning
• Natural Language Processing
• +۵ مورد دیگر

Bo Wu is a Researcher at the MIT-IBM Watson AI Lab in Cambridge, MA, where he conducts pioneering research in deep learning, computer vision, natural language processing, and multimodal learning. Previously, he served as a postdoctoral research scientist at Columbia University after completing his Ph.D. at the Chinese Academy of Sciences (CAS) in Beijing, with additional research experience at Microsoft Research Asia (MSRA) and Academia Sinica. His academic foundation includes: Ph.D. in Computer Science, Chinese Academy of Sciences (CAS) Research internships at Microsoft Research Asia and Academia Sinica Wu's research focuses on advancing situated reasoning in real-world contexts, integrating neuro-symbolic approaches with deep learning for enhanced interpretability. His work spans video question answering, temporal forecasting, and multimodal understanding, with applications in social media prediction, enterprise AI, and personalized dialogue systems. He emphasizes bridging symbolic reasoning with neural networks to develop robust systems capable of handling open-world knowledge and dynamic environments. Analysis of his recent publications reveals three dominant trends: the creation of novel benchmarks for situated video reasoning (STAR, SOK-Bench), development of efficient multimodal architectures for enterprise applications (Granite Vision), and personalization techniques for language models. His research consistently merges computer vision with linguistic understanding while addressing practical constraints like real-time processing and model compression, demonstrating strong industry-academia translation. His scientific excellence is evidenced by prestigious recognitions including: IBM Master Inventor Award (2023) IBM Research Level-A Accomplishment Award (2021) ACL Best Demo Paper Award (2020) ICIP Prediction Challenge Champion (2020) Alibaba Global Vision AI Challenge Top 3 (2018) NIST TAC SM-KBP Top 1 (2019) Wu actively mentors emerging talent, currently recruiting students for vision-language projects. He provides significant academic service as Area Chair for ACM Multimedia, Senior Program Committee Member for AAAI and IJCAI, and organizer of the SMP Challenge at ACM Multimedia since 2017. His leadership extends to CVPR workshops on Multimodal Foundations Models (MMFM) and Multimodal Video Content Understanding (MVCS), while serving on program committees for NeurIPS, CVPR, ACL, and other top-tier conferences. As a core member of the MIT-IBM Watson AI Lab, Wu operates within a unique industry-academia ecosystem that fosters rapid translation of fundamental research into practical applications. His collaborative work with Chuang Gan and other researchers leverages IBM's computational resources and MIT's academic rigor, positioning him at the forefront of enterprise AI innovation where theoretical advances directly address real-world business challenges.

Yuqi Songمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۳ مورد دیگر

Yuqi Song is an Assistant Professor in the Department of Computer Science at the University of Southern Maine (USM), where she joined in August 2023 after completing her Ph.D. at the University of South Carolina. Her interdisciplinary research bridges machine learning with materials science, tourism, and recommender systems. Her educational background includes: Ph.D. in Computer Science, University of South Carolina (2023), supervised by Dr. Jianjun Hu M.S. and B.S. in Computer Science, Chongqing University, supervised by Dr. Ming Gao Dr. Song's research focuses on applying state-of-the-art deep learning techniques—including generative adversarial networks, graph neural networks, and transformer models—to solve real-world problems. She develops AI-driven solutions for materials discovery (predicting crystal structures and properties) and tourism applications (employee turnover prediction systems). Her work uniquely combines computational methods with domain-specific challenges, emphasizing practical implementation through user-friendly tools like her materials informatics web platform MaterialsAtlas.org. Analysis of her recent publications (2023-2025) reveals three dominant research thrusts: (1) materials informatics using transformer-based generative models for crystal structure prediction, (2) robust recommender systems security against data hybrid attacks, and (3) computer vision innovations in depth estimation and medical image analysis. Her work consistently leverages attention mechanisms and cross-disciplinary data integration. Dr. Song actively mentors graduate students, currently advising Reihaneh Maarefdoust (Complex Learning and Machine Learning) and Zahra JahediBashiz (NLP, Generative AI). She teaches core courses including Software Engineering (COS 430) and Artificial Intelligence (COS 470), emphasizing practical programming skills. Her lab seeks motivated students for projects in materials discovery and tourism analytics. She leads a research group focused on interdisciplinary AI applications, collaborating with materials scientists and hospitality industry partners to develop deployable solutions. Current initiatives include deep learning models for predicting piezoelectric properties and generative design of 2D materials, alongside tools for tourism workforce analytics.

Dejan Gjorgjevikjمشاهده پروفایل

استاد

• Pattern Recognition
• Machine Learning
• Software Engineering

Dejan Gjorgjevikj is a Full Professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje. He joined the Department of Computer Science and Engineering in 1992, progressing from Assistant Professor (2004) to Associate Professor (2009) before attaining full professorship in 2014. His international academic engagements include research visits to institutions in Austria, Bulgaria, the Czech Republic, and the UK. Education: Bachelor's and Master's degrees from the Faculty of Electrical Engineering, Skopje (1992, 1997); PhD from the same institution (2004). Research Focus: His primary research explores pattern recognition, machine learning, and software engineering. Recent work emphasizes applications in industrial diagnostics (fault detection in machinery), blockchain security (Ponzi scheme detection), environmental monitoring (air pollution prediction), and human-computer interaction (sensor-based activity recognition). Methodologies frequently involve deep learning architectures like autoencoders, LSTMs, and adversarial networks. Publication Trends: Gjorgjevikj has authored over 90 publications, with recent works demonstrating increased focus on neural network applications in cross-domain problems (mechanical engineering, finance, IoT) and NLP tasks like sarcasm detection. His articles frequently appear in IEEE, Springer, and Elsevier journals. Awards: AAIA’15 Data Mining Competition Award (2015) Projects & Service: Involved in 10+ international/domestic research projects IEEE member since 1991, ACM member since 1997 Program committee member for multiple international conferences

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Jolita Bernatavičienė serves as a Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies within the Image and Signal Analysis Group. With a Doctorate in Technological Sciences (Informatics), she has established herself as a leading researcher in medical image analysis and artificial intelligence applications in healthcare. Her extensive research portfolio spans over 15 years of continuous contributions to the field. Dr. Bernatavičienė's research interests primarily focus on medical image analysis, particularly in ophthalmology and oncology applications. Her work integrates advanced machine learning techniques with medical diagnostics, specializing in eye fundus image analysis for glaucoma detection and prostate MRI analysis for cancer identification. She has made significant contributions to deep learning architectures, signal processing methodologies, and data analysis frameworks applicable to biomedical challenges. Her publication record demonstrates strong trends in applying cutting-edge AI techniques to solve concrete medical problems, with a noticeable shift toward more sophisticated deep learning architectures in recent years. The research spans multiple medical domains including ophthalmology, cardiology, oncology, and renewable energy systems monitoring, reflecting her interdisciplinary approach to data science applications. Leader of International Conference 'Data Analysis Methods for Software Systems (DAMSS)' 2015-2024 Member of IEEE Computer Society section (since 2022) Member of the Council of the Lithuanian Computer Association, Artificial Intelligence Section Member of the Lithuanian Operations Research Society Expert at the Science, Innovation and Technology Agency (MITA) (2020-2022) Dr. Bernatavičienė actively supervises doctoral and master's students, with current doctoral student Roman Surkant working on prostate MRI analysis. She leads multiple significant research projects including 'Developing Talents in Artificial Intelligence to Solve Disruptive Environmental Problems' and serves as scientific leader for the Research Council of Lithuania funded project on cardiac MRI texture analysis. Her work has been supported by various national and international funding mechanisms including COST activities, EuroHPC programs, and Lithuanian national research grants. She is principal investigator for the long-term project developing a database of depersonalized fundus images (2018-2030) and has led numerous projects related to medical image analysis, AI applications in healthcare, and data science methodologies. Her research group maintains strong international collaborations through COST actions and other European research networks.

• Machine Learning
• Deep Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Tadas Žvirblis serves as an Associate Professor within the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies, maintaining his research office at Akademijos St. 4, room 604A in Vilnius. His academic profile bridges theoretical statistics with practical engineering and medical applications through advanced computational methodologies. His research program centers on machine learning and deep learning innovations for complex signal analysis, with dual specializations in biomedical diagnostics (EEG, NIRS, cardiovascular monitoring) and industrial systems (conveyor mechanics, gear fault detection, engine emissions). Key methodological contributions include novel data augmentation techniques for time series, generative modeling of vibration signals, and prognostic frameworks for reliability engineering, demonstrating consistent interdisciplinary collaboration across medical and engineering domains. Analysis of his 13 publications from 2023-2025 reveals a strategic research trajectory applying deep learning to data-scarce scenarios, particularly in biomedical signal interpretation and industrial predictive maintenance. His work shows increasing focus on clinical applications since 2024, including ECMO mortality prediction and aortic morphology studies, while maintaining strong industrial engineering output through IEEE conference publications on conveyor systems and engine diagnostics. Dr. Žvirblis actively supervises doctoral research as Senior Researcher for Gajane Mikalkėnienė's project (2023-2027) developing EEG-based depression diagnosis methods under Informatics field N 009. His grant portfolio includes multiple industry-collaborative projects evidenced by co-authorship with clinical researchers and engineering teams across Lithuania, Poland, and Germany. As a core member of the Interdisciplinary Statistical Research Group, he contributes to the unit's mission of advancing statistical methodologies for real-world data challenges, with particular emphasis on time-series analysis in non-stationary environments. His laboratory work integrates signal processing hardware with deep learning frameworks to address industrial automation and medical monitoring challenges.

Hunjoo Leeمشاهده پروفایل

استادیار

• Reservoir Geomechanics
• Hydraulic Fracturing
• Rock Fracture Mechanics
• +۳ مورد دیگر

Hunjoo Lee is an Assistant Professor in the Department of Chemical Engineering at Oklahoma State University since 2021. His research integrates experimental geomechanics and machine learning for subsurface energy applications. PhD and postdoctoral training in Petroleum Engineering at University of Texas at Austin Focus on reservoir geomechanics and fracture propagation dynamics Research Interests span reservoir geomechanics , hydraulic fracturing , natural fracture interaction , and induced seismicity . His work examines mixed-mode fracture propagation in subsurface conditions using semi-circular bend tests and digital image correlation techniques. Recent Research Trends show expertise in: CO2 storage geomechanics Ionic liquid applications for geothermal systems InSAR-based deformation monitoring Machine learning for petrophysical inversion Fracture diversion in layered formations Professional Activities include Session convener at US Rock Mechanics Symposium Peer reviewer for SPE Journal, Engineering Fracture Mechanics Teaching courses in Advanced Well Stimulation and Production Engineering Advising doctoral and master's thesis research

Indika Kahandaمشاهده پروفایل

استادیار

• Bioinformatics
• Biomedical Informatics
• Machine Learning
• +۳ مورد دیگر

Dr. Indika Kahanda is an Assistant Professor in the School of Computing at the University of North Florida , where he leads the BioMedInfo Lab focusing on bioinformatics and biomedical informatics. Previously, he held the same position at the Gianforte School of Computing (Montana State University) from 2018 to 2023. Education: PhD in Computer Science (2016), Colorado State University MS in Electrical and Computer Engineering (2010), Purdue University BS in Computer Engineering (2007), University of Peradeniya, Sri Lanka His research applies machine learning and natural language processing to large-scale biomedical data, with key focuses on computational methods for functional genomics and automated text mining tools for biomedical literature. Current projects include Pangenomics , student misconception detection , and inconsistency detection in medical literature . Recent publications highlight his work in LLM evaluation for healthcare applications, protein function prediction , and medical transcription error analysis . He contributes to biomedical datasets like miRNAFinder and MLHCBugs, with technical expertise in deep learning, ensemble methods, and metamorphic testing.

• Electrical engineering
• Electronics, sensors and digital hardware
• Distributed computing and systems software
• +۶ مورد دیگر

Masood Parvania is the Roger P. Webb Endowed Professor at the University of Utah in Electrical and Computer Engineering. He serves as Director of the Utah Smart Energy Laboratory (U-Smart) and Co-Director of the NSF WIRED Global Center . His research focuses on mathematical optimization , control theory , and machine learning applications in power system operation, resilience, and interdependent infrastructure modeling. His work addresses critical challenges including: Equity-aware grid restoration Climate-resilient energy systems Cyber-physical security analysis Hybrid energy storage coordination Extreme heat event mitigation strategies Key research trends across his 15 most recent publications reveal deep integration of: Renewable energy with storage systems Machine learning in real-time grid operation Cybersecurity frameworks for critical infrastructure Equity metrics in energy distribution Honors include: IEEE Outstanding Associate Editor Award (2022) University of Utah Presidential Scholar (2020) IEEE Utah Section Outstanding Educator Award (2017) Multiple Best Reviewer and Distinguished Service Awards As Associate Editor for IEEE Transactions on Power Systems , he actively shapes energy research discourse while leading NSF-funded initiatives like: U.S.-Canada Climate-Resilient Grid Center ($90M+ Western EV Infrastructure Scale-Up Wasatch Multi-Modal Corridor Electrification

• Signal Processing
• Computer Vision
• Robotics
• +۵ مورد دیگر

Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.

Nalinaksh S. Vyasمشاهده پروفایل

استاد

• Machine Dynamics
• Condition Monitoring
• Fault Diagnosis
• +۱۰ مورد دیگر

Nalinaksh S. Vyas is a Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). With over three decades of academic and research experience, he has made significant contributions to the fields of machine dynamics, condition monitoring, and fault diagnosis. His work spans both theoretical research and practical applications in various engineering domains. Dr. Vyas completed his B.Tech from IIT Bombay in 1980, followed by an M.Tech in 1983 and PhD in 1986, both from IIT Delhi. His academic journey has been marked by continuous excellence and leadership in mechanical engineering education and research. His research interests focus on Machine Dynamics, Condition Monitoring, Fault Diagnosis, Life Estimation & Prognosis , with specific expertise in Rotor Dynamics, Rail Wheel Dynamics, Automotive Dynamics, System Identification and Parameter Estimation, MEMS, Instrumentation and Sensor Technologies, and Neural Networks. Dr. Vyas has applied these research areas to solve real-world engineering problems across multiple industries. Dr. Vyas's publications over the past two decades demonstrate a consistent focus on mechanical system dynamics, with particular emphasis on rotor systems, railway applications, and advanced signal processing techniques for fault detection. His work bridges theoretical dynamics with practical engineering solutions, often incorporating innovative approaches like wavelet analysis and neural networks for system identification and health monitoring. Featured by India Today (2010), as one of the 20 Innovators Changing Our Lives Awadh Samman, 2009, Govt. of Uttar Pradesh 4th position, HSSC, Madhya Pradesh Board, 1975 Dr. Vyas has led numerous significant research projects and initiatives, including the Technology Mission for Indian Railways (TMIR) and the development of India's first nanosatellite, JUGNU. His work with government agencies like RDSO Lucknow, CSIR-CMERI, and ISRO has resulted in practical technologies that have been implemented in real-world settings, including thermal power plants and railway safety systems. He has also served in various leadership roles including as Vice Chancellor of Rajasthan Technical University and Head of the Department of Mechanical Engineering at IIT Kanpur. His research group at IIT Kanpur operates state-of-the-art laboratories for dynamics testing, condition monitoring, and MEMS sensor development. The team collaborates with industry partners including BHEL, SCL Chandigarh, and Pricol Coimbatore to develop practical engineering solutions with immediate industrial applications.

Joseph S.B. Mitchellمشاهده پروفایل

استاد

• Computational Geometry
• Algorithms and Data Structures
• Optimization
• +۳ مورد دیگر

Joseph S.B. Mitchell is a SUNY Distinguished Professor at the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. His office is located in Math Tower, Room P-138A. His research spans computational geometry, algorithms, optimization, and related fields. Research Interests: Prof. Mitchell's work focuses on fundamental and applied problems in geometric computing, including: Algorithm design for spatial optimization (e.g., art gallery problems, dispersion) Robotics applications (multi-agent path planning, sensor deployment) Efficient solutions for geometric data structures and visibility constraints Recent Publications: His 15 most recent articles (2024-2025) demonstrate a consistent focus on geometric optimization, visibility problems, and algorithm design for polygons and networks. Common themes include approximation algorithms, multi-robot coordination, and combinatorial solutions for spatial challenges. Awards: SUNY Distinguished Professor (recognizing exceptional academic contributions)

• automatic text analysis
• information search
• IT project management
• +۶ مورد دیگر

Natalia Sergeevna Belova is an active Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University). She has been with HSE since 2012, accumulating 13 years of scientific and teaching experience. Her academic journey began with engineering education and progressed through postgraduate studies to earning her Candidate of Technical Sciences degree. Her educational background includes: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specializing in Mathematical and Software Support for Computing Machines 2009: Postgraduate studies at the same institution 2005: Engineering degree from Moscow State Academy of Instrument Engineering and Computer Science Belova's research interests span automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work demonstrates a strong focus on practical applications of computer science, particularly in face recognition, pattern recognition, and educational methodologies for software engineering. She has made significant contributions to the fields of embedded database systems and computer vision. Her publication record shows a clear evolution from foundational work on embedded databases (2009) to advanced research in computer vision and deep learning (2015-2025). The most recent publications focus on artificial intelligence applications in transport design and affect recognition in video, demonstrating her ability to adapt to emerging technologies while maintaining expertise in her core areas. Among her notable achievements: Gratitude from HSE University leadership (2023, 2025) Multiple publication bonuses for high-impact research Recognition as Best Teacher (2016-2017) Membership in the High Professional Potential Group (HSE personnel reserve) Belova has supervised numerous bachelor's theses, guiding students through projects ranging from mobile applications to complex software systems. She has also secured significant research funding, including a Presidential Grant for young doctors of science (2017-2018) for developing pattern recognition methods. Her teaching portfolio includes courses on Group Dynamics and Communication in Software Engineering Professional Practice and Software Engineering Economics, reflecting her dual expertise in technical and soft skills development for future software engineers.

Rickard Karlssonمشاهده پروفایل

مدرس

• Swedish as a second language
• Learner language assessment
• Language development in adults
• +۴ مورد دیگر

Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.