جست‌وجوی استاد و پژوهشگر

• Economics
• Finance
• Gold Investment
• +۶ مورد دیگر

Katarzyna Maria Mamcarz is an Assistant Professor in the Department of Microeconomics and Applied Economics at the Institute of Economics and Finance, Faculty of Economics, Maria Curie-Skłodowski University in Lublin, Poland. She holds a PhD in Economics (specialization: capital markets) and a Master's degree in Economics from the same university, along with a Marketplace Trainer Certificate. PhD in Economics (Capital Markets) Master's in Economics Marketplace Trainer Certificate Her research focuses on alternative investments, particularly gold, examining price determinants, demand/supply dynamics, leverage effects, and causal relationships between gold and other financial assets. She has published extensively on these topics and received a Best Paper Award in 2022 for her analysis of stock-crude oil-gold market linkages. She supervises diploma and master's theses in financial management, including financial reporting and analysis. From 2012-2019, she coordinated the Bologna Process and ECTS system implementation at her faculty, contributing to new study program development. She currently serves as a representative of non-independent staff in the Institute's Scientific Council. Best Paper Award: 'Causal linkages between the stock, crude oil, and gold returns in the Central European region' (2022 Conference of Finance Departments) Her recent publications analyze gold investment strategies during financial crises, VAR modeling of market relationships, and gold's role in diversified portfolios. While grants aren't explicitly mentioned, her work spans financial econometrics, macroeconomic indicators, and educational reforms.

Akhilesh Bajajمشاهده پروفایل

استاد

• Business Information Systems
• Management Information Systems (MIS)
• Database Modeling
• +۱۲ مورد دیگر

Akhilesh Bajaj is the Genave King Rogers Dean of the Collins College of Business and Professor of Business Information Systems at The University of Tulsa. He holds the Chapman Endowed Chair in MIS and has been a key academic and administrative leader since joining the university in 2003. He previously served as Director of the School of Accounting & BIS, Interim Department Chair, and Faculty Senate President, demonstrating deep institutional engagement. Ph.D., Management Information Systems, University of Arizona (1997) MBA, Cornell University (1991) B.Tech, Chemical Engineering, IIT Bombay (1989) His research spans requirements modeling, database and systems design, AI in business, socio-technical systems, and corporate governance . He investigates how information systems enhance organizational performance, with a focus on workflow efficiency, knowledge codification, and decision-making. His interdisciplinary work integrates cognitive theories, social network metrics, and data-centric methodologies. His recent publications explore CEO power, LLMs, CSR, and managerial ability, appearing in top journals like Management Science , JAIS , and IEEE TKDE . His work reflects a strong trajectory in both theoretical modeling and empirical validation, often involving collaborations with students and colleagues. Association of Information Systems Distinguished Member (2025) Faculty Senate Resolution of Appreciation (2022) Collins College Teaching Award (2019) Chapman Endowed Chair (2005–present) National Talent Scholarship (1983) He has advised multiple Ph.D. students and led major research initiatives, including a DoD-funded project on technology insertion in small manufacturers. He has served on numerous university committees, chaired national conferences, and contributed to editorial boards of leading journals. He is actively involved in professional service, including NSF review panels and advisory roles in international conferences. Bajaj leads research teams focused on information systems development, AI applications, and organizational performance. His lab integrates systems analysis with behavioral and economic models, fostering interdisciplinary collaboration across business, engineering, and social sciences.

• Statistical Signal Processing
• Computational Finance
• Nonlinear Time Series Analysis
• +۷ مورد دیگر

George Tzagkarakis is a Researcher at the Foundation for Research and Technology-Hellas (FORTH) and affiliated with the Institute of Computer Science (ICS) and the Signal Processing Laboratory. With over 15 years of experience in Scientific Data Analysis and Computational Finance, his expertise includes multimodal data learning, distributed information processing, mathematical modeling, and risk analytics. He holds two PhDs—one in Computer Science (Statistical Signal Processing) from the University of Crete and one in Finance (Risk Quantification) from the University of Bordeaux—as well as a BSc in Mathematics from the University of Crete. Education: PhD in Computer Science (1st in class, highest honors), University of Crete, Greece PhD in Finance, University of Bordeaux, France BSc in Mathematics (1st in class, highest honors), University of Crete, Greece His research focuses on Statistical Signal Processing , Nonlinear Time Series Analysis , Non-Gaussian Heavy-Tailed Models , Compressive Sensing , Distributed Signal Processing for Smart Sensor Networks , and Machine Learning in Operations Research . His work bridges academic and industrial applications, particularly in remote imaging and quantitative financial analysis. George has been recognized with academic distinctions from the University of Crete, FORTH-ICS, and external state foundations. He has contributed to European and national R&D and R&I projects, demonstrating his ability to transfer research into practical solutions.

• Quantum Computing
• Software Protection
• Data Anonymization
• +۳ مورد دیگر

Arkadiusz Liber is a researcher at the Department of Applied Informatics, Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His research spans quantum computing, software security, and medical data anonymization. Research Interests: Quantum algorithms, software protection mechanisms, medical informatics, and cybersecurity. Key Contributions: Developed quantum signature protocols for medical data, explored function invariants for formula protection, and advanced stock exchange communication algorithms. Notable Publications: Works on Grover's algorithm analysis, quantum circuit implementations, and privacy-preserving frameworks for sensitive data. Contact: Email arkadiusz.liber@pwr.edu.pl

• Neural Networks
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Piotr Ciskowski is a researcher affiliated with the Department of Computer Engineering at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His work bridges computational methods with practical applications in finance, transportation, and building systems. Research Focus : Neural networks, machine learning, and optimization for decision support Application Areas : Forex market analysis, intelligent transportation systems, and adaptive building controls His publications highlight a consistent application of neural networks to complex systems, including urban traffic modeling (2016), forex data streams (2010), and contextual mapping (2006, 2004). Key subfields include recurrent neural networks, self-organizing maps, and technical analysis of financial markets. Contact: piotr.ciskowski@pwr.edu.pl | Office: C-16 building (Technopolis), room P 2.2 | Tel: +48 71 340 7625

• Speech Signal Processing
• Machine Learning
• Noise Reduction
• +۳ مورد دیگر

Ziye Yang is a researcher active in Speech Signal Processing and Machine Learning applications. His work focuses on advanced techniques like weighted prediction error , deep learning models , and noise reduction in audio systems. He has collaborated extensively with Jie Chen and Cédric Richard on topics including speech dereverberation and nonlinear residual echo suppression . His research spans multiple subfields including distributed speech processing , attention-based architectures , and deconvolution regularization . Recent publications (2024-2025) emphasize hybrid methods combining traditional signal processing with modern neural networks, particularly for reverberation modeling and echo suppression. Key trends in his work include Integration of data-driven priors in signal enhancement Development of plug-and-play frameworks for audio processing Application of attention mechanisms in dual-stream networks

Jinan Fiaidhiمشاهده پروفایل

استاد

• Thick Data Analytics
• Deep Learning
• Few Shots Learning
• +۱۲ مورد دیگر

Dr. Jinan Fiaidhi is a Professor of Computer Science at Lakehead University, Canada since 2001. She served as Graduate Coordinator for the MSc and PhD programs in Computer Science and Biotechnology. She holds adjunct positions at the University of Western Ontario. Her academic journey includes degrees from Essex University (PgD, 1983) and Brunel University (PhD, 1986). She has held academic roles at institutions like Sultan Qaboos University and Philadelphia University prior to Lakehead. Her research focuses on Thick Data Analytics , Deep Learning , and Collaborative Learning , with applications in healthcare, medical imaging, and AI-driven diagnostics. She is a Professional Engineer (PEng) in Ontario and a Senior Member of IEEE. She chairs the IEEE Special Interest Group on Big and Thick Data for eHealth and founded the International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) as its Emeritus Editor-in-Chief. Her research is funded by NSERC and MITACS grants. Key projects include developing frameworks like QL4POMR for problem-oriented medical records and applying thick data analytics to Crohn’s disease, ulcerative colitis, and cataract severity analysis. Education: PhD in Computer Science, Brunel University (1986) PgD in Computer Science, Essex University (1983) Awards: None explicitly listed, but holds professional designations: PEng, IEEE Senior Member, ISP (CIPS), and MBCS (Chartered Computing). Grants: NSERC, MITACS (specific projects unspecified). Labs/Teams: Leads IEEE eHealth initiatives and collaborates on projects involving thick data analytics in healthcare interoperability and extreme automation.

• Entrepreneurial Finance
• Angel and Venture Capital Investment
• Investor-Entrepreneur Relationships
• +۴ مورد دیگر

Veroniek Collewaert is a Full Professor of Entrepreneurship and holds leadership roles at Vlerick Business School, including Director of Masters Programmes and Director of the Centre for Excellence in Scale-Ups. She is also a Professor at KU Leuven and previously taught at Maastricht University (2009–2012). Her research focuses on investor-entrepreneur dynamics, scale-up challenges, founding teams, and entrepreneurial biases. She has published in top journals like the Journal of Business Venturing and serves on editorial boards for key entrepreneurship journals. Her expertise includes angel financing, venture capital governance, and HR practices in scaling firms. She organizes Silicon Valley trips for students and leads the European Scaleup Institute. Collewaert actively engages with industry via roles at BAN Vlaanderen (Belgium’s largest angel investor group) and the Europe Startup Nations Alliance (ESNA). Recent work explores CEO narcissism’s impact on funding, regulatory differences in entrepreneurship, and private equity governance. Her articles span topics like crowdfunding signals, HR management, and conflict resolution in entrepreneurial relationships. She earned her doctorate from Ghent University and was a Visiting Scholar at Stanford’s Graduate School of Business.

Stelios M POTIRAKISمشاهده پروفایل

استاد

• Complex Systems
• Time-Series Analysis
• Geophysical Signal Processing
• +۶ مورد دیگر

Stelios M POTIRAKIS is a Professor at the Department of Electrical and Electronics Engineering at the University of West Attica in Athens, Greece. He has held this position since September 2018, following an Associate Professor role at the same institution from March to September 2018, and previously at Piraeus University of Applied Sciences from November 2015 to February 2018. His research focuses on complex systems time-series analysis with applications spanning geophysical phenomena, space weather, biological signals, financial markets, and audio processing. He leads the Electronics and Computer Technologies Laboratory (ECTLab) at the University of West Attica. Dr. POTIRAKIS specializes in digital signal processing, nonlinear dynamics, and mathematical modeling of complex physical systems. His expertise bridges electrical engineering with geophysics, particularly in studying earthquake precursors through electromagnetic emissions, ionospheric disturbances, and atmospheric anomalies. He has developed methodologies to analyze critical phenomena across diverse systems, from phase transitions in physics to market dynamics in finance. His recent publication record demonstrates a strong focus on the intersection of space weather, ionospheric monitoring, and seismic activity. He has pioneered approaches using Very Low Frequency (VLF) signals to detect pre-seismic ionospheric anomalies, contributing to earthquake prediction research. His work also extends to financial markets, where he applies complexity measures to forecast volatility, and to biological signals, where he investigates neural dynamics and spike generation mechanisms. 246 scientific publications with 54,289 reads and 2,419 citations Director of ECTLab (Electronics and Computer Technologies Laboratory) Key contributor to hELlenic Seismo-ElectroMagnetics Network (ELSEM-Net) Active researcher in Swarm mission data analysis for geophysical monitoring Dr. POTIRAKIS maintains an extensive international collaboration network, particularly with Japanese researchers like Masashi Hayakawa, reflecting the global impact of his interdisciplinary research in complex systems analysis and earthquake precursor studies.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Professor Jean-Christophe Nebel is a Professor of Computer Science at Kingston University , Faculty of Science, Engineering and Computing, Computing and Information Systems Department. He holds a PhD in Computing Science from the University of St-Etienne (1997), an MSc in Computing Science (University of Lyon), an MScEng in Electronics & Signal Processing (CPE Lyon), and a Postgraduate Certificate in Higher Education Learning & Teaching. His research focuses on Artificial Intelligence, Machine Learning, Pattern Recognition, Computer Vision , and Bioinformatics , with applications in renewable energy , ecology , and genomics-inspired surveillance . PhD in Computing Science, University of St-Etienne MSc in Computing Science, University of Lyon MScEng in Electronics & Signal Processing, CPE Lyon Postgraduate Certificate in Learning & Teaching, Kingston University His work bridges AI and interdisciplinary domains , including protein structure prediction, human action recognition, and AI-driven environmental monitoring. Key projects include a stochastic context-free grammar framework for proteomics, manifold-based modeling of multivariate sequences, and game-theoretic optimization for smart grids and PPE management during the pandemic. Recent articles explore deepfake detection , air pollution analytics , and honeybee health monitoring via acoustic signals. Notable scientific awards include the IEE Reeve Premium (2004) , Senior Fellow of the Higher Education Academy (SFHEA) , and Senior Member of the IEEE . He led an Innovate UK-funded Knowledge Transfer Partnership (KTP) with Instinet Global Services, achieving the highest "Outstanding" grade. At REF2014, he contributed to a 3* Impact Case Study on pedestrian tracking technology commercialized by Ipsotek and BAe Systems, benefiting entities like the London Eye and the Australian Government. As Director of Kingston University’s Knowledge Exchange & Research Institute for Cyber, Engineering & Digital Technologies , he coordinated the REF2021 submission for Computer Science & Informatics, which achieved 73% world-leading/internationally excellent research. He serves on editorial boards of BMC Bioinformatics , PLOS ONE , and Pattern Analysis & Applications , and evaluates proposals for the EPSRC , NSERC , and international councils.

Min Chenمشاهده پروفایل

استاد

• Multimedia Big Data Analytics
• Multimedia Data Mining
• Machine Learning
• +۴ مورد دیگر

Min Chen is a Professor in the Computing and Software Systems Division at the School of STEM , University of Washington Bothell . Previously, she was a tenured Associate Professor at the Department of Computer Science, University of Montana. Her research focuses on multimedia big data analytics , multimedia data mining , and machine learning , with applications in interdisciplinary projects like endangered language documentation, fake news detection, and cloud-based multimedia systems. Education: Ph.D. in Computer Science from Florida International University (2006) Dr. Chen has published over 90 refereed papers in journals and conferences. Her recent work explores deep learning for multimedia data , cloud-based language tools , and AI applications in social media analysis . She has secured multiple NSF grants and industry awards, including a $140,351 NSF grant (2021-2025) for prosodic analysis in endangered languages. Scientific Awards include the Distinguished Research, Scholarship, & Creative Activities Award (UW Bothell, 2022), Best Demo Award (IEEE MIPR 2021), and Best Paper Award (IEEE IRI 2014). She serves as Chair of IEEE TCMC and Associate Editor for journals like IEEE Transactions on Multimedia . Her teaching includes CSS 343 Data Structures , CSS 484 Multimedia Data Processing , and CSS 584 Multimedia Database Systems . While no explicit student list is provided, her publications suggest collaboration with graduate researchers on projects like MeTILDA and ClickIndia .

Patrick Converyمشاهده پروفایل

استادیار مدعو

• Econometrics
• Financial Markets
• Energy Markets
• +۴ مورد دیگر

Patrick Convery is an Adjunct Assistant Professor at the University of California, Los Angeles (UCLA), affiliated with the Department of Economics. He teaches econometrics, corporate finance, mathematical finance, and an MAE elective in applied big data, emphasizing fundamental understanding and real-world applications. Education: Ph.D. in Physics from UCLA, MBA from Wharton. His research integrates data science and statistical signal processing to analyze and forecast financial and energy markets. He also actively engages in value investing.

• Artificial Intelligence
• Human-Computer Interaction
• Robotics
• +۱۳ مورد دیگر

Assoc. Prof. Dr. Gökhan İnce is a faculty member at Istanbul Technical University (ITU) in the Department of Computer Engineering, under the Computer and Informatics unit. He holds a PhD from Tokyo Institute of Technology and has held research positions at Honda Research Institute in both Japan and Germany. His academic and research profile is deeply rooted in artificial intelligence, human-computer interaction, robotics, and smart wearable technologies. PhD, Tokyo Institute of Technology, 2008–2011 MSc, Technische Universität Darmstadt, 2004–2007 BSc, Istanbul Technical University, 1999–2004 His research spans a wide range of topics including speech recognition, emotion recognition, brain-computer interfaces, e-textiles, fog computing, and IoT-based rehabilitation systems . He actively develops intelligent systems for real-world applications such as telerehabilitation, scoliosis monitoring, and AI-driven enterprise solutions. The recent publication trends show a strong focus on textile-based wearable sensors, multimodal emotion recognition, fog computing frameworks, and AI integration in business processes . His work combines hardware innovation with advanced machine learning, particularly in adversarial and transfer learning paradigms. Scientific Awards and Recognitions: Üstün Başarı Ödülü, YÖK, 2024 En Başarılı Tez Ödülü, ITU, 2024 Best Paper Award, IARIA, 2017 and 2016 Best Presentation Award, Signal Processing Applications Conference, 2016 Best Paper and Best Student Paper, IEA-AIE Conference, 2010 BİDEB Postdoctoral Research Fellowship, 2014 He has led and supervised 30 research projects and over 125 research outputs , including numerous peer-reviewed articles and conference contributions. He serves as Principal Investigator (PI) on multiple ongoing projects in AI, smart textiles, and data integration. He is also a member of professional organizations including IEEE, ISCA, and ISAI, and contributes to open science initiatives. His work is conducted through interdisciplinary teams focusing on smart textiles, robotics, AI systems, and biomedical applications . These teams bridge engineering, computer science, and healthcare to develop next-generation assistive and intelligent technologies.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Ahmet Cüneyd Tantuğ is an Associate Professor in the Department of Artificial Intelligence and Data Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a core faculty member since 2007 and is among the founders of the ITU Natural Language Processing Group. He has held key administrative roles, including Vice Dean (2011–2015) and Head of IT Department at ITU (2012–2020). BSc, MSc, PhD: Istanbul Technical University, Computer Engineering Guest Researcher: University of Copenhagen, Centre for Language Technology (2006–2007) Dr. Tantuğ’s research spans artificial intelligence, natural language processing, machine learning, and deep learning, with a focus on Turkish and other agglutinative languages. His work includes named entity recognition, dependency parsing, machine translation, word sense disambiguation, and text normalization. He has developed linguistic resources for Turkish and low-resource languages such as Amharic, and has contributed to financial AI and temporal expression extraction. His recent publications highlight advancements in Turkish NLP, including optimal vocabulary sizing, tokenization granularity for LLMs, and transformer-based models for named entity recognition. He has led multiple research projects funded by TÜBİTAK, the European Union, and the Ministry of Development. Scientific awards include: Bedri Karafakioğlu Special Award (2000) Siemens Excellence Award (2007) TeknoGirişim Sermaye Desteği Ödülü (2010) He has supervised numerous master’s and PhD students, with theses on topics such as dependency parsing, named entity recognition, and text normalization. He has also served as a consultant for private sector projects in banking, software, and telecommunications, and as a referee for TÜBİTAK, TEYDEB, and the Ministry of Industry and Technology. Dr. Tantuğ leads and participates in active research projects including Turkish text normalization from user-generated content and improving dependency parsing with augmented features. He is a founding member of the ITU Natural Language Processing Group, which continues to be a central hub for NLP research in Turkey.

Burcu Tungaمشاهده پروفایل

دانشیار

• Image Processing
• Applied Mathematics
• Computer Science
• +۱۲ مورد دیگر

Burcu Tunga is an Associate Professor in the Department of Mathematics at Istanbul Technical University, Faculty of Science and Letters. She holds a PhD in Computational Science and Engineering from the same institution and has been an active academic since 2005, progressing from Lecturer to her current rank. She has held key administrative roles, including Deputy Head of Department and membership in university-wide promotion and doctoral committees. PhD: Computational Science and Engineering, Istanbul Technical University (2004–2010) BS: Mathematics, Istanbul Technical University (1995–2000) Her research lies at the intersection of applied mathematics and computer science, with a strong emphasis on image processing, data modeling, and High Dimensional Model Representation (HDMR) . She develops and applies advanced mathematical models for image denoising, color-to-gray conversion, hyperspectral analysis, and machine learning-based diagnostics. Her work integrates fractional calculus, deep learning, and tensor decomposition to solve complex problems in medical imaging, agriculture, and materials science. Recent publications (2022–2025) show a strong trend toward hybrid models combining HDMR with deep learning and fractional operators , applied to tasks such as coffee leaf disease detection, wood defect imaging, and hyperspectral anomaly detection. These reflect a consistent focus on enhancing image quality, feature extraction, and pattern recognition through novel mathematical frameworks. Her scientific contributions are recognized through active research funding and a growing publication record. Key projects include: Anomaly Detection in Hyperspectral Images with HDMR (BAP-funded, 2024–2025) Disease Detection in Coffee Leaves with Deep Learning (completed, 2024) Content-Based Image Retrieval with HDMR (completed, 2018–2021) Burcu Tunga advises several graduate students and collaborates widely across disciplines. She leads research involving tomographic reconstruction, stress wave analysis in trees, and financial time series prediction. Her lab focuses on developing non-destructive evaluation techniques, medical CAD systems, and intelligent image processing algorithms , often leveraging HDMR as a core methodological framework.