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Moncef GabboujView profile

Professor

• Machine learning
• Artificial intelligence
• Signal processing
• +9 more

Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.

Andreas RauberView profile

Associate Professor

• Digital Preservation
• Machine Learning
• Data Citation
• +3 more

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Andreas Rauber is an Associate Professor at TU Wien's Research Department E-Commerce. His work focuses on digital preservation, machine learning, and reproducible research. He leads projects like EOSC-Life (FAIR principles in life sciences) and PLANETS (long-term data access). Key affiliations include the Network Lab and TU Wien's Institute for Software Technology. His research emphasizes data citation frameworks, secure data infrastructure (OSSDIP), and ethical AI practices. He has advised over 20 PhD/Master’s students in areas like adversarial ML and privacy-preserving systems. Notable contributions include DBRepo (semantic databases) and the DBRepo infrastructure for relational data. He collaborates internationally on initiatives like EOSC support offices and cultural heritage projects linking ethnomusicology datasets.

• Audio Processing
• Machine Learning
• Sound Event Detection
• +3 more

Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.

Fikret BasicView profile

Researcher

• Battery Management Systems
• Wireless Communication Security
• Embedded Systems
• +3 more

Fikret Basic is a researcher at the Institute of Technical Informatics at TU Wien, specializing in cybersecurity for embedded systems and battery management systems (BMS). His work focuses on integrating RFID, NFC, and secure communication protocols into industrial and automotive applications. He has led/co-led multiple EU-funded projects including SPiDR, OPEVA, and Intelligent & Networked Embedded Systems. Basic has published extensively at IEEE conferences and received awards such as Scientist of the Year 2024 and AVL Hans List Fonds Preis 2023. His contributions span secure data acquisition, wireless BMS architectures, and authentication mechanisms for IoT devices. Research interests include: Battery Management Systems, Cybersecurity for Industrial Networks, RFID/NFC Applications, Embedded System Security, and IoT Architecture Design. He actively participates in workshops like SPICES 2024 (as Chair) and peer reviews for conferences like EuroPLoP. Basic holds a Dipl.-Ing. (Diploma in Engineering) and a Dr.techn. (Technical Doctorate) in electrical engineering. Projects: SPIDR2 (2025-2028), OPEVA (2023-2025), SPiDR (2021-2024) Grants: AVL Hans List Fonds, Austrian Research Promotion Agency Labs/Teams: Member of TU Wien's Embedded Systems Security Group, collaborates with industrial partners like AVL List GmbH Media engagements include a TV interview in Bosnia discussing academic career paths and a feature in Kleine Zeitung highlighting IT talent development.

Katharina HoedtView profile

Researcher

• Adversarial Examples & Robustness
• Interpretable Machine Learning
• Neural Networks
• +3 more

Katharina Hoedt is a University Researcher and Assistant at the Institute of Computational Perception at Johannes Kepler University Linz (JKU), with a Vienna-based research presence. She holds a PhD in Computer Science from JKU (2020) and has been involved in research roles since 2016, including at the Austrian Research Institute for Artificial Intelligence (OFAI). Her work focuses on adversarial machine learning, model interpretability, and neural network robustness, particularly within music information retrieval domains. Education: PhD in Computer Science (2019–2020), DI (Diploma) in Computer Science (2013–2016), Bachelor of Science in Informatics (JKU Linz). She has taught courses on Machine Learning and Pattern Classification, and Artificial Intelligence at JKU. Research Interests: Adversarial examples and robustness, interpretable machine learning, neural network inner workings, and applications in music classification. Her publications explore adversarial attacks, explanation validity, and model defense strategies in audio and music contexts. Labs/Teams: Active member of the Institute of Computational Perception, collaborating on interdisciplinary projects combining AI with musicology and signal processing.

Markus SchedlView profile

Professor

• Recommender Systems
• Information Retrieval
• Algorithmic Fairness
• +6 more

Markus Schedl is a Full Professor at Johannes Kepler University Linz , Austria, where he leads the Multimedia Mining and Search (MMS) group within the Institute of Computational Perception . He also heads the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab . Education: Computer Science (TU Wien), PhD (JKU Linz), Master's in International Business Administration (WU Wien, University of Gothenburg) Research Interests span recommender systems , information retrieval , algorithmic fairness , user modeling , and machine learning . His recent publications focus on: Multimodal and hybrid AI for human-centric personalization Context-aware music recommendation and emotion recognition Graph neural networks for session-based recommendation Technical and regulatory compliance in fair, transparent, and privacy-preserving systems Projects are funded by the Austrian Science Fund (FWF) , Austrian Research Promotion Agency (FFG) , and European Commission (EC) . He maintains industry collaborations with Siemens, Spotify, and Deezer. Teaching includes courses like Introduction to Machine Learning and Social Media Mining and Analysis at JKU, with guest lecturing experiences at Pompeu Fabra, Queen Mary London, and KTH Stockholm. Labs & Teams: MMS group at JKU's Institute of Computational Perception and HCAI group at LIT AI Lab.

Maja OsojnikView profile

Lecturer

• Sound Art
• Experimental Music
• Electroacoustic Composition
• +5 more

Maja Osojnik is a lecturer for composition at the Salzburg University of Applied Sciences since 2018 and teaches improvisation/KEP Contemporary Music Performance at the Music and Arts Private University of the City of Vienna (MUK) since 2020 within the Faculty of Music. Her research interests span experimental sound art, electroacoustic composition, and contemporary improvisation. Osojnik deconstructs sonic boundaries through lo-fi electronics, field recordings, and unconventional instruments including Paetzold bass recorders, cassette players, and broken sound libraries. Her work explores the limbo between analog and digital, virtual and real spaces, combining experimental techniques with elements of noise, rock, early music, and traditional forms. Her most recent publications reveal trends in radio play hacking, networked performance, and graphic score production. Osojnik's work consistently investigates sound degradation, circuit bending, and the transformation of broken instruments into new sonic palettes. 2023: hörspiel_hacking (radio play composition) 2020: DRUCK (collaborative circuit-bending project) 2018: Berlin Radio Play Festival First Prize (WENDY PFERD TOD MEXICO) 2014: City of Vienna Composition Prize 2009/2019: Austrian State Composition Scholarship Osojnik leads multiple ensembles including Rdeča Raketa, Broken.Heart.Collector, and the Maja Osojnik Band. She founded MAMKA RECORDS in 2018 for self-produced recordings and graphic sound scores. Her sound installations and performance projects often involve custom-built electronics, field recordings, and collaborative networks across Austria, Slovenia, and international festivals.

Stefan BalkeView profile

Researcher

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +3 more

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

Carlo PatronoView profile

Professor

• Nonsteroidal antiinflammatory drugs
• Antiplatelet drugs
• Clinical pharmacology
• +1 more

Carlo Patrono is a distinguished Professor and Chair of Pharmacology at the Catholic University School of Medicine in Rome, a position he has held since 2007. Previously, he served as Professor of Pharmacology at University of Rome 'La Sapienza' 2nd School of Medicine (2001-2006) and University of Chieti 'G. d'Annunzio' School of Medicine (1991-2001). His academic journey began as Assistant Professor at Catholic University School of Medicine (1972-1982), progressing to Associate Professor (1983-1985). Dr. Patrono earned his M.D. Cum Laude from Catholic University School of Medicine, Rome in 1968, following a B.S. in Arts from Liceo Classico Nazareno in 1962. He completed postdoctoral training at Bronx Veterans Administration Hospital and Mount Sinai School of Medicine, New York (1969-1971). His research focuses on nonsteroidal antiinflammatory drugs , antiplatelet drugs , clinical pharmacology , and arachidonic acid metabolism , with significant contributions to understanding cardiovascular pharmacology and platelet function. His work has particular relevance to aspirin therapy and antithrombotic agents. Dr. Patrono has received numerous prestigious awards including the Grand Prize from Fondation Lefoulon-Delalande (2013), the John Vane Award from University of London (2007), and the International Aspirin® Senior Award from Bayer AG (1998). His scholarly impact is reflected in his editorial roles with major journals including Circulation and Arteriosclerosis, Thrombosis and Vascular Biology. As an active researcher, he serves as Principal Investigator for multiple funded projects including studies on antiplatelet effects of aspirin in diabetes patients, funded by Bayer HealthCare AG. He also contributes to European Commission initiatives like the EICOSANOX program investigating eicosanoids and nitric oxide in cardiovascular diseases. Within his institution, Dr. Patrono chairs the Drug Therapy Committee and serves on the Ethics Committee at Catholic University School of Medicine, Rome. He is also a member of the Board of Directors for the Center for Aging Sciences at G. d'Annunzio University Foundation.

Astola Jaakko TapioView profile

Professor

• Signal Processing
• Image Processing
• Nonlinear Signal Processing
• +9 more

Jaakko Tapio Astola is a Professor of Signal Processing at Tampere University of Technology since 1993. He has held prominent roles including Academy Professor (2001–2006) and Associate Professor of Applied Mathematics (1988–1993). His academic journey includes positions at Lappeenranta and Eindhoven Universities of Technology. Astola is a member of the Finnish Academies of Science and Technology, and has received prestigious awards like the EURASIP Fellowship (2012), SPIE Fellowship (2007), and IEEE Fellowship (2000). His research spans signal processing, nonlinear filtering, bioinformatics, and algebraic coding theory. Notable contributions include foundational work on vector median filters and influential textbooks on nonlinear digital filtering. His interdisciplinary work bridges signal processing with systems biology and genetic network modeling. Education Highlights: 1984–1985: Research at Eindhoven University of Technology with Prof. J.H. van Lint's Discrete Mathematics Group 1976–1977: Studies at Kyoto University's Research Institute for Mathematical Sciences Research Interests: Astola's work emphasizes robust filtering techniques, spectral methods, and applications in biomedical signal analysis. His contributions to algebraic signal processing and bioinformatics include advancing Boolean network models for genetic systems and developing systematic bioinformatic analysis frameworks for large-scale genomic data. Awards: EURASIP Fellow (2012) Recipient of 2010 Nokia Award IEEE Fellow (2000) Grants & Advising: While specific grants are not detailed, his extensive academic roles and publications indicate sustained research funding. No student advisees are listed in available texts.

Mitra SanjitView profile

Academic

• Digital Signal Processing
• Analog Signal Processing
• Computer-Aided Design
• +3 more

Sanjit K. Mitra is a Distinguished Professor Emeritus at the University of California, Santa Barbara (UCSB), and a Life Fellow of the IEEE. He holds memberships in prestigious academies including the U.S. National Academy of Engineering, European Academy of Arts & Sciences, and Bavarian Academy of Sciences and Humanities. His career includes roles as Distinguished Professor and Professor at UCSB, UC Davis, and Cornell University, alongside contributions at Bell Labs. Key Positions: Distinguished Professor Emeritus (UCSB), Member of Technical Staff (Bell Labs), 1962–2006. Education: Not explicitly detailed in texts, but his academic lineage spans decades of mentorship. Research focuses on digital and analog signal processing, filter design, and image/video compression. He authored 14 books and over 700 publications, cited 31k+ times (h-index 73). His work has been translated into multiple languages and influenced global engineering curricula. Awards include the IEEE Education Medal (2006), Golden Jubilee Medal (1999), and recognition as a 'Pioneer in Signal Processing' (1998 & 2017). His 48 Ph.D. students include 9 IEEE Fellows and a U.S. NAE member.

Silvia MikschView profile

Professor

• Visual Analytics
• Information Visualization
• Time-Oriented Data Analysis
• +3 more

Prof. Silvia Miksch is a full professor in the Department of Visual Analytics at TU Wien. Her research focuses on visual analytics, time-oriented data visualization, and human-computer interaction. She leads projects in cultural heritage analysis, fraud detection, and pandemic data visualization. Her work bridges computer science with digital humanities, emphasizing user-centric design and uncertainty modeling. Key contributions include guidance systems for VA environments and network visualization frameworks for art history. Affiliations: TU Wien (since 2000+) Research Labs: Network Lab, Visual Analytics Research Group Recent projects explore temporal patterns in artist exhibitions, parameter space exploration, and pandemic data communication. She has advised over 20 PhD/Master’s students, many contributing to VA systems like COVIS and NEVA. Awarded the VGTC Visualization Technical Achievement Award (2024) for foundational work in temporal visualization. Active in IEEE VAST and serves on journal editorial boards. Her labs develop open-source tools for interactive data exploration.

• Audio Engineering
• Music Technology
• Speech Processing
• +3 more

Arnold Esper is a Lecturer at the University of Vienna, specializing in audio engineering and related disciplines. His teaching activities include the course "160065 PR Recording Music and Speech" (offered in 2025S) and prior courses before the 2024W semester. Contact him at arnold.esper@univie.ac.at. Teaching Focus: Audio engineering, music technology, speech processing Research Interests: His work appears centered on technical aspects of sound recording and processing, spanning both musical and speech applications. This aligns with broader fields like digital signal processing and acoustics. Career Timeline: Active teaching roles documented up to the 2025S semester, indicating ongoing academic engagement.

• Signal Processing
• Audio Technology
• Speech Processing
• +5 more

Martin Hagmueller is a researcher at the Institute of Signal Processing and Speech Communication at Graz University of Technology. His work focuses on signal processing, audio technology, and biomedical applications such as vocal fatigue monitoring and speech data analysis. He has contributed to projects involving acoustic sensing, speech corpora management, and bionics. Key research areas include: Acoustic feature extraction for voice analysis Development of speech corpora version control systems Real-time intrusion detection via distributed acoustic sensing Hagmueller has received prestigious awards including the ATC Maastricht Innovation Award (2004) and the 2020 Final Pitch competition recognition. He actively engages in public outreach through events like the Graz Electrical Engineering Days (GEED) exhibitions. Current projects involve acoustic fiber sensing for gas pipelines, high-efficiency refrigeration systems, and clinical applications for dysphonic speakers. He collaborates internationally, evidenced by his involvement in Antarctic research expeditions and interdisciplinary teams.

• Music Information Retrieval
• Artificial Intelligence
• Audio Signal Processing
• +3 more

Gerhard Widmer is a Full Professor at the Institute of Computational Perception within the Faculty of Engineering and Natural Sciences at Johannes Kepler University Linz (JKU). He leads research at the LIT Artificial Intelligence Lab and serves as principal investigator for major projects including the FWF-funded Cluster of Excellence "Bilateral Artificial Intelligence" (2024-2029) and the EU project "Whither Music?" (2022-2026), focusing on AI applications in music cognition and performance analysis. His research spans Music Information Retrieval (MIR) with emphasis on computational modeling of musical expressivity, audio processing, and machine learning applications. Key areas include sound event detection, music transcription, language-based audio retrieval, and modeling of expressive musical performances using deep learning techniques. His work bridges artificial intelligence with musicology to develop systems capable of understanding and generating human-like musical expressions. Analysis of recent publications (2024-2025) reveals a strong trend toward diffusion models for audio generation/manipulation, robustness studies in music emotion recognition, and efficient architectures for sound event detection. There is significant focus on multimodal approaches integrating audio with textual descriptions, knowledge distillation for model compression, and real-time processing solutions for symbolic music analysis and score following. No specific scientific awards are documented in the provided materials. Professor Widmer has supervised 8 research works and manages multiple funded projects: Cluster of Excellence "Bilateral Artificial Intelligence" (FWF, 2024-2029): Investigating human-AI collaborative intelligence "Whither Music?" (EU, 2022-2026): Exploring AI's impact on musical creativity CoMIRVA: Developing music information retrieval visualization tools Sony-CSL project (2021-2024): Industry collaboration on music cognition He directs the Institute of Computational Perception at JKU and is central to the LIT Artificial Intelligence Lab, maintaining active collaborations with the International Society for Music Information Retrieval (ISMIR) community through conferences, shared datasets, and open-source tools like Partitura for symbolic music processing.