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Shruti KshirsagarView profile

Assistant Professor

• Data Science
• Deep Learning
• Machine Learning
• +8 more

Dr. Shruti Kshirsagar is an Assistant Professor and MS Data Science Graduate Coordinator at the School of Computing, Wichita State University (WSU), part of the College of Engineering. She holds a PhD in Telecommunications from INRS (Montreal, Canada), specializing in Speech & Deep Learning, alongside industry experience at EERS Global Technologies and Bosch. Education: PhD in Telecommunications, INRS, Montreal (Canada) Master's and Bachelor's in Electronics & Telecommunication Engineering, University of Mumbai Research interests include Data Science, Deep Learning, Affective Computing, Healthcare AI, Medical Image Processing, and Speech Signal Processing. Her work emphasizes robust solutions in emotion detection, assistive technologies, and healthcare diagnostics. Awards: Recipient of the Best Doctoral Thesis Award (INRS). Grants: Secured multiple research grants (specific names not listed). Advising/Grants: Mentor to graduate students, develops Deep Learning curricula, and leads research projects. Professional memberships include IEEE Signal Processing Society and ISCA. Labs: Maintains a lab focusing on Affective Computing and Signal Processing, with a dedicated lab website.

John HansenView profile

Professor

• Speech Processing
• Speaker Recognition
• Human-Computer Interaction
• +7 more

John H.L. Hansen is a distinguished academic and researcher, currently serving as the Associate Dean for Research and Professor of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas (UTD). He also holds the Distinguished University Chair in Telecommunications Engineering and has a joint appointment in the School of Behavioral and Brain Sciences (Speech & Hearing). His research focuses on robust speech processing, speech enhancement, speaker recognition, and human-computer interaction. He founded the Center for Robust Speech Systems (CRSS) at UTD, which is dedicated to interdisciplinary research in speech processing and language technology. Education: Ph.D. in Electrical Engineering (Georgia Tech, 1988) M.S. in Electrical Engineering (Georgia Tech, 1983) B.S. in Electrical Engineering (Rutgers University, 1982) Research Interests: Dr. Hansen's work spans digital speech processing, speaker trait assessment, speech pathology, and robust speech recognition in adverse conditions such as noise and stress. His contributions include advancements in cochlear implant technology, in-vehicle speech systems, and historical audio recovery (e.g., NASA Apollo missions). Awards & Recognition: IEEE Fellow (2007) ISCA Fellow (2010) Acoustical Society of America 25 Year Award (2010) Recipient of Whitaker Foundation and NSF Awards Grants & Collaborations: He has secured over $12M in external funding from NSF, DARPA, and industry partners like Texas Instruments and IBM. Collaborations include institutions in Japan, Turkey, Denmark, and Spain. His work on the UTDrive project focuses on in-vehicle voice navigation and driver behavior analysis. Labs & Teams: Leads the CRSS, which includes sub-teams like the Cochlear Implant Group and the Speech & Speaker Modeling Group. Active in organizing international conferences like IEEE ICASSP and INTERSPEECH.

Jon FagerströmView profile

Visiting Professor

• Audio Signal Processing
• Digital Signal Processing
• Acoustics
• +5 more

Jon Fagerström is a Visiting Professor at the Department of Information and Communications Engineering, Aalto University. His work focuses on advanced audio signal processing techniques, particularly in velvet noise applications, artificial reverberation, and feedback delay networks. He contributes to the UN Sustainable Development Goal of Quality Education through his academic research in sound engineering. Academic Affiliation: Aalto University Department: Information and Communications Engineering Primary Research Areas: Audio Signal Processing, Acoustics, Sound Synthesis, Virtual Reality Audio Research trends in his publications include innovations in reverberation modeling , decorrelation techniques , and velvet noise applications across audio engineering, VR sound environments, and music technology. Collaborations with researchers like Niko Meyer-Kahlen, Sebastian Schlecht, and Vesa Välimäki highlight his work in feedback delay networks and binaural audio processing.

• Digital Signal Processing
• Spectral Analysis
• Pattern Recognition
• +7 more

Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.

Sourya RoyView profile

Assistant Professor

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +4 more

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

• Automating Design and Diagnosis of Information Systems
• Computer Graphics
• Web Technologies

Konstantin Nikolaevich Kasyan serves as Associate Professor at the Department of Computer Systems and Networks within the Faculty of Computer Science and Technologies at Zaporizhzhia National Technical University, where he has maintained academic activity since 1998. He graduated with honors from Zaporizhzhia Machine-Building Institute's Faculty of Electronic Engineering in 1993, specializing in Radio Engineering with qualification as Radio Engineer. His Candidate of Sciences degree (defended 1998/1999) established his expertise in diagnostic methodologies for electronic systems. His research spans three core domains: automating design and diagnosis of information systems, computer graphics methodologies, and web technologies. This interdisciplinary focus manifests in practical applications ranging from hardware diagnostics to smart home systems. His work demonstrates consistent evolution from foundational electronics reliability research in the 1990s toward contemporary IoT and computer vision applications. His publication trajectory reveals distinct chronological phases: 1990s-2000s concentrated on electro-radio diagnostics and reliability engineering; 2000s-2010s expanded into computer graphics and text recognition; while 2010s-2021 shifted toward IoT integration, smart home technologies, and machine learning applications. This progression reflects both technological advancements and his adaptability across computing subfields. Kasyan teaches modern Internet technologies, computer graphics, computer systems design, and computational methods in scientific research, directly translating his research into pedagogical practice. His academic presence is maintained through Scopus, Web of Science, Google Scholar, and ORCID profiles, indicating active scholarly engagement despite the absence of formal awards or documented grants in available records.

• Artificial Intelligence
• Machine Learning
• Constraint Optimization
• +8 more

Dr. James Atlas is a Senior Lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury's Faculty of Engineering, where he has been employed since July 2018. His primary affiliations include full-time academic responsibilities at this institution. His research spans: Core CS : Artificial intelligence, machine learning, constraint optimization Systems : Distributed and high-performance computing Applied domains : Medical data analysis (CT reconstruction, health prediction), earth/space exploration (flood mapping, environmental monitoring), and multi-agent systems Education : Curriculum development, threshold concept gamification Recent publication analysis (2019-2025) reveals dominant themes in deep learning applications for medical imaging (spectral CT reconstruction, bioacoustic analysis) and environmental sensing (3D tree modeling, vineyard monitoring, flood prediction), with consistent interdisciplinary collaboration. He actively supervises 10 graduate students researching: Machine learning for global flood exposure mapping CT reconstruction algorithms and artifact correction EMG-EEG hybrid prosthetics Hierarchical reinforcement learning Automated 3D digital tree modeling Bioacoustic monitoring systems

• Electroacoustic Music
• Audiovisual Composition
• Acousmatic Music
• +4 more

Gordon Delap is an Associate Professor in the Department of Music at Maynooth University, operating within the Faculty of Arts and Humanities. An acclaimed audiovisual artist and electroacoustic composer from Donegal, Ireland, Delap maintains an active research profile with international collaborations spanning Europe and the Americas. His institutional affiliations include both current and past positions at: Maynooth University (current primary appointment) Nadine Arts Centre, Brussels (resident artist) Edinburgh University (resident artist) Technische Universitaet Berlin (resident artist) SCRIME, University of Bordeaux (resident artist) Delap's research explores the frontiers of sound technology and artistic expression through three interconnected domains: Electroacoustic composition focusing on spatial audio diffusion and acousmatic traditions Physical modeling synthesis developing novel digital instruments Audiovisual integration creating immersive multimedia experiences His recent publications (2021-2025) demonstrate consistent exploration of spatial sound design, with 85% of works utilizing multichannel formats. Compositional themes frequently examine transformation metaphors (insect metamorphosis, elemental states) through spectral processing techniques. Performance contexts show strong presence at international electroacoustic festivals in Europe and Latin America. Delap has received recognition through competition placements including: Finalist, Contemporanea Festival (2018) Finalist, Metamorphoses Competition (2018) His collaborative technical research includes the NESS Project (Large-scale physical modeling synthesis), documented in the Computer Music Journal. Current creative practice continues to investigate spatial audio applications through festival commissions and gallery installations.

• Interactive Audio
• Digital Signal Processing
• Sound Programming
• +6 more

Dr. Martin Jaroszewicz is a Sessional Faculty member at Emily Carr University of Art and Design, specializing in technology-integrated sound disciplines. He holds a PhD in Digital Composition from the University of California Riverside and an MFA in Integrated Composition, Improvisation and Technology from UC Irvine. His academic background includes teaching positions at California universities and a research appointment as Resident Scholar at UC Mexus. His core research domains blend creative and technical audio applications: Interactive audio systems and spatial sound design Digital signal processing algorithms for music/sound VR/AR audio implementation (e.g., game soundtracks) Gesture-based audio interfaces and spectral spatialization Mobile device audio programming frameworks Professionally, he has consulted for organizations like Electronic Arts and presented at institutions including TU Berlin's Institut für Sprache und Kommunikation and The Hague's Institute of Sonology. Significant creative projects include the VR game 'Journey to Foundation,' the memorial installation 'Trauma, Loss, Transcendence,' and the internationally exhibited 'Hyperobject No.1' audiovisual work.