Find Academic Supervisors

• Statistical signal processing
• Robust statistical methods
• Array processing
• +6 more

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Yu XiaoView profile

Associate Professor

• Edge Computing
• Extended Reality (XR)
• Wearable Computing
• +7 more

Yu Xiao is an Associate Professor at the Department of Information and Communications Engineering, Aalto University, specializing in edge computing, extended reality (XR), wearable computing, and crowdsensing. Their research contributes to the UN Sustainable Development Goals, particularly in education and technology innovation. Active in mobile cloud computing and decentralized systems Principal Investigator in EU-funded projects (EMIL, TUTL) Expert in 5G networks, autonomous systems, and human activity recognition Yu Xiao's work spans interdisciplinary domains, including healthcare (cardiovascular resuscitation devices) and urban mobility (autonomous vehicle interactions). They have received multiple awards, including Best Paper Awards and Nokia Foundation Scholarships. Focus on low-latency communication and multiagent reinforcement learning Developed frameworks like FediLive for decentralized social networks Contributed to 128+ publications and software tools Recent collaborations include institutions like Pontificia Universidad Católica de Chile and participation in IEEE committees. Their research integrates blockchain for secure IoT communication and advanced AR applications.

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Esa Rahtu is a Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on computer vision, machine learning, and deep learning applications. He leads projects in image coding, neural networks, 3D reconstruction, object pose estimation, and anomaly detection. Rahtu has contributed to over 98 research outputs since 2017, with recent work emphasizing Gaussian splatting for SLAM, neural radiance fields, and hybrid video codecs for human-machine compatibility. His expertise spans visual-inertial odometry (e.g., ADVIO dataset), LiDAR-based place recognition, and manufacturing quality control systems. Key areas include: 3D scene reconstruction using Gaussian splatting techniques Deep learning models for anomaly detection in industrial processes Hybrid video codecs optimizing human perception and machine processing Multi-sensor fusion for robotic navigation and indoor mapping Notable datasets include ADVIO for visual-inertial odometry and FIORD for 3D reconstruction benchmarking. His research aligns with UN SDG 9 (Industry, Innovation & Infrastructure) and SDG 4 (Quality Education) through advancements in smart manufacturing and educational technology. Rahtu has received continuous research funding, including a grant period from April to June 2018. His work emphasizes practical applications, collaborating on real-world challenges like paper manufacturing quality control and smartphone-based 3D reconstruction.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +3 more

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +6 more

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +3 more

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.

Elena Simona LohanView profile

Professor

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +7 more

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

Pavel ZemcikView profile

Visiting Professor

• Computer Graphics
• Computer Vision
• Machine Vision
• +7 more

Pavel Zemcik serves as a Visiting Professor in the Computational Engineering department at the School of Engineering Sciences, Lappeenranta University of Technology (LUT). His academic work spans multiple domains within computer science with a strong emphasis on visual computing technologies. Dr. Zemcik's research interests encompass a broad spectrum of visual computing disciplines, including computer graphics, computer vision, machine vision, and image processing. His work particularly focuses on light field rendering techniques, 3D display technologies, and advanced wavelet transform applications. He has made significant contributions to GPU acceleration methods for real-time visual processing and has explored applications in both industrial settings and medical imaging. Analysis of his recent publication trends reveals a concentrated research trajectory in light field technologies and 3D display systems over the past five years. His work demonstrates increasing sophistication in handling visual quality metrics, focus management, and compression techniques specifically tailored for 3D displays. The research shows strong interdisciplinary connections between computer graphics, signal processing, and human perception studies. His scholarly output demonstrates consistent productivity across multiple high-impact venues in computer graphics and computer vision. While specific grant information isn't detailed in the available materials, his publication pattern suggests sustained research funding supporting his work in visual computing technologies.

• Terahertz Imaging
• Submillimeter-wave Technology
• Quasioptical Systems
• +5 more

Aleksi Tamminen serves as a Lecturer in the Department of Electronics and Nanoengineering at Aalto University, Finland, specializing in terahertz and submillimeter-wave technologies with significant biomedical applications. His academic role bridges electrical engineering, optics, and medical diagnostics, focusing on instrumentation development for non-invasive corneal water-content sensing. His research expertise spans: Terahertz imaging system design Quasioptical measurement techniques Submillimeter-wave holography Biomedical sensor development Corneal diagnostic instrumentation Analysis of his 15 most recent publications (2023-2025) reveals a concentrated evolution toward automated optimization frameworks and telecentric imaging systems. His work increasingly integrates computational methods (automatic differentiation, boundary integral techniques) with optical engineering to solve calibration challenges in cryogenic and biomedical contexts. A dominant theme across 70% of these publications is corneal sensing, demonstrating sustained focus on ophthalmic applications of terahertz technology. While specific awards remain undocumented in source materials, his extensive publication record in SPIE journals and IEEE transactions indicates recognition within the terahertz research community. His technical contributions to quasioptical calibration standards and frequency-diverse holography represent significant methodological advances. Dr. Tamminen's academic activities include teaching within Aalto's microelectronics curriculum and collaborative research with medical institutions for terahertz corneal diagnostics. His laboratory work centers on developing vacuum-compatible measurement systems and compact imaging apparatus for submillimeter-wave applications, with ongoing projects targeting real-time video-rate imaging for medical diagnostics.

• Machine Learning
• Object-Centric Learning
• Deep Learning
• +5 more

Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi

Antti Ylä-JääskiView profile

Professor

• Mobile Cloud Computing
• Distributed Systems
• Pervasive Computing
• +5 more

Antti Ylä-Jääski is a tenured Professor at Aalto University's Department of Computer Science within the School of Science. He holds roles such as former Vice Head of Department (teaching) and leads the Distributed and Mobile Computing Research Group. His research focuses on distributed mobile computing systems, including mobile cloud computing, edge/fog computing, and energy-efficient communication. He has authored over 135 publications and secured funding from institutions like the Academy of Finland and Business Finland. Education: Doctoral degree in Engineering from Hochschule für Technik Zürich (1993), Master's from Helsinki University of Technology (1986) Research interests span mobile cloud computing, distributed systems, and IoT. Recent work includes vehicular fog computing for 5G, real-time gaze prediction in VR, and edge-assisted video streaming. Awards include multiple Best Paper Awards and teaching accolades, such as the 2009 award for best teaching in the School of Science and Technology. He supervises graduate studies and has advised notable students like Teemu Kämäräinen (2019 Dissertation Award) and Ilya Nikolaevskiy (2018 Award). His projects include 5G@II (2017-2019) and PraNA (2016-2018), focusing on network analytics and programmable systems. Labs/Teams: Leader of the Distributed and Mobile Computing Group, member of Helsinki Institute for Information Technology (HIIT), and Nokia Center for Advanced Research Deputy Director.

• Robot Vision
• Robot Learning
• Computer Vision
• +4 more

Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.

Filip ElvanderView profile

Assistant Professor

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +4 more

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +7 more

Prof. Simona Lohan is a Full Professor at the Electrical Engineering unit of Tampere University, Finland, and a visiting professor at Universitat Autònoma de Barcelona. Her research focuses on wireless positioning, GNSS algorithms, and wearable computing, with an emphasis on privacy-aware localization and interference mitigation. She leads the Signal Processing for Wireless Positioning research group and coordinates the H2020 MSCA European Joint Doctorate A-WEAR (2019-2023). She holds a PhD in Telecommunications from Tampere University of Technology (2003), and has authored/co-authored over 250 peer-reviewed publications. Education background includes an MSc in Electrical Engineering from Polytechnics University of Bucharest (1997), a DEA in Econometrics from École Polytechnique, Paris (1998). Her research spans satellite navigation (Galileo/GPS/GLONASS), UMTS/WCDMA positioning, 5G positioning, IoT localization, and medical applications of positioning systems. She is an associate editor for the RIN Journal of Navigation and IET Journal on Radar, Sonar, and Navigation . Key projects include the H2020 A-WEAR project (wearable health tech), Academy of Finland ULTRA (2020-2022), and SJU GATEMAN (2018-2019). Her work addresses global navigation challenges through LEO-PNT constellations, low-cost positioning solutions for Africa, and fusion of 5G/mmWave radar for airport surveillance. She has pioneered datasets like TUJI1 for indoor localization and developed open-source tools like SyDR for GNSS algorithm benchmarking. Her research themes include: LEO satellite-based positioning systems Anti-spoofing techniques for GNSS RF fingerprinting for device identification Privacy-preserving localization algorithms IoT and industrial internet applications Embedded signal processing systems Her team's innovations bridge communication and navigation domains, with applications in aviation, healthcare, and smart infrastructure. Recent work emphasizes energy-efficient GNSS signal processing and multi-constellation PNT solutions for global accessibility.

• Computer Vision
• Natural Language Processing
• Multimodal Learning
• +2 more

Abduljalil Saif serves as a Research Fellow in the Department of Computer Science at Aalto University, conducting advanced research in artificial intelligence with emphasis on multimodal integration of visual, auditory, and linguistic data streams. His core research domains include Computer Vision , Natural Language Processing , and Audio-Visual Multimodal Learning , where he develops techniques for weakly-supervised pre-training, speaker detection systems, and parameter-efficient adaptation frameworks. Specific methodologies involve attention mechanisms, deep learning architectures, and vision-language alignment strategies. Recent publications (2023-2025) reveal consistent innovation in multimodal system design, with applications spanning active speaker identification, efficient cross-modal transfer, and information retrieval. His work appears in premier venues including SIGIR and VISAPP, demonstrating technical rigor in audio-visual fusion challenges. Dr. Saif actively collaborates within the Laaksonen Jorma research group, contributing to Aalto University's multimedia and computer vision initiatives through joint publications with researchers like Zixin Guo and Selen Pehlivan.