Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.
Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.
Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.
Jari Holopainen is a Senior Lecturer at the Department of Electronics and Nanoengineering , Aalto University. His work focuses on advanced antenna systems, wireless communication, and RFID technologies, with significant contributions to broadband, tunable, and wearable antenna designs. Current affiliation: Aalto University Academic role: Senior Lecturer Research interests span antenna design for mobile terminals, microwave engineering, and machine learning applications in RF systems. His publications highlight innovations in: Bluetooth antennas for metallic smartwatches and jewelry Wideband and dual-polarized antenna arrays RFID transponders with beam steering Machine learning-driven load optimization 3D-printed and capacitive-coupling antenna structures Wave propagation and scattering analysis Scientific contributions include: 15+ peer-reviewed articles (2025-2020) Collaborations with leading researchers in electromagnetics (e.g., Ville Viikari, Pertti Vainikainen)
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.
Dr. Juha Kärkkäinen is a University Lecturer and Principal Investigator at the Department of Computer Science, University of Helsinki, specializing in Algorithmic Bioinformatics. He supervises PhD students in the Doctoral Programme in Computer Science and actively contributes to research outputs and academic events. Institution: University of Helsinki Department: Department of Computer Science Rank: Lecturer His research focuses on string processing , data structures , and combinatorial pattern matching , with recent work on bijective Burrows-Wheeler transforms, LCP arrays, and efficient string indexing. Google Scholar lists publications spanning 2024 to 1999, highlighting his expertise in algorithm design and compression techniques . Notably, he has participated in and organized events like the International Workshop on Combinatorial Algorithms and delivered a keynote speech at the 1st Summer School on Bioinformatics Algorithms. While no explicit awards are detailed, his editorial roles in scientific collections and conferences underscore his academic influence.
Simon Puglisi is a Professor at the University of Helsinki's Department of Computer Science, within the Faculty of Science. His research focuses on algorithms, bioinformatics, data compression, and string processing, with notable contributions to genomic data analysis and efficient indexing techniques. He holds the Alberto Apostolico Best Paper Award (2021) and leads the WILL # CHAIR # BOSSA project (2025–2029). His work includes scalable k-mer indexing tools like Themisto and advancements in Lempel-Ziv compression and suffix tree algorithms. He frequently collaborates internationally, participates in editorial roles for journals like the ACM Journal of Experimental Algorithmics , and contributes to conferences such as the International Symposium on Combinatorial Pattern Matching. Research Interests: Algorithm design for string processing and bioinformatics Efficient data structures for genomic data Compression techniques (e.g., Lempel-Ziv, Burrows-Wheeler) Dynamic and space-efficient algorithms Grants & Projects: WILL # CHAIR # BOSSA (2025–2029) Ongoing collaborations with institutions like the University of Melbourne and King's College London
Archontis Politis is an Assistant Professor in the Department of Computing Sciences at Tampere University's Faculty of Information Technology and Communication Sciences. His research focuses on signal processing, machine learning, and their applications in audio engineering, particularly in spatial audio, sound source separation, and parametric audio coding. He explores topics such as Ambisonics, reverberation control, and neural network-based approaches for audio processing. His work emphasizes spatial audio reproduction, including six degrees of freedom (6DOF) rendering, microphone array processing, and efficient compression techniques for higher-order Ambisonics. He also investigates sound event localization and detection, leveraging machine learning for real-world acoustic scenarios. His contributions span theoretical advancements in spherical harmonics and practical implementations of spatial audio systems. Recent research highlights include developing datasets for music source separation, improving synthetic-to-real generalization in classical music, and creating neural encoding models for irregular microphone arrays. His methodologies often integrate deep learning with traditional signal processing to address challenges in multi-speaker environments and dynamic acoustic scenes.
Ville Jantunen is a Researcher and Supervisor in the Doctoral Programme in Materials Research and Nanosciences. His research focuses on atomistic simulations of materials under extreme conditions, including ion irradiation effects, defect evolution in fusion materials, and nanoparticle dynamics. He is actively involved in the SPATEC project (2022–2026), funded by the Academy of Finland, which explores time and spatial dependence of cascade damage in materials under pulsed ion beams. Key research areas include computational materials science, radiation effects in nanomaterials, and predictive modeling of electronic/atomic phenomena. His work spans both fundamental and applied aspects, with contributions to quantum technology through spin-qubit array studies and fusion energy via tungsten defect analysis. Collaborations include international teams on nanoparticle shape transformation mechanisms and kinetic Monte Carlo simulations. He has organized educational outreach activities like the LEGO lab workshop at Helsinki Natural Science Lyceum (2018), demonstrating engagement in science communication. Publications emphasize interdisciplinary approaches, combining computational methods with experimental insights to address challenges in nanotechnology, fusion materials, and radiation physics.
Dr. Vishnu Unnikrishnan is an Assistant Professor at the Department of Electrical Engineering, Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on energy-efficient high-performance analog/digital/RF integrated circuits and systems in nanometer-scale CMOS technologies. Key areas include time-based data conversion, high-speed serial links, and 5G/6G wireless transceivers. He leads research on analog interfaces using digital/switch components and collaborates with the SoC Hub ecosystem to bridge academic and industrial interests in system-on-chip design. He has secured significant funding, including an EU Marie Curie ITN grant (SMArT) worth €818k and an Academy of Finland Project (2021) of €821k. His work spans over 40 peer-reviewed publications, emphasizing innovations in time-based ADCs, beamforming receivers, and RF system design. Dr. Unnikrishnan actively supervises doctoral and postdoctoral researchers, offers paid master's theses and summer jobs in IC design, and collaborates with industry through the SoC Hub. His research aims to advance cross-technology portable analog interfaces and high-performance mixed-signal systems.
University Lecturer Anu Lehtovuori is affiliated with the Department of Electronics and Nanoengineering at Aalto University, where she actively bridges teaching and research. Her roles encompass academic teaching, project leadership, grant writing (e.g., Academy of Finland applications), and collaboration with doctoral students. She specializes in cutting-edge topics such as antenna design for 5G/6G systems, reconfigurable MIMO architectures, and RF technology for mobile devices. Her research focuses on optimizing antenna performance in compact environments, mitigating interference, and enhancing wireless communication efficiency. Notable areas include wideband antenna systems, decoupling techniques for multi-element arrays, and adaptive antenna-amplifier integration. Lehtovuori emphasizes practical applications, addressing challenges like user interaction effects on mobile antenna performance and minimizing electromagnetic emissions. Research Trends: Dominant themes include 6G IoT antenna solutions, mmWave component integration, and reconfigurable systems leveraging mutual coupling. Grants: Actively pursuing funding through initiatives like the Academy of Finland. Her contributions span both theoretical advancements (e.g., bandwidth optimization algorithms) and industrial applications (e.g., antenna cluster techniques for full-screen smartphones). While no specific awards are documented, her work reflects a strong focus on impactful, industry-relevant innovations.
Tapio Niemi is an Associate Professor (tenure track) in the Department of Physics at Aalto University. He holds a Doctor of Science (Technology) from the former Helsinki University of Technology (now part of Aalto University), awarded in 2002. His research focuses on nanophotonics, plasmonics, quantum dots, and semiconductor materials, with particular expertise in nanofabrication techniques such as block copolymer lithography and nanoimprint lithography. His work contributes to the UN Sustainable Development Goals through advancements in solar cell technology and environmental sensor development. Key research areas include the design of optical devices like microdisk lasers, guided-mode resonance gratings, and plasmonic sensors. He has pioneered studies on ZnO nanowire functionalization and nonlinear optical microscopy, with applications in biomedical imaging and environmental sensing. His interdisciplinary approach integrates nanotechnology, photonics, and materials science to address challenges in energy, health, and sustainability. Education: Doctor of Science (Technology), Helsinki University of Technology (2002) Supervision: Advised doctoral student Tommi Isoniemi on carbon nanotubes and graphene in plasmonics (2017–2021) Labs/Teams: Collaborations include the NanoScience Center (University of Jyväskylä) and Aalto’s nanophotonics research groups Recent work emphasizes high-Q resonators, Purcell-enhanced quantum dot light sources, and UV-curable polymer stamp lithography. His publications span journals like Advanced Optical Materials and Applied Physics Letters , with a focus on practical applications in photonics and nanotechnology.
Viktar Asadchy is an Assistant Professor in the Department of Electronics and Nanoengineering at Aalto University . His research focuses on electromagnetic wave control , photonic time crystals , and metasurface engineering , with applications in nonreciprocal optics , reconfigurable intelligent surfaces , and terahertz technology . He has published extensively on dynamic metamaterials and spatiotemporal modulation. Email: viktar.asadchy@aalto.fi His recent work explores terahertz frequency conversion , inverse-designed time-varying nanostructures , and nonreciprocal metasurfaces . Trends in his publications emphasize harnessing temporal modulation and quasi-bound states to achieve extreme electromagnetic control, including perfect anomalous reflection and energy accumulation in photonic systems.
Katsuyuki Haneda serves as Associate Professor in Aalto University's Department of Electronics and Nanoengineering within the School of Electrical Engineering. Holding a Doctor of Engineering from Tokyo Institute of Technology (2007), his research pioneers high-frequency radio systems including millimeter-wave and Sub-THz communications, with critical applications in medical devices, post-disaster scenarios, and Internet-of-Things networks. Education: Doctor of Engineering, Tokyo Institute of Technology, 2007 Research Focus: Dr. Haneda's work spans Physical layer wireless communications , Antennas and propagation , and RF instrumentation , with groundbreaking contributions to wireless medical applications (notably capsule endoscopy) and in-band full-duplex radio technologies . His group develops advanced channel models for 6G while addressing real-world challenges in post-disaster communications through experimental validation. Publication Trends: Recent 2025 works reveal concentrated advancements in Sub-THz/D-Band communications, featuring innovative antenna designs for 28 GHz systems, channel modeling techniques for 3GPP-like standards, and medical wireless solutions. Publications demonstrate strong experimental emphasis with field measurements across urban, indoor, and medical environments. Scientific Recognition: ISMICT 2019 Best Paper Award (medical wireless) Dual 2013 Best Paper Awards (IEEE VTC Spring & EuCAP) IEICE Best Survey Paper (2015) IEEE AP-S Young Engineer Award (2007) Tokyo Tech Honorary Student Award (2002) Leadership & Collaboration: As Associate Editor for IEEE Transactions on Antennas and Propagation (2012-2016) and Wireless Communications (2013-present), Dr. Haneda shapes field standards. He co-chairs the radio channel working group in COST Action IRACON (CA15104), driving European 5G+/6G research collaboration while supervising graduate researchers in experimental high-frequency communications. Research Infrastructure: The Katsuyuki Haneda Group maintains specialized laboratories for millimeter-wave/Sub-THz channel sounding, antenna characterization, and medical wireless prototyping, supporting hardware-in-the-loop validation for next-generation communication systems.
Sergiy Vorobyov is a Professor at the Department of Signal Processing and Acoustics , Aalto University , Finland. He has held academic and research positions at multiple institutions, including the University of Alberta (Canada), Kharkiv National University of Radio Electronics (Ukraine), RIKEN (Japan), McMaster University (Canada), Duisburg-Essen University and Darmstadt University of Technology (Germany), and Heriot-Watt University (UK). His expertise spans optimization, signal processing, and multi-antenna systems. Dr. Vorobyov holds a Doctoral degree in Natural Sciences from the National Technical University Kharkiv Polytechnical Institute, awarded on January 15, 2002. His research interests focus on optimization and multi-linear algebra applied to signal processing challenges, including statistical and array signal processing, sparse signal processing, estimation and detection theory, and sampling theory. He explores multi-antenna, large-scale, cooperative, and cognitive systems, contributing to advancements in wireless communications and radar engineering. His work aligns with UN Sustainable Development Goals, emphasizing education and innovation. In recent years (2025), his publications emphasize cutting-edge advancements in wireless communications and signal processing. Topics include millimeter-wave MIMO channel estimation, optimization algorithms with momentum-based techniques, vehicular network communications, and robust covariance matrix estimation in challenging noise environments. These contributions highlight his expertise in developing efficient and adaptive methods for modern communication systems. He has received prestigious awards, including: 2004 IEEE Signal Processing Society Best Paper Award 2007 Alberta Ingenuity New Faculty Award 2011 Carl Zeiss Award for teaching and innovative methods 2012 NSERC Discovery Accelerator Award 1st Price Best Paper Award (2015) 1st Price Best Student Paper Award at CAMSAP 2015 As a researcher, Vorobyov has supervised seven theses and led multiple funded projects, such as: AI Based RAN (2023–2025): Scalable AI solutions for 5G/6G networks. MASSIVE AND SPARSE ANTENNA ARRAY PROCESSING FOR MILLIMETERWAVE COMMUNICATIONS (2019–2021): Advanced antenna design and processing techniques. M-CUBE SPA (2017–2021): EU-funded sparse antenna array research. Transmit beamspace for active compressive sensing and communication with multiple waveforms (2016–2020): Radar and MIMO system optimization. He leads the Sergiy Vorobyov Group , focusing on real-time signal processing algorithms and their applications in next-generation wireless systems. His research addresses practical challenges such as efficient channel estimation, robust detection in massive access scenarios, and improving network performance in urban environments.
