Find Academic Supervisors

• Game Studies
• Game Development Culture
• Digital Culture
• +4 more

Solip Park is a Researcher at Aalto University's School of Arts, Design, and Architecture , affiliated with the Department of Art and Media . She is also a doctoral student in the Department of Media, focusing on digital game production culture and immigrant/expatriate game creators . Prior to her academic work, Park co-founded the Nexon Computer Museum in Jeju, South Korea, and has worked in international roles at game companies and startups across the USA, South Korea, and Finland. As a freelance translator and comic artist , she integrates creative methods into her research. Education : Master of Arts (Aalto University) Park's research explores the preservation of game culture through comparative studies of institutions like the Finnish Museum of Games and Nexon Computer Museum. Her methodological innovation combines comic-based research and visual discourse analysis to engage the public. Key themes include translocality , praxiology , and international relations in game development. Her publications span topics like loot box regulation , cross-cultural game jams , and global-local dynamics in the game industry. Park's work has been recognized in the Finnish Game Thesis Competition 2020 and the CHI Conference (honorable mention). Scientific Awards : Honorable mention in CHI Conference on Human Factors in Computing Systems Finnish Game Thesis Competition 2020 award for her Master's thesis Park's interdisciplinary approach bridges game studies , visual arts , and entrepreneurship , reflecting her diverse career trajectory. She actively contributes to game research through empirical studies and public engagement.

Nadia M. AdyView profile

Research Fellow

• Artificial Intelligence
• Computational Creativity
• Reinforcement Learning
• +3 more

Nadia M. Ady serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in artificial intelligence with a focus on computational creativity and intrinsic motivation. Her research explores how to imbue machines with traits of creativity and curiosity through intrinsically motivated learning algorithms, particularly within reinforcement learning frameworks. Her primary research interests span artificial intelligence, machine learning, and cognitive science, with specific emphasis on formalizing psychological concepts like curiosity and competence from Self-Determination Theory for AI applications. She investigates the landscape of intrinsically motivated learning algorithms, diversity progress in goal selection, and the implementation of specific curiosity mechanisms in machine learning systems. Her work bridges theoretical cognitive models with practical AI implementations, often addressing how human variables influence creative AI development. Ady's publication record (2022-2025) reveals a strong interdisciplinary trajectory connecting cognitive science, reinforcement learning, and human-computer interaction. Key themes include the formal specification of competence needs in AI, discriminability-motivated goal selection, and applications of intrinsic motivation to virtual reality environments like dance visualization. Her research consistently addresses the challenge of translating human cognitive traits into computational frameworks while maintaining scientific rigor in modeling psychological constructs. She actively collaborates within Professor Christian Guckelsberger's research group, working alongside researchers including Erik Lintunen, Sebastian Deterding, Markus Laattala, and Perttu Hämäläinen. This collaborative environment spans multiple disciplines, facilitating publications in premier venues such as the Cognitive Science Society, CHI Conference on Human Factors in Computing Systems, and the International Conference on Computational Creativity. Her current projects focus on formalizing curiosity mechanisms and exploring their applications in creative domains and human-AI interaction scenarios.

• Materials Science
• Mechanical Engineering
• Computational Materials Engineering
• +3 more

Zinan Li is a Postdoctoral Researcher at Aalto University's Department of Energy and Mechanical Engineering. Their research focuses on microstructure modeling, crystal plasticity modeling, and integrated computational materials engineering (ICME) approaches. Affiliation: Aalto University Department: Energy and Mechanical Engineering Research Group: Materials to Products Their work explores microstructure-mechanical properties correlation of additive manufacturing (AM) steels and data-driven model development using machine learning. Key research themes include anisotropic fracture behavior, edge fracture analysis, and computational modeling of advanced high-strength steels. Recent publications demonstrate trends in combining experimental data with simulations for fracture prediction in metallic materials. Studies focus on laser powder bed fusion, quenching and partitioning steels, and dual-phase steel mechanics.

• Thermal Engineering
• Heat Transfer
• Renewable Energy Systems
• +3 more

Mohib Rehman is a Postdoctoral Researcher at Aalto University's Department of Energy and Mechanical Engineering. His work focuses on thermal engineering , heat transfer enhancement , and fluid dynamics with applications in renewable energy systems and microchannel heat sinks . He is affiliated with the Energy Conversion and Systems research group. Research Interests Thermal-Hydraulic analysis of heat exchangers Nanofluid and phase-change material applications Microchannel optimization for cooling systems Jet impingement cooling techniques Energy conversion and sustainable technology Publications highlight his expertise in delta-nabla channel configurations , gravitational water vortex exchangers , and ribbed microchannel designs . His work integrates computational fluid dynamics and experimental validation to improve thermal efficiency across diverse applications.

Mohammad DoostaranView profile

Researcher

• Soft Matter Physics
• Biophysics
• Fluid Dynamics
• +3 more

Mohammad Doostaran is a Doctoral Researcher at the Department of Applied Physics, Aalto University. His work focuses on soft matter systems and their interactions with fluid dynamics. Affiliation: Aalto University Department: Applied Physics Research Interests: Soft Matter Physics Biophysics Fluid Dynamics Materials Science His recent research explores mechanical deformations in lipid-based microstructures under microscale fluid flows, contributing to understanding colloidal interactions and soft material behavior.

Sebastiano PeottaView profile

Research Fellow

• Quantum Physics
• Condensed Matter Physics
• Superconductivity
• +3 more

Dr. Sebastiano Peotta is a Research Fellow at Aalto University's Department of Applied Physics, specializing in quantum dynamics and theoretical condensed matter physics. His work focuses on superconductivity, superfluidity, and the role of quantum geometry in flat band systems, particularly in ultracold atomic gases and twisted multilayer materials. Research Focus: Quantum geometry, superfluid weight, flat band systems, and dynamical phase transitions Key Collaborations: Quantum Dynamics research group at Aalto University His publications reveal a strong emphasis on generalized random phase approximation methods and disorder effects in superconductors. He has explored Josephson junctions in graphene double layers, many-body localization phenomena, and effective theories for flat band superconductivity. Scientific Awards: Academy Research Fellow

• Scanning Probe Microscopy
• Atomic Force Microscopy
• Machine Learning in Microscopy
• +3 more

Niko Oinonen is a Visiting Professor at the Department of Applied Physics , Aalto University, specializing in scanning probe microscopy and atomic force microscopy (AFM) techniques. His research focuses on integrating machine learning with nanoscale surface analysis to automate tip functionalization, image interpretation, and structure discovery. Collaborations include researchers from Aalto University and international institutions. Research Trends: Recent work emphasizes computational methods for AFM image analysis, automated microscopy workflows, and machine learning-driven molecular reconstruction. Publications span journals like ACS Nano , Computer Physics Communications , and Science Advances . Contact: Email: ext-niko.oinonen@aalto.fi

Marcel WagenländerView profile

Researcher

• Machine Learning Systems
• Distributed Machine Learning
• Gaussian Processes
• +3 more

Marcel Wagenländer is a Researcher at Imperial College London , affiliated with the Large-Scale Data & Systems Group (LSDS) . His research focuses on Machine Learning Systems , particularly Distributed Machine Learning , Gaussian Processes , and AI Systems , supervised by Professor Peter Pietzuch and collaborating with Professor Mark van der Wilk. His recent publications span top venues like SOSP 2024, HotCloud 2020, and OSDI 2020, addressing challenges in resource management, distributed training, and probabilistic inference. He also contributes to improving benchmarking for Gaussian Process approximations. Marcel earned his Bachelor of Science and Master of Science in Informatics at the Technical University of Munich and is currently interning at Meta in the AI and Systems Co-Design team.

• Additive Manufacturing
• Materials Science
• Artificial Intelligence
• +3 more

Xinyi Yin is a Doctoral Researcher affiliated with the Department of Energy and Mechanical Engineering at Aalto University . They are associated with the research group Materials to Products and contribute to advancements in metal additive manufacturing. Their work focuses on leveraging artificial intelligence for defect detection and localization in 3D printing processes. Research Focus: Additive Manufacturing, Materials Science, Artificial Intelligence Publications: 1 recent article on AI-driven defect detection in metal 3D printing (2025) Email: xinyi.yin@aalto.fi | Phone: +358503134103

• Quantum Computing
• Superconducting Qubits
• Circuit Quantum Electrodynamics
• +3 more

Daniele Trisciani is a Doctoral Researcher at the Department of Applied Physics , Aalto University. His research focuses on experimental quantum computing, specifically within the Superconducting Qubits and Circuit QED group. Affiliation: Aalto University, Department of Applied Physics Email: daniele.trisciani@aalto.fi Research Interests: Quantum computing, superconducting qubits, circuit quantum electrodynamics

Tuomas HarviainenView profile

Professor

• Game Design
• Organizational Learning
• Creative Industry
• +5 more

Tuomas Harviainen serves as Professor of Information Studies and Interactive Media at Tampere University since September 2017 while maintaining affiliation with Aalto University's School of Arts, Design and Architecture. His interdisciplinary work bridges game studies, digital platforms, and organizational learning within Nordic contexts. His research interests focus on Game Design , Digital Distribution , Finnish Creative Industries , and Smart Home Technology . Harviainen examines how gaming mechanics influence consumer behavior, how digital platforms create value (and disvalue), and how cultural contexts shape technology adoption, particularly in Nordic countries. His work often combines qualitative and systems approaches to understand complex human-technology interactions. Analysis of his publication trends reveals a consistent focus on game industry dynamics and digital platform economics, with recent work expanding into smart home ecosystems and identity formation through gaming. His research demonstrates strong connections between business models, user experience, and cultural context. Harviainen supervises graduate theses and has presented internationally, including at the 2019 Mindtrek conference where he contributed to research on Finnish gaming history. His collaborative work spans multiple institutions, particularly with Nordic researchers.

• Machine Learning
• Dynamical Systems
• Partial Differential Equations
• +5 more

Sahel Iqbal is a Doctoral Researcher at the Department of Electrical Engineering and Automation , affiliated with the School of Electrical Engineering at Aalto University . His work bridges Machine Learning with Signal Processing and Partial Differential Equations , focusing on applications in froth flotation and dynamical systems . His research spans: Physics-informed machine learning for mineral processing optimization. Probabilistic modeling using particle filters in experimental design. Parallel-in-time solutions for nonlinear PDEs leveraging Gaussian processes. Publications highlight interdisciplinary trends in Machine Learning (2025), Computer Science (2024), and Mathematics (2024), with subtopics including neural networks , dynamical systems , and probabilistic modeling . He is part of the Sensor Informatics and Medical Technology group, contributing to cutting-edge methodologies in signal processing and automation.

• Machine Learning
• Graph Neural Networks
• Bayesian Inference
• +3 more

Diego Parente Paiva Mesquita serves as a Visitor (Faculty) in the Department of Computer Science under Professor Samuel Kaski's research group. His work bridges theoretical and applied machine learning with emphasis on scalable probabilistic systems. His academic credentials include: Doctor of Technology in Computer Science (awarded December 16, 2021) Bachelor of Computer Science from the Brazilian Ministry of Education (awarded August 23, 2016) Mesquita's research focuses on advancing graph neural networks, Bayesian inference, and incomplete data handling. His fingerprint reveals strong contributions to probabilistic modeling (60% Approximates), random variable analysis (53%), and machine learning systems (50%), with particular innovation in neural network architectures (44%) for complex data structures. Recent publications (2022-2024) demonstrate a cohesive trajectory toward parallelizable machine learning frameworks. Key themes include temporal graph representation learning, embarrassingly parallel Monte Carlo methods, and thin/deep Gaussian process hybrids—addressing critical scalability bottlenecks in probabilistic AI while maintaining theoretical rigor.

• Smart Cities
• Open Data
• Mobile Applications
• +3 more

Hendrik Hielkema is a doctoral student and active researcher at Aalto University's School of Business, focusing on the intersection of technology, urban development, and societal transformation. His work spans open data applications, smart city innovation, and the cognitive impacts of digital learning. Education : Kauppatieteiden maisteri (Master of Science in Economics and Business Administration) in Business Technology from Helsinki School of Economics (now part of Aalto University). Hielkema’s research explores how open data and mobile technologies drive competitive advantage and urban development. He has contributed to academic discourse on smart city initiatives, particularly in Helsinki, and examined the societal implications of internet-driven changes in cognition and work. His publications highlight expertise in computer science, economics, and social sciences, with a focus on digital infrastructure and educational technology. Collaborations span interdisciplinary teams, including co-authors like Hoang, Deng, and Kiravuo. Scientific Awards : Award from Aalto University (2009) Hielkema actively participates in research networks related to UN Sustainable Development Goals, particularly in education and technology. His work has garnered significant citations (93 Scopus citations) and Mendeley readership (230+), indicating broad academic and policy relevance.

• Inverse Problems
• Statistical Learning
• Machine Learning
• +4 more

Abhishake Abhishake is a Postdoctoral Researcher at the Department of Computational Engineering, LUT School of Engineering Sciences, focusing on inverse problems and statistical learning. His research spans mathematics, machine learning, and computational optimization. 2023–present: Postdoctoral Researcher, LUT University, Finland 2021–2022: Postdoctoral Researcher, Technische Universität Berlin, Germany 2018–2021: Postdoctoral Researcher, University of Potsdam, Germany His research interests include inverse problems, regularization methods, and machine learning. He has published extensively on Tikhonov regularization, nonparametric testing, and multi-penalty strategies. His work bridges mathematical theory with applications in pharmacometrics and statistical learning. Key trends in his publications (16 total) include inverse problems in Hilbert scales, regularization techniques for nonlinear statistical learning, and multi-task learning frameworks. Subfields cover oversmoothing penalties, manifold regularization, and convergence analysis.