Find Academic Supervisors

Juha KontinenView profile

Professor

• Mathematical Logic
• Theoretical Computer Science
• Computational Complexity
• +3 more

Juha Kontinen is a Professor and Docent in the Department of Mathematics and Statistics at the University of Helsinki. He serves as a Supervisor for the Doctoral Programme in Mathematics and Statistics. His research focuses on mathematical logic, theoretical computer science, computational complexity, and formal methods. Key areas include dependence logic, team semantics, and their applications in areas like database theory and artificial intelligence. He has been actively involved in numerous research projects funded by organizations such as the Magnus Ehrnrooth Foundation and the Academy of Finland. His work spans foundational studies of computational complexity, formal logics for team semantics, and interdisciplinary applications. Recent projects include exploring discrete differential equations for circuit complexity and probabilistic team semantics with Boolean negation. Kontinen has authored/co-authored over 90 publications, including articles in prestigious journals and conference proceedings. His work bridges theoretical foundations with practical computational challenges, addressing topics like neural network training complexity and hyperproperties in temporal logics. He has also contributed to conference organization, peer review, and doctoral supervision, highlighting his role as a leader in the academic community.

• Machine Learning
• Bioinformatics
• Drug Discovery
• +5 more

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

Aleksanteri Mikulus SladekView profile

Researcher

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +2 more

Aleksanteri Mikulus Sladek serves as a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland, operating under Professor Arno Solin's research group. His institutional affiliation includes the T313 research unit with postal operations based in Espoo. Sladek's research centers on advanced machine learning methodologies, specializing in probabilistic modeling and Bayesian inference frameworks. His work pioneers novel representation techniques including bitstring encoding for approximate inference and algebraic approaches to mixture modeling. He actively contributes to scalable learning algorithms through theoretical innovations in probabilistic graphical models and representation learning architectures. Analysis of his recent publications reveals a distinct trajectory toward efficient computational representations in machine learning, with dual emphases on theoretical rigor and practical implementation. His 2025 work on bitstring representations demonstrates breakthroughs in Bayesian computation, while the 2024 ICLR publication establishes new paradigms for mixture model learning through squaring operations. Sladek operates within Professor Arno Solin's research ecosystem at Aalto University, focusing on probabilistic machine learning systems. The group maintains active participation in premier venues including ICLR and Proceedings of Machine Learning Research, with emphasis on bridging statistical theory with artificial intelligence applications.

• Bayesian Optimization
• Machine Learning
• Artificial Intelligence
• +3 more

Julien Martinelli is a Research Fellow in the Department of Computer Science at Aalto University, Finland. He actively contributes to the research group led by Professor Harri Lähdesmäki, focusing on advanced methodologies in machine learning and optimization with practical applications across scientific domains. Research Interests: Martinelli specializes in Bayesian optimization frameworks, particularly addressing challenges with nuisance parameters, multi-fidelity data sources, and contextual variable selection. His work bridges theoretical machine learning with real-world applications including chemical reaction network inference and model interpretability. Key contributions involve developing robust optimization algorithms that handle unreliable information sources and unknown domain shifts. Publication Trends: Recent work (2022-2025) demonstrates consistent innovation in Bayesian optimization techniques, with increasing emphasis on transfer learning capabilities and human-AI collaboration through preferential feedback systems. His publications reveal strong interdisciplinary connections between computer science, chemistry, and systems biology, particularly through the Reactmine algorithm for chemical network inference. Scientific Awards: No awards or fellowships were documented in the provided information. Advising and Grants: No details regarding graduate student supervision, research grants, or funding sources were mentioned in the available materials. Labs and Teams: Martinelli operates within Professor Harri Lähdesmäki's research group at Aalto University, collaborating extensively with Samuel Kaski and international researchers on cutting-edge machine learning projects.

• Machine Learning
• Probabilistic Modeling
• User Modeling

Alex Henry Benjamin Hämäläinen is a Doctoral Researcher at Aalto University, affiliated with the Probabilistic Machine Learning research group under Professor Samuel Kaski. His work focuses on developing differentiable user models through advanced probabilistic machine learning techniques. His research spans Machine Learning, Probabilistic Modeling, and User Modeling, with emphasis on creating adaptive user simulations using differentiable programming. He investigates uncertainty quantification in behavioral prediction systems and applies reinforcement learning to personalize user interactions in AI-driven environments. His publication at UAI 2023 demonstrates expertise in bridging differentiable methods with user modeling, contributing to subfields like probabilistic inference and behavior prediction. This work reflects broader trends in interpretable AI and human-centered machine learning. As an active member of the Probabilistic Machine Learning group, he collaborates on cutting-edge research in Bayesian inference and scalable user modeling frameworks.

• Bayesian Statistics
• Probabilistic Machine Learning
• Computational Modeling
• +3 more

Osvaldo Antonio Martin is a Research Fellow at Aalto University's Department of Computer Science , affiliated with the Probabilistic Machine Learning research group under Professor Aki Vehtari . His work bridges Bayesian statistical methods, computational modeling, and Python-based probabilistic programming frameworks. Research Interests Bayesian inference and probabilistic programming Statistical software development Computational ecology and biophysics Data-driven modeling for environmental and biological systems Recent Contributions include the development of tools like PyMC and Bambi , which democratize Bayesian modeling in Python. His publications span disciplines from machine learning to molecular evolution, reflecting interdisciplinary applications of Bayesian methods. Publications Focus on probabilistic programming frameworks Applications in environmental systems and protein biophysics Theoretical work on Bayesian inference and prior elicitation Statistical software development for scientific domains

• Machine Learning
• Discrete Mathematics
• Information Theory
• +3 more

Antti Pöllänen is a Doctoral Researcher at the Department of Computer Science, Aalto University. His research spans interdisciplinary areas connecting machine learning, discrete mathematics, and information theory. Role: Doctoral Researcher Institution: Aalto University Department: Computer Science Email: antti.pollanen@aalto.fi Research focuses on: Identifiable causal inference with noisy treatment Geometric packing problems in discrete mathematics Locally repairable codes for distributed storage systems Publications since 2016 demonstrate expertise in theoretical computer science and mathematical optimization. Collaborates with researchers including Pekka Marttinen and Camilla Hollanti.

Alexandru PalerView profile

Associate Professor

• Quantum Computing
• Quantum Software
• Quantum Circuit Compilation
• +2 more

Alexandru Paler serves as an Associate Professor in the Department of Computer Science at Aalto University, Finland, where he leads research in quantum software development. His work focuses on designing compilers and optimization frameworks for quantum circuits, with emphasis on quantum error correction implementation and fault-tolerant quantum computing systems. Based in Espoo at Konemiehentie 2, he maintains active research collaborations through the university's quantum computing initiatives. Dr. Paler's research spans quantum circuit compilation, quantum error correction (particularly surface codes and QLDPC codes), and quantum software optimization. His team develops high-performance quantum compilers for neutral atom architectures and modular superconducting systems, addressing critical challenges in resource estimation and fault tolerance. The Quantum Operating Systems (QUANTUM) research group he contributes to explores scalable quantum software frameworks that bridge theoretical algorithms with practical hardware constraints, with significant work on graph-state compilation and reinforcement learning for circuit optimization. Analysis of his 15 most recent publications (2023-2025) reveals concentrated efforts in quantum compiler design, error correction scalability, and hardware-aware quantum software. Key trends include machine learning applications for decoder optimization, novel approaches to measurement-free error correction, and queuing theory models for fault-tolerant circuit analysis. His work consistently addresses the practical barriers to large-scale quantum computing through compiler innovations and resource-efficient circuit design. Dr. Paler actively participates in the Quantum Operating Systems (QUANTUM) research group within Aalto's Department of Computer Science, focusing on Algorithms and Theoretical Computer Science. This team develops quantum software infrastructure for next-generation quantum hardware, with current projects including Pandora (ultra-large-scale circuit compilation), quantum circuit caching mechanisms, and standardized cell approaches for neutral atom systems. Their research directly supports the transition from theoretical quantum algorithms to executable, error-resilient quantum programs.

Cristian A. Galvis FlorezView profile

Researcher

• Quantum Machine Learning
• Quantum Algorithms
• Quantum State Tomography
• +7 more

Cristian A. Galvis Florez is a doctoral researcher at Aalto University's School of Electrical Engineering, supervised by Professor Simo Särkkä. As part of the QuantLearn project funded by the Research Council of Finland, he explores quantum machine learning algorithms with applications in computational science. His academic journey spans Colombia, Oxford, and Finland, bridging quantum mechanics and machine learning. Education: Master’s in quantum computing, PhD ongoing at Aalto University Research Focus: Quantum state tomography, Gaussian process algorithms, and NISQ device optimization His work integrates quantum computing with machine learning , emphasizing probabilistic modeling and quantum hardware implementation . Recent publications highlight advancements in quantum-assisted regression and state estimation techniques . The QuantLearn project underpins his research, developing algorithms with potential to revolutionize computational domains. His publications demonstrate cross-disciplinary expertise in quantum theory and statistical modeling , leveraging low-rank Gaussian processes for quantum applications.

• Adaptation
• Concept drift
• Evolving data streams
• +3 more

Dr. Indrė Žliobaitė is a researcher in the Department of Computer Science at Aalto University. Her work spans interdisciplinary domains, combining computational methods with ecological and biomedical applications. Adaptation Concept drift Evolving data streams Change detection Predictive analytics Discrimination aware data mining Her research includes modeling respiratory motion for medical applications, analyzing fossil records for paleoecological insights, and developing energy-efficient computing architectures. Publications highlight applications of Bayesian models, mobility trace analysis, and approximate computing techniques.

• Machine Learning
• Robotics
• Autonomous Driving
• +3 more

Jatan Shrestha is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation. His research focuses on applying machine learning techniques to solve complex problems in robotics and autonomous systems. Department: Electrical Engineering and Automation Email: jatan.shrestha@aalto.fi Research Interests: Jatan's work spans machine learning, robotics, and optimization. Key areas include autonomous driving algorithms, multi-agent trajectory planning, visibility-aware navigation, and GPU-accelerated computing for real-time robotic applications. Publication Trends: His research output demonstrates expertise in applying machine learning to hardware optimization (2024), advancing autonomous driving algorithms in dense traffic scenarios (2023), and developing GPU-based optimization frameworks for robotic navigation (2021-2022).

• Bayesian Inference
• Neural Networks
• Statistical Learning
• +6 more

Jorge Loría is a Postdoctoral Researcher at the Department of Computer Science within Aalto University . His research focuses on Bayesian inference, neural networks, and statistical learning, particularly exploring probabilistic modeling with heavy-tailed distributions and uncertainty quantification. Recent work includes advancements in posterior inference for Bayesian neural networks with unbounded variance priors, kernel methods for deep learning, and applications of α-stable distributions in regression and neural processes. His publications span conferences like ICLR and journals such as Statistics and Computing , with a thematic emphasis on robust statistical models and causal inference frameworks. He has not been explicitly associated with teaching roles, formal student advisement, or scientific prizes. Contact details include the email jorge.loria@aalto.fi .

Jouko LampinenView profile

Professor

• Machine Learning
• Bayesian Statistics
• Neural Networks
• +4 more

Jouko Lampinen serves as the Dean of the School of Science (SCI) at Aalto University, Finland, overseeing academic and research operations across the institution. His professional contact includes the dean-sci@aalto.fi email address and phone number +358505604827. Lampinen maintains an active research profile in computational information technology while fulfilling his administrative leadership role, with expertise grounded in advanced algorithmic and statistical methodologies. His research spans machine learning, Bayesian statistics, neural networks, and their applications in brain imaging (fMRI/MEG) and computer vision. Key interests include probabilistic modeling for emotion recognition, object detection in autonomous systems, and medical diagnostics. His work addresses critical challenges in reproducibility, scalability, and interpretation of complex models, bridging theoretical machine learning with practical neuroscience and robotics applications. This interdisciplinary focus demonstrates consistent innovation from the late 1990s through 2018. Analysis of his recent publications reveals a dominant trend in applying Bayesian methods and neural networks to neuroimaging data, with significant contributions to emotion processing algorithms, brain-computer interfaces, and point cloud analysis for autonomous vehicles. His scholarly output shows increasing emphasis on real-world validation of computational models, particularly in medical diagnostics and human-computer interaction contexts, while maintaining foundational work in statistical learning theory. No scientific awards or honors were documented in the provided information. Details regarding student mentorship, grant funding, or specific research teams/labs are absent from the source material, though his deanship implies strategic oversight of research infrastructure within Aalto University's School of Science.

Jussi RintanenView profile

Associate Professor

• Artificial Intelligence
• Automated Planning
• Constraint-Based Search
• +3 more

Jussi Rintanen is an Associate Professor at the Department of Computer Science , Aalto University . He is an internationally leading researcher in constraint-based planning and decision-making , with a focus on applying AI technologies to automating software production and synthesis of intelligent systems . Helsinki University of Technology (Doctoral Degree, 1997) Albert-Ludwigs-University Freiburg (Venia Legendi, 2005) National ICT Australia / Australian National University (Academic Positions) His research spans automated planning , SAT solving , temporal planning , and constraint-based search methods . Recent work highlights symmetry-breaking constraints , partial observability planning , and acyclicity encodings in SAT frameworks. The 15 most recent articles reflect trends in AI planning , graph algorithms , and formal verification , with applications in software synthesis , temporal reasoning , and uncertainty handling . His methodologies integrate integer programming , SMT encodings , and propositional logic .

• 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.