جست‌وجوی استاد و پژوهشگر

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Sammie Kattمشاهده پروفایل

پژوهشگر

• Bayesian Reinforcement Learning
• Partially Observable Markov Decision Processes
• Robotics
• +۲ مورد دیگر

Sammie Katt serves as a Postdoctoral Researcher in the Department of Computer Science at Aalto University's School of Science, specializing in Bayesian Reinforcement Learning for robotics and decision-making under uncertainty. Their work addresses critical challenges in partially observable environments through algorithmic innovation and practical implementations. Dr. Katt's research centers on Bayesian approaches to reinforcement learning, with deep expertise in Partially Observable Markov Decision Processes (POMDPs). They develop scalable algorithms for uncertainty quantification, robot navigation, and real-time decision-making, bridging theoretical advances with robotic applications. Key contributions include BADDr for adaptive POMDP solutions and gym-gridverse for simulation benchmarking. Analysis of Katt's 14 publications (2012-2023) reveals three dominant trends: (1) Bayesian methods for efficient POMDP solving using Monte Carlo tree search and deep learning, (2) Robotics applications in motion prediction, target search, and scene reconstruction, and (3) Framework development for reproducible RL research. Their work consistently emphasizes computational efficiency and real-world applicability in uncertain environments.

• Computer Vision
• Natural Language Processing
• Deep Learning
• +۳ مورد دیگر

Rao Anwer serves as a Visitor (Faculty) and Lecturer in the Department of Computer Science, affiliated with the Laaksonen Jorma research group. Holding a Doctoral degree in Natural Sciences from the Autonomous University of Barcelona (awarded July 18, 2013), his expertise bridges computer vision, natural language processing, and multimodal AI systems with emphasis on cultural diversity and 3D scene understanding. His educational background includes: Doctoral degree in Natural Sciences, Autonomous University of Barcelona (2013) Anwer's research focuses on Computer Vision and Natural Language Processing , specializing in image segmentation, language modeling, and multimodal learning. He pioneers techniques for culturally diverse language evaluation, low-resource language processing, and 3D instance segmentation, addressing critical gaps in AI's ability to handle global linguistic diversity and complex spatial environments through deep feature extraction and self-distillation methods. Analysis of his 2024-2025 publications reveals a decisive shift toward multilingual and multimodal AI systems, particularly in evaluating large language models across 100 languages and developing open-vocabulary 3D vision techniques. His work consistently targets real-world applicability in low-resource settings while advancing theoretical frameworks for continual learning and unknown object discovery in dynamic environments. Anwer actively contributes to the Laaksonen Jorma research group and previously participated in the Academy of Finland-funded Finnish Centre of Excellence in Computational Inference Research (2015-2017), where he investigated deep feature learning and knowledge acquisition for image captioning systems using answer set programming and expert knowledge integration.

Tarja Kokkolaمشاهده پروفایل

پژوهشگر

• Neurodegenerative Diseases
• Alzheimer's Disease Biomarkers
• Type 2 Diabetes
• +۶ مورد دیگر

Tarja Kokkola, PhD, docent, serves as Research Coordinator at the Institute of Clinical Medicine within the School of Medicine at the University of Eastern Finland. She manages quality systems for the Clinical Research Centre, including two ISO 15189-accredited diagnostic laboratories, and contributes to development, validation, and quality assurance of clinical chemistry methodologies. Her research spans multiple critical areas in biomedical science, with particular emphasis on biomarkers for neurodegenerative diseases (especially Alzheimer's disease), type 2 diabetes mechanisms, and sirtuin-related pathways. She has developed expertise in cerebrospinal fluid diagnostics, metabolic phenotyping, and multi-omics integration approaches. Her work bridges clinical laboratory practice with translational research through extensive collaboration in major European research initiatives. Analysis of her 15 most recent publications (2023-2025) reveals a strong focus on Alzheimer's disease biomarkers (6 articles), type 2 diabetes mechanisms (5 articles), and methodological innovations in multi-omics data analysis (4 articles). Her research demonstrates consistent methodological rigor with emphasis on clinical applicability, particularly in differential diagnosis of neurodegenerative conditions and stratification of metabolic disease subtypes. As an active collaborator in the IMI DIRECT consortium and FinnGen project, she contributes to large-scale European efforts to understand disease heterogeneity and develop precision medicine approaches. Her publications consistently appear in high-impact journals across neurology, diabetes research, and analytical methodology fields. Dr. Kokkola also provides lectures and training in quality management, auditing, and risk assessment, supporting the next generation of clinical researchers. Her leadership in maintaining ISO 15189 accreditation for diagnostic laboratories demonstrates commitment to research quality standards essential for translational medicine.

• Biomechanical Simulation
• 3D Target Inference
• Human-Computer Interaction
• +۳ مورد دیگر

Chenyu Li is a Doctoral Researcher at Aalto University's Department of Computer Science in Espoo, Finland. Their work focuses on real-time 3D target inference through biomechanical simulation, combining computer science with human factors research. Their research interests span biomechanical simulation, 3D modeling, and human-computer interaction, with particular emphasis on real-time systems and machine learning applications in computer graphics. Recent publications highlight interdisciplinary work merging computational modeling with biomechanics. Current output includes one notable article presented at the CHI Conference on Human Factors in Computing Systems (CHI '24).