Highlights

  • Rapid completion of the AWS SageMaker proof-of-concept

Syeda Sakira Hassan

Postdoctoral Researcher, Aalto University

syeda (dot) s (dot) hassan (at) aalto (dot) fi

About me

I am an Artificial Intelligence (AI) Scientist with strong backgrounds in Machine Learning and Optimization. 10+ years of industrial and academic experiences in managing, interpreting and analyzing data in order to find patterns. Proficient knowledge in statistics, mathematics, and data analytics. Currently, I am active in the Sensor informatics and medical technology group, supervised by Professor Simo Särkkä, at the Aalto University.
I received my Ph.D. in Signal Processing from the Department of Signal Processing under Professor Heikki Huttunen at the Tampere University. I want to play a significant role in the process of creating and transferring knowledge through research and teaching. My professional objective is to pursue a research career in exploring the applications leveraged by artificial intelligence.

My research interests lie in the area of Machine learning, Deep learning, (Deep) Reinforcement learning, probabilistic inference and Biology and Health powered by AI.


Publications

  • Hassan, Syeda Sakira; Särkkä, Simo. Fourier–Hermite Dynamic Programming for Optimal Control. [To be appeared in] IEEE Transactions on Automatic Control, 2023.

  • Emzir, Muhammad F.; Loppi, Niki A.; Zhao, Zheng; Hassan, Syeda Sakira; Särkkä, Simo. Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors. 25th International Conference on Information Fusion (FUSION), pp. 1-7. IEEE, 2022.

  • Hassan, Syeda Sakira; Särkkä, Simo; García-Fernández, Ángel F. Temporal Parallelization of Inference in Hidden Markov Models. IEEE transactions on Signal Processing, vol-69, pp.4875-4887, 2021.

  • Yaghoobi, Fatemeh; Corenflos, Adrien; Hassan, Sakira; Särkkä, Simo. Parallel Iterated Extended and Sigma-Point Kalman Smoothers. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 5350-5354.

  • Hassan, Syeda Sakira; Huttunen, Heikki; Niemi, Jari; Tohka, Jussi. Bayesian receiver operating characteristic metric for linear classifiers. Pattern Recognition Letters, vol-128, pp.52-59, Dec, 2019.

  • Hassan, Syeda Sakira. Regularization in Machine Learning with Applications in Biology. Tampere University, May, 2019 (Doctoral thesis).

  • Hassan, Syeda Sakira; Mangayil, Rahul; Aho, Tommi; Yli-Harja, Olli; Karp, Matti. Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production. EMBEC & NBC 2017. pp-667-670, 2017. Springer, Singapore.

  • Hassan, Syeda Sakira; Ruusuvuori, Pekka; Latonen, Leena; Huttunen, Heikki. Flow cytometry-based classification in cancer research: a view on feature selection. Cancer informatics. 2015.

  • Urmersbach, Sara; Aho, Tommi; Alter, Thomas; Hassan, Syeda Sakira; Autio, Reija; Huehn, Stephan. Changes in global gene expression of Vibrio parahaemolyticus induced by cold-and heat-stress. BMC microbiology. vol-15,1. pp-229. 2015. BioMed Central.

  • Hassan, Syeda Sakira; Farhan, Muhammad; Mangayil, Rahul; Huttunen, Heikki; Aho, Tommi Bioprocess data mining using regularized regression and random forests. BMC systems biology. vol-7. pp.-1. S-5. 2013. BioMed Central.

  • Hassan, Syeda Sakira. Bioprocess optimization using machine learning methods. Tampere University of Technology, Oct, 2013 (MSc thesis).

Awards and contributions

  • 4 Years grant from Tampere University of Technology's Graduate school.
  • Glaston Hackathon. Nov., 2017. Description: Our team proposed an idea of measuring the glass strength using computer vision techniques. Story and Results

Certifications

  • Fundamentals of Accelerated Computing with CUDA C/C++, NVIDIA Deep Learning Institute
  • AWS SageMaker
  • AWS Personalization
  • Elements of AI
  • Artificial Intelligence: Reinforcement Learning in python
  • Deep Learning Specialization
    • Neural networks and deep learning
    • Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization
    • Structuring Machine LearningProjects
    • Convolutional Neural Networks
    • Sequence Models
  • TensorFlow in Practice Specialization
  • Data Science Natural Language Processing in Python