Applied Scientist at Amazon Web Services - Machine Learning Researcher
I am an applied scientist at Amazon Web Services focusing on privacy and security problems. Prior to AWS, I was an Assistant Professor at the department of Electrical and Computer Engineering of Stevens Institute of Technology. I received my PhD degree from the Signal and Image Processing Institute at the University of Southern California in 2014 under the supervision of Richard M Leahy . My PhD thesis was on Measuring Functional Connectivity of the Brain . After finishing my PhD, my research interests have changed towards machine learning algorithms. I was lucky to work with great researchers Gaël Varoquaux , Bertrand Thirion and Dean Foster.
As a researcher, I am interested in robustness and generalization properties of machine learning models. Below are brief highlights of my research interests.
H. Akrami, S. Aydore, A. A. Joshi, R. M. Leahy, ``Robust Variational Autoencoder for Tabular Data with Beta Divergence'', Uncertainty and Robustness in Deep Learning Workshop, ICML 2020, accesible at arXIV: 2006.08204.
H. Akrami, A. A. Joshi, J. Li, S. Aydore , R. M. Leahy, ``Brain Lesion Detection Using Variational Autoencoder and Transfer Learning'', IEEE International Symposium on Biomedical Imaging (ISBI), Iowa City, Iowa, USA, 2020.
S. Aydore , T. Zhu, D. Foster, ``Dynamic Local Regret for Non-convex Online Forecasting'', In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), December 2019, Vancouver, CANADA, accesible at arXIV: 1910.07927.
S. Aydore , B. Thirion, G. Varoquaux. ``Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data'', In Proceedings of the 36th International Conference on Machine Learning (ICML 2019), June 2019, Long Beach, CA, USA, accesible at arXIV: 1807.11718.
B. Wen, S. Aydore , ``ROMark: A Robust Watermarking System Using Adversarial Training'', Workshop on Machine Learning with Guarantees, NeurIPS 2019, Vancouver, CANADA, 2019, accessible at arXiV: 1910.01221.
T. Zhu, S. Aydore , ``Time-Smoothed Gradients for Online Forecasting'', Time Series Workshop, ICML 2019, Long Beach, CA, USA, 2019, accessible at arXiV: 1905.08850.
S. Aydore , L. Dicker, D. Foster.``A local Regret in Nonconvex Online Learning'', Continual Learning Workshop, NeurIPS 2018, Montreal, Canada, 2018, accessible at arXiv: 1811.05095.
S. Aydore , D. Pantazis, R. M. Leahy, ``A Note on the Phase Locking Value and its Properties,'' NeuroImage, February, 2013.
S. Aydore , S. Ashrafulla, A. A. Joshi, R. M. Leahy, ``A Measure of Connectivity in the Presence of Crosstalk,'' Asilomar Conference on Signals, Systems and Computers, November 3-6, 2013, Pacific Grove, CA, USA.
S. Aydore , D. Pantazis, R. M. Leahy, ``Phase Synchrony in Multivariate Gaussian Data with Applications to Cortical Networks'', IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2012.
S. Aydore , I. Sen, M. Kivanc Mihcak, Y. P. Kahya, ``Classification of Respiratory Signals by Linear Analysis,'' IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, USA, 2009.
S. Aydore , I. Sen, M. K. Mihcak, Y. P. Kahya, ``Classification of Wheeze in Respiratory Sounds by Linear Discriminant Method,'' IEEE Signal and Image Communications Applications, Antalya, Turkey, 2009.
S. Aydore , M. K. Mihcak, A. Akin, R. K. Ciftci, ``On Temporal Connectivity of PFC via Gauss-Markov Modeling of fNIRS Signals,'' IEEE Transactions on Biomedical Engineering, 2010.
PRE-PRINTS
H. Akrami, A.A. Joshi, J. Li, S. Aydore , R. M. Leahy, "Robust Variational Autoencoder", accessible at arXiv: 1905.09961
L. Chen, P. Gautier, S. Aydore , ``DropCluster: A Structured Dropout for Convolutional Neural Networks'', Invited Talk at Women in Machine Learning Workshop, NeurIPS 2019, December 2019, Vancouver, CANADA, accessible at arXiv:2002.02997.
INDUSTRY
S. Aydore , L. Melis, B. Coskun,``RVAE-MMD: Robust Variational Autoencoder with Beta and MMD divergences'', Amazon Machine Learning Conference, October, 2020.
S. Aydore , L. Dicker, K. Torkkola, D. Foster, ``Online Learning for a Non-convex Algorithm at Forecasting'', Amazon Machine Learning Conference, April, 2018.
S. Aydore , L. Razoumov, A. Mahani, ``Speeding up Gamma Fitting in Forecasting Systems'', Amazon Machine Learning Conference, May, 2017.
PATENTS
S. Aydore , B. Coskun, L. Melis, ``Detecting Anomalous Events from Categorical Data Using Autoencoders'', filed by Amazon, June, 2020.
MEDIA COVERAGE
AWS Machine Learning Research Award to support my research at Stevens, 2019.
ICML Diversity and Inclusion Award, 2019.
Google Cloud Platform Research Credits, 2019.
Ming Hsieh Institute Ph.D. Scholar (given to six outstanding senior Ph.D. students in Electrical Engineering Department at USC), 2013.
Best PhD Poster Award, The International Conference on Biomagnetism (BIOMAG), 2012.
National Publication Award from Brain Research Society in Turkey, 2010.
Opportunity Grant from EducationUSA funded by the Bureau of Educational and Cultural Affairs of the US Department of State, 2009.
Viterbi School of Engineering Doctoral Fellowship Award at University of Southern California, 2009.
Necmi Tanyolac Award from Biomedical Engineering Institute at Bogazici University (for contributions to biomedical engineering), 2009.
Graduate scholarship from TUBITAK (The Scientific and Technological Council of Turkey) (2007-2009).
Deep Learning taught in Spring 2019, Fall 2020 at the department of Electrical and Computer Engineering, Stevens Institute of Technology
Programming in Python taught in Fall 2019, Spring 2019 at the department of Electrical and Computer Engineering, Stevens Institute of Technology
Microsoft Research, New York, NY, October, 2019.
Delft Mechanical Engineering Talent Event, TU Delft, The Netherlands, October, 2019.
Amazon Web Services Science Meetings, New York, NY, September, 2019.
Ming Hsieh Institute Ph.D. Scholar (given to six outstanding senior Ph.D. students in Electrical Engineering Department at USC), 2013.
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, June, 2019
Montreal Women in Machine Learning and Data Science meetup, Montreal, CA, December, 2018
Invited Talk at Women in Machine Learning at NeurIPS, Montreal, CA, February, 2019
Bogazici University, Istanbul, Turkey, December, 2018
Sabanci University, Istanbul, Turkey, December, 2018
Acibadem University, Istanbul, Turkey, December, 2018
S2A Seminar at Telecom ParisTech, Paris, France, June 2018.
Neural Networks for Forecasting Demand, Deep Learning Summit, Boston, MA, May, 2018.
Organization of Human Brain Mapping, Annual Meeting, (OHBM 2014), Hamburg, Germany, May, 2014. ( 5% oral talk rate)
Cognitive Neurophysiology Laboratory, University of California Los Angeles, Los Angeles, CA, USA, May, 2014.
Laboratory for Intelligent Imaging and Neural Computing, Columbia University, New York, NY, USA, May 2014.
18th International Conference on Biomagnetism (BIOMAG 2012), Paris, France, August 2012.
Co-organizer of workshop "Synthetic Data Generation: Quality, Privacy, Bias" at ICLR 2021.
Co-leader with H. Akrami of the breakout session "Robust Machine Learning with Bad Training Data" at WiML Un-workshop at ICML 2020.
Area Chair for WiML workshop at NeurIPS 2019, NeurIPS 2020.
Reviewer for conferences NeurIPS ICML, ICLR, AISTATS
Contributions to open source machine learning Python Library scikit-learn
Reviewer for journals NeuroImage, PLOSOne, Mathematical Problems in Engineering
Member of Technical Program Committee for GlobalSIP Symposium on Big Data Analysis and Challenges in Medical Imaging, 2016.