Sergül Aydöre

Senior Applied Scientist at Amazon Web Services - Machine Learning Researcher

I am currently working as a Senior Applied Scientist at AWS AI since 2020. I am passionate about driving the adoption of generative AI in cloud computing and democratizing AI for a wide range of industries. My focus is on developing efficient, scalable, and trustworthy AI solutions. I have led several projects at AWS, including: Amazon Q for Business, Amazon Personalize, AWS Clean Rooms, Amazon Macie. My contributions to these services have involved synthetic data generation, robust machine learning techniques, differential privacy, and encoder-based language models. Recently, I have been working on mitigating hallucinations in our retrieval augmented generation framework within Amazon Q. This involves developing techniques to improve the accuracy and reliability of generated content. 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 , Dean Foster, Aaron Roth, and Michael Kearns.

Research

As a researcher, I am interested in robustness and generalization properties of machine learning models. Below are brief highlights of my research interests.

Robustness to Bad Samples
Robustness to Concept Drift
Robustness to Overfitting

Publications

  • S. Rho, S. Aydore , S. Tang, M. Kearns, A. Roth, Y. Wang, S. Wu, C. Archambeau , "Differentially Private Gradient Boosting on Linear Learners for Tabular Data", TSRML Workshop at NeurIPS 2022.

  • G. Vietri, C. Archambeau, S. Aydore , W. Brown, M. Kearns, A. Roth, A. Siva, S. Tang, S. Wu, "Private Synthetic Data for Multi-task Learning and Marginal Queries", In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

  • Y. Kaya, M. C. Zafar, S. Aydore , N. Rauschmayr, K. Kenthapadi, "Generating Distributional Adversarial Examples to Evade Statistical Detectors", In Proceedings of the 39th International Conference on Machine Learning (ICML 2022).

  • H. Akrami, A.A. Joshi, S. Aydore , R. M. Leahy, "Deep Quantile Regression for Uncertainty Estimation inUnsupervised and Supervised Lesion Detection", IPMI Special Issue, Machine Learning for Biomedical Imaging Journal (MELBA), 2021.

  • H. Akrami, A.A. Joshi, J. Li, S. Aydore , R. M. Leahy, "A Robust Variational Autoencoder using Beta Divergence", Journal of Knowledge-based Systems, 2021.

  • E. Erdemir, J. Bickford, L. Melis, S. Aydore, , ``Adversarial Robustness with Non-uniform Perturbations '', In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), accessible at arXIV: 2102.12002.

  • S. Aydore, W. Brown, M. Kearns, K. Kenthapadi, L. Melis, A.Roth, A. Siva [alphabetical order], ``Differentially Private Query Release Through Adaptive Projection'', In Proceedings of the 38th International Conference on Machine Learning (ICML 2021).

  • H. Akrami, A. A. Joshi, S. Aydore, R. M. Leahy, ``Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection'', International Conference on Information Processing in Medical Imaging, 2021.

  • 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

  • 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, US Patent Number: 11537902, granted in 2023.

  • S. Aydore , W. Brown, M. Kearns, K. Kenthapadi, L. Melis, A. Siva, A. Roth, ``Generating Relaxed Synthetic Data using Adaptive Projection'', filed by Amazon, US Patent Number: 11487765, granted in 2022.

  • S. Upadhyay, S. Aydore , T. Movva, ``Systems and methods for machine-learning augmented application monitoring'', filed by JP Morgan, US Patent number:11151473, granted in 2021.

  • Y. Kaya, M. B. Zafar, N. Rauschmayr, S. Aydore , K. Kenthapadi, ``Detecting suspicious user input distributions in deep neural networks'', filed by Amazon, December, 2021.

  • B. Can, S. Aydore , B. Coskun, W. Ding, Q. Cui, O. Y. Polyakov, ``Multi-cluster based semi-supervised anomaly detection'', filed by Amazon, December, 2021.

  • E. Erdemir, S. Aydore , M. Torkamani, J. Bickford, L. Melis, ``Adversarial Training of Deep Neural Networks Using Non-uniform Perturbations'', filed by Amazon, April, 2021.

MEDIA COVERAGE and blog POSTS

Awards

  • 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).

Teaching

  • Deep Learning taught in Spring 2019, Fall 2020 at the department of Electrical and Computer Engineering, Stevens Institute of Technology

Talks

  • Columbia Center of Artificial Technology Symposium, New York, NY, October, 2022.

  • 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.

Service

  • Co-organizer of workshop Synthetic Data for ML at NeurIPS 2022.

  • General Chair of Women in Machine Learning Workshop at NeurIPS 2022.

  • Co-leader of the breakout session "Does your model know what it doesn't know? Uncertainty Estimation and OOD Detection in DL" at WiML Un-workshop at ICML 2021.

  • 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.

Copyright © Sergul Aydore 2022