About Me
Hi, this is Burak. I am a PhD candidate at Boston University, working under the supervision of Prof. Ayse Coskun in PeacLab research group.
Broadly, I am interested in solving real-world problems using machine learning. My research focuses on designing and developing ML-based frameworks to optimize the performance of large-scale computing systems considering the explainability angle.
But hey, I’m not just about the tech wizardry—I’m also on the prowl for the juiciest updates in the startup universe. My tech stack and some stuff that I have experience on:
- Python, C++, Java (I am not a fan of Java)
- Tensorflow, Pytorch
- Dask, Scrapy
- AWS (ECS, Lambda, Sagemaker, Step Functions, GraphQL, ECR)
- Flask, Django, Docker
- Learning Node.js, React, and Typescript - Only when I have time
Publications
[1] Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim Brandt, Brian Kulis, and Ayse K. Coskun Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems. To be appear in ACM The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), November 2023.
[2] Burak Aksar, Efe Sencan, Benjamin Schwaller, Vitus J. Leung, Jim Brandt, Brian Kulis, and Ayse K. Coskun. Towards Practical Machine Learning Frameworks for Performance Diagnostics in Supercomputers. In ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), June 2023
[3] Burak Aksar, Yara Rizk, Tathagata Chakraborti, Kartik Talamadupula. TESS: A Multi‑intent Parser for Conversational Multi‑Agent Systems with Decentralized Natural Language Understanding Models”. Submitted to ACL 2023.
[4] Tathagata Chakraborti, Yara Rizk, Vatche Isahagian, Burak Aksar, Francesco Fuggitti. From Natural Language to Workflows: Towards Emergent Intelligence in Robotic Process Automation. In International Conference on Business Process Management (BPM), July 2022.
[5] Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim Brandt, Brian Kulis, and Ayse K. Coskun. ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems. In IEEE International Conference on Cluster Computing (Cluster), July 2022. Code
[6] Yijia Zhang, Burak Aksar, Omar Aaziz, Benjamin Schwaller, Jim Brandt, Vitus J. Leung, Manuel Egele, and Ayse K. Coskun. Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation. In High Performance Extreme Computing Conference (HPEC), Sept. 2021.
[7] Burak Aksar, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim Brandt, Manuel Egele, and Ayse K. Coskun. E2EWatch: An End-to-end Anomaly Diagnosis Framework for Production HPC Systems. In International European Conference on Parallel and Distributed Computing (Euro-Par), August 2021. Code
[8] Burak Aksar, Yijia Zhang , Emre Ates, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim Brandt, Manuel Egele, and Ayse K. Coskun. Proctor: A Semi-Supervised Performance Anomaly Diagnosis Framework for Production HPC Systems. In International Supercomputing Conference (ISC-HPC), June 2021. Code
[9] Emre Ates, Burak Aksar, Vitus J. Leung, and Ayse K. Coskun. Counterfactual Explanations for Multivariate Time Series. In Proceedings of IEEE International Conference on Applied Artifical Intelligence (ICAPAI), May 2021. Code
[10] Emre Ates, Yijia Zhang, Burak Aksar, Jim Brandt, Vitus J. Leung, Manuel Egele, and Ayse K. Coskun. HPAS: An HPC Performance Anomaly Suite for Reproducing Performance Variations. In International Conference on Parallel Processing (ICPP 2019), pp. 1-10, Aug. 2019. Code
Experience
IBM AI Research
Research Scientist Intern
May 2022 - Aug. 2022
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Researched the NLP explainability techniques for conversational multi‑agent systems (CMAS)
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Evaluated existing NLP explainability frameworks and prototyped a new framework for a CMAS
IBM AI Research
Research Scientist Intern
June 2021 - Sept. 2021
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Developed a multi-intent classification algorithm with a heuristic parser and natural language understanding models for a multi-chatbot system
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Conducted tests and deployed the parser to the production pipeline
Sandia National Labs
Machine Learning SDE Intern
July 2020 - Sept. 2020
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Designed and developed an ML pipeline for run-time performance anomaly detection
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Deployed the pipeline to a production computing cluster with 1,488 nodes
Sandia National Labs
Machine Learning Research Intern
May 2019 - Aug. 2019
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Researched on hardware & software level performance variations in HPC systems
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Developed LSTM-based machine learning model to forecast time-series based performance metrics in HPC production systems
Education
Boston University
PhD Computer Engineering
2018 - 2022
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GPA: 3.8
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Coursework: Machine Learning, Deep Learning, Learning from Data, Fairness in AI, Advanced Data Structures
Sabanci University
BSc Electronics Engineering
2013 - 2018
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GPA: 3.9
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High Honor(100%) Scholarship