“I took a JavaScript class with Dr. Ozturk, and it was one of the most enjoyable classes I have ever taken. Dr. Ozturk is very methodical, and his explanations are very clear and precise. He is very passionate and enthusiastic about teaching and Computer Science. He always challenges his students to explore topics beyond the class he teaches. He introduced me to Data Science, and I felt in love with the subject. I am very grateful to him for facilitating my admission to the Machine Learning program at GaTech. ”
About
Data Scientist with experience in
• Machine Learning (scikit-learn),
• Natural…
Activity
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It has been a very special year, and I am very proud of the work we have done at the frontier of AI. Thank you TIME ✨ Honored to be recognized and…
It has been a very special year, and I am very proud of the work we have done at the frontier of AI. Thank you TIME ✨ Honored to be recognized and…
Liked by Ozgur Ozturk
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On September 26, tune in for the webinar "Launch Your Career in AI" organized by NVIDIA 🗣 Title: Launch Your Career in AI 🔗…
On September 26, tune in for the webinar "Launch Your Career in AI" organized by NVIDIA 🗣 Title: Launch Your Career in AI 🔗…
Liked by Ozgur Ozturk
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On the importance of "Prompt Engineering". "AI will not take your jobs, but people who master AI will." Join us next week at Harvard Medical School…
On the importance of "Prompt Engineering". "AI will not take your jobs, but people who master AI will." Join us next week at Harvard Medical School…
Liked by Ozgur Ozturk
Experience
Education
Licenses & Certifications
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Neo4j Fundamentals
Neo4j
IssuedCredential ID bmVvNGotZnVuZGFtZW50YWxzLS1nb29nbGUtb2F1dGgyfDEwMzQwNzI4ODM5MjA4MTQyNTgyNQ
Volunteer Experience
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Teaching JavaScript, TypeScript
Local Meetups, Afterschool Programs, Conferences
- Present 10 years 2 months
Education
- Thought JavaScript to Eighth graders, in their weekly afterschool club for a semester.
- Volunteered to present "Introduction to TypeScript" in Atlanta Code Camp and Atlanta meetups of user groups. -
Technical Reviewer
Manning Publications Co.
- Present 11 years 4 months
Education
Reviewed
- Voice Applications for Alexa and Google Assistant
- Torch in Action
- Web Performance in Action
- Sails.js in Action
- Responsive WordPress Theming
Publications
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Feature Extraction and Similarity-based Analysis for Proteome and Genome Databases
The Ohio State University
PhD Thesis
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Vector Space Indexing for Biosequence Similarity Searches
Int’l Journal on Artificial Intelligence Tools
We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries and both (b) pruning…
We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries and both (b) pruning ability and (c) approximation quality for ε-range queries. Results for k-NN queries, which we present here, show that our proposed distances FD2 and WD2 (i.e. Frequency and Wavelet Distance functions for 2-grams) perform significantly better than the others. We then develop effective index structures, based on R-trees and scalar quantization, on top of transformed vectors and distance functions. Promising results from the experiments on real biosequence data sets are presented.
Other authors -
CoMRI: A Compressed Multi-Resolution Index Structure for Sequence Similarity Queries
IEEE Computer Society Bioinformatics Conference (CSB '03)
In this paper, we present CoMRI, compressed multiresolution index, our system for fast sequence similarity search in DNA sequence databases. We employ virtual bounding rectangle (VBR) concept to build a compressed, grid style index structure. An advantage of grid format over trees is subsequence location information is given by the order of corresponding VBR in the VBR list. Taking advantage of VBRs, our index structure fits into a reasonable size of memory easily. Together with a new optimized…
In this paper, we present CoMRI, compressed multiresolution index, our system for fast sequence similarity search in DNA sequence databases. We employ virtual bounding rectangle (VBR) concept to build a compressed, grid style index structure. An advantage of grid format over trees is subsequence location information is given by the order of corresponding VBR in the VBR list. Taking advantage of VBRs, our index structure fits into a reasonable size of memory easily. Together with a new optimized multiresolution search algorithm, the query speed is improved significantly. Extensive performance evaluations on human chromosome sequence data show that VBRs save 80%-93% index storage size compared to MBRs (minimum bounding rectangles) and new search algorithm prunes almost all unnecessary VBRs which guarantees efficient disk I/O and CPU cost. According to the results of our experiments, the performance of CoMRI is at least 100 times faster than MRS which is another grid index structure introduced very recently.
Other authors -
Effective Indexing and Filtering for Similarity Search in Large Biosequence Databases
IEEE International Symposium on Bioinformatics and Bioengineering (BIBE '03)
We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries, (b) pruning ability…
We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries, (b) pruning ability and (c) approximation quality for E-range queries. Results for k-NN queries, which we present here, show that our proposed distances FD2 and WD2 (i.e. Frequency and Wavelet Distance functions for 2-grams) perform significantly better than the others. We then develop effective index structures, based on R-trees and scalar quantization, on top of transformed vectors and distance functions. Promising results from the experiments on real biosequence data sets are presented.
Other authors
Languages
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English
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Turkish
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Beginner German
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Organizations
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Southern Data Science Conference
Program Committee Member
- Present
Recommendations received
3 people have recommended Ozgur
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The medical field isn't always known for being the quickest to embrace new tech, so it's genuinely thrilling to see medical researchers diving…
The medical field isn't always known for being the quickest to embrace new tech, so it's genuinely thrilling to see medical researchers diving…
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