Welcome to my homepage :)

Spring: I'm teaching a course in Responsible AI. Please check out the syllabus here

I have a passion for understanding people's experiences, pain points, and frustrations, and finding solutions to enhance their productivity and satisfaction through technology. Combining this passion with my expertise in data science, machine learning, and user research, I strive to empower people of diverse backgrounds and abilities to lead a more accomplished life.

PhD in computer science from Boston University (specialized in AI, Responsible AI, Human Computation and Human Computer Interaction)
Advised by: Professor Margrit Betke and Professor Danna Gurari

PhD Thesis on "Accurate and Budget-Efficient Text, Image, and Video Analysis Systems Powered by the Crowd"

Principal Product Lead at Microsoft Azure Machine Learning (Area: Responsible AI)

Former data scientist at Rue Gilt Groupe (Rue La La * Gilt)

Experienced in machine learning, user research, human computer interaction, and human computation

Skills

Research Methods

Formative Qualitative Research (e.g. ethnography, contextual inquiry, workplace interviews, Jobs to Be Done, diary studies, competitive analysis etc.)
Evaluative Qualitative Research (e.g. in-person and remote usability & user testing, lab studies, RITE studies, intercept interviews, task analysis etc.)
Quantitative Research (e.g. survey research, experimental design, A/B tests, benchmarking, log data analysis)
Presentation of Concise Findings and Recommendations to Stakeholders

Machine Learning and Data Science

Regression, Classification, Feature Engineering, Decision Trees, Random Forest, SVM, Boosting, Regularization, PCA, Model Selection
Recommender Systems: Collaborative Filtering, Association Rules (Frequent Itemset Mining)
Deep Learning: Keras

Statistical Skills

Hypothesis Testing and Confidence Intervals
Factor Analysis
A/B Testing

Data Analysis Tools

Spark, Snowflake, Scikit-learn, Pandas, NumPy, Matplotlib, Matlab, Weka, NLTK

Programming Languages and Web Technologies

Scala, SQL, Java, Python, C, JavaScript, HTML, CSS

Publications

Google Research Internship

M. Sameki, A. Barua, and P. Paritosh, “QUEST: A Common Sense Approach to Annotating Q&A Content”, Conference on Collective Intelligence, Zurich, Switzerland, July 2018.

M. Sameki, A. Barua, and P. Paritosh, “Rigorously Collecting Commonsense Judgments for Complex Question-Answer Content”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), San Diego, USA, October 2015.

Usability Testing

W. Feng, M. Sameki and M. Betke, “Exploration of Assistive Technologies Used by People with Quadriplegia Caused by Degenerative Neurological Diseases”, International Journal of Human-Computer Interaction, 2017.

Human-in-the-loop Platforms, Human Computation

D. Gurari, K. He, B. Xiong, J. Zhang, M. Sameki, S. D. Jain, S. Sclaroff, M. Betke, and K. Grauman. "Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)." International Journal of Computer Vision (IJCV), 2018.

M. Sameki, T. Zhang, L. Ding, M. Betke, and D. Gurari, “Crowd-O-Meter: Predicting the Vulnerability of Crowd Workers to False News”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2017.

M. Sameki, K. Mays, L. Guo, P. Ishwar, and M. Betke, “An Algorithm for Budget-Optimized Crowdworker Allocation, Applied to the Sentiment Analysis of Political Tweets”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2017.

D. Gurari, K. He, M. Sameki, B. Xiong, J. Zhang, M. Betke, S. Sclaroff, S. Jain and K. Grauman, “Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)”, International Journal of Computer Vision, 2017.

J. Zhang, S. Ma, M. Sameki, S. Sclaroff, M. Betke, Z. Lin, X. Shen, B. Price, and R. Měch, "Salient Object Subitizing", International Journal of Computer Vision (IJCV), 2017.

M. Sameki, M. Gentil, D. Gurari, E. Saraee, E. Hasenberg, J. Y. Wong, and M. Betke, “CrowdTrack: Interactive Tracking of Cells in Microscopy Image Sequences with Crowdsourcing Support”, In Proc. of The Workshop on Human Computation for Image and Video Analysis (GroupSight), in conjunction with AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016. (Best Paper Runner-up Award)

M. Sameki, M. Gentil, K. Mays, L. Guo, and M. Betke, “Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016.

D. Gurari, M. Sameki, and M. Betke, “Investigating the Influence of Data Familiarity to Improve the Design of a Crowdsourcing Image Annotation System”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016.

D. Gurari, M. Sameki, Z. Wu, and M. Betke, “Mixing Crowd and Algorithm Efforts to Segment Objects in Biomedical Images”, The Interactive Medical Image Computation Workshop (IMIC), in conjunction with the conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.

M. Gentil, M. Sameki, D. Gurari, E. Saraee, E. Hasenberg, J. Y. Wong, and M. Betke, “Interactive Tracking of Cells in Microscopy Image Sequences”, The Interactive Medical Image Computation Workshop (IMIC), in conjunction with the conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.

M. Sameki, A. Barua, and P. Paritosh, “Rigorously Collecting Commonsense Judgments for Complex Question-Answer Content”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015

M. Sameki, D. Gurari, and M. Betke, “Predicting the Quality of Crowdsourced Image Drawings from Crowd Behavior”, AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015.

M. Sameki, D. Gurari, and M. Betke, “Characterizing Image Segmentation Behavior of the Crowd”, Collective Intelligence, 2015.

J. Zhang, S. Ma, M. Sameki, S. Sclaroff, M. Betke, Z. Lin, X. Shen, B. Price, and R. Mech, “Salient Object Subitizing”, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015.

D. Gurari, D. Theriault, M. Sameki, B. Isenberg, T. A. Pham, A. Purwada, P. Solski, M. Walker, C. Zhang, J. Y. Wong, and M. Betke, “How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-Experts, and Algorithms”, IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.

D. Gurari, D. Theriault, M. Sameki, and M. Betke, “How to Use Level Set Methods to Accurately Find Boundaries of Cells in Biomedical Images? Evaluation of Six Methods Paired with Automated and Crowdsourced Initial Contours”, The Interactive Medical Image Computation Workshop (IMIC), in conjunction with the conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014. (Best Paper Award for Innovative Idea)