About
I am a Ph.D. in Machine Learning with over seven years of applied research experience and…
Activity
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Excited to release Nemotron-4-340B. More details here: https://lnkd.in/ggMhe8XY
Excited to release Nemotron-4-340B. More details here: https://lnkd.in/ggMhe8XY
Liked by Mehrnaz Amjadi, Ph.D.
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I’m teaching a workshop at the Women in Tech Summit in Warsaw today. It’s the biggest conference for women in tech in Europe. #womenintech #nvidia
I’m teaching a workshop at the Women in Tech Summit in Warsaw today. It’s the biggest conference for women in tech in Europe. #womenintech #nvidia
Liked by Mehrnaz Amjadi, Ph.D.
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NVIDIA Accelerates Inference on Meta Llama 3 https://bit.ly/4b4yyck
NVIDIA Accelerates Inference on Meta Llama 3 https://bit.ly/4b4yyck
Posted by Mehrnaz Amjadi, Ph.D.
Experience
Education
Licenses & Certifications
Volunteer Experience
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Session Chair in Graph Mining
INFORMS
- 9 months
Science and Technology
Organizing a session in “INFORMS Annual Meeting 2019” (Oct 20-23, 2018- Seattle) entitled “Data Mining in Networks (Graph-based Data Mining)” under the “Data Mining” Cluster. This session focuses on Network/Graph algorithms with applications in various areas including but not limited to E-commerce, Economics, Computer Science, Sociology, Healthcare, etc.
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Journal Reviewer
Information Systems Research, MIS Quarterly, E-commerce Research Journals
Science and Technology
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Publications
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Boosted Embeddings for Time Series Forecasting
Conference on Machine Learning, Optimization, and Data Science
Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. In this paper, we propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs whose weights are…
Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. In this paper, we propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs whose weights are incrementally found in a greedy manner over iterations. In particular, we develop a new embedding architecture that improves the performance of many deep learning models on time series using Gradient boosting variant. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.
Other authorsSee publication -
Time Series Anomaly Detection with label-free Model Selection
arXiv:2106.07473
Anomaly detection for time-series data becomes an essential task for many data-driven applications fueled with an abundance of data and out-of-the-box machine-learning algorithms. In many real-world settings, developing a reliable anomaly model is highly challenging due to insufficient anomaly labels and the prohibitively expensive cost of obtaining anomaly examples. It imposes a significant bottleneck to evaluate model quality for model selection and parameter tuning reliably. As a result…
Anomaly detection for time-series data becomes an essential task for many data-driven applications fueled with an abundance of data and out-of-the-box machine-learning algorithms. In many real-world settings, developing a reliable anomaly model is highly challenging due to insufficient anomaly labels and the prohibitively expensive cost of obtaining anomaly examples. It imposes a significant bottleneck to evaluate model quality for model selection and parameter tuning reliably. As a result, many existing anomaly detection algorithms fail to show their promised performance after deployment. In this paper, we propose LaF-AD, a novel anomaly detection algorithm with label-free model selection for unlabeled times-series data. Our proposed algorithm performs a fully unsupervised ensemble learning across a large number of candidate parametric models. We develop a model variance metric that quantifies the sensitivity of anomaly probability with a bootstrapping method. Then it makes a collective decision for anomaly events by model learners using the model variance. Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models. We evaluate our algorithm against other state-of-the-art methods on a synthetic domain and a benchmark public data set.
Other authorsSee publication -
Dynamic Networks: Learning and Decision Making
University of Illinois at Chicago
Several daily phenomena around us can be modeled as time-evolving networks. Working with expressive and tractable models for the evolution of such networks can improve different prediction and decision-making tasks. While the literature has studied many approaches to model such networked phenomena partially, multiple gaps remain. This thesis is an effort to propose novel and scalable models and methods that capture temporal and spatial aspects of graph-structured data.
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Block-Structure Based Time-Series Models For Graph Sequences
arXiv:1804.08796
Although the computational and statistical trade-off for modeling single graphs, for instance, using block models is relatively well understood, extending such results to sequences of graphs has proven to be difficult. In this work, we take a step in this direction by proposing two models for graph sequences that capture: (a) link persistence between nodes across time, and (b) community persistence of each node across time. In the first model, we assume that the latent community of each node…
Although the computational and statistical trade-off for modeling single graphs, for instance, using block models is relatively well understood, extending such results to sequences of graphs has proven to be difficult. In this work, we take a step in this direction by proposing two models for graph sequences that capture: (a) link persistence between nodes across time, and (b) community persistence of each node across time. In the first model, we assume that the latent community of each node does not change over time, and in the second model we relax this assumption suitably. For both of these proposed models, we provide statistically and computationally efficient inference algorithms, whose unique feature is that they leverage community detection methods that work on single graphs. We also provide experimental results validating the suitability of our models and methods on synthetic and real instances.
Other authorsSee publication -
KATRec: Knowledge Aware aTtentive Sequential Recommendations
International Conference on Discovery Science
Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms. Despite recent progress, modeling short-term and long-term behavior of users in such systems is nontrivial and challenging. To address this, we present a solution enhanced by a knowledge graph called KATRec (Knowledge Aware aTtentive sequential Recommendations). KATRec learns the short and long-term interests of users by modeling their sequence of interacted items and…
Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms. Despite recent progress, modeling short-term and long-term behavior of users in such systems is nontrivial and challenging. To address this, we present a solution enhanced by a knowledge graph called KATRec (Knowledge Aware aTtentive sequential Recommendations). KATRec learns the short and long-term interests of users by modeling their sequence of interacted items and leveraging pre-existing side information through a knowledge graph attention network. Our novel knowledge graph-enhanced sequential recommender contains item multi-relations at the entity-level and users' dynamic sequences at the item-level. KATRec improves item representation learning by considering higher-order connections and incorporating them in user preference representation while recommending the next item. Experiments on three public datasets show that KATRec outperforms state-of-the-art recommendation models and demonstrates the importance of modeling both temporal and side information to achieve high-quality recommendations.
Other authorsSee publication -
Managing Adoption under Network Effects
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3383676
We consider the design of promotional pricing strategies to stimulate the adoption of a new product whose consumption features network effects. A key aspect we model is the public uncertainty in the perceived utility of the product across the market and hence the anticipated adoption level. We focus on strategies that naturally arise from two informational considerations: either the firm fully alleviates the perceptional uncertainty so that consumers make informed buying decisions, or induces…
We consider the design of promotional pricing strategies to stimulate the adoption of a new product whose consumption features network effects. A key aspect we model is the public uncertainty in the perceived utility of the product across the market and hence the anticipated adoption level. We focus on strategies that naturally arise from two informational considerations: either the firm fully alleviates the perceptional uncertainty so that consumers make informed buying decisions, or induces the consumers to make their adoption decisions blindly, i.e., based only on the prior public belief. For an uninformed firm, this public signaling of information is orchestrated by: a) providing a vanishingly small discount for early adoption in the case where it is beneficial to have the customers make informed purchase decisions, and b) providing a steep discount for early adoption in the case where it is beneficial to have the customers make their purchase decisions blindly. In the first case, most of the adoption takes place in the later informed period, while in the latter, all adoption takes place in the early blind period. We show that in the system limit where the products become more niche, i.e., the variance of the perception of the product across the target population is small, then inducing informed adoption results in a higher profit than blind adoption. On the other hand, if this variance is high, which is the case for products with a mass appeal, then inducing blind adoption results in a higher profit than informed adoption. Numerical analysis suggests that restricting to these two intuitive strategies incurs negligible loss relative to the optimal promotional pricing strategy.
Other authorsSee publication
Projects
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Directed Data Routing
- Present
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Spatial-Temporal Recommendation Systems
- Present
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Cloudbakers Github Repositories Predictive Analytics
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Target Buy It Again Recommendations
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Instacart Market Basket Analysis
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RecSys Challenge 2019
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"The goal of the challenge is to develop a session-based and context-aware recommender system using various input data to provide a list of accommodations that will match the needs of the user."
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Managing Adoption under Network Effects
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Block-Structure Based Time-Series Models For Graph Sequences
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Prescriptive Analytics for Store Flyer Design
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Honors & Awards
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AT&T Travel Award
T&T Labs Graduate Student Symposium
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MMLS Best Presentation and Poster Finalist
Midwest Machine Learning Symposium(MMLS)
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Graduate College Student Presenter Award
University of Illinois at Chicago Graduate College
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NSF Award
National Science Foundation (NSF)
NSF travel support for SIAM Data Mining(SDM) Doctoral Forum
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Ph.D. Fellowship
Department of Information and Decision Sciences, University of Illinois at Chicago.
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Ranked 1st among B.Sc. Students in Applied Mathematics
University of Tehran
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Member of National Mathematics Olympiad
University of Tehran
Languages
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English
Native or bilingual proficiency
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Persian
Native or bilingual proficiency
More activity by Mehrnaz
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I will be presenting and participating in panel with Charles Wuischpard and Scott Barneson at Google Cloud NEXT 2024 at Las Vegas on April 9 for…
I will be presenting and participating in panel with Charles Wuischpard and Scott Barneson at Google Cloud NEXT 2024 at Las Vegas on April 9 for…
Liked by Mehrnaz Amjadi, Ph.D.
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To all my fellow Iranians 🇮🇷, happy new year! I wish you the best in health, wealth, and success in this new year. #nowruz
To all my fellow Iranians 🇮🇷, happy new year! I wish you the best in health, wealth, and success in this new year. #nowruz
Liked by Mehrnaz Amjadi, Ph.D.
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