Ameer Haj-Ali
- Electrical and Electronic Engineering
- Cellular and Molecular Neuroscience
- Hardware and Architecture top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Co-authors
- Shahar KvatinskyRotem Ben-HurNimrod WaldRonny RonenDebjyoti BhattacharjeeIon StoicaKrste AsanovićQijing Huang
- Topics
- Advanced Memory and Neural Computing (6 papers)Ferroelectric and Negative Capacitance Devices (5 papers)Parallel Computing and Optimization Techniques (4 papers)
- Journals
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and SystemsIEEE Transactions on Circuits and Systems I Regular PapersIEEE Micro
- Partner nations
- IsraelUnited StatesEcuador
In The Last Decade
Ameer Haj-Ali
10 papers receiving 259 citations
Peers
Comparison fields: 5 of 32
- Electrical and Electronic Engineering 197
- Cellular and Molecular Neuroscience 67
- Hardware and Architecture 64
- Artificial Intelligence 47
- Computer Networks and Communications 46
Countries citing papers authored by Ameer Haj-Ali
This map shows the geographic impact of Ameer Haj-Ali's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ameer Haj-Ali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ameer Haj-Ali more than expected).
Fields of papers citing papers by Ameer Haj-Ali
This network shows the impact of papers produced by Ameer Haj-Ali. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ameer Haj-Ali. The network helps show where Ameer Haj-Ali may publish in the future.
Co-authorship network of co-authors of Ameer Haj-Ali
This figure shows the co-authorship network connecting the top 25 collaborators of Ameer Haj-Ali. A scholar is included among the top collaborators of Ameer Haj-Ali based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ameer Haj-Ali. Ameer Haj-Ali is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning | 5 |
| 2 | 10 | |
| 3 | Ansor: Generating high-performance tensor programs for deep learning | 24 |
| 4 | Deep Reinforcement Learning in System Optimization. | 2 |
| 5 | 76 | |
| 6 | 12 | |
| 7 | 16 | |
| 8 | 39 | |
| 9 | 53 | |
| 10 | 29 |
About Ameer Haj-Ali
Ameer Haj-Ali is a scholar working on Computational Mathematics, Hardware and Architecture and Information Systems, having authored 10 papers that have together received 266 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (6 papers), Ferroelectric and Negative Capacitance Devices (5 papers) and Parallel Computing and Optimization Techniques (4 papers). The work is most often cited by research in Hardware and Architecture (64 citations), Computational Mathematics (5 citations) and Cellular and Molecular Neuroscience (67 citations). Ameer Haj-Ali has collaborated with scholars based in Israel, United States and Ecuador. Frequent co-authors include Shahar Kvatinsky, Rotem Ben-Hur, Nimrod Wald, Ronny Ronen, Debjyoti Bhattacharjee, Ion Stoica, Krste Asanović, Qijing Huang, John Wawrzynek and William S. Moses. Their work appears in journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on Circuits and Systems I Regular Papers and IEEE Micro.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.