Frances-A Feedback
+ ++We would like to hear from users of the Frances-A tool. +Please fill out one of the following forms based on your use of Frances-A. +Alternatively, you can send us an email. +
+ + ++ +
+ +diff --git a/_data/alumni.yml b/_data/alumni.yml index 8fbd5ad..3cc9564 100644 --- a/_data/alumni.yml +++ b/_data/alumni.yml @@ -1,4 +1,15 @@ -# Postdoctoral and PhD Graduates: +# Postdoctoral Graduates: +- name: Dr. Ali Ghanbari + status: Postdoctoral Fellow + email: alig@iastate.edu + site: https://ali-ghanbari.github.io/ + img: ali.jpg + +- name: Dr. Breno Dantas Cruz + status: Postdoctoral Fellow + email: bdantasc@iastate.edu + site: https://www.cs.iastate.edu/people/breno-dantas-cruz + img: breno.jpg - name: Dr. Hoan A. Nguyen status: Postdoctoral Fellow @@ -6,18 +17,44 @@ site: https://sites.google.com/site/nguyenanhhoan/home img: hnguyen.jpg -- name: Hamid Bagheri - status: PhD - email: hbagheri@iastate.edu - site: http://www.cs.iastate.edu/people/hamid-bagheri - img: hbagheri.jpg - - name: Dr. Zhen Yu status: Postdoctoral Fellow email: yuzhen3301@gmail.com site: http://design.cs.iastate.edu img: zyu.jpeg +# PhD Graduates: + +- name: Dr. Mohammad Wardat + status: PhD Summer'23 + email: wardat@iastate.edu + site: https://www.cs.iastate.edu/people/mohammad-wardat + img: wardat.jpg + +- name: Dr. Sumon Biswas + status: PhD Spring'22, MS Spring'21 + email: sumon@iastate.edu + site: https://sumonbis.github.io/ + img: sbiswas.jpg + +- name: Dr. Rangeet Pan + status: PhD Spring'22 + email: rangeet@iastate.edu + site: https://rangeetpan.github.io + img: pan.jpg + +- name: Dr. Samantha Khairunnesa + status: PhD Summer'21, MS Fall'17 + email: sammy@iastate.edu + site: https://www.linkedin.com/in/samantha-syeda + img: skhairunnesa.jpg + +- name: Dr. Hamid Bagheri + status: PhD Spring'21 + email: hbagheri@iastate.edu + site: http://www.cs.iastate.edu/people/hamid-bagheri + img: hbagheri.jpg + - name: Dr. Md. Johirul Islam status: PhD Summer'20, MS Fall'19 netid: mislam @@ -116,6 +153,12 @@ # B.S. Graduates +- name: Huaiyao Ma + status: B.S. Fall'22 + email: huaiyao@iastate.edu + site: https://www.info.iastate.edu/individuals/info/294581/Ma-Huaiyao + img: huaiyao.jpg + - name: Nathaniel M Wernimont status: B.S. Spring'20 email: natew@iastate.edu diff --git a/_data/grants.yml b/_data/grants.yml index 1e0f50c..dc5f9b2 100644 --- a/_data/grants.yml +++ b/_data/grants.yml @@ -596,3 +596,81 @@ Data-driven discoveries are permeating critical fabrics of society. Unreliable discoveries lead to decisions that can have far-reaching and catastrophic consequences on society, defense, and the individual. Thus, the dependability of data-science lifecycles that produce discoveries and decisions is a critical issue that requires a new holistic view and formal foundations. This project will establish the Dependable Data Driven Discovery (D4) Institute at Iowa State University that will advance foundational research on ensuring that data-driven discoveries are of high quality. The activities of the D4 Institute will have a transformative impact on the dependability of data-science lifecycles. First, the problem definition itself will have a significant impact by helping future innovations beyond academia. While the notion of dependability is well-studied in the computer-systems literature, challenges in data science push the boundary of existing knowledge into the unknown. This institute's work will define D4, and increase data science's benefit to society by providing a transformative theory of D4. The second impact will come from the process of shared vocabulary development facilitated by this institute, and its result that would encourage experts across TRIPODS disciplines and domain experts to collaborate on common goals and challenges. Third, the institute will set research directions for D4 by providing funding for foundational research, which will have a separate set of impacts. Fourth, the institute will facilitate transdisciplinary training of a diverse cadre of data scientists through activities such as the Midwest Big Data Summer School and the D4 workshop. The project will advance the theoretical foundations of data science by fostering foundational research to enable understanding of the risks to the dependability of data-science lifecycles, to formalize the rigorous mathematical basis of the measures of dependability for data science lifecycles, and to identify mechanisms to create dependable data-science lifecycles. The project defines a risk to be a cause that can lead to failures in data-driven discovery, and the processes that plan for, acquire, manage, analyze, and infer from data collectively as the data-science lifecycle. For instance, an inference procedure that is significantly expensive can deliver late information to a human operator facing a deadline (complexity as a risk); if the data-science lifecycle provides a recommendation without an uncertainty measure for the recommendation, a human operator has no means to determine whether to trust the recommendation (uncertainty as a risk). Compared to recent works that have focused on fairness, accountability, and trustworthiness issues for machine learning algorithms, this project will take a holistic perspective and consider the entire data-science lifecycle. In phase I of the project the investigators will focus on four measures: complexity, resource constraints, uncertainty, and data freshness. In developing a framework to study these measures, this work will prepare the investigators to scale up their activities to other measures in phase II as well as to address larger portions of the data-science lifecycle. The study of each measure brings about foundational challenges that will require expertise from multiple TRIPODS disciplines to address. +- key: grant-nsf-2120448 + agency: NSF + primary: true + title: "Collaborative Research: CCRI: ENS: Boa 2.0: Enhancing Infrastructure for Studying Software and its Evolution at a Large Scale" + start_date: 2021-10-01 #Roughly + url: "https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120448&HistoricalAwards=false" + amount: $824,474.00 + PI: Hridesh Rajan + coPIs: + end_date: 2024-09-30 #Roughly + abstract: > + In today’s software-centric world, ultra-large-scale software repositories, e.g. GitHub, with hundreds of thousands of projects each, are the new library of Alexandria. They contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important research hypotheses. However, the current barrier to entry is prohibitive and only a few with well-established infrastructure and deep expertise can attempt such ultra-large-scale analysis. Necessary expertise includes: programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization. The need to have expertise in these four different areas significantly increases the cost of scientific research that attempts to answer research questions involving ultra-large-scale software repositories. As a result, experiments are often not replicable, and reusability of experimental infrastructure low. Furthermore, data associated and produced by such experiments is often lost and becomes inaccessible and obsolete, because there is no systematic curation. Last but not least, building analysis infrastructure to process ultra-large-scale data efficiently can be very hard. + + This project will continue to enhance the CISE research infrastructure called Boa to aid and assist with such research. This next version of Boa will be called Boa 2.0 and it will continue to be globally disseminated. The project will further develop the programming language also called Boa, that can hide the details of programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization from the scientists and engineers and allow them to focus on the program logic. The project will also enhance the data mining infrastructure for Boa, and a BIGDATA repository containing millions of open source project for analyzing ultra-large-scale software repositories to help with such experiments. The project will integrate Boa 2.0 with the Center for Open Science Open Science Framework (OSF) to improve reproducibility and with the national computing resource XSEDE to improve scalability. The broader impacts of Boa 2.0 stem from its potential to enable developers, designers and researchers to build intuitive, multi-modal, user-centric, scientific applications that can aid and enable scientific research on individual, social, legal, policy, and technical aspects of open source software development. This advance will primarily be achieved by significantly lowering the barrier to entry and thus enabling a larger and more ambitious line of data-intensive scientific discovery in this area. +- key: grant-nsf-2223812 + agency: NSF + primary: true + title: "SHF:Small: More Modular Deep Learning" + start_date: 2022-10-01 #Roughly + url: "https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223812&HistoricalAwards=false" + amount: $580,000.00 + PI: Hridesh Rajan + coPIs: + end_date: 2025-09-30 #Roughly + abstract: > + This project will study a class of machine learning algorithms known as deep learning + that has received much attention in academia and industry. Deep learning has a large + number of important societal applications, from self-driving cars to question-answering + systems such as Siri and Alexa. A deep learning algorithm uses multiple layers of + transformation functions to convert inputs to outputs, each layer learning higher-level + of abstractions in the data successively. The availability of large datasets has made it + feasible to train deep learning models. Since the layers are organized in the form of a + network, such models are also referred to as deep neural networks (DNN). While the jury + is still out on the impact of deep learning on the overall understanding of software's + behavior, a significant uptick in its usage and applications in wide-ranging areas and + safety-critical systems, e.g., autonomous driving, aviation system, medical analysis, + etc., combine to warrant research on software engineering practices in the presence of + deep learning. One challenge is to enable the reuse and replacement of the parts of a + DNN that has the potential to make DNN development more reliable. This project will + investigate a comprehensive approach to systematically investigate the decomposition of + deep neural networks into modules to enable reuse, replacement, and independent evolution + of those modules. A module is an independent part of a software system that can be tested, + validated, or utilized without a major change to the rest of the system. Allowing the + reuse of DNN modules is expected to reduce energy and data intensive training efforts + to construct DNN models. Allowing replacement is expected to help replace faulty + functionality in DNN models without needing costly retraining steps. + + The preliminary work of the investigator has shown that it is possible to decompose fully + connected neural networks and CNN models into modules and conceptualize the notion of + modules. The main goals and the intellectual merits of this project are to further expand + this decomposition approach along three dimensions: (1) Does the decomposition approach + generalize to large Natural Language Processing (NLP) models, where a huge reduction in CO2e + emission is expected? (2) What criteria should be used for decomposing a DNN into modules? + A better understanding of the decomposition criteria can help inform the design and + implementation of DNNs and reduce the impact of changes. (3) While coarse-grained + decomposition has worked well for FCNNs and CNNs, does a finer-grained decomposition of + DNNs into modules connected using AND-OR-NOT primitives a la structured decomposition has + the potential to both enable more reuse (especially for larger DNNs) and provide deeper + insights into the behavior of DNNs? The project also incorporates a rigorous evaluation plan + using widely studied datasets. The project is expected to broadly impact society by informing + the science and practice of deep learning. A serious problem facing the current software + development workforce is that deep learning is widely utilized in our software systems, but + scientists and practitioners do not yet have a clear handle on critical problems such as + explainability of DNN models, DNN reuse, replacement, independent testing, and independent + development. There was no apparent need to investigate the notions of modularity as neural + network models trained before the deep learning era were mostly small, trained on small + datasets, and were mostly used as experimental features. The notion of DNN modules developed + by this project, if successful, could help make significant advances on a number of open + challenges in this area. DNN modules could enable the reuse of already trained DNN modules in + another context. Viewing a DNN as a composition of DNN modules instead of a black box could + enhance the explainability of a DNN's behavior. This project, if successful, will thus have a + large positive impact on the productivity of these programmers, the understandability and + maintainability of the DNN models that they deploy, and the scalability and correctness of + software systems that they produce. Other impacts will include: research-based advanced + training as well as enhancement in experimental and system-building expertise of future + computer scientists, incorporation of research results into courses at Iowa State University + as well as facilitating the integration of modularity research-related topics, and increased + opportunities for the participation of underrepresented groups in research-based training. diff --git a/_data/members.yml b/_data/members.yml index eaa6f7c..1139e7c 100644 --- a/_data/members.yml +++ b/_data/members.yml @@ -19,60 +19,48 @@ site: https://www.cs.iastate.edu/people/shibbir-ahmed img: sahmed.jpg -- name: Sumon Biswas +- name: Fraol Batole status: PhD - email: sumon@iastate.edu - site: http://web.cs.iastate.edu/~sumon/ - img: sbiswas.jpg - -- name: Yijia Huang - status: PhD - email: hyj@iastate.edu - site: https://www.cs.iastate.edu/people/yijia-huang - img: yhuang.png - + email: fraol@iastate.edu + site: https://fraolbatole.github.io/ + img: fraol.jpg + - name: Sayem Imtiaz status: PhD - email: liyp0095@iastate.edu + email: sayem@iastate.edu site: https://www.cs.iastate.edu/people/sayem-mohammad-imtiaz img: simtiaz.jpg - -- name: Yupei Li + +- name: Ruchira Manke status: PhD - email: liyp0095@iastate.edu - site: https://www.cs.iastate.edu/people/yuepei-li - img: blank.png - -- name: Samantha Khairunnesa - status: PhD - email: sammy@iastate.edu - site: http://www.cs.iastate.edu/people/samantha-syed-khairunnesa - img: skhairunnesa.jpg + email: rmanke@iastate.edu + site: https://tads.research.iastate.edu/people/ruchira-manke + img: ruchira.jpg - name: Giang Nguyen status: PhD email: gnguyen@iastate.edu - site: http://design.cs.iastate.edu - img: gnguyen.png + site: https://www.cs.iastate.edu/gnguyen + img: giang.jpeg + +- name: David OBrien + status: PhD + email: dobrien@iastate.edu + site: https://davidmobrien.github.io/ + img: david.png -- name: Rangeet Pan +- name: Astha Singh status: PhD - email: rangeet@iastate.edu - site: http://www.cs.iastate.edu/people/rangeet-pan - img: pan.jpg + email: asthas@iastate.edu + site: https://www.astha-singh.com/ + img: astha.png -- name: Mohammad Wardat +- name: Deepak-George Thomas status: PhD - email: wardat@iastate.edu - site: https://www.cs.iastate.edu/people/mohammad-wardat - img: wardat.jpg + email: dgthomas@iastate.edu + site: https://deepakgthomas.github.io/ + img: deepak.jpg # Master's Students: # Bachelor's Students: - -- name: Xuan-Long Vu - status: BS - email: longvu@iastate.edu - site: http://design.cs.iastate.edu - img: vu.jpg diff --git a/_includes/home_page/carousel.html b/_includes/home_page/carousel.html index 97e005a..76a8ef7 100644 --- a/_includes/home_page/carousel.html +++ b/_includes/home_page/carousel.html @@ -74,6 +74,20 @@ alt="End of the year lunch, December 2018"> +
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+ +We would like to hear from users of the Frances-A tool. +Please fill out one of the following forms based on your use of Frances-A. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances-A tool. +Please fill out one of the following forms based on your use of Frances-A. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances-A tool. +Please fill out one of the following forms based on your use of Frances-A. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances tool. +Please fill out one of the following forms based on your use of Frances. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances tool. +Please fill out one of the following forms based on your use of Frances. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances tool. +Please fill out one of the following forms based on your use of Frances. +Alternatively, you can send us an email. +
+ + ++ +
+ ++We would like to hear from users of the Frances tool. +Please fill out one of the following forms based on your use of Frances. +Alternatively, you can send us an email. +
+ + + + + ++
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+Here is a collection of control flow tools similar to Frances. +If you know of a tool not listed here, please let us know. +
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+If you find any problems, would like to see any features added, or just have general comments, please take our survey or email us at sondag@cs.iastate.edu. +We are very interested to hear who is using our tool and in what ways. +This will help us develop future versions of this tool. +
+ + + + + + + ++ is an active project. +If there are any features you would like to see added, or if you find any bugs, please notify us. +In the future, we plan to release the source code as an open source project. +For now, please continue to use the web interface to the tool. +Our current work and future plans for this tool are the following: +
++Compiler and programming language implementation courses are integral parts of many computer science curricula. +However, the range of topics necessary to teach in such a course are difficult for students to understand and time consuming to cover. +In particular, code generation is a confusing topic for students unfamiliar with low level target languages. +We present Frances, a tool for helping students understand code generation and low level languages. +The key idea is to graphically illustrate the relationships between high level language constructs and low level (assembly) language code. +By illustrating these relationships, we take advantage of the students existing understanding of some high level language. +We have used Frances in a compiler design course and received highly positive feedback. +Students conveyed to us that Frances significantly helped them to understand the concepts necessary to implement code generation in a compiler project. +
+ + + + (x86) +(alt) ++
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| +Code Generator - x86 AT&T + | +Code Generator - At&T (alternate link) + |
| +Code Generator - MIPS + | +Code Generator - x86 Intel + |
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| Figure 1: Example usage of Frances tool. Left side shows user input code and legend. Right side shows example output. |
+Lessons using the Frances tool are available: +Code generation lessons. +
+ + ++If you have any comments, find any problems, +or would like to see any features +added, please give us feedback through one of the following +forms: + student, + teacher, + other. +Alternatively, feel free to +email us at sondag@iastate.edu. +Also, we are very interested to hear who is using our tool and in what ways. +This will help us develop future versions of this tool. +
+ ++ was developed originally for debugging as part of a larger framework. +After using this tool, it was clear that it was extremely helpful with understand assembly language +and control flow. +With this is mind, we aimed to develop this tool further with these ideas in mind. +Furthermore, a major goal was to make the user interface as simple as possible. +The main features of are: +
+ is currently being used for both research and in the classroom. +In the classroom, these uses include learning and understanding the following concepts: +
+We named the tool in honor of + +Frances E. Allen +. +She received the Turning award for pioneering contributions to the theory and +practice of optimizing compiler techniques that laid the foundation for modern +optimizing compilers and automatic parallel execution. + +
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+If you find any problems, would like to see any features added, or just have general comments, please take our survey or email us at sondag@cs.iastate.edu. +We are very interested to hear who is using our tool and in what ways. +This will help us develop future versions of this tool. +
+ + + + + + + ++ is an active project. +If there are any features you would like to see added, or if you find any bugs, please notify us. +In the future, we plan to release the source code as an open source project. +For now, please continue to use the web interface to the tool. +Our current work and future plans for this tool are the following: +
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