Okd migration

_data/alumni.yml

@@ -1,23 +1,60 @@
-# 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
   email: hoan@iastate.edu
   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

_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.

_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

_includes/home_page/carousel.html

@@ -74,6 +74,20 @@
            alt="End of the year lunch, December 2018">
       </a>
     </div>
+    <div class="carousel-item">
+      <a href="http://design.cs.iastate.edu/people.html">
+      <img class="d-block img-fluid"
+           src="/img/lab-social-2021.jpg"
+           alt="Lab Social gathering, December 2021">
+      </a>
+    </div>
+    <div class="carousel-item">
+      <a href="http://design.cs.iastate.edu/people.html">
+      <img class="d-block img-fluid"
+           src="/img/2021-grad.jpg"
+           alt="Graduation of Dr. Hamid Bagheri and Dr. Samantha S. Khairunnesa">
+      </a>
+    </div>
   </div>
   <!--
     Carousel controls are temporarily commented-out because they seem to not be

_papers/ASE-23/index.md

@@ -0,0 +1,24 @@
+---
+key: ASE-23
+permalink: /papers/ASE-23/
+short_name: ASE '23
+title: Mutation-based Fault Localization of Deep Neural Networks
+bib: |
+  @inproceedings{ghanbari2023deepmufl,
+    author = {Ghanbari, Ali and Thomas, Deepak-George and Arshad, Muhammad Arbab and Rajan, Hridesh},
+    title = {Mutation-based Fault Localization of Deep Neural Networks},
+    booktitle = {ASE'2023: 38th IEEE/ACM International Conference on Automated Software Engineering},
+    location = {Kirchberg, Luxembourg},
+    month = {September 11--15},
+    year = {2023},
+    entrysubtype = {conference},
+    abstract = {
+      Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in top-1 position.
+    }
+  }
+kind: conference
+download_link: ase23.pdf
+publication_year: 2023
+tags:
+  - boa
+---