may 2024 update

Author: fraolBatole

.DS_Store

@@ -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
@@ -12,6 +23,14 @@
   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

_data/alumni.yml

@@ -11,18 +11,6 @@
 
 # Postdocs:
 
-- name: Breno Dantas Cruz
-  status: Postdoctoral Fellow
-  email: bdantasc@iastate.edu
-  site: https://www.cs.iastate.edu/people/breno-dantas-cruz
-  img: breno.jpg
-
-- name: Ali Ghanbari
-  status: Postdoctoral Fellow
-  email: alig@iastate.edu
-  site: https://ali-ghanbari.github.io/
-  img: ali.jpg
-
 # PhD Candidates:
 
 - name: Shibbir Ahmed
@@ -36,13 +24,7 @@
   email: fraol@iastate.edu
   site: https://fraolbatole.github.io/
   img: fraol.jpg
-
-- name: Usman Gohar
-  status: PhD
-  email: ugohar@iastate.edu
-  site: https://www.cs.iastate.edu/ugohar
-  img: usman.jpg
-
+  
 - name: Sayem Imtiaz
   status: PhD
   email: sayem@iastate.edu
@@ -79,12 +61,6 @@
   site: https://deepakgthomas.github.io/
   img: deepak.jpg
 
-- name: Mohammad Wardat
-  status: PhD
-  email: wardat@iastate.edu
-  site: https://www.cs.iastate.edu/people/mohammad-wardat
-  img: wardat.jpg
-
 # Master's Students:
 
 # Bachelor's Students:

_data/members.yml

@@ -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
+---

_papers/.DS_Store

@@ -0,0 +1,26 @@
+---
+key: EMSE-23
+permalink: /papers/EMSE-23/
+short_name: EMSE '23
+title: "What Kinds of Contracts Do ML APIs Need?"
+bib: |
+  @article{Khairunnesa2023,
+    author = {Samantha Syeda Khairunnesa and Shibbir Ahmed and Sayem Mohammad Imtiaz and Hridesh Rajan and Gary T. Leavens},
+    title = {What Kinds of Contracts Do ML APIs Need?},
+    journal = {Empirical Software Engineering},
+    volume = {1},
+    number = {1},
+    article = {1},
+    month = {March},
+    year = {2023},
+    publisher = {Springer},
+    address = {Hanover, Pennsylvania, USA},
+    abstract = {Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kinds of contracts help API users catch errors at earlier stages in the ML pipeline. We describe an empirical study of posts on Stack Overflow of the four most often-discussed ML libraries: TensorFlow , Scikit-learn, Keras, and PyTorch. For these libraries, our study extracted 413 informal (English) API specifications. We used these specifications to understand the following questions. What are the root causes and effects behind ML contract violations? Are there common patterns of ML contract violations? When does understanding ML contracts require an advanced level of ML software expertise? Could checking contracts at the API level help detect the violations in early ML pipeline stages? Our key findings are that the most commonly needed contracts for ML APIs are either checking constraints on single arguments of an API or on the order of API calls. The software engineering community could employ existing contract mining approaches to mine these contracts to promote an increased understanding of ML APIs. We also noted a need to combine behavioral and temporal contract mining approaches. We report on categories of required ML contracts, which may help designers of contract languages.},
+    doi={10.1145/nnnnnn},
+  }
+kind: journal
+download_link: emseMLContract.pdf
+publication_year: 2023
+tags:
+  - mdl
+---

_papers/ASE-23/ase23.pdf

@@ -45,5 +45,5 @@ extra_download_links:
   - { link: dataset.zip , title: Dataset}
 publication_year: 2019
 tags:
-  - boa
+  - d4
 ---

_papers/ASE-23/index.md

@@ -41,5 +41,5 @@ kind: conference
 download_link: ml-fairness.pdf
 publication_year: 2020
 tags:
-  - boa
+  - d4
 ---