updated papers + members

Author: fraolBatole

.DS_Store

@@ -6,29 +6,41 @@
   site: https://sites.google.com/site/nguyenanhhoan/home
   img: hnguyen.jpg
 
-- name: Hamid Bagheri
-  status: PhD Spring'21
-  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
 
+- 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
   site: https://scholar.google.com/citations?user=HdY_VO4AAAAJ&hl=en
   img: jislam.jpg
-  
-- name: 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. Ganesha Upadhyaya
   status: PhD Fall'17, MS Spring'15
@@ -122,6 +134,12 @@
 
 # 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/alumni.yml

@@ -14,14 +14,14 @@
 - name: Breno Dantas Cruz
   status: Postdoctoral Fellow
   email: bdantasc@iastate.edu
-  site: https://people.cs.vt.edu/bdantasc/
+  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: alighanbari.jpeg
+  img: ali.jpg
 
 # PhD Candidates:
 
@@ -31,18 +31,18 @@
   site: https://www.cs.iastate.edu/people/shibbir-ahmed
   img: sahmed.jpg
 
+- name: Muhammad Arshad
+  status: PhD
+  email: arbab@iastate.edu
+  site: https://www.cs.iastate.edu/people/arbab
+  img: arbab.jpg
+
 - name: Fraol Batole
   status: PhD
   email: fraol@iastate.edu
   site: https://fraolbatole.github.io/
   img: fraol.jpg
 
-- name: Sumon Biswas
-  status: PhD
-  email: sumon@iastate.edu
-  site: https://sumonbis.github.io/
-  img: sbiswas.jpg
-
 - name: Usman Gohar
   status: PhD
   email: ugohar@iastate.edu
@@ -59,25 +59,25 @@
   status: PhD
   email: dobrien@iastate.edu
   site: https://davidmobrien.github.io/
-  img: david.jpg
+  img: david.png
 
 - name: Giang Nguyen
   status: PhD
   email: gnguyen@iastate.edu
   site: https://www.cs.iastate.edu/gnguyen
   img: giang.jpeg
 
-- name: Rangeet Pan
+- name: Astha Singh
   status: PhD
-  email: rangeet@iastate.edu
-  site: https://rangeetpan.github.io
-  img: pan.jpg
+  email: asthas@iastate.edu
+  site: https://www.astha-singh.com/
+  img: astha.png
 
-- name: Mojdeh Saadati
+- name: Deepak-George Thomas
   status: PhD
-  email: msaadati@iastate.edu
-  site: https://www.cs.iastate.edu/people/mojdeh-saadati
-  img: saadati.jpg
+  email: dgthomas@iastate.edu
+  site: https://deepakgthomas.github.io/
+  img: deepak.jpg
 
 - name: Mohammad Wardat
   status: PhD
@@ -94,15 +94,3 @@
   img: ruchira.jpg
 
 # Bachelor's Students:
-
-- name: Ian Gluesing
-  status: BS
-  email: ianmg@iastate.edu
-  site: https://www.linkedin.com/in/iangluesing
-  img: ian.jpg
-
-- name: Huaiyao Ma
-  status: BS
-  email: huaiyao@iastate.edu
-  site: https://www.info.iastate.edu/individuals/info/294581/Ma-Huaiyao
-  img: huaiyao.jpg

_data/members.yml

@@ -0,0 +1,24 @@
+---
+key: ESEC-FSE-22
+permalink: /papers/ESEC-FSE-22/
+short_name: ESEC-FSE '22
+title: "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software"
+bib: |
+    @inproceedings{obrien23shades,
+        author = {David OBrien and Sumon Biswas and Sayem Mohammad Imtiaz and Rabe Abdalkareem and Emad Shihab and Hridesh Rajan},
+        title = {23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software},
+        booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
+        location = {Singapore, Singapore},
+        month = {November 14--18},
+        year = {2022},
+        entrysubtype = {conference},
+        abstract = {
+            In software development, the term “technical debt” (TD) is used to characterize short-term solutions and workarounds implemented in source code which may incur a long-term cost. Technical debt has a variety of forms and can thus affect multiple qualities of software including but not limited to its legibility, performance, and structure. In this paper, we have conducted a comprehensive study on the technical debt in machine learning (ML) based software. TD can appear differently in ML software by infecting the data that ML models are trained on, thus affecting the functional performance of ML systems. The growing inclusion of ML components in modern software systems have introduced a new set of TDs. Does ML software have similar TDs to traditional software? If not, what are the new types of machine learning specific technical debts? Which ML pipeline stages do these debts appear? Do these debts differ in ML tools and applications and when they get removed? Currently, we do not know the state of the ML TDs in the wild. To address these questions, we mined 68,820 self admitted technical debts (SATD) from all the revisions of a curated dataset consisting of 2,641 popular ML repositories from GitHub, along with their introduction and removal. By applying an open-coding scheme and following upon prior works, we provided a comprehensive taxonomy of ML SATDs. Our study analyzes ML SATD type organizations, their frequencies within stages of ML software, the differences between ML SATDs in applications and tools, and quantifies the removal of ML SATDs. The findings discovered suggest implications for ML developers and researchers to create maintainable ML systems.
+        }
+    }
+kind: conference
+download_link: 23Shades-fse22.pdf
+publication_year: 2022
+tags:
+  - boa
+---

_papers/.DS_Store

@@ -0,0 +1,39 @@
+---
+key: ICLR-22
+permalink: /papers/ICLR-22/
+short_name: ICLR '22
+title: "A Global Convergence Theory for Deep ReLU Implicit Networks via Over-Parameterization"
+bib: |
+  @inproceedings{gao22global,
+    author = {Tianxiang Gao and Hailiang Liu and Jia Liu and Hridesh Rajan and Hongyang Gao},
+    title = {A Global Convergence Theory for Deep ReLU Implicit Networks via Over-Parameterization},
+    booktitle = {ICLR'22: The 10th International Conference on Learning Representations},
+    location = {Virtual},
+    month = {April 25-April 29},
+    year = {2022},
+    entrysubtype = {conference},
+    abstract = {
+      Implicit deep learning has received increasing attention recently, since
+      it generalizes the recursive prediction rules of many commonly used
+      neural network architectures. Its prediction rule is provided implicitly
+      based on the solution of an equilibrium equation. Although many recent
+      studies have experimentally demonstrates its superior performances, the
+      theoretical understanding of implicit neural networks is limited. In
+      general, the equilibrium equation may not be well-posed during the training.
+      As a result, there is no guarantee that a vanilla (stochastic) gradient
+      descent (SGD) training nonlinear implicit neural networks can converge.
+      This paper fills the gap by analyzing the gradient flow of Rectified Linear
+      Unit (ReLU) activated implicit neural networks. For an m-width implicit
+      neural network with ReLU activation and n training samples, we show that
+      a randomly initialized gradient descent converges to a global minimum at a
+      linear rate for the square loss function if the implicit neural network is
+      over-parameterized. It is worth noting that, unlike existing works on the
+      convergence of (S)GD on finite layer over-parameterized neural networks,
+      our convergence results hold for implicit neural networks, where the
+      number of layers is infinite.
+    }
+  }
+kind: conference
+download_link: implicit_nn.pdf
+publication_year: 2022
+---