Ph.D. Defense Video: https://www.youtube.com/watch?v=gpuhqjKNnDg Thesis Statement: Knowledge-infused Learning is a class of Neuro-Symbolic AI techniques that incorporate broader forms of knowledge (lexical, domain-specific, common-sense, and constraint-based) into addressing limitations of either symbolic or statistical AI approaches, such as model interpretations and user-level explanations. Compared to powerful statistical AI that exploit data, KiL benefit from data as well as knowledge. Manas Gaur's Ph.D. Defense talk investigate the knowledge-infusion strategy in two important ways. The first is to infuse knowledge to make any classification task explainable. The second is to achieve explainability in any natural language generation tasks. The defense demonstrated the effective strategies of knowledge infusion that bring five characteristic properties in any statistical AI model: (1) Context Sensitivity, (2) Handling Uncertainty and Risk, (3) Interpretable in learning, and (4) User-level Explainability, across natural language understanding (NLU) tasks. Along with proven methodological contributions in AI made by the Manas Gaur's dissertation, it also introduces Knowledge-intensive Language Understanding tasks, a variant of General Language Understanding (GLUE) tasks that challenges AI and NLU research on explainability and interpretability. Furthermore, the Defense showcased the utility of incorporating diverse forms of knowledge: linguistic, commonsense, broad-based, and domain-specific. As the Defense illustrated the success in various domains, achieving state-of-the-art in specific applications, and significant contributions towards improving the state of machine intelligence, Manas also mentioned about careful steps to prevent errors arising due to knowledge infusion. The Defense also described Manas's future research direction towards Deep Knowledge Infusion, which would be pivotal in propelling machine understanding.

1. Ph.D. Dissertation Defense
Knowledge-infused Learning
Artificial Intelligence
Institute
Manas Gaur
mgaur@email.sc.edu
Artificial Intelligence Institute
Department of Computer Science and
Engineering
University of South Carolina
1
Advisor:
Dr. Amit P. Sheth
Committee Members
Dr. Biplav Srivastava
Dr. Krishnaprasad Thirunarayan
Dr. Valerie L. Shalin
Dr. Jyotishman Pathak
Dr. Pooyan Jamshidi
Dr. Vignesh Narayanan
Dr. Lorne Hofseth
March 25, 2022

2. 2
A bit about me and my Ph.D. journey
M. Gaur (2022)
Netaji Subhas University of Technology
2013
B.S, Computer Science
Advisor: Dr. Ram Shringar Raw
Delhi Technological University
2015
M.S, Software Engineering
Advisor: Dr. Vinod Kumar Panchal
Dr. Talel Abdessalem Dr. Petko Bogdanov
UofSC CS and AI Institute
Advisor: Dr. Amit P. Sheth
“You have to work harder than I do”

3. Knowledge-infused Learning
Artificial Intelligence
Institute
3
Manas Gaur
Ph.D. Defense

4. 4
Goal:
Use Human Knowledge to make AI Explainable and Interpretable.
Vision:
We want to make next generation neuro-symbolic AI approach
inspired by human’s ability to combine data and knowledge.
M. Gaur (2022)

5. M. Gaur (2022) 5
A DARPA Perspective on Artificial Intelligence

6. M. Gaur (2022) 6
Low-level Data
Sensors, Text,
Image, and
Collection
Neural Network and
Deep Learning
Decisions/Actions
System 1
Statistical AI is a Black Box

7. 7
M. Gaur (2022)
Can a model capture context and handle uncertainty in input?
Can user-level explanations be obtained from the success or
failure of an AI model?
Can we control an AI model by making it interpretable?
How can we make an AI model self-explainable?
Statistical AI alone is not enough!!

8. M. Gaur (2022) 8
Sheth and Thirunarayan, Duality of Data and Knowledge, IEEE Computer Society 2021
Low-level Data
Sensors, Text,
Image, and
Collection
Neural Network and
Deep Learning
Knowledge Graph
(Labeled Nodes and
Edges)
Symbolic
Reasoning
Decisions/Actions
System 1
System 2
Neural Network
and Deep Learning
Decisions/Actions
System 1
Low-level Data
Sensors, Text,
Image, and
Collection

9. M. Gaur (2022) 9
History
McCarthy and Hayes - 1968
Some Philosophical Problems from the
Standpoint of Artificial Intelligence
Douglas Hofstadter - 1979
Gödel, Escher, Bach
Amit Sheth -
2001: World Model and its utility in enhancing
search, personalization, and profiling
2002: Semantic Enhancement Engine to
improve information retrieval using ontologies
2005: Semantics for the semantic web: The
implicit, the formal and the powerful
2010: Computing for Human Experience
Leslie Valiant - 2006
Robust logics
How machines can acquire and manipulate
commonsense knowledge ?
Daniel Kahneman - 2011
Thinking Fast and Slow
Stitching System 1 and System2 : NeuroSymbolic AI
Amit Sheth - 2017
Knowledge will propel machine understanding of
content; Semantic-Cognitive-Perceptual
Computing
Gary Marcus - 2019
Rebooting AI
AI need a hybrid “Knowledge-driven” approach

10. 10
Knowledge Graphs
(KG)
M. Gaur (2022)
1. Machine readable structured representation of
knowledge
1. Consisting of entities, entity types, and
relationships in various forms (e.g., labeled
property graphs and RDFs).
Speer et al. AAAI’17
Vrandečić et al. ACM Comm’14
Gaur et al. ICSC’19
Miller, ACM Comm’95
ConceptNet
World War I fought_with Poisonous Gas
Subject Predicate Object

11. 11
Knowledge Graphs (KG)
M. Gaur (2022)
Question
Response
With KG you can answer any open
domain question.

12. 12
M. Gaur (2022)
Statistical AI alone is not enough!!
Can a model capture context and handle uncertainty in input?
Can user-level explanations be obtained from the success or
failure of an AI model?
Can we control an AI model by making it interpretable?
How can we make an AI model self-explainable?

13. 13
M. Gaur (2022)
Questions are Assessors
Assessors
Context Sensitivity
Handles Uncertainty and Risk
Interpretability
User-level Explainability

14. 14
M. Gaur (2022)
Scope (Data and Method)
Reddit
Mental Health
(2005-2018)
>13 Million
posts
>2 Million
Users
Twitter
(Event-Specific)
>80 Million
tweets
> 15 Million
Users
(Aggregated)
Clinical Diagnostic
Interviews
180 Patients
60 minutes interview
(manually transcribed)
>13.8 Million clinical
notes on >124K
patients with mental
health conditions
Heterogenous Dialog
datasets with >100K
dialogs on Politics, Travel,
News, Mental Health, and
Geography
Classification Models Generative Models

15. 15
M. Gaur (2022)
Attention Neural
Network
(Vaswani et al.
NIPS’17)
Large Amount of
Textual Data
Objective Function:
Word co-occurrences
Standard Training Process of Language
Model
AI in Natural Language Understanding (NLU)

16. 16
M. Gaur (2022)
Attention Neural
Network
(Vaswani et al.
NIPS’17)
Large Amount of
Textual Data
AI in NLU
Foundational Models:
1. Transformers
(Vaswani et al. NIPS’17)
2. Autoencoders
(Kramer AIChE’91 and Hinton &
Salakhutdinov. Science’04)
3. Recurrent Neural Networks
(Williams et al. Nature’86)
4. Long Short Term Memory
(Hochreiter & Schmidhuber Neural
Computation’97)

17. 17
{AI + NLU} for Classification Task
What would have
happened if Facebook was
present in World War I ?
What would have
happened if Facebook was
present in World War II ?
What
Happened
Facebook
World
War
I
SIMILAR
What
Happened
Facebook
World
War
II
M. Gaur (2022)
https://gluebenchmark.com/
(Example from Quora
Question Pairs Dataset)
Similarity computing
function
Probability distribution of
a sentence

18. 18
What would have
happened if Facebook was
present in World War I ?
World War I <fought_with> Trenches
World War I <fought_with> Poisonous Gas
World War I <fought_with> Guns
Trenches
Poisonous
Gas
Guns
What
Happened
Facebook
World
War
I
M. Gaur (2022)
{AI + Knowledge + NLU} for Classification Task
External knowledge in the form of
<Subject><Predicate><Object>

19. 19
What would have
happened if Facebook was
present in World War II ?
World War II <fought_with> Ships
World War II <fought_with> Fighter Planes
World War II <fought_with> Tanks
Ships
Fighter
Planes
Tanks
What
Happened
Facebook
World
War
I
M. Gaur (2022)
{AI + Knowledge + NLU} for Classification Task

20. 20
Comparison with & without Knowledge
SIMILAR
Without generic
world knowledge
DIFFERE
NT
With generic world
knowledge
M. Gaur (2022)
(BERT) Neural
Language Model
without Knowledge
on Quora Question
Pairs
Neural Language
Model with
Knowledge on
Quora Question
Pairs (KI-BERT)
[Faldu et al. 2021]
70%
72%
%age
correct
classification

21. 21
M. Gaur (2022)
Context Sensitivity:
Two sentences are different
because of the concepts World
War I and World War II
Bottom Line
Bottom Line: With Knowledge, AI
can be made sensitive towards a
context.
SIMILAR
Without generic
world knowledge
DIFFERENT
With generic world
knowledge

22. 22
M. Gaur (2022)
Why is it Important: Complex Environment Kursuncu and Gaur et al.
CSCW’19
“#MyJihad is to my prayer for
mother, then father, then god,
then other relatives in …”
“I asked about the paths to
Paradise It was said that there
is no path shorter than Jihad;
killing of apostates”
Non-Extremist Extremist
Extremist Extremist

23. 23
M. Gaur (2022)
Why is it Important Kursuncu and Gaur et al. CSCW’19
“#MyJihad is to my prayer
for mother, then father, then
god, then other relatives in
…”
“I asked about the paths to
Paradise It was said that
there is no path shorter
than Jihad; killing of
apostates”

24. 24
M. Gaur (2022)
Why is it Important Kursuncu and Gaur et al. CSCW’19
paradise
killing
Hate
attack
s
violence
Context understanding
Deeper
Semantics
jihad
paradise
god
prayer
jihad jihad
jihad
Recall
Baseline Religion
+
Ideology
Religion
+
Hate
Hate
+
Ideology
All Three
82% 82%
85%
88%
94%

25. 25
AI for NLU in a Generative Task
Attention Neural Network
(Vaswani et al. NIPS’17)
Large Amount of Textual Conversational
Data in General Domain
M. Gaur (2022)
Trained Generative Model

26. 26
{Statistical AI + NLU} for Sentence Generation
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
The model can
generate and ask
either of these
questions
They are either bad
questions or irrelevant
( A clinician won’t ask
either of these)
M. Gaur (2022)
A model trained to asked questions
Risky

27. 27
{Statistical AI + NLU} for Sentence Generation
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
The model can
generate and ask
either of these
questions
M. Gaur (2022)
A model trained to asked questions

28. 28
{Statistical AI + Knowledge + NLU} for Generation
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
M. Gaur (2022)
Do you feel nervous?
More than half the days
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?
Knowledge
Infusion using
Medical
Questionnaire
(MedQ)
These questions are
medically valid and safe.
Roy and Gaur et al. ACL’22
Safety
Checks

29. 29
M. Gaur (2022)
Do you feel nervous?
More than half the days
T5
(Raffel et.
al.
ACL’20)
KI
Attention
Model
(Ours)
30.6%
17.1%
10.6%
13.3%
KI: Knowledge Infusion
{Statistical AI + Knowledge + NLU} for Generation
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?
Roy and Gaur et al. ACL’22

30. 30
M. Gaur (2022)
Knowledge
If is cause then symptom
If is symptom then medication
If is medication then treatment
Probability next question generation is
Process
Safety Checks
Do you feel nervous?
More than half the days
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?

31. 31
Bottom Line
M. Gaur (2022)
Knowledge
Uncertainty and Risk:
Model can sense:
1. When the generated question is unsafe
2. When the generated question is safe
Do you feel nervous?
More than half the days
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?

32. 32
Stitching the Classification and Generation
Tasks
M. Gaur (2022)
Context Sensitivity
Classification Task Generation Task
Handling Uncertainty or Risk
Human
Annotation
Experience
(e.g. History)
Web Search
Corpus
Expert
Guidelines
Generative
Output
Classification
Output
Labeled Dataset

33. 33
Stitching the Classification and Generation
Tasks
M. Gaur (2022)
Context Sensitivity
Classification Task Generation Task
Handling Uncertainty or Risk
Generative
Output
Classification
Output
Labeled Dataset
Experience
(e.g. History)
Web
Search
Corpus
Expert
Guidelines
Interpretability
Experience
(e.g. History)
Web
Search
Corpus
Expert
Guidelines
User-level Explainability
matching

34. 34
Stitching the Classification and Generation
Tasks
M. Gaur (2022)
Context Sensitivity
Classification Task Generation Task
Handling Uncertainty or Risk
Generative
Output
Classification
Output
Labeled Dataset
Interpretability
User-level Explainability
Information
graph (KG,
Lexicons,
etc.)
Kashyap and
Sheth CIKM’94

35. 35
What do we learn from these Examples?
M. Gaur (2022)
Context Sensitive Capture:
Statistical AI is opinionated based on the text it sees
and input is partial representation of the world.
Uncertainty and Risk:
Statistical AI, fail to establish the connection between
input and output
User-level Explainable:
Statistical AI’s explanations are system-oriented
and
not rich enough for user-level understanding.
Interpretable:
A Statistical AI model that you can understand and
control
Transferable:
Statistical AI learns the data and not the task
Knowledge can highlight the context in input.
Knowledge can assess risky prediction
Knowledge can mend the focus of statistical AI.
Knowledge can enable User-level explanations
Knowledge can help in generalize across tasks

36. 36
Assessment
M. Gaur (2022)
Assessors Statistical AI
Context Sensitivity
Handles Uncertainty and Risk
Interpretability
User-level Explainability

37. 37
Thesis Statement : Knowledge-infused Learning (KiL)
M. Gaur (2022)
Knowledge-infused Learning is a class of Neuro-Symbolic AI
techniques that incorporate broader forms of knowledge
(lexical, domain-specific, common-sense, and constraint-based)
into addressing limitations of either symbolic or statistical AI
approaches,
such as model interpretations and user-level explanations.
Compared to powerful statistical AI that exploit data, KiL benefit
from data as well as knowledge.

38. 38
Types of Knowledge-infused Learning
M. Gaur (2022)
Shallow Infusion : Navigating the black box from Outside
1. Capture Context : 45-89% better over State-of-the-art [Gaur et al. AAAI’20, ICHI’21]
2. Minimize uncertainty in model by 61-83% [Gaur et al. WWW’19, PLoS’21, ACL’22]
3. Post-Hoc Explainability [Kursuncu and Gaur et al. CSCW’19, Gaur et al. AMIA’21]
Semi-Deep Infusion : Navigating the black box from Inside
1. Capture Context : 61-94% better over State-of-the-art [Gaur et al. CIKM’18, JMIR’21]
2. Minimize uncertainty in model by 47-84% [Gaur et al. DSAA’18, Roy and Gaur et al. IJCAI’22*,
ACL’22]
3. Explainability [Gaur et al. AAAI’22, Roy and Gaur et al. IJCAI’22, ACL’22]
4. Interpretability [Kursuncu and Gaur et al. CSCW’19, Roy and Gaur et al. IJCAI’22, ACL’22]
* submitted

39. 39
Characteristics (X) - Contributions (Y)
Graph
M. Gaur (2022)
Entity
Normalization
Retrieval and
Ranking
Optimization
Loss Function
Evaluation
Metrics
Context
Sensitive
Uncertainty
and Risk
Interpretability User-level
Explainability
Transferability
ICHI’21 PLoS’21
ICHI’21
CIKM’18
WWW’19
PLoS’21 ACL’22
WWW’19 PLoS’21
CIKM’18 AAAI’22
DSAA’1
8
JMIR’21
AAAI’20 AAAI’20
ACL’22 JMIR’21
AAAI’22
CSCW’19
AAAI’20
AMIA’21
AAAI’22
AAAI’22
Ontology/KG
ISWC’18 ICSC’19
ISWC’18 ICSC’19
ISWC’18 ICSC’19

40. 40
From GLUE to KILU: Application Contribution
M. Gaur (2022)
Corpus of
Linguistic
Acceptability
Summarizing
Clinical
Interviews
DSM-5 and PHQ-9
Flesch Reading,
Divergence,
Theme Overlap
Matthew Correlation
Rich
Evaluation
metrics
Stanford
Sentiment
Treebank
Assessing Severity in
User-generated Content
Ordinal Error, Perceived
Risk Measure, Ranked
Precision/Recall
DSM-5 and Drug
Abuse Ontology
Accuracy
Question NLI
ConceptNet and
WordNet
Concept Mover
Distance, BLEURT
F1-Score and
Accuracy
User-language
Paraphrase
Corpus
Microsoft
Paraphrase
Corpus
Recognizing
Textual
Entailment
Conversational
Information Seeking
Process Knowledge
NLG
Gaur et al. JMIR’21 Gaur et al. Pone’21,
WWW’19, ACL’ 22
Gaur et al. AAAI’22 Roy & Gaur et al. ACL
ConceptNet,
WikiNews, Wikipedia ,
MS-MARCO
Logical Coherence,
Semantic Relevance,
BLEURT
Accuracy
PHQ-9, GAD-7,
C-SSRS
Accuracy
Avg. # Unsafe
Matches, Avg.
#KG concept
Matches,
Avg. Sq. Rank
Error
Reagle & Gaur
FirstMonday’22
Sellam et al. BLEURT ACL’20, Nguyen et al. MS MARCO, NIPS’16,
Kusner et al. Word Mover ICML’15, Cameron et al. PREDOSE JBI’13
General Language Understanding Evaluation
(GLUE) (Wang et al. ICLR’19)
Knowledge-intensive Language Understanding (KILU)
(Sheth and Gaur IEEE IC’21)
Modified and Adapted from McCarthy et al. IGI’12

41. 41
M. Gaur (2022)
I. Knowledge-infusion for Suicide Risk Classification
II. Knowledge-infusion for Language Generation

42. 42
M. Gaur (2022)
I. Knowledge-infusion for Suicide Risk Classification
II. Knowledge-infusion for Language Generation

43. 43
M. Gaur (2022)
No Risk Suicidal
Ideation
Suicidal
Behaviors
Suicidal
Attempt
7%
12%
25%
37%
Probability of
Admission to
Hospital
● 80% of the patients suffering from
Borderline Personality Disorder
have suicidal behavior.
● 5-10% of whom commit suicide.
● Individuals may attempt to conceal
suicidal thoughts in Clinical Settings
● They express freely on Social Media
○ r/SuicideWatch
341, 000 Subscribers
● Current models that predict Suicide
Risk are not clinically grounded and
explainable
Why Suicide Risk Classification?
Individuals with Borderline Personality Disorder (2019)
Veen et al. BMC’19

44. 44
M. Gaur (2022)
How does annotator look at a user’s post in
r/SuicideWatch
I do have a potential to live a decent life but not
with people who abandon me. Hopelessness
and feelings of betrayal have turned my nights
to days. I am developing insomnia because of
my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've been
diagnosed with borderline for a while, and I’m
just going to isolate myself and sleep forever.
Label: Moderate Risk
( UMD Suicidality Dataset
Shing et al. CLPsych’18 )
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Annotator Perspective for Moderate Risk

45. 45
M. Gaur (2022)
How does model look at a user’s post?
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Annotator Perspective for Moderate Risk
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Low Risk

46. 46
M. Gaur (2022)
Starting simple
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Low Risk
53%
SVM-L
Recall

47. 47
M. Gaur (2022)
A complex model
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Low Risk
53%
SVM-L
Recall
Shing et al.
CLPsych’18
57%
CNN
Kim
EMNLP’14

48. 48
M. Gaur (2022)
Story always starts with a POST
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Low Risk
53%
SVM-L
Recall
Shing et al.
CLPsych’18
57%
CNN
Kim
EMNLP’14
CNN + GL
Sawhney et
al. ACL’22
62%
Gambler Loss
Ziyin et al. NIPS’19

49. 49
M. Gaur (2022)
Something is wrong with the labels
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Low Risk
No Risk
Low Risk
Moderate
Risk
Severe
Risk
Without Definitions these
labels are subject to different
interpretations/thresholds
Suicide
Indication
Suicide
Ideation
Suicide
Behavior
Suicide
Attempt
Columbia Suicide Severity Rating Scale
Posner et al. Columbia Univ’08
Concept Classes

50. 50
M. Gaur (2022)
Shallow Infusion: Locate and Embed Concept
Phrases
Suicide
Indication
Suicide
Ideation
Suicide
Behavior
Suicide
Attempt
I do have a potential to live a decent life but not
with people who [abandon me]. [Hopelessness]
and [feelings of betrayal] have turned
my [nights to days]. I am developing [insomnia]
because of my [restlessness].
I just [can’t take it anymore]. Been [abandoned]
yet again by someone I cared about. I've been
[diagnosed with borderline] for a while, and I’m
just going to [isolate myself] and [sleep forever].
Gaur et al. WWW’19
Concept
Phrases
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and
sleep forever.
Annotator Perspective for Moderate Risk
( UMD Suicidality Dataset
Shing et al. CLPsych’18 )

51. 51
M. Gaur (2022)
Gaur et al. WWW’19
CNN
Layers
Suicide
Indication
Suicide
Ideation
Shallow Infusion: Locate and Embed Concept
Phrases
I do have a potential to live a decent life but not
with people who [abandon me]. [Hopelessness]
and [feelings of betrayal] have turned my [nights
to days]. I am developing [insomnia] because of
my [restlessness].
I just [can’t take it anymore]. Been [abandoned]
yet again by someone I cared about. I've been
[diagnosed with borderline] for a while, and I’m
just going to [isolate myself] and [sleep forever].

52. 52
M. Gaur (2022)
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Moderate Risk
Shallow Infusion: Locate and Embed Concept
Phrases
Gaur et al. WWW’19

53. 53
M. Gaur (2022)
Shallow Infusion improves Recall
53%
SVM-L
Recall
Shing et al.
CLPsych’18
57%
CNN
Gaur et al.
WWW’19
CNN + GL
Sawhney
et al.
ACL’22
62%
I do have a potential to live a decent life but
not with people who abandon me.
Hopelessness and feelings of betrayal have
turned my nights to days. I am developing
insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned
yet again by someone I cared about. I've
been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep
forever.
Model’s Perspective for Moderate Risk
74%
CNN with
Semantic
Embedding
Loss
Gaur et al.
WWW’19
Gaur et al. WWW’19
CNN +
Concept
Phrase
Gaur et al.
WWW’19
84%
SOTA

54. 54
M. Gaur (2022)
Gaur et al. WWW’19
Gaur et al. PLoS’21
Numbers are SNOMED-CT / ICD-10 ids
Isolate
myself
Feelings of
Betrayal
Lack of Trust
704373005
Social Isolation
77096008
Hopelessness
Restlessness
Just Can't...
Abandoned
Sleep Forever
Feeling hopeless
30707703
Feeling agitated
24199005
Feeling abandoned
225015008
Feeling suicidal
225457007
Emotional State
106126000
Mental State Finding
36456004
Disturbance in thinking
26628009
Suicidal thoughts
6471006
Multi-hop
User-level Explainability
Why Moderate Risk?

55. 55
M. Gaur (2022)
Explainability as a metric
Gaur et al. WWW’19
Gaur et al. PLoS’21
Perceived
Risk
Measure
37%
54%
16%
14%
61%
47%
(I)
(II)
: Perceived Risk Measure (PRM)
SVM-L
Shing et al.
CLPsych’18
SVM-L
+
Concept
Phrases
WWW’19
+Supportive
Label
PLoS’21
+Supportive
Label
PLoS’21
CNN with
Semantic
Embedding
Loss
. WWW’19
CNN +
Concept
Phrase
WWW’19
Annotator Disagreements
Annotators who agree with the predictions
mis-classification

56. 56
Where are we?
M. Gaur (2022)
Assessors Statistical
AI
Shallow
Infusion
Context Sensitivity
Handles Uncertainty and
Risk
Is Interpretable?
Is User-level Explainable?
Gaur et al. WWW’19, PLoS’21, AMIA’21
Kursuncu et al. CSCW’19
● Concept Classes are
Explainable
● Explainability as a metric
prevent uncertain predictions
Shallow Infusion

57. 57
M. Gaur (2022)
Gaur et al. CIKM’18
Words
Words
Sentences
Sentences
Self-correlation
Or Self-
Attention
Self-correlation
Or Self-
Attention
Concept Classes
User-level
Posts
Cross-correlation
Or
Cross-Attention
AI Models learn by Similarities

58. 58
Modulating between Embedding Spaces
M. Gaur (2022)
Gaur et al. CIKM’18
75%
CN
Pk
Decoder
Encoder
posts
Concept Classes
Autoencoder
● Representation Learners
● Representation Modulators

59. 59
Semantic Encoding and Decoding
M. Gaur (2022)
Gaur et al. CIKM’18
Posts-by-Posts Self-Attention Matrix
Concept-by-Concept Self-Attention Matrix
Linear Projection between two
embedding spaces
Sylvester Equation
Simoncini et al. SIAM’16

60. 60
Semantic Encoding and Decoding
M. Gaur (2022)
Gaur et al. CIKM’18
Machine Learning
Model
Sylvester Equation
Simoncini et al. SIAM’16
Input Layer
Feed Forward
Neural Network
δ : Tunable Parameter for Knowledge
Infusion
(1-δ) : Forces Knowledge Infusion
Allow model interpretability

61. 61
Really struggling with my bisexuality which is causing chaos in my relationship with
a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now
starting to get drunk because I can’t cope with the obsessive, intrusive thoughts,
and need to get out of my head.
Don’t want to live anymore. Sexually assault, ignorant family members and my never
ending loneliness brights up my path to death.
I do have a potential to live a decent life but not with people who abandon me.
Hopelessness and feelings of betrayal have turned my nights to days. I am
developing insomnia because of my restlessness. I just can’t take it anymore. Been
abandoned yet again by someone I cared about. I've been diagnosed with borderline
for a while, and I’m just going to isolate myself and sleep forever.
Really struggling with my bisexuality which is causing chaos in my relationship with
a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now
starting to get drunk because I can’t cope with the obsessive, intrusive thoughts,
and need to get out of my head.
Don’t want to live anymore. Sexually assault, ignorant family members and my never
ending loneliness brights up my path to death.
I do have a potential to live a decent life but not with people who abandon me.
Hopelessness and feelings of betrayal have turned my nights to days. I am
developing insomnia because of my restlessness. I just can’t take it anymore. Been
abandoned yet again by someone I cared about. I've been diagnosed with borderline
for a while, and I’m just going to isolate myself and sleep forever.
δ = 1.0 (No
Knowledge)
δ = 0.84 (16% knowledge)
Interpretability with Semi-Deep
Infusion
Really struggling with my bisexuality which is causing chaos in my relationship with
a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m now
starting to get drunk because I can’t cope with the obsessive, intrusive thoughts,
and need to get out of my head.
Don’t want to live anymore. Sexually assault, ignorant family members and my never
ending loneliness brights up my path to death.
I do have a potential to live a decent life but not with people who abandon me.
Hopelessness and feelings of betrayal have turned my nights to days. I am
developing insomnia because of my restlessness. I just can’t take it anymore. Been
abandoned yet again by someone I cared about. I've been diagnosed with borderline
for a while, and I’m just going to isolate myself and sleep forever.
δ = 0.71 (29% knowledge)
Expert Evaluation Agreement: 84%
Really struggling with my bisexuality which is causing chaos in my relationship
with a girl. Being a fan of LGBTQ community, I am equal to worthless for her. I’m
now starting to get drunk because I can’t cope with the obsessive, intrusive
thoughts, and need to get out of my head.
Don’t want to live anymore. Sexually assault, ignorant family members and my
never ending loneliness brights up my path to death.
I do have a potential to live a decent life but not with people who abandon me.
Hopelessness and feelings of betrayal have turned my nights to days. I am
developing insomnia because of my restlessness. I just can’t take it anymore.
Been abandoned yet again by someone I cared about. I've been diagnosed with
borderline for a while, and I’m just going to isolate myself and sleep forever.
δ = 0.66 (34% knowledge)

62. 62
Reduction in Uncertainty / Risky Predictions
M. Gaur (2022)
Gaur et al. CIKM’18
Large Feature Set
[Gkotsis, Nature’17]
False
Alarms
Description of
Concept Classes
DSM-5: Knowledge Source
for Mental health Conditions
domain-specific
ontology
30%
CNN
4%
3%
2.5% 1.12%
CNN
Random Forest
Adding various forms of knowledge

63. 63
Intermediate Conclusions
M. Gaur (2022)
Gaur et al. CIKM’18
● Posts and Concept Classes bring Context Sensitivity inside the model
● Uncertainty in controlled due to Weight Matrix
● Explainability is achieved by analyzing Weight Matrix
● Interpretation can be gauged by tuning δ

64. 64
Where are we?
M. Gaur (2022)
Assessors Statistical
AI
Shallow
Infusion
Semi-Deep
Infusion
Classificatio
n
Context Sensitivity
Handles Uncertainty and Risk
Is Interpretable?
Is User-level Explainable?
Gaur et al. CIKM’18, ICHI’21

65. 65
M. Gaur (2022)
I. Knowledge-infusion for Suicide Risk Classification
II. Knowledge-infusion for Language Generation

66. 66
Process Knowledge
M. Gaur (2022)
Has subject wished he was dead or wished
he could go to sleep and not wake up?
YES / NO
Has subject had any thoughts of killing
himself?
YES / NO
Has subject been thinking about how he
might do this?
YES / NO
Has subject has these thoughts and some
intentions of acting on them?
YES / NO
Process Knowledge for Suicide Risk Classification
Roy and Gaur et al. IJCAI’22*
Suicide
Indication
Suicide
Ideation
Suicide
Behavior
Suicide
Attempt
Concept Classes

67. 67
Semi-Deep Infusion: Process Knowledge
M. Gaur (2022)
Really struggling with my bisexuality which is causing chaos in my
relationship with a girl. Being a fan of LGBTQ community, I am equal
to worthless for her. I’m now starting to get drunk because I can’t
cope with the obsessive, intrusive thoughts, and need to get out of my
head.
Don’t want to live anymore. Sexually assault, ignorant family
members and my never ending loneliness brights up my path to
death.
I do have a potential to live a decent life but not with people who
abandon me. Hopelessness and feelings of betrayal have turned my
nights to days. I am developing insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned yet again by someone I
cared about. I've been diagnosed with borderline for a while, and I’m
just going to isolate myself and sleep forever.
Has subject wished he was dead or wished
he could go to sleep and not wake up?
YES
Has subject had any thoughts of killing
himself?
YES
Has subject been thinking about how he
might do this?
NO
Has subject has these thoughts and some
intentions of acting on them?
NO
Y = Suicide Ideation
Simple Text Classification Process Knowledge-based Classification
Roy and Gaur et al. IJCAI’22

68. 68
Semi-Deep Infusion: Process Knowledge
M. Gaur (2022)
Really struggling with my bisexuality which is causing chaos in my
relationship with a girl. Being a fan of LGBTQ community, I am equal
to worthless for her. I’m now starting to get drunk because I can’t
cope with the obsessive, intrusive thoughts, and need to get out of my
head.
Don’t want to live anymore. Sexually assault, ignorant family
members and my never ending loneliness brights up my path to
death.
I do have a potential to live a decent life but not with people who
abandon me. Hopelessness and feelings of betrayal have turned my
nights to days. I am developing insomnia because of my restlessness.
I just can’t take it anymore. Been abandoned yet again by someone I
cared about. I've been diagnosed with borderline for a while, and I’m
just going to isolate myself and sleep forever.
Simple Text Classification Process Knowledge-based Classification
Roy and Gaur et al. IJCAI’22

69. 69
Semi-Deep Infusion: Process Knowledge
M. Gaur (2022)
Process Knowledge-based Classification
Roy and Gaur et al. IJCAI’22

70. 70
Semi-Deep Infusion: Process Knowledge
M. Gaur (2022)
Roy and Gaur et al. IJCAI’22
Rudin Nature’19
Process Knowledge Structure in C-SSRS
C-SSRS: Columbia Suicide Severity Rating Scale
Decision Tree:
Optimize by Bernoulli loss
Don’t want to live anymore. Sexually assault, ignorant family
members and my never ending loneliness brights up my path
to death. [...] I've been diagnosed with borderline for a while,
and I’m just going to isolate myself and sleep forever.
Has subject had any thoughts of killing
himself? YES
p
psub

71. 71
Semi-Deep Infusion: Process Knowledge
M. Gaur (2022)
Roy and Gaur et al. IJCAI’22
ERNIE: Zhang et al. ACL’19
W2V: Mikolov et al.
NIPS’13
Process Knowledge Structure in C-SSRS
C-SSRS: Columbia Suicide Severity Rating Scale
ERNI
E
W2V ERNI
E
W2V
ERNI
E W2V
simF = Gaussian simF = Cosine
Vanilla Baseline
62%
69% 69%
78%
71%
72%
AUC-ROC
W2V: Word2Vec

72. 72
Semi-Deep Infusion: Process Knowledge Explanations
M. Gaur (2022)
Roy and Gaur et al. IJCAI’22*,
ACL’22
Process Knowledge Structure in C-SSRS
C-SSRS: Columbia Suicide Severity Rating Scale
I wish I could give a shit about what would
make it to the front page. I have been there
and got nothing. Same as my life. I do have
a gun.’, ’I thought I was talking about it. I am
not on a ledge or something, but I do
have my gun in my lap.’, ’No. I made sure
she got an education and she knows how to
get a job. I also have recently bought her
clothes to make her more attractive. She
has told me she only loves me because I
buy her things.
1. Wish to be dead - Yes
2. Non-specific Active Suicidal
Thoughts - Yes
3. Active Suicidal Ideation with
Some Intent to Act - Yes
4. Label: Suicide Behavior or
Attempt
Self Explainable for an end-user
(1,2,3 verify adherence to the
clinical guideline on diagnosis
which a clinician understands)
47%
70%
XLNet
Yang et al.
NIPS’19
Process
Knowledge (Ours)
Agreement with Experts

73. 73
Generative Models: Matching with Process Knowledge
M. Gaur (2022)
Gaur et al. AAAI’22
● Are the generated questions contextual and diverse? (Minimize
Redundancy)
● Are the generated questions semantically related with each other?
● Are the generated questions in logical order? (Process Knowledge)

74. 74
Generative Models: Matching with Process Knowledge
M. Gaur (2022)
Generator
Network
Generator
Network
Passages
related to
posts (P)
Capture Context:
1. TF-IDF / BM-25
2. Hyperlinks ( Asai et al. ICLR’20 )
3. Maximize Inner Product Search (MIPS) (Bachrach et al.
RecSys’14) (Lewis et al. NIPS’20)
Reward
Process Knowledge
Semantically related Questions Logical Order
Retriever

75. 75
Generative Models: Matching with Process Knowledge
M. Gaur (2022)
Generator
Network
Passage Context
Controlled by KG
Reward
Maximize Inner Product Search (MIPS)
Between KG and Passages
Capture Context
Semantic Relations
Logical Order
Passage Context
Controlled by KG
Reward
Generator
Network
Evaluator
Network
Constraint on
Logical Order
Retriever Retriever
Generator-Evaluator Pairing forces
Order
Capture Context
Semantic Relations
Logical Order

76. 76
Ordering the Generated Questions
M. Gaur (2022)
Gaur et al. AAAI’22
Cross entropy loss
Reverse Cross entropy loss

77. 77
Semi-Deep Infusion in Generative Models
M. Gaur (2022)
Gaur et al. AAAI’22
More Examples:
https://github.com/manasgaur/AAAI-22
Simplified Query:
Bothered by trouble concentrating while reading newspaper or watching television
● Do you have a hard time falling
asleep and staying asleep?
● Do you feel like you sleep a lot but
are still tired?
● Would you like to know about some
major sleep disorders?
● Would you like to know about the 5
major sleep disorder types?
1. Do you feel like you sleep a lot but are still tired?
2. How many hours of sleep do you get on average
each night?
3. How long have you struggled with sleep
difficulties
4. Have you been diagnosed with any sleep
disorder?
Baseline Generator (T5) (Raffel et al.
JMLR’20)
Our Approach
Symptoms Symptoms Clarification Symptoms Cause
Cause and
Symptoms
Diagnosis

78. 78
Where are we?
M. Gaur (2022)
Assessors Statistical
AI
Shallow
Infusion
Semi-Deep
Infusion for
Classification
Semi-Deep
Infusion for
Generative
Models
Context Sensitivity
Handles Uncertainty and Risk
Is Interpretable?
Is User-level Explainable?
Gaur et al. CIKM’18
Roy and Gaur et al. ACL’22
Roy and Gaur et al. IJCAI’22

79. 79
M. Gaur (2022)
Summarizing Technical Contributions (KiL can do, statistical AI cannot)
Concept Classes Knowledge Attention
Shallow Infusion Shallow and
Semi-Deep Infusion
❏ Capture
Context
❏ Handle Uncertainty and
Risk
❏ Model Interpretability
❏ User-level Explainability
❏ Self Explainable Model
Knowledge graph
Context Controller
Logical Order
Constraints
Semi-Deep Infusion
❏ Process-Controlled
Question Generation
Process Knowledge
Semi-Deep Infusion

80. 80
Datasets for AI+NLU Research Community
M. Gaur (2022)
Corpus of
Linguistic
Acceptability
Summarizing
Clinical
Interviews
DSM-5 and PHQ-9
Flesch Reading,
Divergence,
Theme Overlap
Matthew Correlation
Rich
Evaluation
metrics
Stanford
Sentiment
Treebank
Assessing Severity in
User-generated Content
Ordinal Error, Perceived
Risk Measure, Ranked
Precision/Recall
DSM-5 and Drug
Abuse Ontology
Accuracy
Question NLI
ConceptNet and
WordNet
Concept Mover
Distance, BLEURT
F1-Score and
Accuracy
User-language
Paraphrase
Corpus
Microsoft
Paraphrase
Corpus
Recognizing
Textual
Entailment
Conversational
Information Seeking
Process Knowledge
NLG
Gaur et al. JMIR’21 Gaur et al. Pone’21,
WWW’19, ACL’ 22
Gaur et al. AAAI’22 Roy & Gaur et al. ACL
ConceptNet,
WikiNews, Wikipedia ,
MS-MARCO
Logical Coherence,
Semantic Relevance,
BLEURT
Accuracy
PHQ-9, GAD-7,
C-SSRS
Accuracy
Avg. # Unsafe
Matches, Avg.
#KG concept
Matches,
Avg. Sq. Rank
Error
Reagle & Gaur
FirstMonday’22
General Language Understanding Evaluation
(GLUE) (Wang et al. ICLR’19)
Knowledge-intensive Language Understanding (KILU)
(Sheth and Gaur IEEE IC’21)
Modified and Adapted from McCarthy et al. IGI’12

81. 81
M. Gaur (2022)
Concerns regarding Knowledge Infusion
Knowledge-infusion is a necessary bias for controlling AI, but there are concerns
1. Extrapolation or Overgeneralization of the Model:
a. Sentence: Your mom and dad are toxic.
b. Paraphrased with Statistical AI: Toxicity is in your mom and dad
c. Paraphrased with Knowledge-infused Learning: Your parents are radioactive
Reagle and Gaur FirstMonday’22
2. Disparity:
a. Sentence: She has her boundaries for a reason.
b. Paraphrased with Statistical AI: She has her borders for a factor.
c. Paraphrased with Knowledge-infused Learning: She has her bound/limits for a
purpose/cause.

82. 82
M. Gaur (2022)
Further Research Questions : Deep Infusion Gaur et al. AAAI’19
● We know Neural Models learn by performing abstraction
at each layer
○ Which layer require Knowledge?
○ How much knowledge infusion needs to happen to
orient the layer’s representation towards the task?
● Overgeneralization or Extrapolation of the Neural Models due to
Knowledge Infusion can be controlled:
○ By understanding the concept a neuron has learnt
○ Removing unwanted neurons (Deterministic Dropout)
Deep Infusion a.k.a
Layerwise Knowledge
Infusion

83. 83
Thesis Publications
[AAAI 2022] MG, KG, VS, HJ. ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and
Knowledge Graphs
[ACL 2022] RS, AN, MG, A Risk-Averse Mechanism for Suicidality Assessment on Social Media
[IEEE IC 2021] MG, KF, AS. "Semantics of the black-box: Can knowledge graphs help make deep learning systems more
interpretable and explainable?."
[IEEE IC 2021] AS, MG, KR, KF. "Knowledge-intensive language understanding for explainable ai."
[PLoS 2021] MG, VA, AA, UK, TK, JB, JP, AS. "Characterization of time-variant and time-invariant assessment of suicidality on
Reddit using C-SSRS."
[JMIR 2021] MG, VA, UK, AA, VS, TK, JB, MN, AS. "Knowledge-infused abstractive summarization of clinical diagnostic interviews:
Framework development study."
[ICHI 2021] MG, KR, AS, BS, AS. "“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers
during COVID-19 on Reddit."
[AMIA 2021] MG, RT, TK, AS, JP. "Comparing Suicide Risk Insights derived from Clinical and Social Media data."

84. 84
Thesis Publications
[AAAI 2020] MG, CA, SA, WG, KZ, AJ. "Unsupervised detection of sub-events in large scale disasters."
[HT 2020] MG, UK, AS, RW, SY. "Knowledge-infused deep learning."
[AAAI 2019] MG, UK, AS, "Knowledge infused learning (k-il): Towards deep incorporation of knowledge in deep learning."
[IEEE IC 2019] AS, MG, UK, RW. "Shades of knowledge-infused learning for enhancing deep learning."
[WWW 2019] MG, AA, JS, UK, TK, RK, AS, RW, JP. "Knowledge-aware assessment of severity of suicide risk for early
intervention."
[ICSC 2019] MG, AA, UL, UK, TK, AG, AS, RW, JP. "Question answering for suicide risk assessment using reddit."
[ICSC 2019] MG, SS, AG, AS. "empathi: An ontology for emergency managing and planning about hazard crisis."
[CIKM 2018] MG, UK, AA, AS, RD, TK, JP, "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit
Posts to DSM-5 for Web-based Intervention."
[DSAA 2018] QH*, MJ*, IMD*, MG*, LZ, "A hybrid recommender system for patient-doctor matchmaking in primary care."

85. 85
Other Publications
[ECML 2021] KR, QZ, MG, AS, "Knowledge infused policy gradients with upper confidence bound for relational bandits."
[IJID 2021] SB., MS, MG, AM. "Impact of reproduction number on the multiwave spreading dynamics of COVID-19 with
temporary immunity: A mathematical model."
[SocInfo 2020] TW, HI, UK, MG, VL, TK, AS, IBA, "Alone: A dataset for toxic behavior among adolescents on twitter."
[KDD 2020] NS, MG, SB, CR, SB, AS "EXO-SIR: An epidemiological model to analyze the impact of exogenous infection of covid-
19 in india."
[CSCW 2019] UK, MG, CC, AA, TK, VS, DA, IBA, AS. "Modeling islamist extremist communications on social media using
contextual dimensions: religion, ideology, and hate."
[ISWC 2018] AG, MG, SS, TK, AS, Personalized Health Knowledge Graph.
[ICHI 2018] QH, IMD, MJ, MG, LZ. "A collaborative filtering recommender system in primary care: Towards a trusting patient-
doctor relationship."
[WI 2018] SB, MG, BB, VS, AS, BM. Enhancing crowd wisdom using explainable diversity inferred from social media.

86. Ph.D. @ Kno.e.sis & AIISC
Artificial Intelligence
Institute
86
M. Gaur (2022)

87. 87
M. Gaur (2022)
Publications and Contribution to Research Community
Publications → 38
Venues:
AAAI, ECML-PKDD, ACL,
CIKM, WWW, CSCW, ISWC,
DSAA, ICHI, AMIA, JMIR,
PLoS One, etc.
18 (Conferences), 9
(Journals), 3 (AAAI
Symposiums), 3 (Book
Chapters), 3 (IEEE
Magazine), and 2
(Workshops)
Resources (>1K
downloads)
Suicide Risk Severity Lexicon
[WWW 2019]
Services
DSM-5 and DAO KG [CIKM 2018]
Reddit CSSRS Dataset v1.0
[WWW 2019]
Reddit CSSRS Dataset v2.0
[PLoS One 2021]
Moments of Change Dataset
[NAACL CLPsych 2022]
PRIMATE (submitted to
CLPsych)
PC Member: ACL-RR, NeurIPS,
CIKM, Web Conference, AAAI,
EMNLP, NAACL, Big Data,
ICWSM, Web Science, etc.
Application Track Chair:
ISIC
Co-Editor: Knowledge-infused
Learning (IEEE) &
Personal Knowledge Graphs:
Methodology, Tools, and
Applications (IET-UK)
Session Chair: ICSC & WI
Nominated for UofSC
Breakthrough Research
Awards

88. 88
M. Gaur (2022)
Technical Outreach
Tutorials Workshops Invited Talks
Knowledge-infused Deep Learning
[ ACM HT 2020]
Knowledge-infused Reinforcement
Learning [KGC 2022]
Explainable AI using KG
[ ACM CoDS-COMAD 2021]
Explainable Data for AI in
Cyber Social Threats &
Public Health [ AAAI ICWSM
2021]
Knowledge-infused Mining &
Learning [ACM SIGKDD 2020]
Knowledge-infused Learning
[Knowledge Graph Conf. 2021]
Knowledge-infused Learning
[ACM CIKM 2022]
PODCASTS
Chaos
Orchestra

89. 89
Grant Proposals (DoD, AFoRL, NSF, NIH, etc.)
M. Gaur (2022)
Partners
&
Collaborators
Maria
Liakata
Adam
Tsakalidis
Time-Sensitive
Sensing of Language
in User Generated
Content ($24K)
Development of an Instrumented,
Intelligent Infant Interaction Laboratory for
the Prediction of ASD ($39K)
Jessica
Bradshaw
Ugur
Kurşuncu
Valerie
Shalin
Advancing Neuro-symbolic
AI with Deep Knowledge-
infused Learning ( $140K )
Amit
Sheth
Valerie
Shalin
Amit
Almor
Amitava
Das
KREDIT: Knowledge infoRmEd
NLU for Deception IdenTification
Sriram
Natarajan
Qi
Zhang
Meera
Narasimhan
A Knowledge-Enhanced Approach
to Contextual and Personalized
Virtual Health Assistant ($1M)
Nitin
Agarwal
UALR
Huan
Liu
ASU
Raminta
Daniulaityte
ASU
Srinivasan
Parthasarathy
OSU
Carlos
Castillo
UPF
Spain
EPSRC-UKRI
NSF
AFoRL
NSF
UofSC
Lead Contributor
Technical Contributor
Lead Contributor
Technical Contributor
Lead Contributor
Ponnurangam
Kumaraguru
IIIT-H, India

90. 90
Research and Industry
Internships Fellowships

91. 91
Fortunate to Mentor 20 students
2 High School
8 Masters
5 Early Ph.Ds.
5 Undergraduate

92. 92
Dr. Krishnaprasad
Thirunarayan
Dr. Jyotishman Pathak
Dr. Amit P. Sheth
(Advisor)
Dr. Biplav Srivastava
Dr. Lorne Hofseth
Dr. Valerie L. Shalin
Thanks to my Amazing Committee Members
Dr. Vignesh Narayanan
Dr. Pooyan Jamshidi

93. 93
Thanks to Mentors
Dr. Sanjaya
Wijeratne
(Holler.io)
Dr. Kalpa
Gunaratna
(Samsung
Research)
Dr. Shreyansh
Bhatt
(Amazon
Research)
Dr. Saeedeh
Shekarpour
(Univ. of Dayton)
Dr. Sarasi
Lalithsena
(IBM Research)
Dr. Hemant
Purohit
(George
Mason Univ.)
Dr. Sujan
Perera
(Amazon
Research)
Dr. Ugur
Kurşuncu
(Georgia State
Univ.)
Dr. Ke Zhang
(Dataminr)
Dr. William
Goves
(Dataminr)
Dr. Sam
Anzaroot
(Verneek)
Dr. Qiwei
Han
(NOVA SBE)
Dr. Alejandro
Jaimes
(Dataminr)
Dr.
Ramakanth
Kavuluru
(Univ. of
Kentucky)
Dr. Vijay
Srinivasan
(Samsung
Research)
Dr. Petko
Bogdanov
(State University
of New York,
Albany)

94. 94
Thanks to Friends
A
I
I
S
C
&
K
N
O
E
S
I
S

95. 95
Acknowledgement

96. 96
Ph.D. and The Ultramarathons
● Mountaineer Rumble 50K (~33 miles)
○ Overall: 10 out of 44
○ Age Group: 6
● Carolina Reaper 50K (~33 miles)
○ Overall: 13
○ Age Group: 10
● Hilton Head Half Marathon (~13.11 miles)
○ Overall: 16 out of 656
○ Age group: 4 out of 59
● Uptil now
○ Distance Covered: 2,210 miles
○ Ran all the highest peaks in and
around South Carolina

97. 97
THANK YOU!
Questions?