-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.qmd
More file actions
249 lines (213 loc) · 36.8 KB
/
index.qmd
File metadata and controls
249 lines (213 loc) · 36.8 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
---
title: "Ehud Karavani"
date-modified: last-modified
pagetitle: "CV | Ehud Karavani" # Avoid "Ehud K | Ehud K" because of `title`, specify it's cv
format:
html:
css: cv-style.css
listing:
- id: publication-list
contents: ../publications/*/*.qmd
type: table
filter-ui: false
sort-ui: false
sort: "date desc"
date-format: YYYY
fields: [date, title, publication, doi]
field-display-names:
publication: "Venue"
doi: "DOI" # TODO: make DOI links clickable
---
[{{< iconify bi envelope-fill >}} Email](mailto:ehudkaravani@gmail.com){.btn .btn-primary .btn-sm .rounded} [{{< iconify bi linkedin >}} LinkedIn](https://linkedin.com/in/ehudk){.btn .btn-primary .btn-sm .rounded} [{{< iconify simple-icons googlescholar >}} Scholar](https://scholar.google.com/citations?user=KAzt_pYAAAAJ&hl=en){.btn .btn-primary .btn-sm .rounded} [{{< iconify bi medium >}} Medium](https://medium.com/@ehudkr){.btn .btn-primary .btn-sm .rounded} [{{< iconify bi github >}} GitHub](https://github.com/ehudkr){.btn .btn-primary .btn-sm .rounded}
## {{< iconify material-symbols format-ink-highlighter >}} Highlights
- Machine learning researcher and causality expert with 9 years of experience in healthcare
- Applied researcher and data science working solo, as tech-lead, or as squad leader
- EB1-A visa approved (US)
- 1st author Cell paper and co-inventor on 3 US patents
- Excellent Python coder
- Creator of [`causallib`](https://github.com/IBM/causallib) - an open-source Python package for causal inference.\
800+ stars and 100+ forks on Github.
- Developed high-throughput frameworks for quasi-experiments\
from statistical engines to dashboards exploring results and supporting decisions
- Received an *IBM Research Accomplishment* award (2023)
- Communicator: PyData conference speaker, lecturer, and podcast interviewee
Causal inference, machine learning, deep learning, statistics, data viz, communication, Python
## {{< iconify bi person-fill >}} About me
I bridge the gap between rigorous statistical learning and robust software engineering. I specialize in automating analyses into scalable frameworks. Whether it’s architecting the backend of a statistical engine or designing visualization-heavy dashboards, I close the loop between complex modeling, reusable tools and actionable stakeholder insights.
As a project leader, I translate vague business/research questions into concrete hypotheses, manage agile milestones, collaborate with international peers, and navigate the nuances between technical and non-technical communication.
A T-shaped integrative thinker. I thrive on cross-pollinating fields: I have adapted biostatistics concepts to tailor modern transformer-based deep-learning architectures for biological data, applied machine learning theories of invariance to promote fairness in genetic risk scores, and used formal causal graphs to deconfound learning from multiple sources and drastically improving model generalization. I'm also a passionate advocate of synthesizing engineering practices into research practices: academic workflows as git workflows, test-driven modeling, and Clean Code for research code.
## {{< iconify bi briefcase-fill >}} Experience
+----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2017 – present | ::: cell_title |
| | **Staff Machine Learning Researcher**\ |
| | *Causal Machine Learning for Healthcare and Life Science, IBM-Research* |
| | ::: |
| | |
| | - Creator of [**`causallib`**](https://github.com/IBM/causallib) – a one-stop-shop open-source Python package for flexible causal inference modeling. |
| | |
| | - *Received an IBM Research Accomplishment award (2023)* |
| | |
| | - Client project leader from start to finish: eliciting information from domain experts, translating vague clinicians' questions into concrete statistical hypotheses, answering them, and communicating the findings |
| | |
| | - Led, designed, and engineered a reusable framework for drug discovery, applying high-throughput causal inference to observational healthcare data |
| | |
| | - Managing a team of 5 researchers.\ |
| | Leading the scientific pipeline, system design, and visualization app |
| | |
| | - Generating 100s of hypotheses in minutes |
| | |
| | - Serving 4 external engagements with top pharma clients, bringing millions in revenue |
| | |
| | - Individual Contributor (IC)\ |
| | Causal inference consultant for projects in the US, UK, France, Japan, Kenya, South Africa, and Switzerland |
| | |
| | - Led global strategy at IBM Research for causality in drug discovery |
| | |
| | - Steered research agenda and technical focus areas, reporting directly to Research VPs |
| | - Oversaw research of subgroup discovery for adaptive experimentation using Bayesian inference |
| | |
| | - Mentored 10+ students and interns\ |
| | Onboarding lead, onboarding 10+ researchers |
| | |
| | - Teaching academics how to apply software development fundamentals to research, delivering maintainable production-grade research code |
| | |
| | - Published 10+ papers and issued 3 US patents |
| | |
| | 2024: |
| | |
| | - "GLM-ification" of deep learning models, bringing established concepts from biostatistics into transformer-based deep-learning LLM-like models, tailoring them for biology. |
| | |
| | - Implemented concepts from generalized linear (mixed) models (e.g., zero-inflated negative-binomial regression or random effects) in PyTorch |
| | |
| | - Deconfounded learning from multiple fragmented sources improving model generalization |
| | |
| | - Identified and quantified data confounding bias (batch-like effects) leading to poor generalization in clients |
| | |
| | - Deconfounded learning using approaches from domain adaptation, invariant risk minimization, and conditional autoencoders, drastically improving model generalization |
| | |
| | 2025: |
| | |
| | - Quantum Advantage task force member: developing and testing quantum algorithms for combinatorial optimization problems |
| | - Applied AI and analytical approaches to improve quantum algorithms, like finding better initial parameters, reducing variational optimization rounds hardware usage and saving costs |
+----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2022 | ::: cell_title |
| | **Principle Statistician**\ |
| | *Laboratory for Gait & Neurodynamics, Ichilov Hospital* |
| | ::: |
| | |
| | - Lead statistician in a clinical study |
| | - Bayesian hierarchical/multilevel models and causal inference for gait analysis in multiple sclerosis patients |
| | - Bayesian multilevel models for hurdle models of repeated patients’ measurements |
| | - Formal causal inference with DAGs for minimizing inessential tests, saving over 3 hours of unnecessary tests by clinicians per patient. |
+----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2016 – 2017 | ::: cell_title |
| | **Teaching Assitant**\ |
| | *The School of Computer Science, Hebrew University of Jerusalem* |
| | ::: |
| | |
| | - Introduction to Data Science |
| | |
| | - Workshop in Computational Bioskills |
+----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2015 – 2016 | ::: cell_title |
| | **Research Associate / Computational Biologist\ |
| | ***Institue for Medical Research Israel-Canada, Hadassah Hospital* |
| | ::: |
| | |
| | - Developed novel methodologies for finding high-resolution protein-RNA interactions using high-volume RNAseq data |
+----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
## {{< iconify bi mortarboard-fill >}} Education
+-------------+-------------------------------------------------------------------------------------------------------------------+
| 2016 – 2019 | ::: cell_title |
| | **M.Sc. in Computer Science and Computational Biology**\ |
| | *Faculty of Science, the Hebrew University of Jerusalem, Israel* |
| | ::: |
| | |
| | Thesis: *quantifying the utility of embryo selection using genomic prediction of traits*\ |
| | [published in Cell](https://www.cell.com/cell/fulltext/S0092-8674(19)31210-3) |
| | |
| | - Predicting physical traits from DNA (GWAS) using classical, machine learning, and deep learning methods |
| | |
| | - Pioneering the effects of prediction-based embryo selection in IVF |
+-------------+-------------------------------------------------------------------------------------------------------------------+
| 2013 – 2016 | ::: cell_title |
| | **B.Sc. in Computer Science and Computational Biology**\ |
| | *Faculty of Science, the Hebrew University of Jerusalem, Israel* |
| | ::: |
| | |
| | - *Dean's List of Academic Excellence (2016)* |
| | - Research scholarship from IMRIC (2016) |
| | |
| | Bachelor's thesis published in [Nucleic Acids Research](https://academic.oup.com/nar/article/46/19/10380/5064785) |
+-------------+-------------------------------------------------------------------------------------------------------------------+
## {{< iconify fluent people-community-24-filled >}} Outreach
- [PyData speaker](https://youtu.be/7RUkcZEyhQM?si=gR84iGnuIez5WXGj)
- [Causal Bandits podcast interviewee](https://youtu.be/Q7sinHrknC8?si=9dH5kqIfNGhvjuB8)
- DataNights causality series lecturer
- Recurring [DataHack](https://www.datahack.org.il/) mentor and judge
- Co-Organized the 2018 Atlantic Causal Inference Conference Data Challenge
- TDS [Editor’s Pick](#0) on Medium
## {{< iconify bi code-slash >}} Skills
+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Programming skills | - Python & its scientific and ML stack (fluent) |
| | |
| | - Pandas, Polars, DuckDB, Ibis, Numpy, Scikit-Learn, PyGAM, Statsmodels, PyTorch (lightning), PyMC, Bambi, Arviz, Matplotlib, , Seaborn (objects), Altair, Streamlit, cvxpy, Pydantic, Hydra, Ray... |
| | |
| | - R (when needed) |
| | |
| | - SQL (but Ibis when possible) |
| | |
| | - Git + GitHub |
| | |
| | - Continuous development (Travis, GitHub Actions) |
| | |
| | - Linux and remote development (Cloud/AWS + Jupyter lab / VS Code) |
| | |
| | - Jupyter, Quarto, Latex, Typst |
+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Technical skills | - Causal Inference |
| | |
| | - Machine Learning and Deep Learning |
| | |
| | - Statistics and Bayesian Inference |
| | |
| | - Data Visualization |
| | |
| | - Verbal & written communication |
| | |
| | - Programming, software engineering and development |
+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Languages | - Fluent English |
| | |
| | - Native Hebrew |
+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| General | - Data enthusiast |
| | |
| | - Musician 🎸, hiker / backpacker 🏔️ |
| | |
| | - Friendly 🙂 |
+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
## {{< iconify bi trophy-fill >}} Awards
+----------+------------------------------------------------------------------------------------------------------------------------------------+
| 2023 | IBM-Research Accomplishment |
| | |
| | For my work on causallib and research engagement with the Cleveland Clinic Foundation |
+----------+------------------------------------------------------------------------------------------------------------------------------------+
| 2019 | Best of RSNA |
| | |
| | For the paper *Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms*, published in Radiology |
+----------+------------------------------------------------------------------------------------------------------------------------------------+
| 2019 | Best Talk: Israeli Population Genetics Meeting |
| | |
| | For the paper *Screening Human Embryos for Polygenic Traits has Limited Utility* |
+----------+------------------------------------------------------------------------------------------------------------------------------------+
| 2019 | Featured Theory of the issue (Cell) |
| | |
| | For the paper *Screening Human Embryos for Polygenic Traits has Limited Utility* |
+----------+------------------------------------------------------------------------------------------------------------------------------------+
| 2016 | Dean's list of academic excellence |
+----------+------------------------------------------------------------------------------------------------------------------------------------+
## {{< iconify bi file-earmark-text-fill >}} Publications
::: {#publication-list style="padding-bottom:0em;"}
:::
[*May go out of date. Please see my [Google Scholar page](https://scholar.google.com/citations?user=KAzt_pYAAAAJ&hl=en) for the most up-to-date information.*]{style="color: gray;"}