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I am a research assistant in the <ahref="https://emtiyaz.github.io/">Approximate Bayesian Inference Team</a> at the <ahref="https://aip.riken.jp/">RIKEN Centre for Advanced Intelligence Project</a> (Tokyo, Japan).
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I am an undergraduate researcher at the School of Computing, University of Utah supervised by <a
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href="http://varunshankar.com/">Professor Varun Shankar</a>. Our work
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invovles designing novel methodologies to solve partial differential equations with physics-informed machine learning techniques.
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From 2017 to 2018, I worked at the <ahref="https://www.esa.int/">European Space Agency</a> as a <ahref="https://www.esa.int/About_Us/Careers_at_ESA/Graduates_Young_Graduate_Trainees">‘Young Graduate Trainee’</a> in the <ahref="https://www.esa.int/gsp/ACT/">Advanced Concepts Team</a> (Netherlands).
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I worked with <ahref="https://www.esa.int/gsp/ACT/team/dario_izzo/">Dr. Dario Izzo</a> on a policy search method combining imitation learning and trajectory optimization.
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I am also a research intern at the <ahref="https://team-approx-bayes.github.io/">Approximate Bayesian Inference Team</a> at the <a
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href="https://aip.riken.jp/">RIKEN</a> Centre for Advanced Intelligence Project supervised by <a
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href="https://gmmarconi.github.io/">Dr. Gian Maria Marconi</a> and <ahref="https://www.thomasmoellenhoff.net/">Dr. Thomas
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Möllenhoff</a>. Our research focuses on curriculum learning.
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In 2017, I graduated with an MSc in Artificial Intelligence from the <ahref="https://www.ed.ac.uk/informatics">University of Edinburgh</a> specialising in machine learning and computational neuroscience.
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I did my thesis under <ahref="https://www.nottingham.ac.uk/psychology/people/mark.vanrossum">Prof. Mark van Rossum</a> on (neuronal) population coding.
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I also worked as a teaching assistant for <ahref="http://www.drps.ed.ac.uk/16-17_Archive_at_01-09-2016/dpt/cxinfr10069.htm">IAML</a> (computer lab tutorials) and <ahref="http://www.drps.ed.ac.uk/16-17_Archive_at_01-09-2016/dpt/cxinfr08023.htm">DMMR</a> (small-group teaching).
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Previously, I was a Machine Learning Engineer at <ahref="https://omdena.com/">Omdena</a> working for the World Resources Institute. I have also worked as a Lead Machine
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Learning Engineer for TrashOut and a Data Scientist for SevaExchange.
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In 2016, I graduated with a Bachelors in Computer Science and Mathematics (joint course) from <ahref="https://www.imperial.ac.uk/computing">Imperial College London</a>.
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During the summers, I did software internships at <ahref="https://www.ocadotechnology.com/">Ocado Technology</a>and <ahref="https://www.diamond.ac.uk/">Diamond Light Source</a>.
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I am also an astrodynamics and fluid dynamics team
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member at <ahref="https://www.conexresearch.com/">Conex Research</a>, an international team of students and young professionals developing science based space mission proposals.
<strong>October 2021 - Present</strong>: Working as a research intern at the ABI team focusing on curriculum learning.</p>
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<strong>September 2021 - Present</strong>: Working as an undergraduate researcher at The University of Utah focusing on physics-informed machine learning.</p>
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<strong>March 26, 2021</strong>: Will be joining the <ahref="https://amlab.science.uva.nl/">Amsterdam Machine Learning Lab</a> as a PhD candidate supervised by <ahref="https://enalisnick.github.io/">Eric Nalisnick</a> in September
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<strong>February 2021 - September 2021</strong>: Worked at World Resources Institute as a Machine Learning Engineer on identifying
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economic and financial incentives for forest and landscape restoration using NLP. The open source code and the KDD 2021 conference
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paper for this project are available below.</p>
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<strong>January 2021 - September 2021</strong>: Started working at Omdena as a Software Engineer and Platform Engineer managing and optimizing their Airtable
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database, making a community engagement measuring system, and creating and launching <ahref="https://omdenaai.github.io/">OmdenaLore</a>, a first of its kind AI-4-good library.</p>
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<strong>October 2020 - December 2020</strong>: Worked at SevaExchange as a Data Scientist on recommender systems to connect volunteers with
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volunteer opportunities.
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<strong>March 10, 2021</strong>: Talk on <ahref="/pres/mem-exp/riken_seminar_march2021.pdf">Identifying Memorable Experiences of Learning Machines</a>at <ahref="https://aip.riken.jp/video/aip-open-seminar-16/">RIKEN AIP Open Seminar</a>
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<strong>June 2020 - August 2020</strong>: Worked at Omdena's AI-4-good projects as a Lead Machine Learning Engineer.
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<strong>July 2019 - June 2023</strong>: Undergraduate degree in Computer Science and Engineering at SRM Institute of Science and
I’m currently interested in variational inference, kernel methods (including Gaussian processes) and reinforcement learning. Previously I have worked on topics in optimal control/trajectory optimization and computational neuroscience.
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I am currently interested in physics-informed machine learning and how physics-informed neural network (PINN) models can be used to
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simulate and solve partial differential equations that
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occur in various physical settings. I am specifically trying to work towards finding solutions to known failure modes of PINNs which occur with
I often speak at events to encourage students to consider a path in Machine Learning. I share some of those
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events below:
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<ol><ahref="https://youtu.be/C_V3EKr0rCw"><strong>Bronx Science Machine Learning club</strong></a>: I discussed different ways students can start learning Machine Learning
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concepts, make unique and interesting projects (and share them), and approach internship opportunities.</ol>
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<ol><ahref="https://youtu.be/qWMOFWCCC9Y"><strong>Devs' Street event</strong></a>: I discussed about my past projects and the lessons I
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learned from them on applying Machine Learning in industry. I shared my personal experience looking for internship opportunities and
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making presentable projects.</ol>
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<ol><ahref="https://youtu.be/um73FhxAjBs"><strong>NeurIPS 2020 Nairobi meetup</strong></a>: I co-hosted this online meetup for NeurIPS
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2020 where we invited researchers and practitioners from Stanford University, George Washinton University, Facebook AI Residency program, Amazon, Spotify,
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Google, Instadeep, and NVIDIA
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research divisions.</ol>
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{% for post in site.posts %}
@@ -166,7 +199,7 @@ <h2>Other notes</h2>
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Adapted from <astyle="font-size:small;" href="https://leonidk.com/">Leonid Keselman's fork</a> of <astyle="font-size:small;" href="https://jonbarron.info">John Barron's website</a>.
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Adapted from <astyle="font-size:small;" href="https://github.com/dvtailor/dvtailor.github.io">Dharmesh Tailor's fork</a> of <astyle="font-size:small;" href="https://github.com/leonidk/new_website">Leonid Keselman's website</a>.
In this paper we propose a Knowledge Management Framework based on NLP techniques that would tackle challenges of resource-intensive nature, lack of comprehensive central information sources, and overlapping jurisdictions in policy analysis. The framework is designed to be platform-, language- and policy-agnostic. To classify financial incentives in restoration policies, both Sentence-BERT and Cross-Encoders performed well. For sentence classification inference with Sentence-BERT, a random forest classifier can be used to assign a category to a given sentence using the learned embeddings from the SBERT model.
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