Aaditya Landge
Manager, ML Engineering at Twitter
Engineering Manager @Twitter
(Notifications Team)
I'm currently managing the Notifications Product Experience team at Twitter. Our team is responsible for sending personalized, relevant and timely notifications to Twitter customers so they never miss what's happening on Twitter. 

My past experience includes
- Recommender systems
- Product development across Consumer and Ads within Twitter
- Research and development of large scale distributed data analysis algorithms on supercomputers, computational topology and advanced algorithms
- Data and scientific visualization
- System Software (Device drivers) for NVIDIA GPUs
Twitter
https://www.linkedin.com/company/twitter
San Francisco, California
https://media-exp1.licdn.com/dms/image/C560BAQHRsTnqOuV4tA/company-logo_100_100/0/1622574251992?e=1655942400&v=beta&t=l-yW9EmrsG8cURA78kF_BFi2lxXr3MlQavUcU_SpbaA
Manager, ML Engineering
Twitter
https://www.linkedin.com/company/twitter
https://media-exp1.licdn.com/dms/image/C560BAQHRsTnqOuV4tA/company-logo_100_100/0/1622574251992?e=1655942400&v=beta&t=l-yW9EmrsG8cURA78kF_BFi2lxXr3MlQavUcU_SpbaA
Senior ML Engineer
Twitter
https://www.linkedin.com/company/twitter
San Francisco, California
https://media-exp1.licdn.com/dms/image/C560BAQHRsTnqOuV4tA/company-logo_100_100/0/1622574251992?e=1655942400&v=beta&t=l-yW9EmrsG8cURA78kF_BFi2lxXr3MlQavUcU_SpbaA
Software Engineer
Scientific Computing and Imaging Institute, University of Utah
https://www.linkedin.com/company/university-of-utah---employment
Current Research: 
Large-scale parallel data analysis algorithms for scientific simulations:
  - Developed large scale parallel topological analysis techniques for in-situ (on-the-fly) topological feature extraction of scientific simulations by adding less than 1% overhead and a 10x increase in analysis frequency
  - Developed a C/C++/MPI-based library for parallel topological analysis of scientific simulations that is scalable to 16,000 nodes (256,000 CPU cores) and deployed on the world’s fastest supercomputers (previous known implementation scaled to only 4,000 cores)
  - Developed theoretical proofs and formal descriptions of above techniques requiring theoretical understanding of topology concepts

Past Research:
Analyzing and visualizing performance data obtained from profiling massively parallel simulation codes executed on supercomputers
- Developed a data visualization tool called Boxfish to visualize network traffic on a supercomputer interconnect using Python, PyOpenGL that helped design better communication strategies for parallel applications
Salt Lake City
https://media-exp1.licdn.com/dms/image/C510BAQEVsCSmV6ss1Q/company-logo_100_100/0/1519856644367?e=1655942400&v=beta&t=oimB5hytMr1oytxPDBYibjplQ7P8oXQ4VQJ7I2px3wM
Graduate Research Assistant
Lawrence Livermore National Laboratory
https://www.linkedin.com/company/lawrence-livermore-national-laboratory
- Developed scalable algorithm for parallel merge tree computation used in topological analysis using C++/MPI
 - Gained experience in working with large parallel high performance computing systems, and various   developer tools for parallel programming
 - Established long term collaborations with scientists such that the projects became part of my thesis.
Livermore
https://media-exp1.licdn.com/dms/image/C560BAQFMlbXI0yNbpw/company-logo_100_100/0/1625587530285?e=1655942400&v=beta&t=pOB2lXbBJroayHmPBDL2UF7u-PnZVfuvnvga91ZGlVc
Research Intern - Data Analysis Group
Lawrence Livermore National Laboratory
https://www.linkedin.com/company/lawrence-livermore-national-laboratory
Analysis of complex performance data obtained from large scale parallel simulations: Performed canonical correlation analysis (CCA) of performance data and simulation output to identify performance bottlenecks.
Livermore, CA
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Research Intern - Data Analysis Group
NVIDIA
https://www.linkedin.com/company/nvidia
Worked on the CUDA profiler team. 
Responsibilities involved developing functionality in the CUDA driver (like exposing hardware performance counters and GPU machine code instrumentation of CUDA kernels) to support key profiler features. 

Gained deep understanding and knowledge of the GPU architecture, and CUDA performance optimizations.
https://media-exp1.licdn.com/dms/image/C560BAQFDs6GbpvE3zA/company-logo_100_100/0/1561949205873?e=1655942400&v=beta&t=jgULdCi6Edb1aJTr5PRXC68Zo18x-UpNboXqFaq0KRk
System Software Engineer
NVIDIA
https://www.linkedin.com/company/nvidia
Worked as a part time intern in the Video Team during last year of undergraduate program.

Developed a Windows application using C/C++/CUDA that performed intelligent scaling (resizing) of digital photographs to reduce the space required to store photographs

Developed a hand gesture recognition system:
- Created a hand gesture image database of about 9000 images with various gestures
- Ported object classification routines from OpenCV to CUDA in order to accelerate the classifier training process - reduced training time from several days to withing a day
- Enabled real time interaction with the computer/laptop using a webcam and hand gestures
https://media-exp1.licdn.com/dms/image/C560BAQFDs6GbpvE3zA/company-logo_100_100/0/1561949205873?e=1655942400&v=beta&t=jgULdCi6Edb1aJTr5PRXC68Zo18x-UpNboXqFaq0KRk
Intern
MS
Computing (Data Analysis and Management)
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University of Utah
https://www.linkedin.com/school/university-of-utah/
B.Tech.
Computer Science
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Savitribai Phule Pune University
https://www.linkedin.com/school/university-of-pune/
Secondary School Certificate
Loyola High School
ACM
Student Member
IEEE
Student Member
The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation run time.
In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’14
http://www.sci.utah.edu/publications/Lan2014a/Landge_SC2014.pdf
As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.
Exploring Power Behaviors and Trade-offs of In-situ Data Analytics
Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, of SC '13
http://dl.acm.org/citation.cfm?doid=2503210.2503303
The placement of tasks in a parallel application on specific nodes of a supercomputer can significantly impact performance. Traditionally, this task mapping has focused on reducing the distance between communicating tasks on the physical network. This minimizes the number of hops that point-to-point messages travel and thus reduces link sharing between messages and contention. However, for applications that use collectives over sub-communicators, this heuristic may not be optimal. Many collectives can benefit from an increase in bandwidth even at the cost of an increase in hop count, especially when sending large messages. For example, placing communicating tasks in a cube configuration rather than a plane or a line on a torus network increases the number of possible paths messages might take. This increases the available bandwidth which can lead to significant performance gains.

We have developed Rubik, a tool that provides a simple and intuitive interface to create a wide variety of mappings for structured communication patterns. Rubik supports a number of elementary operations such as splits, tilts, or shifts, that can be combined into a large number of unique patterns. Each operation can be applied to disjoint groups of processes involved in collectives to increase the effective bandwidth. We demonstrate the use of Rubik for improving performance of two parallel codes, pF3D and Qbox, which use collectives over sub-communicators.
Mapping Applications with Collectives over Sub-communicators on Torus Networks
roceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’12
http://dl.acm.org/citation.cfm?id=2389128
The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.
Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations
IEEE Transactions on Visualization and Computer Graphics (TVCG), (InfoVis’12)
http://www.sci.utah.edu/publications/landge12/Landge-boxfish-infovis12.pdf
Lawrence Livermore National Laboratory
Outstanding Poster Award - Summer Interns Poster Symposium
One of the six invited fully-funded students. Presented a poster on "Scalability and Power Efficiency of In-Situ Analysis Workflows"
The Salishan Conference on High Speed Computing
Invited Student
Extreme Computing Research Center, King Abdullah University of Science and Technology
Visiting Scholar
Invited to attend a two weeks rigorous training program on high performance computing technologies at Chicago, IL hosted by Argonne National Lab and fully-funded by Department of Energy.
Argonne Training Program on Extreme Scale Computing (ATPESC-2013)
Invited Student
Scientific Computing and Imaging (SCI) Institute
Best Poster Award at SCIx - 2011
Advanced Algorithms
Graduate Level
Advanced Scientific Computing I
Graduate Level
Advanced Scientific Computing II
Graduate Level
Computational Topology
Graduate Level
Data Mining
Graduate Level
Information Visualization
Graduate Level
Linear Models
Graduate Level
Machine Learning
Graduate Level
Scientific Visualization
Graduate Level
Introduction to Big Data with Apache Spark
CS100.1x (edX UC-Berkley)
In the area of deploying machine learning systems, Domain Adaptation is an important task which we encounter in real world. Here the goal is to build our model based on some fixed source domain and then deploy it to one or more different target domains. In many applications, it is expensive and time consuming to collect labeled training samples. On the other side, classifiers trained with only a limited number of labeled patterns are usually not robust. In practice, the computational cost for domain adaptation will grow fast as the data sets become larger and more unlabelled data is cheaply available.
In this paper, we consider a semi-supervised domain adaptation technique named DTMKL(Domain Transfer Multiple Kernel Learning) which can learn robust classifiers with only a limited number of labeled patterns from the target domain by leveraging a large amount of labeled training data from other auxiliary(which we call source) domains. Under the framework of DTMKL, we propose an approach based on intelligent sampling on the unlabeled data which reduces the running time without any significant impact on accuracy.
Academic Coursework Project - Machine Learning : Scalable Domain Adaptation via Intelligent Sampling
Present day scientific simulations rely heavily on extremely powerful compute resources for their computation needs. These resources are typically supercomputers with hundreds of thou- sands of CPU cores. Analyzing the performance of these simulations becomes a complex task given the sheer complexity and size of these supercomputers. Conventional methods for an- alyzing the performance of these systems include profiling the simulation code and collecting hardware metrics like flop counts, cache hits/misses, branches taken, etc. from the cores. But with today’s architectures, one can collect quite a wide range of hardware metrics from every compute core, generating a complex dataset of performance metrics. Analyzing this dataset for optimization guidelines becomes a difficult task even for performance experts.
In this project, we analyze the performance of simulations by observing the relations between two data sets - the performance metrics, and the output of the simulation. Understanding the interdependence of these two data sets leads to a better understanding of the behavior of these complex simulations on these large machines. To find correlations between these two datasets we take help of Cross Correlation Analysis (CCA) to understand which performance metrics are most correlated to the features in the simulation output. We demonstrate how our analysis helps in making important inferences about the performance behavior of the MIRANDA simulation on the IBM BlueGene/P system at Lawrence Livermore National Laboratory.
Academic Coursework Project - Data Mining: Understanding the Performance of Massively Parallel Simulations using Canonical Correlation Analysis
Children
Atmabodh
Atmabodh is an NGO in Pune, India that works towards spreading computer literacy amongst under privileged children. We conducted lessons for children aged between 9 - 13 yrs at the computer labs in VIT, Pune after school hours. 

I was instrumental in organizing a month long computer basics workshop for 500 children aged between 9 - 13 yrs in Jan, 2008.
Founding Member
https://ca.linkedin.com/in/3stanjung
Toronto, ON
Tristan Jung
Senior Engineering Manager & Toronto Engineering Site Lead
https://www.linkedin.com/in/hmudumba
San Francisco Bay Area
Harshavardhan Mudumba Venkata
Software Engineering Manager at Twitter | Ex Facebook, Amazon
https://www.linkedin.com/in/shang-shang-5b78562a
Seattle, WA
Shang Shang
Manager, Machine Learning Engineering at Twitter
https://in.linkedin.com/in/kusumita-dasgupta-62516b9
Bengaluru
Kusumita Dasgupta
Senior Engineering Manager @ Twitter
https://www.linkedin.com/in/ashokpancilypoothiyot
San Francisco Bay Area
Ashok Pancily Poothiyot
Product at Instagram | Yale | GaTech | IIT
https://www.linkedin.com/in/ushakolluru
San Francisco Bay Area
Usha Kolluru
Quality Engineering | Team lead | Cloud Security
https://www.linkedin.com/in/markshumphrey
New York, NY
Mark Humphrey
Vice President Of Engineering at Compass /
https://www.linkedin.com/in/cuihang
Boulder, CO
Hang Cui
Senior Director Of Engineering at Compass, ex-Google
https://www.linkedin.com/in/lily-xie-39b5b882
United States
Lily Xie
--
https://www.linkedin.com/in/wei-jiang-5713b91b
San Francisco Bay Area
Wei Jiang
Software Engineer at Airbnb
https://www.linkedin.com/in/kksheng
Sunnyvale, CA
Kaikai Sheng
Software Engineer
https://www.linkedin.com/in/gilbert-gao-8a889925
San Francisco Bay Area
Gilbert Gao
Software Engineer in ML infrastructure ｜Apple
https://www.linkedin.com/in/yan-wang-14b07667
United States
yan wang
Software Engineer at Google
https://www.linkedin.com/in/feiye0
San Francisco Bay Area
Fei Ye
Software Engineer at Google
https://www.linkedin.com/in/yuanqingyecmu
Pittsburgh, PA
Yuanqing Ye
Software Engineer at Google
https://www.linkedin.com/in/krishnavishal
Redmond, WA
Vishal Krishna
Machine Learning Engineer at Twitter
https://www.linkedin.com/in/yuxingz
San Francisco, CA
Yuxing Zhang
Software Engineer at Uber, not planning to work outside of SF Bay Area in the near future. Also not planning to work in hedge funds.
https://www.linkedin.com/in/stevenecpierre
San Francisco, CA
Steven Pierre
Senior Software Engineer at Apple
Valerio Pascucci
      
          



            
              
        
              
  

    
        Aaditya is a brilliant graduate student. I know he will have a great career. 
I challenge anyone to find another Master student who has published 
three papers at the Supercomputing conference and one at the 
InfoVis conference (the top in two fairly different fields). This stellar record is rivaled only by Aaditya's communication skills and ability to work in a team. One aspect where he is also probably unique is the ability to perform high quality technical work while also being able to engage effectively users to understand their needs and requirements. Aaditya will be a great asset for any organization dealing with high performance computing and advanced technology in general. 
Anyone who will hire Aaditya will be fortunate to have him in his/her organization!  


Liked by Aaditya Landge
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After 7 incredible years at Twitter, I have decided to jump back into the startup world! This time I am joining Lillian Chou, Jordan Zamir and…
Liked by Aaditya Landge
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Glad to get National Science Foundation (NSF) support for my research. Best part about this grant --- it will get me and my PhD student to…
Liked by Aaditya Landge
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Here's an excellent breakdown on how our team personalizes invitations back to Twitter via notifications using machine learning. Well done, Prakhar…
CUDA Users Group
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https://www.linkedin.com/groups/1627257
ISCR Scholar Program Alumni Group at LLNL
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https://www.linkedin.com/groups/4031966
OpenCL Developers
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University of Utah Student Alumni
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SCI Institute
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https://www.linkedin.com/groups/1131577
Data Visualization
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Visual Analytics
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https://www.linkedin.com/groups/80552
VIT PUNE Alumni Association
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https://www.linkedin.com/groups/82394
Apache Spark
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CUDA Developers
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High Performance Computing (HPC).
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