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
Manager, ML Engineering
San Francisco, California
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Twitter
https://www.linkedin.com/company/twitter
Senior ML Engineer
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Twitter
https://www.linkedin.com/company/twitter
Software Engineer
San Francisco, California
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Scientific Computing and Imaging Institute, University of Utah
https://www.linkedin.com/company/university-of-utah---employment
Graduate Research Assistant
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
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Lawrence Livermore National Laboratory
https://www.linkedin.com/company/lawrence-livermore-national-laboratory
Research Intern - Data Analysis Group
- 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
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Lawrence Livermore National Laboratory
https://www.linkedin.com/company/lawrence-livermore-national-laboratory
Research Intern - Data Analysis Group
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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NVIDIA
https://www.linkedin.com/company/nvidia
System Software Engineer
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.
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NVIDIA
https://www.linkedin.com/company/nvidia
Intern
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
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Computing (Data Analysis and Management)
MS
University of Utah
https://www.linkedin.com/school/university-of-utah/
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Computer Science
B.Tech.
Savitribai Phule Pune University
https://www.linkedin.com/school/university-of-pune/
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Secondary School Certificate
Loyola High School
ACM
Student Member
IEEE
Student Member
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
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.
http://www.sci.utah.edu/publications/Lan2014a/Landge_SC2014.pdf
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
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.
http://dl.acm.org/citation.cfm?doid=2503210.2503303
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
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.
http://dl.acm.org/citation.cfm?id=2389128
Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations
IEEE Transactions on Visualization and Computer Graphics (TVCG), (InfoVis’12)
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.
http://www.sci.utah.edu/publications/landge12/Landge-boxfish-infovis12.pdf
Outstanding Poster Award - Summer Interns Poster Symposium
Lawrence Livermore National Laboratory
Invited Student
The Salishan Conference on High Speed Computing
One of the six invited fully-funded students. Presented a poster on "Scalability and Power Efficiency of In-Situ Analysis Workflows"
Visiting Scholar
Extreme Computing Research Center, King Abdullah University of Science and Technology
Invited Student
Argonne Training Program on Extreme Scale Computing (ATPESC-2013)
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.
Best Poster Award at SCIx - 2011
Scientific Computing and Imaging (SCI) Institute
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)
Academic Coursework Project - Machine Learning : Scalable Domain Adaptation via Intelligent Sampling
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 - Data Mining: Understanding the Performance of Massively Parallel Simulations using Canonical Correlation Analysis
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.
Children
Atmabodh
Founding Member
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.
https://ca.linkedin.com/in/3stanjung
Tristan Jung
Senior Engineering Manager & Toronto Engineering Site Lead
Toronto, ON
https://www.linkedin.com/in/hmudumba
Harshavardhan Mudumba Venkata
Software Engineering Manager at Twitter | Ex Facebook, Amazon
San Francisco Bay Area
https://www.linkedin.com/in/shang-shang-5b78562a
Shang Shang
Manager, Machine Learning Engineering at Twitter
Seattle, WA
https://in.linkedin.com/in/kusumita-dasgupta-62516b9
Kusumita Dasgupta
Senior Engineering Manager @ Twitter
Bengaluru
https://www.linkedin.com/in/ashokpancilypoothiyot
Ashok Pancily Poothiyot
Product at Instagram | Yale | GaTech | IIT
San Francisco Bay Area
https://www.linkedin.com/in/ushakolluru
Usha Kolluru
Quality Engineering | Team lead | Cloud Security
San Francisco Bay Area
https://www.linkedin.com/in/markshumphrey
Mark Humphrey
Vice President Of Engineering at Compass /
New York, NY
https://www.linkedin.com/in/cuihang
Hang Cui
Senior Director Of Engineering at Compass, ex-Google
Boulder, CO
https://www.linkedin.com/in/lily-xie-39b5b882
Lily Xie
--
United States
https://www.linkedin.com/in/wei-jiang-5713b91b
Wei Jiang
Software Engineer at Airbnb
San Francisco Bay Area
https://www.linkedin.com/in/kksheng
Kaikai Sheng
Software Engineer
Sunnyvale, CA
https://www.linkedin.com/in/gilbert-gao-8a889925
Gilbert Gao
Software Engineer in ML infrastructure ｜Apple
San Francisco Bay Area
https://www.linkedin.com/in/yan-wang-14b07667
yan wang
Software Engineer at Google
United States
https://www.linkedin.com/in/feiye0
Fei Ye
Software Engineer at Google
San Francisco Bay Area
https://www.linkedin.com/in/yuanqingyecmu
Yuanqing Ye
Software Engineer at Google
Pittsburgh, PA
https://www.linkedin.com/in/krishnavishal
Vishal Krishna
Machine Learning Engineer at Twitter
Redmond, WA
https://www.linkedin.com/in/yuxingz
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.
San Francisco, CA
https://www.linkedin.com/in/stevenecpierre
Steven Pierre
Senior Software Engineer at Apple
San Francisco, CA
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!  


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…
https://www.linkedin.com/signup/cold-join
Liked by Aaditya Landge
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…
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Liked by Aaditya Landge
Here's an excellent breakdown on how our team personalizes invitations back to Twitter via notifications using machine learning. Well done, Prakhar…
https://www.linkedin.com/signup/cold-join
Liked by Aaditya Landge
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CUDA Users Group
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ISCR Scholar Program Alumni Group at LLNL
https://www.linkedin.com/groups/4031966
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OpenCL Developers
https://www.linkedin.com/groups/1729897
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University of Utah Student Alumni
https://www.linkedin.com/groups/91396
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SCI Institute
https://www.linkedin.com/groups/1131577
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Data Visualization
https://www.linkedin.com/groups/2244682
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Visual Analytics
https://www.linkedin.com/groups/80552
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VIT PUNE Alumni Association
https://www.linkedin.com/groups/82394
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Apache Spark
https://www.linkedin.com/groups/7403611
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CUDA Developers
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High Performance Computing (HPC).
https://www.linkedin.com/groups/87791