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For Sanghani Center students interning at tech companies, corporations, and research labs from coast to coast, the benefits are threefold: they gain on the job work experience; they contribute to solving real-world problems; and what they learn enhances their own research interests.

Summer 2024 finds them at places as varied at GE Healthcare, Amazon, National Renewable Energy Laboratory, the Washington Post, Citigroup, Honeywell, Google, and Honda, to name only a few.

“As evidenced by this year’s internship opportunities, businesses in all fields are relying more and more on data and AI to improve efficiency and gain insights and our students are well prepared to help them accomplish their goals in that regard,” said Naren Ramakrishnan, Sanghani Center director.

Following is a list of Sanghani Center interns – where they are working and what they are doing:

Eman Abdelrahman,  a Ph.D. student in computer science, is a graduate student intern at Lawrence Livermore National Lab in Livermore, California. She is working on enhancing autonomous Graphical User Interface (GUI) intelligent agents leveraging large multimodal models. Her advisor is Ismini Lourentzou.

Ahmed Aredah, simultaneously pursuing a Ph.D. in civil engineering and a master’s degree in computer science, is an infrastructure software development intern at Moffatt & Nichol in the FlexTerm Simulation Division in Norfolk, Virginia. He is working on simulating ports operations, full stack/DevOps development, and data analysis. His advisors are Hesham Rakha and Hoda Eldardiry.

Vasanth Reddy Baddam, a Ph.D. student in computer science, is a research scientist intern at the Honda Research Institute in Detroit, Michigan. He is currently developing frameworks and algorithms to enable robots to navigate through human spaces more effectively and to make the robots more socially compliant. His advisor is Hoda Eldardiry.

Rayan Bouhal, an undergraduate student in computer science, is a software engineering intern at Honeywell HCE in Richmond, Virginia. He is working on the Niagara Framework. His advisor is Hoda Eldardiry.

Si Chen, a Ph.D. student in electrical and computer engineering, is a research engineer intern on the trust layer team at Salesforce EinsteinGPT in San Francisco, California. She is working on content moderation for large language models. Her advisor is Ruoxi Jia.

Yusuf Dalva, a Ph.D. student in computer science, is a research scientist/engineer intern at Adobe Research, Creative Media Lab (CML) in Seattle, Washington. He is working on harmonized, multi-layer image generation. His advisor is Pinar Yanardag.

Pradyumna Upendra Dasu, a master’s degree student in computer science, is a machine learning intern at Blue Triangle Technologies, Inc. in Mechanicsville, Virginia. He is contributing to the building of a machine learning pipeline for yielding actionable insights from web performance data. His advisor is Edward Fox.

Tanner Fredieu, a Ph.D. student in electrical and computer engineering, is a software systems engineer intern in the Mission Systems Engineering Division at NASA’s Jet Propulsion Laboratory in Pasadena, California, where he is leading the design, development, and deployment of deep learning-based computer vision systems for satellites operating in low-Earth orbit. Fredieu’s internship, which began last summer, was extended throughout the 2023-2024 academic year and will end in September. His advisor is Lynn Abbott.

Alvi Md Ishmam, a Ph.D. student in computer science, is an artificial intelligence/machine learning Ph.D. intern at GE Healthcare in San Ramon, California. He is working to develop vision language model on 2d/3d bio medical images. His advisor is Chris Thomas.

Fausto German Jimenez, a Ph.D. student in computer science, is a data science and analytics intern on the TV+ Revenue and Subscription team at Apple in Culver City, California. He is applying advanced data science techniques to drive engagement and subscriptions to the Apple TV+ service. His advisor is Chris North.

Feiyang Kang, a Ph.D. student in electrical and computer engineering, is a research intern at NVIDIA Research, in Santa Clara, California. He is on the augmented video (AV) perception team, researching data-centric problems in perception for autonomous vehicles. His advisor is Ruoxi Jia.

Vanshaj Khattar, a Ph.D. student in electrical engineering, is a graduate research intern at the National Renewable Energy Laboratory (NREL) in Golden, Colorado. He is working on the problem of critical load restoration during power blackouts caused by extreme events. More specifically, he is using reinforcement learning techniques to address uncertainties in load demands and network topology during the critical load restoration process, an approach aimed at improving the resilience of power systems in the face of unforeseen disruptions. His advisor is Ming Jin.

Sha Li, a Ph.D. student in computer science, is an engineering and business operations intern at the Washington Post in Washington, D.C.  She is on the Personalization and Artificial Intelligence team, working on open-domain question answering tools. Her advisor is Naren Ramakrishnan.

Harish Babu Manogaran, a master’s degree student in computer engineering, is a perception intern at a stealth startup in Palo Alto, California. He is working on computer vision for augmented reality (AR)/virtual reality (VR) applications. His advisor is Anuj Karpatne.

Faizan Manzoor, a master’s degree student in electrical and computer engineering, is a Power System Engineering Division intern at Mitsubishi Electric Power Products Inc in Warrendale, Pennsylvania. His advisor is Ming Jin.

Makanjuola Ogunleye, a Ph.D. student in computer science, is a data science intern in the Artificial Intelligence Division at Intuit Inc., Mountain View, California. He is working on improving the experiences of Intuit Assist (one of Intuit’s most powerful generative AI products) for customers, using large language models and retrieval augmented generation. His advisor is Ismini Lourentzou.

Shailik Sarkar, a Ph.D. student in computer science, is an engineering and business operations intern on the Personalization and Artificial Intelligence team at the Washington Post in Washington, D.C. He is exploring innovative applications of artificial intelligence and natural language processing in news consumption. His advisor is Chang Tien-Lu.

Medha Sawhney, a Ph.D. student in computer science, is a deep learning automation intern at NVIDIA, working with the Deep Learning Focus Group, in Santa Clara, California. She is responsible for developing computer vision and reinforcement learning solutions to automate graphics processing unit (GPU) validation. Her advisor is Anuj Karpatne.

Gurkirat Singh, a master’s degree student in computer science, is a sales and trading summer analyst at Citigroup’s Markets Division in Houston, Texas. He is working on the Electrical Reliability Council of Texas (ERCOT) Power Trading desk where he is developing quantitative tools and strategies to improve trade research and execution. His advisor is Hoda Eldardiry.

Anushka Sivakumar, a master’s degree student in computer science, is a software development engineer intern at Berkley Alternative Markets Tech, W.R. Berkley Corporation in Virginia. She is a backend engineer supporting the development of innovative applications in the commercial insurance industry. Her advisor is Chris Thomas.

Wenjia Song, a Ph.D. student in computer science, is a software engineer intern at Google Cloud in Sunnyvale, California. She is developing unsupervised approaches for Gmail spam detection. Her advisor Danfeng (Daphne) Yao.

Afrina Tabbasum, a Ph.D. student in computer science, is an applied science intern at Amazon in Bellevue, Washington. She is working on satellite imagery with the Geospatial Science team. Her co-advisors are Hoda Eldardiry and Ismini Lourentzou.

Zain ul Abdeen, a Ph.D. student in electrical engineering, is a graduate research intern at the National Renewable Energy Laboratory (NREL), in Golden, Colorado. He is working on machine learning based intrusion detection and mitigation strategies to improve smart grid resilience. His advisor is Ming Jin. 

Summer offers an opportunity for graduate students at the Sanghani Center to gain real-world experience in their research focus areas by working at major companies and labs across the country. This year these include places like Amazon AWS in Seattle, Washington; JPMorgan Chase & Co in New York City;  the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts; Bosch in Pittsburgh, Pennsylvania; and the Intel Lab in Santa Clara, California.  

Following is a list of Sanghani Center students – where they are and what they are doing:

Satvik Chekuri, a Ph.D. student in computer science, is a natural language processing research intern working remotely with a Deloitte Audit and Assurance Data Science team in New York City. The team’s research focuses on the intersection of knowledge graphs and Large Language Models (LLMs) in the financial domain. His advisor is Edward A. Fox.

Hongjie Chen, a Ph.D. student in computer science, is a research scientist intern at Yahoo Research in Sunnyvale, California, working remotely with the advertising team. His advisor is Hoda Eldardiry.

Humaid Desai, a master’s degree student in computer science, is a software engineer intern at Ellucian in Reston, Virginia, working remotely. He is contributing to Ellucian’s SaaS-based solutions using React.js, Node.js, and AWS cloud technologies. His advisor is Hoda Eldardiry.

Jianfeng He, a Ph.D. student in computer science, is an applied scientist intern working onsite at Amazon AWS in Seattle, Washington, where he is researching text summarization. His advisor is Chang-Tien Lu.

Adheesh Juvekar, a Ph.D. student in computer science, is an applied scientist intern working on generative artificial intelligence onsite at Amazon in Boston, Massachusetts. His advisor is Ismini Lourentzou.

Myeongseob Ko, a Ph.D. student in electrical and computer engineering, is a machine learning research intern onsite at Bosch in Pittsburgh, Pennsylvania, where he is working on a diffusion model. His advisor is Ruoxi Jia.

Shuo Lei, a Ph.D. student in computer science, is a graduate research intern onsite at Intel Labs in Santa Clara, California. She is working on developing a new few-shot learning method for multi-modal object detection to lower the effort of human annotation, training effort, and domain adaptation while meeting accuracy requirements for industrial usage. Her advisor is Chang-Tien Lu.

Wei Liu, a Ph.D. student in computer science, is a business intelligence intern at Elevance Health in Indianapolis, Indiana, working remotely with the data analysis team. Her advisor is Chris North.

Amarachi Blessing Mbakwe, a Ph.D. student in computer science, is an artificial intelligence research associate intern at JPMorgan Chase & Co in New York City, working onsite. She is conducting research on natural language processing-related problems that involve applying Large Language Models (LLMs) in finance. Her advisor is Ismini Lourentzou.

Makanjuola Ogunleye, a Ph.D student in computer science is a data scientist intern at Intuit, working onsite with the company’s AI Capital team in Mountain View, California. He is contributing to key machine learning products. His advisor is Ismini Lourentzou.

Mandar Sharma, a Ph.D. student in computer science, is a Ph.D. software engineering intern at Google AI in Kirkland, Washington, where he is working onsite on integrating state-of-the-art in natural language processing to the services provided by Google’s Cloud AI platforms. His advisor is Naren Ramakrishnan.

Ying Shen, a Ph.D. student in computer science, is a research intern onsite at Apple in New York City, where she is working on diffusion models. Her co-advisors are Lifu Huang and Ismini Lourentzou.

Afrina Tabassum, a Ph.D. student in computer science, is a research intern at Microsoft in Redmond, Washington, working onsite. She is co-advised by Hoda Eldardiry and Ismini Lourentzou.

Chiawei Tang, a master’s degree student in computer science, is a software engineer intern onsite at Juniper Network in Sunnyvale, California. His work involves creating a simulator designed to emulate the data output from wired network devices such as routers and switches. This strategic initiative facilitates system scalability testing for developers and significantly mitigates the financial impact associated with the procurement of physical hardware. His advisor is Chris Thomas.

Muntasir Wahed, a Ph.D. student in computer science, is a research intern onsite at IBM Research Almaden Lab in San Jose, California, working on the development and application of foundation models. His advisor is Ismini Lourentzou.

Zhiyang Xu, a Ph.D. student in computer science, is an applied scientist intern onsite at Amazon Alexa in Sunnyvale, California, where he is working on improving dialogue systems. His advisor is Lifu Huang.  

Raquib Bin Yousuf, a Ph.D. student in computer science, is among 25 students from 19 colleges chosen to attend the Washington Post Engineering class in Washington, D.C., this summer. He is working with state of the art artificial intelligence systems to develop new technology for the Washington Post. His advisor is Naren Ramakrishnan.

Yi Zeng, a Ph.D. student in computer engineering, is a research scientist intern onsite at Meta in Menlo Park, California, working on artificial intelligence fairness, finding ways to make state of the art AI systems more robust and responsible. His advisor is Ruoxi Jia.

Jingyi Zhang, a Ph.D. student in computer science, is a graduate intern working remotely with Amgen’s Computational Biology Group within Clinical Biomarkers & Diagnostics in Thousand Oaks, California. She is taking an active role in developing a data and analytics platform as well as participating in prostate therapeutic area translational computational biology. Her advisor is Lenwood Heath.

Shuaicheng Zhang, a Ph.D. student in computer science, is a summer intern onsite at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts, where he is conducting research on the generative graph foundation model. His advisor is Dawei Zhou.

Xiaona Zhou, a Ph.D. student in computer science, is a University Research Association Sandia Graduate Summer Fellow at Sandia National Labs in Livermore, California. She is onsite working on anomaly detection in time series data. Her advisor is Ismini Lourentzou.

Virginia Tech and Amazon gathered for a Machine Learning Day held at the Virginia Tech Research Center — Arlington on April 25 to celebrate and further solidify their collaborative Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning.  

Announced last year, the initiative — funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s campus in Blacksburg and at the Innovation Campus in Alexandria — supports student- and faculty-led development and implementation of innovative approaches to robust machine learning, such as ensuring that algorithms and models are resistant to errors and adversaries, that could address worldwide industry-focused problems. Read full story here.

Afrina Tabassum, a Ph.D. student in computer science, was attracted to the Sanghani Center by the trending research conducted by faculty for improving machine learning algorithms and their application to other fields.

Her research interests lie in machine learning and self-supervised learning, particularly designing novel representation learning objectives for multi-modal data. “I was really attracted to this area of research by an urge to use deep learning in order to make people’s lives easier,” she said.

One of the projects Tabassum is working on at the Sanghani Center is “Hard Negative Sampling Strategies for Contrastive Representation Learning,” a collaboration with her advisors, Hoda Eldardiry and Ismini Lourentzou and a fellow Ph.D. student.

Their paper introduces Uncertainty and Representativeness Mixing (UnReMix) for contrastive training, a method that combines importance scores that capture model uncertainty, representativeness, and anchor similarity. 

“We verify our method on several visual, text and graph benchmark datasets and perform comparisons over strong contrastive baselines,” said Tabassum, “and to the best of our knowledge, we are the first to consider representativeness for hard negative sampling in contrastive learning in a computationally inexpensive way.”

Experimental and qualitative results so far have demonstrated the effectiveness of their proposed approach, she said.

Tabassum is also part of a team from Lourentzou’s PLAN Lab which is competing in the Alexa Prize Taskbot Challenge 2.

“Ten teams across the world were selected to build a taskbot to assist in cooking and performing other tasks around the house. Our bot will be able to make adaptable conversation a reality by allowing customers to follow personalized decisions through the completion of multiple sequential subtasks and adapt to the tools, materials, or ingredients available to the user by proposing appropriate substitutes and alternatives,” she said.

In addition to working on adapting instructions according to the user needs, she is serving as student team leader with responsibilities that include setting clear team goals and short-term deadlines and delegating tasks among all the team members. 

Projected to graduate in 2024, Tabassum would like to pursue a career in industry research.

A Virginia Tech team of  five computer science Ph.D. students at the Sanghani Center for Artificial Intelligence and Data Analytics is one of 10 university teams selected internationally to compete in the Alexa Prize TaskBot Challenge 2. The team will design multimodal task-oriented conversational assistants that help customers complete complex multistep tasks while adapting to resources and tools available to the user, such as ingredients or equipment. Read more here.