Stony Brook University Computer Science

The Conference on Computer Vision and Pattern Recognition (CVPR) 2024, regarded as one of the most prestigious and highest-impact conferences, has recognized Prof. Rameau as an Outstanding Reviewer.  This recognition was given to the top 2% of reviewers for their high-quality reviews (as judged by their Area Chairs) among 9,872 reviewers.  This is the second time Prof. Rameau has received the Outstanding Reviewer Award from CVPR. Link: https://x.com/CVPR/status/1793616950314369239

  Professors Niranjan Balasubramanian and Michalis Polychronakis, from the Department of Computer Science, have each received Amazon Research Awards to further advance their fields of research.  Balasubramanian’s research focuses on the potential of Large Language Models (LLMs) for autonomous execution of complex tasks. He will use the Amazon funding to create a controlled environment, a complex task testbed, where LLMs can be rigorously evaluated. This testbed features innovative assessment criteria beyond typical accuracy metrics, a sandbox execution environment with mock APIs, and natural language descriptions of complex goals.   This research bridges the gap between theoretical promise and real-world implementation. By developing a controlled environment for LLMs, Balasubramanian aims to unlock their potential while ensuring safety. His work contributes to advancing AI technologies and addressing real-world challenges in a thoughtful and systematic manner.  Polychronakis’ Amazon funding will allow him to continue to explore ways to improve software security and enhance memory safety. His research aims to address the challenges posed by memory corruption vulnerabilities, which are still a major source of system compromise and malware infection. Despite the advantages of modern memory-safe languages like Go and Rust, most existing software are still written in memory-unsafe languages like C and C++. The familiarity of developers with C and C++, vast code bases in these languages, and their efficiency hinder efforts to migrate to memory-safe alternatives.  To address this issue, Polychronakis is developing SafeTrans, a system that automates the conversion of existing C/C++ code to Rust. Rust with its memory safety features and low runtime overhead, is a great candidate to replace memory-unsafe languages in critical systems. SafeTrans seeks to accelerate the adoption of memory-safe languages by automating elements of the migration process while lowering the risk of memory-related vulnerabilities. His research helps the larger goal of increasing software security and making systems more resistant to modern vulnerabilities.  Both researchers received approximately $100k in funding which includes Amazon credits.  The Amazon Research awards recognize the innovative contributions of Niranjan Balasubramanian and Michalis Polychronakis, and their research assistants. Both initiatives demonstrate a commitment to innovation and real-world impact, shaping the future of technology.   Written by Sahil Sarna  

  Computer Science students have the opportunity to gain various experiences during their visit to the SBU NY campus.  Four students from the Department of Computer Science at SUNY Korea achieved three meaningful awards in the three biggest Hacks competitions at Stony Brook University in New York. These include “Best Sustainability Hack” in SBU Hopper Hacks 2024, “Best Social Impact” in SBUHacks VI, and “Best Integration of Customer Segmentation Utilizing AI” in Softheon Hack@CEWIT. These events were held online from January 29 to February 4, February 9 to February 11, and March 1 to March 3, 2024, respectively, at Stony Brook University. Biniam Markos, Eunwoo Choi, Ulukbek Aitmatov, and Younwoo Ki from SUNY Korea won the  “Best Sustainability Hack” as one team in SBU Hopper Hacks 2024. Hopper Hacks is a hackathon centered around social good, lasting 24 hours, and open to all students at Stony Brook University. SUNY Korea CS students were also awarded in the same competition in 2022. The same team also won the “Best Social Impact” in SBUHacks VI. SBUHacks extends over 48 hours, encouraging limitless creativity. During SBUHacks, students are tasked with pushing their boundaries and pursuing projects aligned with their interests. Additionally, Biniam Markos, Eunwoo Choi and Younwoo Ki from SUNY Korea, along with Alissa Burich, majoring in Business Management from SBU in New York, won “Best Integration of Customer Segmentation Utilizing AI” in Softheon Hack@CEWIT 2024, which is the second-place prize in this competition. Notably, SUNY Korea students have been consistently recognized in this competition, with another group securing the highest award last year. At Softheon Hack@CEWIT, participating students can develop software and automation solutions tailored for health insurers and governmental health agencies.   Written by Student Reporter, Hyeonha Hwang (hyeonha.hwang@stonybrook.edu)

  The recent months have been particularly prolific for Prof. Rameau, showcasing a series of achievements highlighting SUNY-K’s role in the global academic community. From publications in renowned venues to global collaborations, Prof. Rameau’s contributions not only contribute to the development of smart cities and autonomous driving but also bring valuable recognition to our institution. Contributions to Autonomous Driving: Professor Rameau's core research involves creating novel computer vision and AI solutions for autonomous driving and smart cities. In collaboration with the VDCLab directed by Prof. Kum at the Graduate School of Mobility, KAIST, he has co-authored two papers on the concept of High-Definition (HD) maps. These have recently emerged as an essential tool for the future of autonomous driving. HD maps provide detailed, digital representations of roads, incorporating both geometric shapes and semantic information about road elements such as lanes and traffic signs.In the first work, published in CVPR-W 2023 [1], the research team introduced InstaGraM (Instance-level Graph Modeling for Vectorized HD Map Learning), a novel approach for creating HD maps directly from images. They developed a novel graph neural network capable of generating detailed, vectorized HD maps in real-time using a rig of surrounding cameras mounted on a car. Another piece of research, recently published in RA-L [2], showcases a concrete application of HD maps for place recognition. This strategy effectively combines visual information with the HD mapping approach described earlier, significantly enhancing the robustness of existing localization solutions in urban environments.     Aside from his research on HD maps for autonomous driving, Prof. Rameau has also recently developed innovative solutions for the calibration and deployment of CCTV cameras to track vehicles in 3D and geolocalize them. This work, entitled "CCTV-Calib," has been published in the journal Machine Vision and Applications (MVA) [3].   Associate Editor at RA-L: Professor Rameau was recently appointed as Associate Editor of IEEE Robotics and Automation Letters. With a current impact factor of 5.2, this Q1 journal is regarded as one of the most influential in the field of Robotics. RA-L publishes peer-reviewed articles featuring innovative research ideas and application results in robotics and automation, which cover theoretical findings and detailed application case studies. Professor Rameau's responsibilities include organizing and supervising the manuscript review process in the field of "Vision and Sensor-Based Control." Adjunct Faculty Position at NAAMII (Nepal):   Since September 2023, Prof. Francois Rameau has been an Adjunct Faculty member at NAAMII (Nepal Applied Mathematics and Informatics Institute), where he collaborates with experts from world-renowned institutions. NAAMII is a non-profit research institute founded by Nepali researchers dedicated to establishing a solid foundation for scientific research in Nepal in the areas of informatics, applied mathematics, and AI. It also aims to contribute to the democratization and decentralization of AI globally. Within this framework, Prof. Rameau is part of the research group PUSHVIC, which explores novel spatial computing challenges, such as place recognition in implicit representations [4], employing pose regression through diffusion models.     Exchange with French Universities:   Through an international PHC-STAR (NRF) project involving SUNY-Korea, KAIST, and two French universities (the University of Burgundy and the University of Picardy), Prof. Rameau hosted a Ph.D. student at our institution in the fall semester of 2023 to conduct research on object detection using event-based cameras. In return, he visited the University of Burgundy in December 2023 to strengthen our collaboration with partner universities, co-supervise students, and deliver a seminar to the research team at the ICB Laboratory (UMR 6303 CNRS). This partnership with French universities and research centers is expected to expand in the coming years, fostering a range of exchanges and collaborative research initiatives.   References: [1] Juyeb SHIN, Francois RAMEAU, Hyeonjun JEONG, and Dongsuk KUM, “InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning", CVPRW - Vision-Centric Autonomous Driving (VCAD), June 2023, Vancouver. [2] Hyeonjun JEONG, Juyeb SHIN, Francois RAMEAU, and Dongsuk KUM “Multi-Modal Place Recognition via Vectorized HD Maps and Images Fusion for Autonomous Driving", Robotics and Automation Letters (RA-L), 2024. [3] Francois RAMEAU, Jaesung CHOE, Fei PAN, Seokju LEE, and In So KWEON “CCTV-Calib: a Toolbox to Calibrate Surveillance Cameras Around the Globe", Journal of Machine Vision and Applications, 2023. [4] Rashik SHRESTHA, Bishad KOJU, Abhigyan BHUSAL, Danda Pani PAUDEL, and Francois RAMEAU, “CaLDiff: Camera Localization in NeRF via Pose Diffusion", Arxiv, 2024.  

Our CS department is pleased to annount that Professor Aruna Balasubramanian and Niranjan Balasubramanian are the winners of the Fall 2023 SBU Office of the Vice President for Research (OVPR) Seed Grant Program competition.  Office of the Vice President awarded $1,000,000 in total funding to support 21 research projects.  Both professors were submitted an abstract and brief proposal, including a timeline that demonstrated how this seed funding would help to develop a highly competitive proposal for extramural funding. Sixty-six applications were received from Stony Brook colleges, including, but not limited to, the College of Arts and Sciences, the College of Engineering and Applied Sciences, the Renaissance School of Medicine, the School of Communication and Journalism, the School of Dental Medicine, and the School of Health Professions.  For more information about the OVPR Seed Grant Program and Fall 2023 Seed Grant cycle, please read this article.  Please see the attached abstract. 

  Professors Aruna and Niranjan Balasubramanian were elated to find out that their mobile phone energy research was recognized with the 2023 Internet Measurement Conference (IMC 23) Test of Time award. They recently participated in a Q&A session with undergraduate student  Kimberly Xiao to detail their award winning paper, Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications.[1] Q. How did this project start? Niranjan: This measurement study examined how network transmissions consume energy on mobile devices and allowed for the design of various energy-efficient algorithms for mobile network applications. This work started when Aruna and I were PhD students at UMass. As part of their doctoral program, we had to implement a project that combined a research area with another research area. I chose to combine my work in information retrieval with systems and worked with Prof. Arun Venkatramani, who was Aruna’s PhD advisor at UMass. Back in 2008 there was some work on mobile, handheld devices, but mobile phones were about to explode in the consumer world. Arun stressed that energy and power consumption was going to be critical and the idea was born. Q. What were some of the challenges you faced? Aruna: There was a new phone called Nokia N95 phone that was released in 2006 which was one of the first programmable phones; so we were excited to work with it.  But at that time, there was really no easy way to measure power consumption. Niranjan: To begin with, I did some measurements where I repeatedly sent data of varying sizes over the network and waited until the phone’s battery would die. This told you how much power the entire data transmission consumed. This was a rather crude and time-consuming way to measure energy but early on we started seeing some energy patterns. The problem was that the pattern we were seeing didnt make a lot of sense. Click here to read an in depth description of the patterns the researchers observed and the concept of tail energy. Q. Why do you think this research paper received a Test of Time award? Aruna: There are two things in this paper that attract attention. The first is the novelty. Everyone intrinsically knew that power would be an important consideration for battery-operated phones, but power measurement tools were not available. So there is a competitive advantage to the research. The second is that this paper goes against conventional wisdom. It showed that energy and performance are not always correlated. Usually, the thinking is that if you improve network performance (i.e., time to send data), then you also save energy. But our work showed that this is not always the case. And more importantly, energy consumption not only depends on how much data you send, but also on how you send the data (i.e., in bulk, or in chunks). Follow-up research designed intelligent ways to send data to reduce power consumption based on how the radio state machine is designed. Even beyond cellular communication, several researchers have since used this idea of carefully looking at the state machine to improve performance. Q.    Would there be a difference in data if the study used today’s smartphones instead of  the Nokia N95? Would the study have to be modified if the device used was different? Aruna: Actually this is one of the reasons for the wide reach of this work; our observation is not tied to a single phone, a single application or even any particular cellular standard. When we did this work in 2009, the cellular standard was 3G. Our measurements showed that the tail energy problem is in the 3G radios which were used by all phones at that time. Since then cellular networks have evolved to 4G/LTE and 5G. Interestingly, this exact tail energy problem has been observed in 4G/LTE as well as 5G. Since latency is an important criterion, the radios still try to stay in high-energy states after transmission. Q.    What was the thought process behind naming your protocol TailEnder, a term often used in cricket? Arun at UMass came up with this name. Tailenders in cricket refers to the last set of batters on a team. They are the “tail” of the team’s batting order. Since our transmission algorithm was related to reducing or ending the impact of tail energy, it can also be called a “TailEnder”. Q.     As a Test of Time award-winning paper, has this paper changed or influenced your current research? Aruna: I built my career on measuring, characterizing, and improving power consumption on phones. Specifically, I have used this idea that improving performance does not always mean improving power/energy, in many different research areas. For example, we found that when loading Web pages, making the Web page load faster does not always mean you are reducing power. Niranjan: Aruna and I are working on sustainable Natural Language Processing (NLP). With the prevalence of Large Language Models (LLMs), energy considerations have become even more important now than they were a decade ago. Aruna and I have been studying and improving energy consumption of these LLMs these past few years. In our recent work called “Irene,” we show how to accurately predict energy consumption and bottlenecks in transformer-based neural networks. Q. Do you see companies using TailEnder in their products in the future?  Aruna: It is always hard to know whether a company adopts ideas from a paper. Once you publish, the idea is in the public domain and anyone can use it… However, in some cases you do know when a work builds on your work. For example, AT&T has this really nice tool called ARO (Mobile Application Resource Optimizer). This tool helps developers design applications that are more power efficient. They used this idea of tail energy in both 3G and LTE to provide best practices for different applications including video applications. You can learn more about it here. Similarly, after the paper came out, some researchers from T-Mobile spoke to us about the work and our findings. The nice thing about this work is that once you know about the tail energy problem you can design applications to get around the problem. Q. Do you have any memorable experiences from the time you worked on the project? Niranjan: We were trying to prove some bounds on our algorithm and the three of us went to Antonio’s Pizza, a popular Pizza place in Amherst. We were discussing the proof while eating, and Arun had a breakthrough and wrote the proof on a napkin. We then went back and transcribed it. This is the one vivid memory I have of writing this paper. Aruna: We almost did not make it to the deadline for the paper. We submitted this paper at 2.59am for a 3.00am deadline. We then went back to our apartment to eat some Maggi (an Indian version of instant noodles). There is no way I would have imagined then that this paper would win a Test-of-time award.   More about the patterns that were observed and the findings: Suppose I wanted to upload 100 Mb of data from my phone using a cellular network. If I sent it in one transmission, then the total energy it took was less than if I sent it in ten transmissions of 10 Mb chunks each. One might guess that there was some overhead to each.  But what was more puzzling was that the total energy consumed increased dramatically, if we waited longer between each transmission than if we sent the transmissions right after each other. Arun kept asking me why! He was saying things like “congestion control misbehavior” which I had no clue about. I hadn't even taken a networking class (I probably should have).  I knew very little about cellular networks then, and knew even less about congestion control. So I turned to Aruna and repeated Arun’s question. She then jumped in and we tried to figure out an explanation. That is how this project began. Aruna: I could see why Arun was asking that question. But I had no clear answer either at that time. Then a few months later, I happened to be at a (different, unrelated) seminar at Dagstuhl in Germany. Dagstuhl is a castle in a remote location in Germany where Computer Scientists get together to exchange ideas about different research topics. At the dinner table, there was a researcher from Nokia sitting next to me. I mentioned to him about Niranjan’s measurements and observations. He didn't have an answer either, but he said we should look at the cellular radio state machine. That sparked the idea for the rest of the work. Niranjan: When we looked at the state machine of the cellular radio, we were able to answer the question that has been bugging us for over a year. So the radio is in three states. First is the low power state when no data is being sent; in this state the radio does not really expend any power. When there is data to send, the radio moves to an intermediate state and a high power state; the higher the power the faster you can send data, but also the faster you drain the battery. But after sending the data, the radio does not immediately go back to the low power state. It stays in a high power state for a little time in case there is more data to send. This is clearly a waste of battery. We call this the “tail energy”.  But the radio expends this tail energy for performance reasons. If you move back to the low power state immediately after sending data, and more data has to be sent, you will incur delays. The cellular radio wants to avoid this, so it stays in the high power state just in case. So going back to our puzzling observation, we could now explain why we were seeing the high overhead and why sending data chunks right after each other is better than waiting longer. We did some careful experimentation to measure this tail energy and found that 3G radios expend considerable tail energy. So when you send packets in intervals, there are long periods of time that the radio is in a high power state even when no data is being sent—>more tail energy->quicker battery drain. In 2021 researchers at the University of Michigan and the University of Minnesota documented that this tail energy problem exists even in 5G (https://feng-qian.github.io/paper/5g_sigcomm21.pdf). What is more interesting: they also used the state machine analysis to measure the tail energy in 5G radios.  

  Thursday, 23 November, was a special night for the female students in Computer Science when they got together, and with Professor Aruna Balasubramanian, in what they all hope will be the first of many such events. SiEun Park (CS, U3):  Last Thursday, female students and Prof.  Aruna Balasubramanian from the computer science department gathered together for the first time. Even though they are from diverse backgrounds, the reason they came together is to foster community, collaborate, and empower. The event was organized by CS Juniors and Seniors and supported by the department. The event started off with introducing each member regarding their biography, the reason they chose CS, and any problems they are facing. Because it was the first-ever time for getting together, it provided a unique platform for female students to connect, share experiences, and inspire one another in their pursuit of excellence in the field. One of the key objectives of the event was to create a supportive network that would enable female students to share insights, exchange ideas, and form lasting connections.  The night was mainly led by Prof. Balasubramanian, visiting professor from Stony Brook University, NY. Prof.  Balasubramanian aims to address problems which CS female students face. In CS, women are underrepresented, so it is important to make sure that they are also provided a supportive environment to thrive. She is a part of WiCS (Women in Computer Science) association in Stony Brook University, and other associations which help to connect CS female students. She talked about how our students get to join such meetings in order to receive support from one another. Professor Aruna Balasubramanian: Computer science as a discipline has historically suffered from underrepresentation of many demographics. In 2019, the department of Computer Science at Stony Brook formed a diversity committee to broaden participation in Computer Science, and I chair this committee. My goal in SUNY Korea is to reach out to the Women students in the department, who are severely underrepresented.  The women students in the department self-organized and planned a dinner event for a first ice-breaker meeting. I am meeting many of the students for the first time, so the goal of the dinner was to introduce myself and make sure the students are comfortable sharing their experience with me. The students were extremely forthcoming and really bright; it ended up being a very fun evening. This is only the first step. Our plan is to meet regularly over the year to understand the challenges faced by students and to create a support system that is self-sustaining.    

  Professor Yoon Seok Yang from the Computer Science Department was a featured speaker at the Korea Times Forum: Survival and Growth Strategies in the AI Era, which took place on Wednesday, October 18th, at the Korea Chamber of Commerce and Industry building in Seoul. Professor Yang took part in the initial panel discussion, which focused on the transformative influence of AI technology on human society and addressed the ethical concerns accompanying its advancement. Along with Professor Yang, Uhm Yul, Director General of the Artificial Intelligence Policy Bureau at the Ministry of Science and ICT, Chang Joong-ho, a professor at Seoul School of Integrated Sciences & Technologies (aSSIST), and Kim Myoung-shin, Principal Policy Officer at LG AI Research were also esteemed participants of the discussion panel.  Professor Yang highlighted: “I have worked in Silicon Valley for over ten years, and I was part of the AlphaGo developing team, and all developers say that the AI developing trend is so fast that even they don’t know how and when AI will become the dominant force in the future world.  AI innovation holds significant potential across diverse industries. Specifically, it plays a pivotal role in the medical industry, aiding the development of new drugs and medical treatments; contributes to risk assessment and investment analysis within the financial industry; and its language model technologies reshapes operations within the business sector. These advancements all influence our present way of life.   When considering the ethical factors, we cannot stop the growth in technological advancement; rather we need to focus on minimizing side effects through regulations. Emphasizing the risk factors is crucial when developing new technologies, and simultaneously, safeguarding personal information must be a paramount concern." Professor Yoon Seok Yang is an assistant professor in the Computer Science department at SUNY Korea, Stony Brook University. Professor Yang completed his Ph.D. in Electrical and Computer Engineering from Texas A&M University. Prior to his academic role, he contributed as a research scientist at the Neuromorphic Computing Lab at Intel Labs in California, and worked in Google as a silicon and research engineer in the Tensor Processing Unit.  To access more about forum click this LINK.

Computer Science major Sooa Kim won 1st place in the 2023 Jeju Digital Social Innovation Hackathon Competition. The Hackathon competition, hosted by the Jeju Special Self-Governing Province, was held on September 21st – 22nd. The theme of this year’s competition was proposing ideas for products or services that solve societal issues using digital technology. Sooa Kim came in first by proposing a customized power monitoring platform. “In Jeju, which has declared carbon neutrality since 2012 and is actively working to expand new and renewable energy sources, citizens can use the time-of-use electricity rate system that takes into consideration the characteristics of new and renewable energy. The time-of-use rate system is a shifting of electricity rates based on the season and time of day, and the ultimate goal is to match the supply curve of renewable energy and the demand curve for electricity. Accordingly, we proposed “Jirit” (찌릿, the sound of an electric shock), a customized power monitoring platform that encourages citizen participation by providing real-time monitoring of power usage and user-centered data analysis that induces demand response using incentives. Through this, we can expect economic benefits for electricity users and a carbon reduction effect through the time-based rate system. It was a meaningful time to address the shortcomings of the existing power analysis platform with technology optimized for Jeju Island's unique power system. I had the opportunity to receive feedback from professional mentors regarding the issues that needed improvement when the platform is put into use, and this led me to consider a more user-centered approach.    

A new brain-inspired computing research lab, led by Prof. Yoonseok Yang, has recently been established in the department. The Brain-Inspired Computing Lab (BCL) is focused on developing new computing technologies inspired by the human brain. The BCL has joined the Intel Neuromorphic Research Community (INRC), a consortium of academic, government, and industry groups around the world that are working to advance neuromorphic computing. The lab has already attracted four undergraduate students and one PhD student. Prof. Yang is a leading expert in neuromorphic computing. His research has focused on developing new hardware and software platforms that can mimic the way the brain processes information. The BCL's work is expected to make significant contributions to the field of neuromorphic computing and could lead to the development of new and powerful computing technologies. Lab link: https://sites.google.com/view/suny-bcl/home    

  During the summer Prof. Byungkon Kang held special lecture sessions for students invited from the Incheon Academy of Science and Arts (인천과학예술영재학교). The 17-hour lecture spanned two days from Jul. 17th to the 18th touching on a list of subjects including introductory machine learning, natural language processing, and probabilistic reasoning. Lecture sessions were accompanied by hands-on programming exercise sessions where the participants were shown how to implement basic machine learning systems using PyTorch. The lectures were concluded by project-oriented workshops to help students apply AI techniques to solve various engineering and scientific problems.  

  Prof. Byungkon Kang is part of a team that has won a major grant from the Institute for Information & Communication Technology Promotion (IITP).   The project (IITP grant RS-2023-00259678) is a joint effort by Inha University, Incheon National University, and SUNY Korea. It is intended to support graduate students including employees at local industries as part-time graduate students. Such part-time students will be eligible for up to 90% tuition support by this project. Regular graduate students can also be supported as RAs. The annual budget allocated to SUNY Korea amounts to 100M KRW (about 75K USD). The theme of SUNY Korea's part is designing safety and convenience features of smart vehicles. Such features include, but are not limited to, the following scenarios: Hazardous situation detection, safety-aware path planning, and designing cost-effective on-board recognition systems. We expect this project to be a multi-disciplinary research involving many sub-areas of AI (such as computer vision, decision making, natural language understanding, etc.) and possibly other engineering fields (such as mechanical and electrical engineering).    

Exchanges of students and faculty between SUNY-Korea and the “Home Campus” at Stony Brook re-affirm the very-close link between both institutions and offer opportunities for numerous enriching experiences, academic and non-academic, for all involved.  Such exchanges have always been envisaged (and the department has had visiting faculty from the Home Campus), but, unfortunately, they became impossible during the COVID pandemic.  So, the department is now especially pleased to announce that professors Aruna Balasubramanian and Niranjan Balasubramanian will be with us for the Fall-2023 and Spring-2024 semesters.  They will teach several undergraduate and graduate courses and also collaborate on research with the local faculty.   (From left to right) Prof. Aruna Balasubramanian / Prof. Niranjan Balasubramanian Prof. Aruna Balasubramanian received her PhD from the University of Massachusetts (Amherst) and has been with SBU since 2015.   Her work incorporates elements of both networking and systems in the context of mobile devices. The goal of that work is to design mobile protocols/systems that radically improve performance. Current mobile system designs make assumptions that, while true for the wired environment, are not valid for the mobile environment. Instead, she wants to re-architect mobile systems to better support current and next-generation applications. Dr. Balasubramanian's recent projects include MobileHub (reduces energy consumption of always-on sensing applications by leveraging heterogenous hardware), WProf (a tool that identifies the bottlenecks during page load), and FindAll (makes mobile applications less reliant on the cloud by building a local search engine on phones, that lets users search locally).   Prof. Niranjan Balasubramanian received his PhD from the University of Massachusetts (Amherst) and has been with SBU since 2015.  His research is motivated by the challenge of building systems that can extract, understand, and reason with information present in natural language texts. His research interests are in two broad areas: NLP and information retrieval. He has worked on different projects in areas like Question Answering at a 4th Grade Level, Event Schema Generation from news stories, Machine Learning for Information Retrieval, Energy-efficient Mobile Search, and Automatic Wikipedia Pages.

We are proud to announce that Prof. François Rameau has, in collaboration with colleagues at Laval University (Canada) and Adobe, co-authored a paper set to appear in the prestigious IEEE Transactions on Pattern Analysis and Machine Intelligence.  The journal is among the most highly ranked in the world: all respected journal rankings place it in the top five worldwide  in computer science (and related fields).   The research, entitled "A Perceptual Measure for Deep Single Image Camera and Lens Calibration", introduces a novel deep learning-based camera calibration method. Additionally, the team developed a new perceptual measure to quantify the impact of camera parameters on human perception for various tasks such as virtual object insertion and compositing.   Additional information can be found on the project webpage: https://lvsn.github.io/deepcalib/    

Prof. Zhoulai Fu has received a Starting Program Grant from the National Research Foundation of Korea to support his research in Numerical Software. His work focuses on developing reliable and efficient numerical software using advanced programming languages and software engineering solutions. The grant provides funding from  late 2022 to early 2025.   Numerical software is used across various fields, including artificial intelligence, engineering, robotics, and medical imaging, but it can produce misleading or erroneous results if not built with care. Professor Fu's project aims to address this challenge by building trustworthy and performant numerical software with formal methods.   As part of this effort, he is investigating the use of pure 16-bit neural networks over 32-bit for image classification problems. Image classification involves automatically categorizing images into predefined categories and is an important problem in machine learning and everyday life, such as in facial recognition. Professor Fu's current work seeks to explore the surprising performance benefits of using 16-bit neural networks over the standard, widely used 32-bit neural networks.