Stony Brook University Computer Science

Three undergraduate students (Dongyeong Kim, Gilho Kim, and Myeongkjun Kim), together with one Ph.D. student (Si Yong Lee), have published a research paper in IEEE Access, a Q1-ranked international journal, demonstrating an outstanding educational and research achievement. This work was conducted under the supervision of Prof. Yoon Seok Yang and reflects the strong research-oriented educational environment fostered through close faculty mentorship. The research focuses on improving the training and inference efficiency of spiking neural network (SNN)–based object detection models for low-power, real-time edge AI applications. To address the high computational cost of conventional CNN-based detectors such as YOLO, the group enhanced a Sigma–Delta spiking YOLOv3 framework by incorporating advanced training and optimization techniques. The proposed methods improved convergence, detection accuracy, and energy efficiency, and were validated using multiple real-world datasets. In addition, the trained model was successfully deployed on Intel’s Loihi 2 neuromorphic platform, demonstrating its feasibility for real-time and low-power operation. Through this project, undergraduate students gained hands-on experience in advanced AI research, from algorithm development to deployment on a neuromorphic hardware platform. This work demonstrates the value of active undergraduate participation in research and the CS Department’s commitment to integrating education and cutting-edge research.

  Two CS undergraduates, Juyoung Yun and Sol Choi, were the lead authors on a paper that won the Best Paper Award at the 2025 International Conference on Neural Information Processing (ICONIP), a long-running and well-regarded conference in the field of AI. The other authors were their supervisors: Profs. Francois Rameau, Prof. Byungkon Kang, and Prof. Zhoulai Fu. The paper is “Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited Learning”. Modern AI models are usually trained in 32-bit “full precision,” which is accurate but expensive in terms of memory and computation. Their work shows that one can safely train neural networks entirely in 16-bit precision, without the usual 32-bit “safety net,” even on modest hardware. A practical recipe, validated by theory and extensive experiments, is given for choosing the right settings so that training remains stable and the final model quality matches (and sometimes even rivals) 32-bit training. This is particularly important for settings where computing resources are limited, such as smaller labs, edge devices, or student projects that cannot rely on large GPU clusters. By cutting the precision in half while preserving performance, our approach makes state-of-the-art neural network training more accessible and more energy-efficient. This efficiency and practicality were key reasons the paper received the award.

Brain-inspired Computing Lab (BCL), led by Prof. Yoon Seok Yang, was selected to participate in the 2025 Preliminary Startup Package program organized by the Ministry of SMEs and Startups of Korea. BCL’s selected project, “Next-Generation AI Neuromorphic Processor Semiconductor Design”, is about developing a new type of on-device AI chip for edge computing that works more like the human brain. Most of today’s AI systems use GPUs, which are powerful but consume a lot of electricity and cost a great deal to run. As AI models get bigger and more complex, this problem is becoming even more serious. BCL aims to create a processor based on neuromorphic computing, which mimics the way neurons and synapses in the brain work. This makes it possible to run AI much faster and with far less energy, which is especially important for edge devices like self-driving cars, robots, and smart devices that need to work in real time.  In the project, BCL plans to design the chip itself, build a test version using an FPGA board, and make sure it can handle well-known datasets like ImageNet. BCL are also creating easy-to-use software tools so that developers can work with their chips just like they would with existing platforms such as TensorFlow or PyTorch. In the future, the goal is to make these chips available for many different applications and contribute to AI technology that is not only more powerful but also more energy-efficient and accessible.

  The department is pleased to welcome two new faculty members whose teaching and professional experiences further enrich our department's commitment to high-quality education and innovation. Prof. JiHyun Jeong joins the department as an Assistant Professor. She recently completed her Ph.D. in Information Science from Cornell University, where her research focused on Human-AI interaction, trust, and collaboration. Her dissertation explored how autonomous agents can balance performance with social considerations when interacting with humans. Prof. Jeong brings a strong interdisciplinary background in human-computer interaction, AI-driven social behaviors, and team cooperation technologies. She has also worked as a PhD intern at Motional and has received multiple awards, including an Honorable Mention at ACM DIS and a DARPA grant as co-author. Her research expands the department's strength in socially aware AI, human-agent interaction, and computational models of cooperation. Prof. James Finn joins as a Teaching Professor, bringing a wealth of international teaching and industry experience. He holds a Ph.D. in Computer Science from Princeton University and has taught computer science and engineering at institutions such as Stanford University, San Francisco State University, and King Mongkut’s University of Technology Thonburi (Thailand). His background includes roles as a software engineer at Yelp, technical trainer for Hewlett Packard and Tibco Software, and senior technical writer at MacUser Magazine. Prof. Finn’s extensive experience in technical instruction and global education will greatly enhance our students’ learning experience, particularly in programming and foundational CS courses.

    Prof. François Rameau and his team from SUNY Korea participated in the 5th Annual Nepal AI School (ANAIS), held from December 27, 2024, to January 6, 2025, in Kathmandu, Nepal. On January 1st, Prof. Rameau delivered a captivating lecture on the transformative field of Computer Vision, offering participants an in-depth journey through pivotal research papers and groundbreaking advancements that have shaped the field, particularly in 3D reconstruction. His session provided invaluable insights into the historical and technical context of Computer Vision, emphasizing how cutting-edge developments are empowering machines to perceive, interpret, and interact with the world in unprecedented ways. Participants praised the lecture for its clarity and relevance, which bridged foundational concepts with real-world applications. The Annual Nepal AI School, organized by the Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), is a premier platform in South Asia, bringing together international scientists, faculty, and participants for 11 intensive days of AI-focused lectures, labs, and project work. The program covers a comprehensive range of topics, from fundamental principles to the latest advancements in AI research and applications, fostering collaboration and innovation in the field. https://nepalschool.naamii.org.np/home https://www.linkedin.com/posts/naamiinepal_anais2024-naamii-researcheducateinnovate-activity-7284120674200178688-rb6P?utm_source=social_share_send&utm_medium=member_desktop_web  

  Jaemin Cho, a graduate student from the Computer Science MS program, achieved a remarkable feat by winning the prestigious Hackathon Challenge for Layering Detection in Anti-Money Laundering. This challenge, hosted during the Annual Nepal AI School (ANAIS) 2024 in Nepal on December 26th,2024 to January 6th, 2025, called for innovative solutions to detect layering in financial transactions—a critical step in combating money laundering. The competition focused on creating advanced methodologies to identify and prevent layering, a key stage in the money laundering process. The significance of this challenge extends beyond the technical aspects. It aims to prevent financial crimes by identifying illicit transactions early, ensure compliance with global regulations and laws, and protect economies by mitigating illicit activities that can destabilize financial systems. As the winner, Jaemin has earned accolades and the opportunity to be hired by F1Soft International, one of Nepal’s leading financial technology companies. This recognition highlights the practical and transformative applications of his skills in financial technology. The ANAIS 2024 event was a vibrant platform, featuring 191 participants and 22 speakers representing 18 countries, fostering a truly global exchange of ideas. Twenty-two competing teams, each comprising five members, showcased their innovative solutions to challenges, including the F1Soft Money Laundering Challenge and the Startup Challenge. Participants had the opportunity to present their work, sharpen their skills, and contribute to impactful problem-solving on a global scale. The event’s theme, "Big Things Can Happen from Small Places," underscored the potential for groundbreaking ideas to emerge from any part of the world. ANAIS 2024 was made possible through the support of its sponsors and partners. The title sponsor, F1Soft International Pvt. Ltd., and the silver sponsor, YoungInnovations Pvt. Ltd., provided vital backing for the event. The venue partner, Premier International IB Continuum School, hosted the event, while the AI Hackathon received additional sponsorship from UK International Development, The Asia Foundation, Data for Development in Nepal, eSewa Ltd., and Fonepay. Further support from Google, SecurityPal, and Fusemachines reinforced the collaborative spirit of this initiative. Jaemin’s accomplishment not only showcases his talent and dedication but also brings pride to our academic community. His success serves as an inspiration to all students, proving that with hard work, innovation, and determination, transformative contributions can be achieved on a global stage.   https://nepalschool.naamii.org.np/home https://www.linkedin.com/posts/naamiinepal_anais2024-naamii-researcheducateinnovate-activity-7284739753776660480-MO5w?utm_source=social_share_send&utm_medium=member_desktop_web https://www.linkedin.com/posts/naamiinepal_anais2024-naamii-researcheducateinnovate-activity-7285464529910603776-VdZT?utm_source=social_share_send&utm_medium=member_desktop_web

Prof. Kang’s latest publications include a paper in the proceedings of the 2024 International Conference on Information and Knowledge Management (CIKM). This is a premier AI conference well-known for its papers on data mining, machine learning, and information management. It is recognized by many academic institutions, including NRF in Korea, as a class-A CS conference (CORE rank A).  The paper is about a machine learning algorithm that bypasses a shortcoming of AutoDiff, a core component in deep learning that enables us to train complex models. When training a model that uses linear algebraic routines, such as determinants, AutoDiff will give suboptimal performance. The paper proposes a reparameterization algorithm that yields the correct learning scheme as well as potentially saving more time by taking advantage of the parallelizability of the new formulation. A second paper, with Ph.D student Joonkyu Han as the main contributor, was published in the proceedings of the 29 th IEEE Pacific Rim International Symposium On Dependable Computing (PRDC). This is a well-regarded conference that showcases a variety of novel papers on software reliability, safety, security, and dependability. The paper deals with the use of visual and spatial information to authenticate a user during a login process. A fall-back authentication is an extra step of authentication taken when the user forgets his/her password. Typically-used secure question or email verification approaches have their shortcomings in terms of memorability and security. This work provides a way to authenticate a user by exploiting visual navigation through a virtual map; that is, a successful navigation through a map constitutes a successful login. Humans are particularly well-suited to remembering visual navigation, a fact that motivates and justifies our approach. Prof. Kang was also recognized as an Outstanding Reviewer for the 35th British Machine Vision Conference (November 2024), a highly ranked conference on ahigh-ranking computer vision.

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.