Steven Wang

🚀 BostonHacks 2024! Last weekend, Andrew Sun and I won 2nd place for the CometCare track at BostonHacks 2024, which was our first Hackathon. We had a lot of fun working from the ground up to create a final product called GoodDeeds, a platform designed for NGOs, non-profits, and other local organizations looking to post about events requiring community involvent across the spectrum of urgency, ranging from severe post-disaster recovery efforts to more relaxed community service initiatives. 🏢 For Organizations Organizations can sign up to create events, specifying a location, urgency rating, volunteer needs, and a description of the event. 🙋 For Volunteers Individuals like you and me can sign up as volunteers with a specified “home” location and a distance radius we’re willing to travel. Volunteers can view all events within their set radius, filter by date and start time, and sign up. Their email is added to a mailing list for updates, and they can reach out to event organizers or ask questions to an OpenAI chatbot directly within the app. 🎮 XP Levels To encourage community involvement, we introduced a game-like feature: XP levels for individual users that increase with volunteering attendance. Higher XP points are awarded for events with higher severity ratings (like post-hurricane cleanup). Users and organizers can see a leaderboard displaying top volunteers with their XP points and approximate location. 📣 Real-Time Notifications We integrated a notification system, allowing organizers to alert individuals within a set radius about upcoming events or updates for events they’ve signed up for, powered by the Twilio SendGrid API. The emails look nice, too! 💻 Tech Stack We had a lot of fun learning Streamlit, a frontend library built in Python, along with building a Flask backend and using a PostgreSQL database hosted on Neon. The frontend was deployed on Streamlit Cloud, while the backend was deployed on Render. Given the short time constraint, we’re thrilled with what we accomplished, and we look forward to future development! 🌐 Check It Out! Demo Video: https://lnkd.in/ecJyGufP Live Site: https://lnkd.in/ey5ht6yG (starts cold, may take a while on first signup) Source Code: https://lnkd.in/ew7PJySZ

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Today, I am excited to have presented my research project on Shop the Look: Discover Your Style With AI and Visual Search for Fashion (https://lnkd.in/gvdPsm5s) on the Industrial Day at the University of Massachusetts Dartmouth! 🎉 My project focuses on building a custom YOLOv5 model to detect fashion accessories like shirts, pants, shoes, handbags, and sunglasses. The goal is to streamline product recommendations and transform visual inspiration into actionable shopping options, much like Amazon's and Pinterest's "Shop the Look" feature. The pipeline contains the following steps:👇 1️⃣ Data Collection • Scraped 100 images per class from Pinterest, focusing on various clothing accessories. 2️⃣ Data Annotation • Annotated images using Label Studio, creating bounding boxes around objects and exporting them in YOLO format. 3️⃣ Model Training • Trained a YOLOv5 model using YOLOv5-large weights, fine-tuned with PyTorch to match custom classes. 4️⃣ Feature Extraction • Used a pre-trained ResNet-50 model to extract feature embeddings from the input images for further analysis and recommendation. 5️⃣ Running Inference • Applied the trained model to new images, detecting clothing accessories using OpenCV-DNN, and providing similar fashion recommendations based on the detected objects. 6️⃣ Visualizing the Results • Displayed detected objects with bounding boxes and labels to validate the model's accuracy, while offering recommendations for similar fashion items. GitHub repository for the project: https://lnkd.in/gNKuteeq For more details, check out Kunlong Gu's insightful Pinterest blog: https://lnkd.in/gN87digb A big thank you to Kunlong Gu for sharing this fantastic resource! This project draws inspiration from both Pinterest Engineering and Amazon Science. I am currently exploring Spotify's ANNOY (Approximate Nearest Neighbors Oh Yeah) for generating similar recommendations on an article in a low-latency runtime environment. Stay tuned. I will share the updates soon! 💫 🙌 References: • Approximate Nearest Neighbors by Spotify: https://lnkd.in/gPMAq6NM • Vector Similarity Search by Google: https://lnkd.in/gfY3UGC5 • Efficient Similarity Search by Facebook: https://lnkd.in/gn_EkHa6 It was a great opportunity to connect with fellow students and share insights. #YOLOv5 #ResNet50 #DataAnnotation #ComputerVision #FashionRecommendation #IndustrialDay

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This past weekend, I had the amazing opportunity to build out a new idea at the Berkeley AI Hackathon. It was a great time, and I learned a lot from the experience. Our team built Billy, a social media platform to help people engage more thoughtfully within the political sphere by helping make their ideas for bills come to life. Thanks for having me and thanks to my team for helping building such a cool platform! Billy's goal is to foster informed political engagement online. Using AI and a vast database of American legislation, users can write their bills. Once a bill is created with Billy, it's posted for others to interact with—exploring ideas, voting on issues, and leaving comments. Billy promotes thoughtful dialogue, new perspectives, and idea-sharing, creating a space for public political discussion. To learn more about how we built it, feel free to check out this dev post. Also, check out our demo in this video. https://lnkd.in/e3Um8SGf https://lnkd.in/eGueJYAe #AI #Hackathon #BerkeleyAI #PoliticalEngagement #CivicTech #Innovation #MachineLearning #WebDevelopment #Nextjs #OpenAI

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Knock knock! 🚪 Who's there? 🤔 It's GLaMM! 🌟 Excited to share that our paper, GLaMM: Pixel Grounding Large Multimodal Model, will be presented at CVPR 2024! 🎉🎉 tomorrow (Jun 20, 2024, Thursday) at 10:30 AM, poster #326! 🔥 Why GLaMM is special 🙌: 1. First end-to-end LMM tackling multiple vision-language tasks like Grounded Conversation Generation (GCG), image captioning, region-captioning, referring expressions segmentation, visual question answering, and more in a single model. 💥 2. We developed a fully automatic image annotation pipeline to build GranD dataset. 📊✨ 3. Fully open-source: Code, models, dataset, and dataset generation pipeline. 🚀💻 Check it out: GranD Dataset: https://lnkd.in/eYuKia9C GranD-f Dataset: https://lnkd.in/dYcjC_e8 Code: https://lnkd.in/d3yFnPRt (Do not forget to ⭐️ our repo). Come meet the team (Hanoona, Sahal, Salman, Ming-Hsuan, Fahad) at Arch4A-E at poster #326 to know more! 😊👩‍💻👨‍💻 #CVPR2024 #MultimodalModels #VisionLanguage

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🌟 Career Kickstarter Event Recap 🌟 Had an amazing time attending Northeastern University’s Career Kickstarter-Fall '24 event at the Silicon Valley campus! 🎉 The event provided valuable insights and resources that left me feeling empowered and ready to take my next career steps. Key highlights: ✅ Building a Personal Brand: A deep dive into defining and expressing my unique value. ✅ Experiential Learning: It’s incredible to see the emphasis Northeastern places on hands-on experience. The team shared how vital this is in shaping career success. ✅ Networking: The interactive session allowed me to connect with peers and professionals, expanding my network and gaining new perspectives. ✅ Career Planning: I walked away with a clear plan and SMART goals for the upcoming months, which has already given me a great sense of direction. Huge shoutout to the career development team—Emanuel Cifuentes Machado, M.A. and Natasha Odama for their dedication and guidance throughout the event. #NUCareerKickstarter #CareerGrowth #ProfessionalDevelopment #SiliconValleyCareers #ExperientialLearning #NetworkingOpportunities #CareerPlanning #NortheasternUniversity #SVCampus

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LATE POST : Week 2 Update from the Headstarter AI's Fellowship! As part of the fellowship, I have had to learn an entirely new tech stack in order to be able to proceed through the assigned projects. I’ve been building My Pantry, a web app designed to help you manage your pantry. It’s powered by Next.js, Firebase, Material UI, and the Meta Llama 3.1 8B Instruct API. Key Features: Inventory Management: Keep track of everything in your pantry with ease. Recipe Suggestions: Discover personalized recipe ideas using the items in the pantry with help from Meta AI. Coming Soon: Adding computer vision to automatically recognize and log items using photos. Check it out: https://lnkd.in/dVtYDpa8 While the UI and the features are very basic, I will be enhancing it as I go upwards with the learning. A huge thanks to the Headstarter team for this fantastic learning experience. #Headstarter #WebDevelopment #NextJS #Firebase #MaterialUI #LearningJourney #CodingCommunity

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𝗧-𝟴 𝗗𝗮𝘆𝘀 𝘁𝗼 𝗚𝗛𝗖 𝟮𝟬𝟮𝟰: 𝗝𝗼𝗶𝗻 𝗠𝘆 𝗕𝗿𝗮𝗶𝗻𝗱𝗮𝘁𝗲 𝗼𝗻 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗙𝗔𝗔𝗡𝗚 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲! One of the aspects of GHC I'm most excited about is participating in 𝐁𝙧𝒂𝙞𝒏𝐝𝒂𝒕𝒆𝘀. Braindates offer an incredible opportunity to connect with like-minded individuals, share experiences, and gain new insights in a more intimate setting. They truly embody the spirit of GHC by fostering personal connections that contribute to our collective growth. I believe strongly in GHC's mission because we share the same ideology of helping women navigate the tech industry. This year's theme, "We + Me", emphasizes how our journeys contribute to a greater collective impact. Navigating our way through big companies like FAANG isn't an easy task. Beyond keeping up with regular job responsibilities, there's always so much happening around us. It's crucial to ensure that, in addition to professional development, we don't miss out on personal growth opportunities. During my time at Microsoft, I had the incredible opportunity to be part of the Aspire Leadership Council, participate in hackathons, lead an early-in-career group in the Growth Groups community, and serve as a speaker for a series organized by Microsoft for undergraduate students. These experiences have significantly contributed to both my personal and professional growth. Having worked there for three years, I've gathered valuable tips on how to make the most out of your experience in a FAANG company. I'd love to share these insights and learn from yours! In that spirit, I'm hosting a Braindate titled: "𝙈𝒂𝙭𝒊𝙢𝒊𝙯𝒊𝙣𝒈 𝒀𝙤𝒖𝙧 𝙁𝑨𝘼𝑵𝙂 𝙀𝒙𝙥𝒆𝙧𝒊𝙚𝒏𝙘𝒆: 𝙃𝒐𝙬 𝙩𝒐 𝑫𝙞𝒗𝙚𝒓𝙨𝒊𝙛𝒚 𝒂𝙣𝒅 𝑻𝙝𝒓𝙞𝒗𝙚" If this aligns with your interests, let's come together to empower each other and make the most of our individual and collective experiences. Feel free to reach out or sign up for the Braindate session here - https://lnkd.in/gJ5JbEbM Looking forward to connecting with you all! 🌟 Wᴇ + Mᴇ = Eᴍᴘᴏᴡᴇʀᴇᴅ Tᴏɢᴇᴛʜᴇʀ 🌟 #GHC2024 #AnitaBOrg #WePlusMe #Braindate #WomenInTech #Networking #FAANG #PersonalDevelopment #AspireLeadersCouncil #Hackathons

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Yesterday, I was lucky to attend another awesome Supermomos event on LLM-based applications at Microsoft's Times Square office. Thanks to Abby T. and Steven Kell for organizing! 🧑‍💻It was great learning about the Agentic RAG architecture from Yujian Tang (OSS4AI). I enjoyed the live coding workshop to actually build a RAG agent together and interact with it in realtime - particularly the part where you could see its internal "thoughts" (e.g. "I need to do X. I have a tool that does X. Let me formulate a text query to accomplish X"). Although it was only a simple example with PDFs of 10-Ks from Lyft and Uber, the implications of being able to give tools and agency to an AI is powerful. 🗜️I never thought I'd find learning about the inner workings of vector search algorithms so fascinating, but Zain Hasan (Weaviate) really delivered. As a "visual storyteller" myself, I really appreciated how he broke down Scalar, Product, Binary, and Matryoshka Quantization. Just when I thought that degraded retrieval recall was a given compromise for compressing vectors, he brought it home by unveiling Weaviate's innovative "Adaptive Retrieval" algorithm that achieves 30x+ reduction in memory usage at the cost of only ~5% retrieval recall. #GenAI #AgenticRAG #RAG #Vectorsearch

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The Babel Effect: Multilingual Performance Discrepancies in LLMs Excited to share my research, "The Babel Effect," which explores performance gaps in Large Language Models (LLMs) between high-resource languages like English and low-resource ones like Nepali. The paper dives into tokenization challenges, data scarcity, and training inefficiencies. We propose solutions like data augmentation and improved tokenization to address these issues. Read more here: https://lnkd.in/deWwJ4uN #AI #NLP #Tokenization #ML

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Check out our new paper! It’s also my first arxiv!

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🌉 Dead Week at UC Berkeley 🌉 May has swept through Berkeley, bringing with it the annual tradition known as Dead Week. As the buzz of Telegraph Avenue dims slightly, we undergrads know it’s our cue to hunker down and hit the books one last time before finals. Here are some key points I've been focusing on as we wrap up this dead week! Lesson 1: Structure in Chaos Berkeley’s Dead Week is ironically alive with a flurry of review sessions and last-minute project deadlines. I've found that a structured approach—breaking down study sessions into focused blocks—helps me navigate this intense week. The serene backdrop of the Bay Area, from the calming waters by the marina to the redwoods around campus, provides the perfect study break scenery to clear my mind and reset. Lesson 2: The Strength of Shared Struggles There's something about the communal silence of Mainstacks Library during Dead Week that reminds you you're not alone. Joining forces with other students, we grind out practice exams and Pomodoro sessions until 2 am. It's these moments of collaboration that transform challenging content into shared victories. Lesson 3: Well-being as a Priority Dead Week at Berkeley is not just about academic survival but also about maintaining well-being amidst the madness. I make it a point to keep up with my runs through the Fire Trails, ensuring that my mental and physical health aren’t sidelined during these critical times. Lesson 4: Reflection and Readiness As I review notes and practice problems, I’m also reflecting on how each semester builds more than just technical skills. It hones resilience, deepens patience, and expands my understanding of what I can achieve under pressure. With finals on the horizon, I feel prepared and eager to tackle the challenges ahead. To my peers also in the midst of Dead Week, let’s push through to the finish line with determination and spirit! 💪 What are your Dead Week strategies? Let’s share some wisdom and encouragement as we gear up for finals! 🚀 #UCBerkeley #ComputerScience #DeadWeek #FinalsPreparation #GoBears

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🎉 Thrilled to announce that I have three papers accepted at EMNLP 2024! 🚀 Here’s a glimpse into each: 1️⃣ Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (led by myself, co-authored with Zack Hui and Gary Liu) 🌟 In this work, we tackle the limitations of generative models for extractive machine reading comprehension (MRC) by introducing QASE, a lightweight module that significantly improves performance without increasing computational demands. QASE enables PLMs to surpass GPT-4o in few-shot settings, achieving or exceeding state-of-the-art (SOTA) results across multiple datasets.   📄 Read more: https://lnkd.in/esuJC5Ga 2️⃣ Defending Against Social Engineering Attacks in the Age of LLMs (jointly led by myself, Tharindu Kumarage, and Amrita Bhattacharjee) 🌟 This paper explores how LLMs can both facilitate and defend against chat-based social engineering (CSE) attacks. We introduce SEConvo, a novel dataset simulating CSE attacks and defenses in real-world contexts. Our proposed framework, ConvoSentinel, systematically flags malicious messages and improves detection across entire conversations, offering a cost-effective and adaptable solution.   📄 Read more: https://lnkd.in/ebvDQBkm 3️⃣ A Survey on Open Information Extraction from Rule-based Models to Large Language Models (jointly led by Pai Liu, myself, and Ziwei (Sara) Gong, and more) 🌟 This comprehensive survey covers the evolution of OpenIE from 2007 to 2024, mapping its trajectory from rule-based methods to neural and pre-trained LLMs. It highlights key datasets, evaluation metrics, and offers insights into how traditional OpenIE research can inform future developments in the LLM era.   📄 Read more: https://lnkd.in/eJ8D_sBz #EMNLP2024 #NLP #AI #LLMs #Cybersecurity #InformationExtraction

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Over the past few weeks, many people have reached out to me to learn more about the work I did during my internship at American Express, where I focused on 𝗲𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗮𝘂𝘀𝗮𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝘂𝘀𝗲𝗱 𝗯𝘆 𝗹𝗶𝗻𝗲 𝗮𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀. To share my technical insights, I’ll be detailing my experience in a series of posts, starting with an important concept I worked on: the distinction between predictive and causal modeling. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝘃𝘀. 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: 𝗪𝗵𝘆 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱 In data science, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is widely used to forecast future outcomes based on past data. For example, a retail company might use a predictive model to estimate next month’s product sales using historical trends, seasonality, and customer behavior. While useful, this approach only tells us 𝘸𝘩𝘢𝘵 𝘮𝘪𝘨𝘩𝘵 𝘩𝘢𝘱𝘱𝘦𝘯 but not 𝘸𝘩𝘺 it happens. Enter 𝗰𝗮𝘂𝘀𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, which goes a step further to uncover the cause-and-effect relationships behind the outcomes. Instead of just predicting future sales, causal modeling answers questions like, 𝘋𝘪𝘥 𝘢 𝘳𝘦𝘤𝘦𝘯𝘵 𝘮𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘤𝘢𝘮𝘱𝘢𝘪𝘨𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘤𝘢𝘶𝘴𝘦 𝘵𝘩𝘦 𝘴𝘢𝘭𝘦𝘴 𝘣𝘰𝘰𝘴𝘵, 𝘰𝘳 𝘸𝘢𝘴 𝘪𝘵 𝘥𝘶𝘦 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳 𝘧𝘢𝘤𝘵𝘰𝘳𝘴? 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Predictive models might show a correlation, but correlation doesn’t equal causation. If we want to make data-driven decisions—whether it’s in marketing, healthcare, or finance—we need to know what actions directly influence outcomes. For example, knowing that a campaign caused an increase in sales is far more valuable than just predicting sales trends. In my next post, I’ll discuss different causal algorithms, including the ones I used in my project. Stay tuned and follow for more! #internship #healthcare #finance #machinelearning

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Check out this hackathon project that me and Pranay Harpalani created.

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Finished a 9 month Software Engineering Bootcamp with Springboard! PERN full stack web development skills achieved. I'm now transitioning into Headstarter Software Engineering fellowship program where I will be building AI driven projects with other teammates. My gameplan: 1. Continue the data structure/algorithm grind. Daily LeetCode problems alternating between Python and JavaScript. Can try more languages as I acquire them. Learn at least one new algorithm or concept daily. 2. Continue acquiring breath/depth of knowledge in available technologies. There's a lot of libraries/tools out there so I want to focus on the most impactful and efficient ones with thorough documentation. 3. Develop sight-reading skills for code. Similar to sight-reading sheet music, I want to be able to look at any code and figure out how things work as quickly as possible. 4. Implement personal projects. Some ideas include abstracting Optometry decision making into a weighted graph data structure with the end node being the most accurate diagnosis. Subjective refraction can also be abstracted as well since input is simply a Boolean provided by the patient (one or two becomes 0 or 1). A directed graph would work nicely. 5. Find a paid internship/job. This would help apply my skills to a real world application and within a company framework. Also, I got bills to pay like everyone else. Very excited for what's to come! https://lnkd.in/gJUk-RkD

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Week 4 of CUNY Tech Prep: I'm excited to be working with Daniel Chen and Andrew Lam, RT(R) throughout this semester in building a simple reinforcement learning data science project. Daniel is amazing at coding and has won several Hackathons in the past, including the CTP Hackathon that I participated over this past summer. Andrew and I met through a mutual friend, and we have many interests in common, including a background working in healthcare. This week, I watched a video on Clustering in Data Science, which I have attached below for your reference. This video introduced me to the concept of Clustering, which is the grouping of data based off a measure of similarity. The video continues to show a demo of creating dendrograms from a dataset in R. I thought that it was interesting because I recently worked with R and RStudio in a research project for Collegiate Science and Technology Entry Program (CSTEP). I am excited to recreate the same demonstration using Pandas and NumPy in Python.

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🚀 What's a more iconic duo than recruiters and the STAR method? 🎓 College students and GenAI! TLDR: 🤖 Interview robot me on my new portfolio website: xavierpark.com 𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻: Growing up in a Korean household, selling myself has always been one of the most challenging parts of the recruiting process. 🏠 It's always awkward to communicate my past projects/experiences while resisting the urge to downplay my achievements. 🌟 Deep down, I'm quite confident and proud of what I've done, but showing that outwardly is something I definitely still need to work on. But in the meantime… 𝗧𝗮𝘀𝗸: Eliminate all emotions. Become Spock. 🖖 𝗔𝗰𝘁𝗶𝗼𝗻: By leveraging Facebook’s FAISS library, I created a vector database from over 30 of my own personal answers to common interview questions. 📚 I then developed an AI chatbot using the Retrieval-Augmented Generation (RAG) framework to simulate myself providing answers that might come up about my resume. 🤖 𝗥𝗲𝘀𝘂𝗹𝘁: At least until AI develops emotions, recruiters now have a more interactive way to understand my qualifications without answers being impeded by years of cultural nuance and personal affectations. 🌐 This bot can be found on my new portfolio website at xavierpark.com. I encourage you all to use it while you can! 🎯 As affordable as the brand new GPT-4o Mini model is, I’m still a broke college student. 💸 I’m also a bit worried about malicious bots running up my usage, but that’s for a later conversation with my friends over at CHEQ. 😅 **𝗡𝗼𝘁𝗲:** Data for the bot is still a WIP. Coming up with good sample responses takes forever. ⏳ Feel free to give it a try and let me know your thoughts! 💬

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I'm grateful to have had the opportunity to travel back to dear NYC for a couple of days and present about some fundamental theoretical components of the Aerospike vector database. Thank you Microsoft and PyData NYC for hosting and putting on this event, and thank you Arthur Anderson for your wonderful portion of the presentation! (Side remark: after the presentation, I was absolutely stoked when someone told me that I should be a professor after college 😄) I'm taking advantage of this post to provide a few small updates: Going forward, I'll be working on more software engineering tasks with the Aerospike Graph team, and I'll simultaneously learn more about distributed systems, get deeper into algorithmic design & analysis, and spend more time with C++. I plan to post something more reflective about my gap semester experience and what I have learned , but here is a list of some other things that I have enjoyed recently outside of my internship: - 📓 Reengaging with my Applied Mathematics courses in nontraditional ways. Without exams to study for, I've taken full advantage of this time to relearn a slew of topics from original principles, and this has been a tremendously fruitful and satisfying process. Additionally, considering the context in which mathematical concepts were discovered has boosted my appreciation for my interests. I highly recommend "The History of Mathematical Proof in Ancient Traditions" by Karine Chemla if the math-history intersection intrigues you. - 🥾 Getting outdoors more often. Whether it's walking my dog, running, or backpacking for a couple of days, I've been making a conscious effort to rejuvenate my mind and avoid any shred of mental stagnancy at all costs. Side note: I saw my first barn owl last night (indeed, I love birdwatching). - 📚 🇫🇷 Cultivating a couple of daily habits. I have made doing (duo-ing?) my Duolingo lessons a priority, and I now realize that my years of Latin are finally paying off with other European languages. I've also made daily reading a priority, and I have set a fairly modest goal of one book a month. I'm right on track so far! I'm very proud of (1) my work and (2) my work-life balance thus far, and I'm absolutely looking forward to the upcoming academic year. There are various reflections that I've unearthed over the past 6 months or so, and I certainly plan to articulate them here towards the end of this summer!

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Exciting news from Cornell University! 🌟 Cornell has joined the U.S. Commerce Department's AI Safety Institute Consortium (AISIC) to support the development and deployment of trustworthy and safe artificial intelligence technologies. As a Computer Science freshman at Cornell's College of Engineering, this initiative resonates deeply with my aspirations in AI and machine learning. 🤖 The AISIC consortium, established by the National Institute of Standards and Technology (NIST), aims to bring together AI creators and users, academics, nonprofit organizations, and government and industry researchers. With more than 200 members, including companies and startups of all sizes, research teams, and representatives of professions deeply engaged in AI's use, the consortium is set to develop evidence-based guidelines and standards for AI measurement and policy. 🌍 Cornell's involvement in the consortium underscores its commitment to advancing safe and trustworthy methods, practices, and policies in AI, contributing to positive global impact. Across its campuses in Ithaca and New York City, Cornell is a hub of AI developers and users, including researchers who are creating new ways to use knowledge, ideas, and tools to have a positive global impact. 🌟 I am proud to be part of a university that is at the forefront of addressing the critical need for safe and trustworthy AI. This initiative aligns perfectly with my goal of becoming a Machine Learning engineer and exploring how AI can enhance productivity and competitiveness in business. 📈 #AI #MachineLearning #CornellUniversity #AISIC #AIethics #TechInnovation https://lnkd.in/e9rYfbtf

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