A college student has revealed how he was able to stay focused and motivated during an intense period in which he applied for 456 internships.

Oliver Wu is a junior studying computer science at the University of Michigan. He plays an active role in campus life, where he's involved with the university's Asian American community and plays volleyball.

Over the past four months though, Wu has been focused on another task on top of his studies and extracurricular activities: landing an internship.

College students today are increasingly mindful of their future career prospects. A survey of students due to graduate in 2023 conducted by job website Handshake found around half were planning to apply to more jobs, while one third were looking at a more diverse range of roles, and one fifth were starting their search sooner.

Wu has only just started out as a junior, but he's already thinking about the future. He told Newsweek he is seeking a "career in tech," though remains flexible about where that will take him.

"I would love to use my skills to develop solutions in sustainability and environmental protection," he said. "However, I do recognize that I am currently still a college student so there is a lot to learn and my plans may change once I get more experience in the industry."

That desire to seek experience has seen him embark on an exhaustive search to land an internship with a top company. It's a search that began before he even started at college, when he started noticing openings being posted on online job boards over the summer.

"I started applying in July and soon I hit 200 applications," he said. Wu said he "stopped looking at the number of applications" fairly early into the process, but quickly developed a daily routine.

"Usually, I would open up two or three job boards, see what new jobs were posted, and then apply to all of the jobs if the salary, location, roles etc. met what I was looking for," he said. "I also kept track of which companies I had referrals to and checked on a weekly basis if those companies had opened up their applications."

Wu said prior to starting college on his very best days he was completing "15 to 20 applications a day," but that slowed down once he was in class. "On days where I did not apply as much, I would practice my technical interview skills," he added.

He insists he never set out to apply for 456 internships though. "It just kind of happened after applying day in day out," he said. Wu attributes that to the fact that he started applying earlier and continued until late in the year.

During this period, there were times when he felt "burned out" though. "The hardest part was staying positive and working hard, despite having hundreds of rejections," he said.

In those periods, he always made sure to take time off to recharge. He remained motivated though. "I did not want to feel regret that I could have tried harder, so I made up my mind to pursue this with everything I had," he said. He attributes some of that to his religious faith and the belief that whether he succeeded or failed "God has a plan for me."

That plan saw Wu complete an astonishing 56 interviews, as well as 30 technical assessments, 22 second- and third-round assessments and four final rounds off the back of those initial applications.

As stressful as it might have been, those interviews and assessments have proven invaluable. "I feel much less nervous and familiar with the process. Additionally, I know what to expect, and the areas which I need to improve at," he said.

More importantly, at the end of such an intense period of pressure, Wu had something to celebrate. "I ended up accepting an offer at Ford as an enterprise technology intern," he revealed. Wu said the moment he learned he had landed the internship he "felt like a massive weight had been lifted off my shoulders."

"I was in class at the time and I remember stepping out, going into the hallway and jumping up and down while silently screaming in excitement for around 10 minutes," he said. "I ended up landing two more offers, but ultimately accepted Ford."

Eager to share his news and highlight the work that went into it, Wu posted a video to TikTok under the handle oliesandroid revealing how the 456 applications had ultimately been "worth it" in the end. The video has been watched 2.7 million times.

Reflecting on the experience, Wu has one piece of advice for anyone looking to land an internship. "Network," he said. "A big mistake I made was not networking properly and being scared to network and relying on cold applications instead. If I could do it all over again, I would definitely network more. Take a deep breath, and relax, this is a marathon not a sprint."

Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground.

Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground.

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College Student Sends 456 Applications, Gets Accepted Into One Internship - Newsweek

Data Science, the practice of deriving insights from data, has become a global phenomenon. In India, the expanding tech industry seeks proficient data-driven professionals, elevating a Bachelors Degree in Data Science to a highly sought-after qualification. The process of choosing the best program among several options can be difficult. To assist you in navigating this dynamic landscape, presented here is a comprehensive guide to the premier Bachelor in Data Science programs available for enrollment in 2024.

Indian Institute of Technology (IITs): These prestigious institutions consistently rank among the best for Data Science globally. Each IIT offers variants of the program, with highlights like:

IIT Madras B.Sc. in Programming and Data Science integrates core programming principles with advanced data science applications, offering a comprehensive understanding. Graduates acquire a versatile skill set for innovative solutions in IT and analytics.

IIT Delhis B.Tech. in Computer Science and Engineering, specializing in Data Science, merges robust foundational computer science knowledge with industry-centric data science modules, creating a curriculum that blends theoretical understanding with practical relevance.

IIT Bombays B.Tech. in Mathematics and Computing, specializing in Data Analytics, harmonizes mathematical rigor with hands-on data analysis skills. The program equips students with a robust foundation for analytical applications in various domains.

International Institute of Information Technology (IIITs): Renowned for their focus on IT, IIITs offer rigorous data science programs:

IIIT Hyderabads B.Tech. in Data Science and Computer Science offers in-depth knowledge of algorithms, data structures, and statistical methods. The program ensures a comprehensive understanding of foundational concepts for real-world applications.

IIIT-Delhis B.Tech. in Computer Science and Engineering, specializing in Data Science, prioritizes hands-on projects and industry collaborations. The program ensures practical experience and real-world insights for aspiring data professionals.

National Institute of Technology (NITs): NITs deliver high-quality technical education, with strong data science programs in:

NIT Warangals B.Tech. in Computer Science and Engineering, specializing in Data Science, integrates a thorough CS curriculum with elective courses in data science, providing students with a well-rounded education in both areas.

NIT Surathkals B.Tech. in Computer Science and Engineering, specializing in Data Analytics, empowers students with proficiency in data mining, machine learning, and data visualization, fostering expertise in these critical domains.

Indian Statistical Institute (ISI): A premier institute for statistics, ISI offers a unique data science program:

B.Math. (Hons.) in Statistical Computing and Data Analytics provides a rigorous foundation in statistics and mathematics, emphasizing computational tools for data analysis, ensuring students are well-equipped for analytical challenges.

BITS Pilani: This reputed private university provides a well-rounded data science education:

B.E. (Hons.) Computer Science with a specialization in Data Science merges core CS fundamentals with intensive data science courses and industry internships, providing students with a holistic and practical education.

Delhi Technological University (DTU): A leading engineering university in Delhi, DTU offers a promising data science program:

B.Tech. in Computer Science and Engineering, specializing in Data Science, boasts a well-structured curriculum enriched with hands-on projects and industry exposure, ensuring students acquire practical skills aligned with industry demands.

Amity University: This renowned university offers a flexible and dynamic data science program:

B.Sc. (Hons.) Data Science covers diverse data science areas and permits customization through electives, enabling students to tailor their education to specific interests within the expansive field of data science.

Jain University: Jain Universitys School of Data Science & Technology (SDST) offers a unique program:

B.Sc. (Hons.) Data Science prioritizes real-world applications, industry collaborations, and project-based learning, ensuring students gain practical experience and insights essential for success in the dynamic field of data science.

Evaluate university infrastructure, ensuring ample computational resources, well-equipped lab facilities, and robust career guidance services, to provide students with a conducive environment for learning and professional development.

Commencing a Bachelors degree in Data Science marks an exciting milestone. Conduct a thorough assessment of your priorities, diligently research universities, and choose a program that aligns seamlessly with your aspirations and preferred learning approach. Keep in mind, that the path to becoming a proficient data professional commences with making the right choice.

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Top Data Science Graduation Programs to Enroll in for 2024 - Analytics Insight

Roch Gurin, chair of computer science and engineering at the McKelvey School of Engineering and the Harold B. & Adelaide G. Welge Professor of Computer Science at Washington University in St. Louis, has received a two-year $207,394 grant from the National Science Foundation to enhance the ability of a 3D atmospheric simulation software to rapidly simulate how Earths atmosphere responds to changes in its chemical composition.

The software, GEOS-Chem, is designed to study climate change, and the project brings together expertise in computer and atmospheric science and includes McKelvey Engineering faculty Kunal Agrawal, a professor of computer science and engineering, and Randall Martin, the Raymond R. Tucker Distinguished Professor in energy, environmental and chemical engineering and model scientist of the GEOS-Chem project.

By improving the speed at which those simulations can run, the project aims to improve researchers ability to simulate and understand how Earths atmosphere evolves, contributing to climate change research and mitigation with potential economic and societal impacts.

Read more on the McKelvey School of Engineering website.

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Gurin wins grant to enhance atmospheric simulation speed - The Source - Washington University in St. Louis

Computing is the cornerstone of modern society and the training and education offered at Willamettes School of Computing and Information Sciences has never been more important for those looking to find solutions to the worlds greatest challenges. Yet, according to Assistant Professor of Computer Science Fred Agbo, a key demographic is being left out of computing education: adults.

Computer literacy isnt just important for job and career success. For older adults, Agbo says that computing can provide other benefits in their daily lives including helping older adults with cognitive development, building confidence, and empowering active citizenship. But lack of funding and attention have made gaining these crucial skills more challenging for adults.

Agbos research on broadening participation in computing (BPC) was recently accepted at the Technical Symposium on Computer Science Education, one of the worlds premier computer science conferences. We spoke with Professor Agbo to learn more about his research and about how adults can be brought into the computing revolution.

1. What is BPC and what does it mean to make computing more inclusive?

Agbo: BPC means broadening participation in computing. It is a program that engages the computing community to design strategies and frameworks to increase the participation of underrepresented groups in computing.

The goal of the BPC program is to democratize computing education by ensuring that more folks who normally would not have access to computing literacy are motivated to study computer science and even pursue a career in it. Moreover, this program promotes diversity, equity, and inclusion in all educational contexts such as the K-12, colleges, and post-college adults.

While there is evidence on BPC in K-12 education, there are limited studies that showcase adults in computing education. This is a fundamental gap that must be addressed. My research amplifies this gap, advocates for BPC in adult education, and aims to inspire discussion about how to advance computing education for adults.

2. Why is it so important for adults to receive computer education?

Agbo: In a digitally evolving world and with the rapid diffusion of technology influencing every aspect of life, everyone needs to be computer literate. Computer literacy in this context entails acquiring 21st-century skills such as creativity, computational thinking, and problem-solving abilities, which can be applied in all areas of life.

Adults (those with work-life, retirees, or senior citizens alike), do not just need these skills to remain relevant at their jobs or change careers, but also to unravel daily contemporary problems. In addition, adults can develop their cognitive capabilities through computing education, which will keep them active as they age. Computing education can also empower adults to uphold lifelong learning through citizenship education.

3. What does your research suggest about past efforts to expand computing education for adults?

Agbo: The digital disparity between older adults and the younger generation is not clear in the academic literature. My research investigated this gap by systematically examining the literature and found that attempts towards BPC in adult education started as far back as the 1980s. However, there has been little to no significant progress made over the years.

This study also investigated the learning outcomes for adults and found that there are positive gains for BPC in adults education. For example, studies show that computing education for adults increases their motivation, interest, self-confidence, and computational knowledge.

4. Why has computing education been slow to expand to adults?

Agbo: Its difficult to say. However, there is evidence that suggests limited funding to support the development of BPC in adult education. Another issue that may have limited BPC in adult education is the faint attention it receives from computing educators and scholars. Advocacy for BPC in adult education is necessary, which is one of the contributions of this paper.

5. How can we help improve adult computer literacy?

Agbo: Computer science educators and researchers must identify the need for inclusion in designing strategies, frameworks, and curricula for BPC where adult education context is also considered a significant part of the program. Funding should be made available to the community of computing educators to carry out studies in developing curricula for computing education in adults.

Thankfully, the Special Interest Group on Computer Science Education a highly respected symposium in the community has recognized this need. This paper will hopefully engender actions towards developing adults computing education.

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Five questions on adult computer literacy with Assistant Professor of Computer Science Fred Agbo - willamette.edu

The first documented case of pancreatic cancer dates back to the 18th century. Since then, researchers have undertaken a protracted and challenging odyssey to understand the elusive and deadly disease. To date, there is no better cancer treatment than early intervention. Unfortunately, the pancreas, nestled deep within the abdomen, is particularly elusive for early detection.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) scientists, alongside Limor Appelbaum, a staff scientist in the Department of Radiation Oncology at Beth Israel Deaconess Medical Center (BIDMC), were eager to better identify potential high-risk patients. They set out to develop two machine-learning models for early detection of pancreatic ductal adenocarcinoma (PDAC), the most common form of the cancer. To access a broad and diverse database, the team synced up with a federated network company, using electronic health record data from various institutions across the United States. This vast pool of data helped ensure the models' reliability and generalizability, making them applicable across a wide range of populations, geographical locations, and demographic groups.

The two models the PRISM neural network, and the logistic regression model (a statistical technique for probability), outperformed current methods. The teams comparison showed that while standard screening criteria identify about 10 percent of PDAC cases using a five-times higher relative risk threshold, Prism can detect 35 percent of PDAC cases at this same threshold.

Using AI to detect cancer risk is not a new phenomena algorithms analyze mammograms, CT scans for lung cancer, and assist in the analysis of Pap smear tests and HPV testing, to name a few applications. The PRISM models stand out for their development and validation on an extensive database of over 5 million patients, surpassing the scale of most prior research in the field, says Kai Jia, an MIT PhD student in electrical engineering and computer science (EECS), MIT CSAIL affiliate, and first author on an open-access paper in eBioMedicine outlining the new work. The model uses routine clinical and lab data to make its predictions, and the diversity of the U.S. population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions, like a few health-care centers in the U.S. Additionally, using a unique regularization technique in the training process enhanced the models' generalizability and interpretability.

This report outlines a powerful approach to use big data and artificial intelligence algorithms to refine our approach to identifying risk profiles for cancer, says David Avigan, a Harvard Medical School professor and the cancer center director and chief of hematology and hematologic malignancies at BIDMC, who was not involved in the study. This approach may lead to novel strategies to identify patients with high risk for malignancy that may benefit from focused screening with the potential for early intervention.

Prismatic perspectives

The journey toward the development of PRISM began over six years ago, fueled by firsthand experiences with the limitations of current diagnostic practices. Approximately 80-85 percent of pancreatic cancer patients are diagnosed at advanced stages, where cure is no longer an option, says senior author Appelbaum, who is also a Harvard Medical School instructor as well as radiation oncologist. This clinical frustration sparked the idea to delve into the wealth of data available in electronic health records (EHRs).

The CSAIL groups close collaboration with Appelbaum made it possible to understand the combined medical and machine learning aspects of the problem better, eventually leading to a much more accurate and transparent model. The hypothesis was that these records contained hidden clues subtle signs and symptoms that could act as early warning signals of pancreatic cancer, she adds. This guided our use of federated EHR networks in developing these models, for a scalable approach for deploying risk prediction tools in health care.

Both PrismNN and PrismLR models analyze EHR data, including patient demographics, diagnoses, medications, and lab results, to assess PDAC risk. PrismNN uses artificial neural networks to detect intricate patterns in data features like age, medical history, and lab results, yielding a risk score for PDAC likelihood. PrismLR uses logistic regression for a simpler analysis, generating a probability score of PDAC based on these features. Together, the models offer a thorough evaluation of different approaches in predicting PDAC risk from the same EHR data.

One paramount point for gaining the trust of physicians, the team notes, is better understanding how the models work, known in the field as interpretability. The scientists pointed out that while logistic regression models are inherently easier to interpret, recent advancements have made deep neural networks somewhat more transparent. This helped the team to refine the thousands of potentially predictive features derived from EHR of a single patient to approximately 85 critical indicators. These indicators, which include patient age, diabetes diagnosis, and an increased frequency of visits to physicians, are automatically discovered by the model but match physicians' understanding of risk factors associated with pancreatic cancer.

The path forward

Despite the promise of the PRISM models, as with all research, some parts are still a work in progress. U.S. data alone are the current diet for the models, necessitating testing and adaptation for global use. The path forward, the team notes, includes expanding the model's applicability to international datasets and integrating additional biomarkers for more refined risk assessment.

A subsequent aim for us is to facilitate the models' implementation in routine health care settings. The vision is to have these models function seamlessly in the background of health care systems, automatically analyzing patient data and alerting physicians to high-risk cases without adding to their workload, says Jia. A machine-learning model integrated with the EHR system could empower physicians with early alerts for high-risk patients, potentially enabling interventions well before symptoms manifest. We are eager to deploy our techniques in the real world to help all individuals enjoy longer, healthier lives.

Jia wrote the paper alongside Applebaum and MIT EECS Professor and CSAIL Principal Investigator Martin Rinard, who are both senior authors of the paper. Researchers on the paper were supported during their time at MIT CSAIL, in part, by the Defense Advanced Research Projects Agency, Boeing, the National Science Foundation, and Aarno Labs. TriNetX provided resources for the project, and the Prevent Cancer Foundation also supported the team.

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New hope for early pancreatic cancer intervention via AI-based risk prediction - MIT News