BECOMING, ALWAYS

Strangers and Struggles in a New World

16 May 2025

A recent report by the Swedish Association of University Teachers and Researchers echoes a reality I know all too well. As a PhD student and early-stage researcher in Poland, the quality of life issues raised in the study feel painfully familiar. What I once imagined as a calm and inspiring academic journey quickly unraveled into a bureaucratic labyrinth—and the nightmare began as early as the first week. The difficulty of securing the right to stay in Poland, particularly due to the scarcity of available appointment slots for a temporary residence permit, continues to be an uphill battle. Unlike EU citizens who navigate a more streamlined process, I am left with an inefficient, disheartening path that drags on endlessly. Strangely, it seems that only institutional connections with the immigration offices can unlock appointments—an implicit reflection on how systems sometimes reward proximity over fairness.

This prolonged instability has taken a toll on both my quality of life and mental health. Even after eight months, I find myself mentally fatigued, weighed down by the cumulative stress of opening a bank account, searching for a home, and navigating countless government offices for registration. Still, I am without a work laptop—so my personal one has unwillingly become my academic lifeline. Every day, I haul it back and forth, slowly erasing the line I once drew between work and personal life. I had envisioned this fellowship as a period of growth and ambition. Instead, I feel like a shadow of the researcher I set out to become—disillusioned, anxious, and lacking the mental space to even focus on developing my project. I often wonder: how can brilliance bloom in soil that is so dry and cracked?

The PhD journey, once a beacon of purpose, now feels like a cautionary tale. Ironically, when I assert my rights as a Marie Skłodowska-Curie Actions (MSCA) Doctoral Fellow, as outlined clearly in the MSCA handbook—where host institutions are obligated to assist with administrative processes such as visa and residence permits—I am met with accusations of arrogance or disrespect. It is disheartening to be painted as problematic for simply requesting the support I was promised. I ask myself: what does it mean to uphold dignity in a system that often seems indifferent to it?

And so, here I am—navigating not only a research project but an entire infrastructure that feels unwelcoming. Each day may bring a new lesson in resilience. Our academic journeys deserve more than silent suffering. However, each day may also offer a new perspective on life. Perhaps the persistence of these unchanging academic challenges is not just a test of endurance, but a signal—reminding me that there are paths elsewhere, places that will truly value who I am and foster real growth, both personally and professionally.

26 February 2025

Moving to a new opportunity does not always mean stepping into a better environment. The struggles I face now feel eerily familiar—most of them have little to do with the research itself. Preparing for a presentation about my work should have been exciting, a chance to showcase my academic background, project objectives, and research plans. Instead, it only served as a reminder of the endless roadblocks. The lack of necessary data left me stranded, and project mismanagement only deepened the frustration. With every passing day, my motivation wanes, and the passion that once fueled my academic journey continues to slip away.

Then there are the logistical nightmares—visa applications, endless bureaucratic red tape, and the exhausting process of securing temporary residence as a foreign researcher. The lack of administrative support from my employer makes everything needlessly difficult. For months, anxiety has been creeping in as I navigate a system that seems designed to be as inefficient as possible. The foreigner department’s website is as bad as ever—confusing, outdated, and utterly unhelpful. Maybe this is a sign that it is time to rethink my path.

Attending scientific meetings, once something I looked forward to, has only reinforced my disillusionment. I expected intellectual engagement, thought-provoking discussions, and valuable insights to refine my work. Instead, I encountered dismissiveness, condescension, and outright disrespect. There was no constructive criticism—just gatekeeping disguised as expertise. This experience has made one thing clearer than ever: academia may not be worth it. The decision to leave is becoming less of a possibility and more of an inevitability.

20 December 2024

The first year of a PhD is a whirlwind of ideas and ambitions, where every choice shapes the trajectory of your academic journey. Writing a review paper often seems like an early milestone—a chance to showcase your grasp of the field while establishing yourself as a knowledgeable voice. Yet, for those engrossed in research, the time and effort required for a strong review can feel like a diversion from achieving tangible results.

A wise alternative is to prioritize the research itself. By weaving literature insights directly into the project, one can stay informed while maintaining focus on outcomes that matter. Tracking key publications and even preprints ensures you remain updated without the added pressure of formalizing findings into a standalone review. This approach channels your time into meaningful progress rather than dispersing efforts, avoiding premature proposals that might later prove challenging.

Every PhD journey is unique, and while a review paper might be a pivotal step for some, it is not a universal necessity. Whether you choose to dive into a review early or let it emerge naturally within your research, the goal remains the same: to contribute meaningfully to your field and community. Focus on what propels your work forward, and the rest will follow.

10 November 2024

Moving to a new country is like diving headfirst into a novel where you are barely fluent in the language, and everyone else has already read the plot. Each day, I find myself stumbling through moments of awe and confusion. Even the simplest things — directions, food labels, conversations — feel like riddles waiting to be solved. My comfort zone has become a distant memory, replaced by a world where each small interaction is a test of patience and adaptability. One month down, with possibly three years and eleven months to go. Yet I still brace myself every time I venture out to tackle the mysteries of grocery aisles or government offices. Even routine tasks like opening a bank account took nearly three weeks, leaving me to wonder if the Institute had anticipated these bureaucratic adventures in my fellowship. I would not call it entitlement, but as an MSCA Fellow, I have a right to require support for all the administrative procedures that come with being an early-stage researcher in a foreign country.

Then there is the research program and doctoral school — an entirely different kind of adventure. Long hours of data extraction, thanks to the computational complexity of implemented algorithm, have left me bleary-eyed. I have had a few presentations in this first month, each a fresh reminder of my own nerves and the gnawing doubt about whether I truly understand my own research. The language barrier adds yet another layer. Asking for technical help or sharing ideas with peers requires careful phrasing and humility. I second-guess my words, wondering if I have managed to communicate my thoughts clearly or if something essential has slipped through the cracks. It is a strange feeling, standing before an audience and realizing that while I am trying to bridge a language gap, I am also trying not to trip over my own words. Somehow, I will either find resilience in all this fumbling or surrender and go back to where I came from.

Each day, I find myself juggling between moments of optimism — trusting that I will eventually feel at home — and flashes of pessimism, wondering if I will always feel like a stranger. Let us see where this journey takes me, and decide by then whether there is still a reason, or even a glimmer of hope, to keep pursuing this doctorate degree.

Learning with Machines (and Technology)

Online vs. Classroom: Which Enrollment Type is Right? (09 September 2023)

(The associated codes and implementations in this project is located in the Jupyter notebook.)

Background and Objective

You are working as a data scientist at a local University. The university started offering online courses to reach a wider range of students. The university wants you to help them understand enrollment trends and identify what contributes to higher enrollment, particularly whether the course type (online or classroom) is a factor.

Executive Summary

Enrollment counts are analyzed to test hypotheses. Machine learning models are provided to predict future enrollment counts.

Data Preprocessing

Data is cleaned using pandas, missing values are filled, and data types corrected. The dataset contains 1850 courses: 1375 online and 475 classroom-based.

Hypothesis Testing

A Mann-Whitney U Test indicates a significant difference between online and classroom enrollments. Online courses attract more students.

Regression Model

Linear Regression and Elastic Net models predict enrollment counts with similar RMSEs. TPOT automation finds a pipeline with similar performance.

Conclusion

Any model can be deployed; predictions differ minimally from actual enrollment.

Machine Learning for Loan Approval: A Balancing Act Between Accuracy and Fairness (27 September 2023)

(The associated codes and implementations in this project is located in the Jupyter notebook.)

Background and Objective

A start-up wants to automate loan approvals by predicting whether a loan will be paid back. The classifier should prioritize identifying loans that will not be repaid.

Executive Summary

Borrower features are analyzed. Models are trained and evaluated to classify loans accurately, focusing on recall for unpaid loans.

Data Preprocessing

The dataset has no null values. Target variable is not_fully_paid. Class imbalance exists: 8045 fully paid loans and 1533 not fully paid.

Feature Engineering

Highly correlated features are reduced. PCA is used to reduce dimensions to 2, explaining 99.9% variance. One-hot encoding applied for categorical variable purpose.

Classifier

XGBoost classifier used. Recall on testing set is initially 10%. Applying SMOTE balancing improves recall to 89%. TPOT automated pipeline confirms gradient-boosted tree classifier performs best on balanced data.

Conclusion

XGBoost Classifier is recommended due to high recall on unpaid loans.

Bike Sharing Demand in South Korea (20 September 2023)

(The associated codes in this project is located in the Jupyter notebook.)

Data Preprocessing

Column names shortened. Dataset contains no missing values. Numeric columns include floats and integers; categorical columns are strings.

Analysis and Modeling

Summer and non-holidays show highest bike rentals. Random Forest model predicts rentals with R² ≈ 0.79. Important predictors: hour and temperature; least important: snowfall.

Conclusion

Decision tree-based models are effective; Random Forest recommended for predictions.

Disaster Learning of a Machine Learning Model (15 September 2023)

(The associated codes and implementations in this project is located in the Jupyter notebook.)

Objective

Classify Titanic passenger survival.

Data Preprocessing

Drop PassengerId, ticket, name. Encode sex, impute missing age and embarked. One-hot encode pclass and embarked.

Modeling

Logistic Regression, KNN, and GBDT models evaluated. GBDT achieves highest accuracy (~88%). TPOT finds a similar GBDT pipeline.

Dashboards

Listen to the Sound of a Dashboard (Spotify 2023) (16 September 2023)

(The associated codes and implementations in this project is located in the Jupyter notebook and Power BI eXchange file.)

Analysis

Kaggle dataset of top 953 streamed songs in 2023. Taylor Swift has most tracks (34), The Weeknd next (22). Popular songs tend to be non-instrumental with high danceability and ~122 bpm.

Web Scraping

Scrape the Malt Extract from Your Beer (22 August 2023)

Data scraped from Boozy using Beautiful Soup. Extracted: Name, Price, Star Rating, Number of Reviews.

Sample Data

Name	Price	Rating	No. of Reviews
Engkanto Mango Nation - Hazy IPA 330mL Bottle ...	543.0	5.0	2
Engkanto High Hive - Honey Ale 330mL Bottle 4-...	407.0	4.0	5
Engkanto Green Lava - Double IPA 330mL Bottle ...	594.0	5.0	1
Engkanto Live It Up! Lager 330mL Bottle 4-Pack	407.0	5.0	1
Engkanto Paint Me Purple - Ube Lager 330mL Bot...	543.0	5	1

Highest and Lowest Rated Products

Top 4 highest-rated beers with ≥5 reviews:

Name	Rating	Reviews
Pilsner Urquell 330mL Bottle Pack of 6	5.0	12
Sapporo 330mL Bundle of 6	5.0	12
Stella Artois 330mL Bottle Bundle of 6	5.0	11
Paulaner Weissbier Dunkel 500mL Bottle	5.0	11

Four lowest-rated beer products:

Name	Rating	Reviews
Rochefort 8 330mL	3.0	1
Royal Dutch Ultra Strong 14% 500mL	3.5	2
Tiger Crystal 330mL Can	3.8	5
Paulaner Weissbier Party Keg 5L	3.8	5