vault/C&Q/Growth.md

Career Growth Plan: Alvis Logins (2026–2031)

Profile: PhD Computer Science (Aarhus 2020), Research Team Lead at Huawei Moscow, 5+ years industry, publications at WWW/ICDE/IEEE.
Current salary: €4,000 net/month · Target: ≥€5,600 net/month (+40%) within 5 years.
Direction: Applied statistics, mathematical optimization, network performance — not pure ML.

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Part 1 — Skills to Reinforce Now

These are the foundational gaps to close in Year 1. Ordered by urgency and impact on all four tracks (job market, publications, academic profile, startup).

1.1 Mathematical Optimization (highest priority)

You have the intuition from your PhD (discrete optimization in graphs) but need to deepen continuous/convex and stochastic optimization theory, with exercise-level fluency.

Core gaps:

• Convex analysis: sets, functions, duality theory, KKT conditions, Lagrangian relaxation — solve textbook problems fluently, not just read proofs
• Interior-point methods and first-order methods (proximal gradient, ADMM) — both theory and implementation
• Stochastic optimization: stochastic programming, sample average approximation, chance constraints
• Network optimization specifics: min-cost flow, facility location LP/IP relaxations, column generation
• Combinatorial optimization refresh: polyhedral theory, totally unimodular matrices, submodularity

Anki strategy for optimization:

• Cards for definitions (convex set, strong duality, Slater’s condition) — both formal definition and geometric intuition on each card
• “Pattern recognition” cards: “Given this problem structure → what formulation?” e.g., “minimize max of affine functions → LP”
• “Algorithm selection” cards: “Problem with structure X → use method Y because Z”
• “Name ↔ result” cards for major theorems: Farkas’ lemma, minimax theorem, Carathéodory’s theorem, weak/strong duality, complementary slackness
• Weekly target: 15–20 new cards, review backlog daily (~20 min during commute/breaks)

1.2 Applied Statistics & Probability

Your copula/latency work shows you can use advanced tools, but you need systematic depth for publication-quality rigor and interview readiness.

Core gaps:

• Hypothesis testing beyond basics: multiple testing corrections (Bonferroni, BH), permutation tests, bootstrap confidence intervals
• Time series: stationarity tests (ADF, KPSS), ARIMA/SARIMA, spectral analysis, Granger causality (you’ve touched this — formalize it)
• Extreme value theory and heavy-tailed distributions — directly relevant to network latency modeling
• Bayesian inference fundamentals: conjugate priors, MCMC, hierarchical models
• Copula theory formalization: Sklar’s theorem proof, tail dependence, goodness-of-fit tests for copulas
• Causal inference: potential outcomes framework, do-calculus basics, instrumental variables

Anki strategy for statistics:

• Formula cards with “when to use” context on the reverse
• Distribution cards: “Name ↔ PDF ↔ key properties ↔ typical application domain”
• “Assumption violation → consequence → fix” triplets (e.g., “heteroskedasticity → biased SE → use HC robust SE or GLS”)
• Test selection cards: “I want to compare X → use test Y → assumptions are Z”

1.3 Queueing Theory & Stochastic Network Calculus

This is your niche differentiator. Few people combine WiFi/networking domain knowledge with rigorous queueing theory. Deepening this makes you uniquely valuable.

Core gaps:

• Classical queueing: M/M/1, M/M/c, M/G/1 (Pollaczek-Khinchine), networks of queues (Jackson, BCMP)
• Heavy-traffic approximations, diffusion limits
• Network calculus: deterministic (min-plus algebra) and stochastic (moment generating function bounds, effective bandwidth/capacity)
• The TR-452 ΔQ framework you’ve already explored — formalize and publish
• Scheduling theory: weighted fair queueing, deficit round-robin, their optimality properties

Anki strategy:

• “Model ↔ assumptions ↔ key formula ↔ when it breaks” cards
• Little’s Law and its generalizations — drill until automatic
• Network calculus: service curve, arrival curve, backlog/delay bounds — card per concept

1.4 Systems Engineering & Software Architecture

You self-identified this as weak. For team leadership and startup credibility, you need to speak this language fluently.

Core gaps:

• Design patterns for data-intensive systems (read “Designing Data-Intensive Applications” by Kleppner — one chapter per week)
• System design interview patterns: load balancers, message queues, database sharding, caching strategies
• ns-3 simulation architecture: you use it at work, formalize your understanding for publications
• CI/CD and testing practices for research code — important for both startup and team leadership credibility

1.5 Leadership & Management

Core gaps:

• Engineering management frameworks: Will Larson’s “An Elegant Puzzle”, Camille Fournier’s “The Manager’s Path”
• Research team metrics: how to measure and communicate research output to non-technical stakeholders
• Hiring and mentoring: structured interviews, onboarding processes
• Presentation skills: conference talks, internal tech talks, executive summaries

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Part 2 — Target Jobs (Years 3–5)

2.1 Industry: Research Team Lead / Principal Researcher

Company	Location	Role Type	Est. Salary (net/mo)	Notes
Nokia Bell Labs	Murray Hill / Paris / Cambridge	Senior Research Scientist	€5,500–7,500	Strong optimization research culture; publishes actively
Ericsson Research	Stockholm / Herzogenrath	Senior Researcher / Expert	€5,000–6,500	5G/6G network optimization; research-friendly culture
Nokia (product R&D)	Espoo / Budapest / Berlin	Principal Engineer	€5,000–6,000	More applied; WiFi 7/8 standards work
Yandex Research	Moscow / remote	Senior Researcher / Team Lead	₽500–700K (~€5,000–6,500)	Strong ML but open to OR; volatile geopolitically
Qualcomm Research	San Diego / Amsterdam	Staff Engineer — WiFi	$160–200K (~€6,000–7,500 net)	WiFi PHY/MAC optimization; very technical
INRIA	Paris / Sophia Antipolis	Chargé de Recherche (CR)	€3,500–4,500 net	Permanent public research; great for academic track
Broadcom	Various EU offices	Principal Engineer — WiFi	€5,500–7,000	WiFi chipset; heavy optimization work
Siemens (Digital Industries)	Munich	Senior Data Scientist / Expert	€5,000–6,000	Industrial networking, TSN
InterDigital	London / Rennes	Senior R&D Engineer	€5,000–6,000	Standards research, patents

Key insight: For the 40% raise, Nordics + Germany + Benelux are the sweet spot — high salaries, strong telecom ecosystem, good academic connection opportunities. Switzerland pays highest but cost of living erodes the advantage. US roles pay most in absolute terms but require visa sponsorship.

2.2 Academic: Part-Time → Full-Time Track

Institution	Country	Why it fits	Action needed
Aalborg University (AAU)	Denmark	Strong wireless/networking group; you interned there 2018	Reconnect with contacts, propose adjunct/visiting role
DTU (Technical University of Denmark)	Denmark	Operations research + telecom groups	Cold approach or via Aarhus network
Aarhus University	Denmark	Your alma mater — strongest existing network	Contact Panagiotis Karras (supervisor), explore доцент-like role
KTH Royal Institute of Technology	Sweden	Optimization and communication systems groups	Target postdoc or researcher affiliation
TU Delft	Netherlands	Strong OR group (Centrum Wiskunde & Informatica nearby)	Network via conferences
HSE Moscow	Russia	Flexible industry-academia arrangements	Easiest entry point while in Moscow
Skoltech	Russia	Your MSc institution; applied math focus	Strong existing connections
MIPT	Russia	Your BSc institution; they hire part-time lecturers	Lowest barrier to start teaching

Strategy: Start with MIPT/HSE/Skoltech part-time teaching (Year 1–2) while building European connections. Transition to a Nordic/Dutch adjunct or visiting position (Year 3–4) when you relocate.

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Part 3 — Publication Strategy

3.1 Target Venues (ranked by fit, not prestige)

Tier 1 — Top targets (high impact, directly in scope):

• Operations Research (INFORMS) — your optimization + network application niche
• IEEE/ACM Transactions on Networking (ToN) — network optimization, scheduling
• Performance Evaluation (Elsevier) — queueing, network performance modeling
• Queueing Systems (Springer) — if your work has queueing-theoretic depth

Tier 2 — Strong venues, good acceptance for applied work:

• Computer Networks (Elsevier) — WiFi/access network optimization
• IEEE Communications Letters (you’ve published here — keep going)
• IEEE Transactions on Wireless Communications — WiFi signal/channel estimation
• Annals of Applied Statistics — if your copula/latency work has statistical depth
• Mathematical Programming (Springer) — if you have a strong theoretical result

Tier 3 — Conferences (for visibility and networking):

• IEEE INFOCOM — networking, performance
• ACM SIGMETRICS — performance modeling and measurement
• IFIP Performance — network performance evaluation
• WiOpt — wireless network optimization (perfectly targeted)
• INFORMS Annual Meeting — operations research community

3.2 Concrete Paper Ideas (from your current work)

#	Working Title	Key Method	Target Venue	Timeline
1	Copula-Based Latency Dependence Modeling in WiFi Access Networks	Gumbel/Clayton copulas, goodness-of-fit	Performance Evaluation or Annals of Applied Statistics	Year 1 Q1–Q2
2	Stochastic Network Calculus Meets ΔQ: Unifying Quality Attenuation Frameworks	Laplace-Stieltjes transforms, service curves	Queueing Systems or SIGMETRICS	Year 1 Q2–Q4
3	Link-Adaptation-Aware Frame Aggregation for MCS Convergence in 802.11be	Your patent work, formalized	IEEE Trans. Wireless Communications	Year 1–2
4	Causal Analysis of Throughput-Latency Coupling in OFDMA Schedulers	Granger causality, CUSUM, CCF	IEEE INFOCOM or Computer Networks	Year 1–2
5	Optimal Resource Allocation in WiFi 7 MLO: An Integer Programming Approach	IP/LP relaxation, column generation	Operations Research or WiOpt	Year 2
6	A Survey: Mathematical Optimization in WiFi MAC Layer Design	Review paper, positions you as domain expert	IEEE Communications Surveys & Tutorials	Year 2–3

Target pace: 2 papers/year minimum, ideally 3. By Year 5, you need 10–15 post-PhD publications for a credible academic profile.

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Part 4 — Expert Networking & Personal Branding Strategy

This section is arguably the highest-leverage activity. Academic positions and senior industry roles are filled through networks more than job boards. Networking is a skill that compounds — start now, even if it feels awkward.

4.1 Leverage Your Huawei Position (Year 1–2)

Your current role gives you access to a global R&D network. Use it before you leave.

Internal Huawei network:

• Standards bodies. Huawei is a major contributor to IEEE 802.11 (WiFi), 3GPP, and ITU-T. Volunteer to attend or contribute to a standards working group meeting. Even observer status puts you in rooms with engineers from Qualcomm, Intel, Broadcom, Nokia, Ericsson. One standards meeting is worth 50 LinkedIn messages.
• Huawei HQ connections. You interned in Dongguan (2023). Re-engage those contacts. Propose a joint paper or technical report with the HQ WiFi team — cross-site publications look good and build lasting relationships.
• Internal tech talks. Offer to give talks on your optimization/statistics work at other Huawei labs (e.g., the Paris/Munich/Stockholm offices). This builds your name inside the company and gives you contacts at EU offices — useful if you want a Huawei transfer as a stepping stone to relocation.
• Patent co-inventors. Your patent collaborators are natural allies. Maintain those relationships — co-inventors often become co-authors or referees later.

External via Huawei affiliation:

• When you attend conferences, your Huawei badge opens doors. Engineers from competitor companies (Nokia, Ericsson, Qualcomm) are curious about Huawei’s approach and will talk to you at poster sessions and coffee breaks.
• Propose that Huawei sponsor or co-sponsor a workshop at a relevant conference (WiOpt, INFOCOM). Workshop organizers build strong networks fast.

4.2 Reactivate Academic Networks (Year 1, ongoing)

Immediate actions (this month):

1. Email Panagiotis Karras (your PhD supervisor, now at Aarhus/elsewhere). Brief update on your work, mention your publication plans, ask if he’d be open to co-supervision or co-authorship. PhD supervisors are your #1 academic asset — keeping this relationship warm is non-negotiable.
2. Email your Aalborg collaborators from the 2018 internship. Propose catching up, share a draft of your current work.
3. Email the Singapore Management University contact from 2019. They work on optimization — a natural fit.
4. Email the University of Macao contact from 2019. Offer to co-author on any shared interest.
5. Email your INRIA contact from 2013. Even if it was a long time ago, INRIA researchers tend to be stable in their positions and welcoming to former interns.

Template for reconnection emails:

Subject: Update from [your name] — potential collaboration on [topic]

Hi [name], I hope you’re doing well. I wanted to reach out after some time — I’ve been at Huawei Moscow leading a research team on WiFi optimization, and my work has been moving toward [applied statistics / network calculus / optimization]. I’m currently working on [specific paper idea] and thought of you because [specific connection to their work]. Would you be open to a brief call to explore whether there’s a collaboration opportunity? I’m also considering visiting [their city] for [conference/reason] later this year.

Key principle: Every reconnection email should contain a specific potential collaboration, not just “catching up.” Academics respond to concrete proposals.

4.3 Conference Networking Plan

Conferences are where jobs, collaborations, and reputations are made. Budget for 2–3 conferences per year. If Huawei won’t pay, invest your own money — the ROI is massive.

Year 1 conference targets:

Conference	When (typical)	Why	Networking goal
WiOpt	Jun–Jul	Perfectly targeted: wireless + optimization	Meet 3–5 potential collaborators; give a talk
IEEE INFOCOM	May	Large, prestigious; network performance	Meet industry researchers from Nokia/Ericsson
IFIP Performance	Nov	Queueing + network performance community	Connect with academic queueing theorists

Conference networking tactics (specific and actionable):

1. Before the conference: Read the accepted papers list. Identify 5–10 people whose work connects to yours. Email 3–5 of them: “I’ll be at [conf], I work on [X] which connects to your recent paper on [Y]. Would you have 15 minutes for a coffee?”
2. During the conference: Attend talks in your target area. Ask one question per session — this makes you visible. At poster sessions, spend time with junior researchers (PhD students) — they become future collaborators and they remember who was kind to them.
3. After the conference: Within 48 hours, send follow-up emails to everyone you had a meaningful conversation with. Reference something specific from the conversation. Propose a concrete next step (“I’ll send you the draft we discussed” or “Let’s set up a call to explore the joint problem”).
4. Give talks, not just attend. Submit papers or workshop proposals. A 15-minute talk does more for your reputation than 3 days of passive attendance.

4.4 Build an Online Presence (Year 1, low effort, high compound returns)

alogins.net — transform it into a research homepage:

• Add a “Research” page: list your publications with PDFs (where copyright allows), your current research interests, and 2–3 open problems you’d like to collaborate on
• Add a “Blog” section: write 1 post per month (~500 words) about a technical topic from your work. Examples: “Why copulas matter for network latency modeling”, “A practical guide to stochastic network calculus”, “What WiFi 7 MLO means for quality optimization”. This positions you as a thoughtful expert, not just a paper-producer
• These posts become material for talks, they attract search traffic from other researchers, and they give you something to share when networking

Google Scholar profile: Ensure it’s complete and up to date. This is the first thing any academic or research recruiter checks.

LinkedIn (low priority but nonzero): Post occasional technical insights (~1/month). Connect with people you meet at conferences. Recruiters from Nokia, Ericsson, Qualcomm actively search LinkedIn for senior researchers.

Twitter/X or Mastodon (optional): Some academic communities (operations research, networking) are active there. Follow key researchers, share your papers when published. Low effort, slow payoff.

4.5 Build a Local Moscow Network (Year 1–2)

While you’re still in Moscow, build a local expert network:

• HSE/Skoltech/MIPT seminars: Attend 1–2 per month in applied math, optimization, or data science. Introduce yourself to the organizers.
• Moscow Data Science / ML meetups: Give a talk on your work. The Moscow tech community is active and well-connected internationally.
• Yandex Research seminars: Some are open to external visitors. This is also reconnaissance for a potential job move.
• Co-supervision: Offer to co-supervise a master’s student at MIPT or HSE. This costs ~2h/week but gives you an academic track record item and a potential co-author.

4.6 Strategic Relationship Map

Maintain a simple spreadsheet (or Notion database) tracking your professional relationships:

Name	Affiliation	How met	Last contact	Strength (1–5)	Next action	Potential value
P. Karras	Aarhus	PhD supervisor	[date]	5	Send draft of copula paper	Reference letters, co-authorship
[Aalborg contact]	AAU	2018 internship	[date]	2	Reconnection email	Adjunct position, Nordic network
[HQ colleague]	Huawei Dongguan	2023 internship	[date]	3	Propose joint paper	Cross-site publication
…	…	…	…	…	…	…

Rules:

• Review the spreadsheet monthly
• No contact should go >6 months without interaction if Strength ≥ 3
• Every interaction should have a concrete next step
• Before any job application, check who in your network connects to that organization

4.7 Networking Time Budget

Activity	Frequency	Time/instance	Monthly total
Reconnection/follow-up emails	4/month	20 min	~1.5 h
Conference attendance	2–3/year	3–4 days	~1 h amortized
Local seminar attendance	2/month	2 h	4 h
Blog post writing	1/month	2–3 h	2.5 h
Relationship spreadsheet review	1/month	30 min	0.5 h
LinkedIn/online presence	2/month	15 min	0.5 h
Total			~10 h/month

This is about 2.5 hours per week — significant but manageable. The key is consistency, not intensity.

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Part 5 — Learning Curriculum

5.1 Three-Year Overview

Period	Optimization	Statistics	Queueing/Networks	Systems/Leadership	Startup
Y1 Q1–Q2	Boyd & Vandenberghe chapters 1–8 with exercises	Casella & Berger review (ch. 6–10)	Kleinrock Vol.1 ch.1–5	“Designing Data-Intensive Applications” ch.1–6	MVP voice assistant: Whisper+Qwen+HA integration
Y1 Q3–Q4	Boyd chapters 9–11 + Stanford EE364A problem sets	Copula theory (Nelsen); time series (Hamilton ch.1–5)	Stochastic Network Calculus (Jiang & Liu) ch.1–4	“An Elegant Puzzle”	User testing (5–10 users), iterate
Y2 Q1–Q2	Bertsekas “Nonlinear Programming” selected chapters	Causal inference (Hernán & Robins) Part I	Kelly “Reversibility and Stochastic Networks”	System design interview prep	Decide go/no-go on startup
Y2 Q3–Q4	Stochastic programming (Birge & Louveaux) ch.1–5	Bayesian methods (Gelman et al.) selected chapters	ns-3 deep dive: custom module, validated simulation	“The Manager’s Path”	If go: register ИП, first paying users
Y3 Q1–Q2	Integer programming (Wolsey) selected chapters	Advanced: empirical processes or high-dimensional stats	Publish survey paper on optimization in WiFi MAC	Job search preparation	Scale or maintain as side project
Y3 Q3–Q4	Review + specialize based on publication needs	Review + specialize	Active conference networking	Interview preparation	—

5.2 Detailed Year 1 Curriculum (Week-by-Week)

Daily schedule:

• 1h intense study (new material + exercises)
• 1–2h revision (Anki reviews + re-reading notes + light exercises)
• Best split: morning — Anki review (30 min), commute — Anki (20 min), evening — new material (1h), before bed — light review (30 min)

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Block 1: Convex Optimization Foundations (Weeks 1–12)

Textbook: Boyd & Vandenberghe, “Convex Optimization” (free PDF at web.stanford.edu/~boyd/cvxbook/) Video lectures: Stanford EE364A on YouTube (Stephen Boyd) — watch corresponding lecture after reading chapter.

Week	Chapter / Topic	Exercises	Anki cards to create
1	Ch.2 (2.1–2.3): Convex sets, examples	2.1, 2.2, 2.4, 2.7, 2.12	Definitions: convex set, convex hull, cone, affine set, hyperplane, halfspace, polyhedron
2	Ch.2 (2.4–2.6): Operations preserving convexity, separating hyperplanes, dual cones	2.15, 2.19, 2.24, 2.28, 2.33	Separation theorems, supporting hyperplane, dual cone properties
3	Ch.3 (3.1–3.2): Convex functions, examples	3.2, 3.5, 3.6, 3.16, 3.21	Key functions (log-sum-exp, norms, perspectives), first/second order conditions
4	Ch.3 (3.3–3.5): Operations preserving convexity, conjugate function, quasiconvexity	3.22, 3.24, 3.36, 3.42, 3.49	Conjugate function rules, composition rules, quasiconvex examples
5	Ch.4 (4.1–4.4): Optimization problems, LP, QP, SOCP	4.1, 4.3, 4.8, 4.11, 4.15	Problem hierarchy: LP ⊂ QP ⊂ SOCP ⊂ SDP ⊂ convex
6	Ch.4 (4.5–4.7): SDP, geometric programming, vector optimization	4.17, 4.25, 4.43, 4.44	SDP formulation patterns, GP log-transformation trick
7	Ch.5 (5.1–5.3): Lagrange dual, weak and strong duality	5.1, 5.3, 5.5, 5.7, 5.13	Lagrangian, dual function, weak duality (always holds), strong duality + Slater’s
8	Ch.5 (5.4–5.5): KKT conditions, sensitivity analysis	5.20, 5.21, 5.27, 5.31, 5.39	KKT conditions (primal feasibility, dual feasibility, complementary slackness, stationarity)
9	Review week. Re-do failed exercises from weeks 1–8. Intensive Anki.	Redo 3 hardest problems per chapter	Consolidate; merge similar cards
10	Ch.6 (6.1–6.4): Approximation and fitting — least-norm, least-squares, Chebyshev	6.2, 6.5, 6.9	Pattern cards: “fitting problem type → convex formulation”
11	Ch.7 (7.1–7.4): Statistical estimation — ML, MAP, experiment design	7.1, 7.4, 7.7	Connection between ML estimation and convex optimization
12	Ch.8: Geometric problems	8.1, 8.4, 8.8 + implement one problem in CVXPY	CVXPY syntax cards: objective, constraints, solve

Milestone: By week 12, you should be able to formulate a convex optimization problem from a written description, identify its type, write the dual, and solve it numerically in CVXPY.

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Block 2: Applied Statistics Refresh (Weeks 7–20, overlapping with Block 1)

Textbook: Casella & Berger, “Statistical Inference” (for theory); Wasserman, “All of Statistics” (for breadth); Nelsen, “An Introduction to Copulas” (for your copula paper).

Week	Topic	Source	Exercises	Anki
7–8	Sufficient statistics, exponential families, MLE theory	C&B ch.6-7	5 problems per chapter	Definitions, Fisher information, Cramér-Rao bound
9–10	Hypothesis testing: NP lemma, LRT, UMP tests	C&B ch.8	8.1, 8.3, 8.10, 8.17, 8.25	Test types, Type I/II errors, power function
11–12	Confidence intervals, bootstrap, permutation tests	Wasserman ch.8-9	Code 3 bootstrap examples in Python	Bootstrap bias, CI interpretation, pivotal quantities
13–14	Multiple testing: Bonferroni, BH procedure, FDR	Wasserman ch.10 + papers	Simulate FDR control in Python	FDR vs FWER, when to use which
15–16	Copula theory: Sklar’s theorem, Archimedean copulas, tail dependence	Nelsen ch.1–4	4 exercises per chapter	Copula families (Gumbel, Clayton, Frank, t), Kendall’s tau ↔ copula parameter
17–18	Copula estimation and goodness-of-fit	Nelsen ch.5 + Genest & Favre (2007)	Fit copulas to your latency data	GoF tests for copulas, AIC/BIC for copula selection
19–20	Time series basics: stationarity, ACF/PACF, ARIMA	Hamilton ch.1–3 or Shumway & Stoffer	Fit ARIMA to network traffic data	Stationarity tests (ADF, KPSS), Box-Jenkins procedure

Milestone: By week 20, you should have a near-complete draft of Paper #1 (copula-based latency modeling).

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Block 3: Queueing Theory Foundations (Weeks 13–26)

Textbook: Kleinrock, “Queueing Systems, Vol. 1: Theory” (classical, rigorous); supplement with Harchol-Balter, “Performance Modeling and Design of Computer Systems” (modern, excellent intuition).

Week	Topic	Exercises	Anki
13–14	M/M/1: birth-death process, steady state, Little’s Law	Kleinrock ch.1–2, 5 problems	Little’s Law (L = λW), PASTA, utilization
15–16	M/M/c, M/M/c/K, Erlang-B and Erlang-C formulas	Kleinrock ch.3, 5 problems	Erlang formulas, blocking probability
17–18	M/G/1: Pollaczek-Khinchine formula, residual life, PASTA	Kleinrock ch.5, 3 problems + 2 from Harchol-Balter	P-K formula, residual service time, busy period
19–20	Priority queues, vacation models	Harchol-Balter ch.28–30	Preemptive vs non-preemptive, conservation laws
21–22	Networks of queues: Jackson networks, BCMP theorem	Harchol-Balter ch.24–25	Product form, traffic equations, open vs closed networks
23–24	Intro to stochastic network calculus: arrival/service curves, backlog/delay bounds	Jiang & Liu ch.1–3	Min-plus convolution, effective bandwidth, σ/ρ characterization
25–26	Network calculus applications to WiFi: EDCA, aggregation, scheduling	Your own research + Ciucu et al. papers	Connect theory to your WiFi domain knowledge

Milestone: By week 26, you should have enough queueing theory to write Paper #2 (stochastic network calculus + ΔQ unification).

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Block 4: Ongoing Parallel Tracks (throughout Year 1)

Systems reading (1 chapter/week, ~45 min):

• Weeks 1–12: Kleppner, “Designing Data-Intensive Applications” — one chapter per week
• Weeks 13–24: Larson, “An Elegant Puzzle” — one chapter per week
• Weeks 25–36: Fournier, “The Manager’s Path” — one chapter per week

Startup track (weekends, 3–4h/week):

• Weeks 1–8: Refine voice assistant MVP (Piper/Silero + Whisper + Qwen3.5 on 1070Ti). Get pipeline stable with Pipecat.
• Weeks 9–16: Integrate with Home Assistant. Define 10 core voice commands for smart home control. Russian TTS pipeline with ruaccent/runorm.
• Weeks 17–24: Beta test with 5–10 users (friends, family, Home Assistant community). Collect feedback.
• Weeks 25–36: Iterate based on feedback. Write a blog post about the architecture (networking + branding). Decide go/no-go.

Treat the startup as a learning vehicle: practice system design, user research, product thinking, and public writing. If it takes off, great. If not, you’ve built skills and portfolio pieces.

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Part 6 — Anki System Design

Your tendency to forget details, names, and formulas makes Anki your most important tool. Here’s a system designed for math/engineering study.

6.1 Deck Structure

Career Study/
├── Optimization/
│   ├── Definitions & Concepts
│   ├── Theorems & Proofs (key steps)
│   ├── Problem Patterns (formulation recognition)
│   └── Algorithm Selection
├── Statistics/
│   ├── Distributions & Properties
│   ├── Tests & Procedures
│   ├── Formulas (with "when to use")
│   └── Assumption Checks
├── Queueing Theory/
│   ├── Models & Formulas
│   ├── Theorems (Little, PASTA, Jackson, PK)
│   └── Network Calculus
├── Names & Faces/
│   ├── Key researchers in your field
│   ├── Conference contacts
│   └── Who published what (paper ↔ author ↔ key result)
└── Systems & Leadership/
    ├── Design patterns
    ├── Architecture concepts
    └── Management frameworks

6.2 Card Design Principles (for math/engineering)

1. Minimum information principle. One fact per card. “What are the KKT conditions?” is too broad. Instead: “What is the stationarity condition in KKT?” / “What is complementary slackness in KKT?”
2. Always include “when/why”. Front: “Pollaczek-Khinchine formula.” Back: not just the formula, but “Applies to M/G/1 queues. Gives mean number in system using only first two moments of service time. Key insight: you don’t need the full service distribution.”
3. Use cloze deletions for formulas. “Little’s Law: L = {{c1::λ}} × {{c2::W}}” — this forces active recall of each component.
4. Create “reverse” cards for important concepts. Forward: “What does Slater’s condition guarantee?” → “Strong duality.” Reverse: “What condition guarantees strong duality for convex problems?” → “Slater’s condition (strictly feasible point exists).”
5. Image cards for geometric intuition. Screenshot key figures from Boyd’s book. “What does this picture illustrate?” → “Separating hyperplane theorem.”
6. “Name ↔ Result” cards for networking. “Who is Mor Harchol-Balter?” → “CMU professor, performance modeling, wrote the PFSC textbook, works on scheduling theory.” This helps at conferences.

6.3 Daily Anki Workflow

• Morning (20 min): Review due cards (~50–100 cards once system is mature). Use FSRS algorithm for optimal scheduling.
• Commute (20 min): Continue reviews if backlog exists; otherwise browse “Names & Faces” deck.
• After study session (10 min): Create 5–10 new cards from today’s material. Do not batch card creation — create immediately while the material is fresh.
• Weekend (30 min): Review and clean up cards created during the week. Merge duplicates, improve wording, add images.

Target steady state: ~300–500 active cards after 3 months, ~1000–1500 after a year. At 90% retention, daily reviews should take 15–25 minutes.

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Part 7 — Timeline and Milestones

Year 1 (2026–2027)

• Complete Boyd chapters 1–8 with exercises (month 3)
• Submit Paper #1: copula-based latency modeling (month 5)
• Complete queueing theory foundations — Kleinrock + Harchol-Balter (month 6)
• Reconnect with 5+ academic contacts (month 2)
• Attend 2 conferences: WiOpt + INFOCOM or IFIP Performance (throughout year)
• Start part-time teaching at MIPT or HSE (semester 2)
• Launch blog on alogins.net: 6+ technical posts (throughout year)
• Voice assistant MVP with 5+ beta users (month 6)
• Submit Paper #2: stochastic network calculus + ΔQ (month 8)
• Anki system: 500+ active cards, daily habit established (month 3)
• Read “Designing Data-Intensive Applications” (month 3)
• Read “An Elegant Puzzle” (month 6)

Year 2 (2027–2028)

• Complete stochastic optimization + integer programming blocks
• Submit 2–3 more papers (target: 4–5 total post-PhD by end of year)
• Teach one semester course (optimization or applied ML)
• Co-supervise 1 master’s student
• Negotiate promotion at Huawei or begin external job search
• Startup: go/no-go decision, register if viable
• Attend 2–3 conferences, give at least 1 talk
• Begin Bayesian inference and causal inference study blocks

Year 3 (2028–2029)

• Job change. Target: Senior Researcher / Team Lead at Nokia, Ericsson, or equivalent. Minimum €5,600 net.
• 6–8 post-PhD publications total
• Apply for adjunct/visiting researcher position at European university
• Submit survey paper on optimization in WiFi MAC
• Active presence in 2+ research communities (recognized by name at conferences)

Year 4 (2029–2030)

• Settle into new role, build team, deliver results
• 8–10 publications total; target 1 top-tier venue (Operations Research, Math Programming, or ToN)
• Increase teaching load to 1 course/semester if feasible
• Begin supervising PhD student(s) if university-affiliated
• Startup: either generating revenue or gracefully wound down

Year 5 (2030–2031)

• 10–15 post-PhD publications
• Earning ≥€5,600 net/month (40% increase achieved)
• Established as recognized expert in network optimization + applied statistics niche
• Active academic affiliation (adjunct or visiting researcher)
• Positioned for tenure-track application at Nordic/EU university within next 5 years
• Relationship network: 50+ active professional contacts across industry and academia

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Appendix A — Key Resources

Textbooks (priority order)

1. Boyd & Vandenberghe — “Convex Optimization” (free PDF) — THE optimization textbook
2. Harchol-Balter — “Performance Modeling and Design of Computer Systems” — best modern queueing book
3. Kleinrock — “Queueing Systems, Vol. 1” — classical, rigorous
4. Nelsen — “An Introduction to Copulas” — for your Paper #1
5. Wasserman — “All of Statistics” — concise, broad stats reference
6. Kleppner — “Designing Data-Intensive Applications” — systems architecture
7. Bertsekas — “Nonlinear Programming” — after Boyd, go deeper
8. Jiang & Liu — “Stochastic Network Calculus” — your niche
9. Hamilton — “Time Series Analysis” — for applied time series work
10. Birge & Louveaux — “Introduction to Stochastic Programming” — Year 2

Online Courses (free)

1. Stanford EE364A — Convex Optimization I (YouTube + Stanford Online)
2. Stanford EE364B — Convex Optimization II (YouTube)
3. MIT OCW 6.079 — Introduction to Convex Optimization
4. MIT OCW 6.262 — Discrete Stochastic Processes (Robert Gallager)
5. Brady Neal — Causal Inference course (YouTube + free textbook)
6. Hernán & Robins — “Causal Inference: What If” (free PDF, Harvard)

Tools

• CVXPY (Python) — convex optimization modeling
• statsmodels + scipy.stats — statistical analysis
• SimPy — discrete event simulation in Python
• ns-3 — network simulation (you already use this)
• AnkiDroid — spaced repetition (already using)

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Appendix B — Salary Benchmarking Reference

Location	Role	Gross/year (est.)	Net/month (est.)	Notes
Germany	Senior Data Scientist	€80–102K	€4,200–5,200	Glassdoor 2026; Munich/Berlin premium
Germany	Team Lead / Principal	€95–120K	€4,800–5,800	With PhD + 5yr exp
Sweden (Stockholm)	Senior Researcher (Ericsson)	SEK 640–770K	€4,500–5,500	Ericsson Glassdoor; competitive benefits
Finland (Espoo)	Senior Researcher (Nokia)	€70–90K	€4,000–5,000	Lower tax; strong benefits
Netherlands	Senior DS / Principal	€85–110K	€4,500–5,800	30% ruling for expats (5 years)
Denmark	Senior Researcher	DKK 55–70K/mo	€4,500–5,500	High tax but strong purchasing power
Switzerland	Senior Researcher	CHF 120–160K	€6,500–8,500	Highest gross; highest cost of living
France (INRIA)	Chargé de Recherche	€48–60K	€3,200–4,000	Permanent; great academic freedom
USA (remote for EU co.)	Staff/Principal Engineer	$160–220K	€5,500–7,500	Visa complexity; best absolute pay
Moscow	Team Lead (Huawei/Yandex)	₽5–8M/yr	€4,000–6,000	Volatile with exchange rate

For 40% increase (€5,600+ net): Most reliable paths are Germany (Principal level), Netherlands (with 30% ruling), Sweden, or Denmark. Switzerland is the safest bet financially.