TENZIN TSERING NYC, USA Email: tenzintsering1608@gmail.com LinkedIn: https://www.linkedin.com/in/tenzintsering1608 GitHub: https://github.com/ttsering4 Website: https://tenzintsering.xyz PROFESSIONAL SUMMARY Operations, People & Independent Research Office Manager · HR Product Intern · Business Administration (A.S.) · NYC I keep busy teams organized, communicate clearly with people at every level, and dig into data or technical problems when the work calls for it. Open to roles in research, HR, operations, and admin. US citizen, no sponsorship needed. I'm someone who likes keeping systems and people in sync. That might mean running the front desk of a busy tax office, sourcing candidates for an HR product, or teaching myself a technical project from scratch. The through-line is the same: listen carefully, stay organized, and finish what I start. Most recently I worked as an HR Product Intern at EMRA AI and as Office Manager at H&R Block, where I was promoted within six weeks. On my own time I also built an EEG decoding project that classifies imagined movement with up to 83% accuracy. I like work where communication, detail, and curiosity all matter. I'm open to roles across research, HR, operations, and administration. US citizen, authorized to work with no sponsorship needed. WORK HISTORY Independent Researcher | Self-directed EEG and BCI Research | June 2026 to Present Built a four-class motor imagery decoder (EEGNet, 3,508 params) on BCI Competition IV-2a for left hand, right hand, feet, or tongue from four seconds of scalp EEG. Cross-day evaluation: best subject 83%, nine-subject mean 57.7%. • 83% best-subject accuracy (chance 25%) • EEGNet · 3,508 parameters · braindecode • Cross-day train/test · 9 subjects HR Product Intern | EMRA AI | April 2026 to June 2026 · Remote Supported an HR hiring product by sourcing candidates, coordinating interview schedules, screening applicants, and relaying client feedback to guide product improvements. • Candidate sourcing & screening • Interview & meeting coordination • Client feedback for product Office Manager | H&R Block | December 2023 to April 2026 · Queens, NY Promoted from Receptionist within six weeks. Managed the front desk for a seven-person tax office, scheduled six preparers each season, and handled high-volume calls and sensitive financial information with care. • 1,500+ appointments per season • 80 to 150+ calls/day at peak • Promoted within six weeks • Confidential ID & IRS PIN handling Front Desk Associate | Elite Fitness | September 2023 to April 2024 · New York Welcomed members and ran the front desk at a private gym of 30 to 40 members. Handled memberships, questions, and kept the desk and records in order. • Member welcome & front desk • 5 to 10 new memberships/month • Records & desk upkeep EDUCATION Associate of Science, Business Administration | LaGuardia Community College (CUNY), New York, NY | Expected 2027 | in-progress Part-time study alongside full-time seasonal work. Building a foundation in operations, management, and professional communication. Focus: Business operations, Organizational management, Professional communication, Records & administration Self-Directed Study: EEG, BCI & Applied ML | Independent | 2025 to Present | in-progress Structured self-study behind the BCI Competition IV-2a decoder: literature, experiment workflow, compact CNNs for EEG, and clear reporting. Focus: EEG preprocessing (4-38 Hz), Four-class motor imagery, EEGNet / braindecode, Cross-day evaluation PROJECTS HR Attrition Dashboard | Power BI | 2025 Built an interactive Power BI dashboard to study employee attrition and guide HR retention decisions. • Imported raw CSV data and cleaned it in Power Query by removing duplicates, fixing errors, and setting data types • Created an AttritionCount column and DAX measures to calculate the attrition rate as a percentage • Designed bar charts, KPIs, pie charts, and tables to present clear and useful insights CERTIFICATIONS & AWARDS Project Management, Google / Coursera. 2025 Agile with Jira, Atlassian / Coursera. 2025 FEATURED PROJECT Reading movement intent from EEG Decoding imagined movement from scalp EEG. 83% accuracy on a four-way choice where guessing scores 25%. I trained a compact neural network (EEGNet, 3,508 parameters) to tell which of four movements a person was imagining: left hand, right hand, feet, or tongue, from four seconds of scalp EEG. On held-out recordings from a different day, it reaches 83% on the best subject and 57.7% averaged across nine people. Chance is 25%. Brain-computer interfaces let people control devices by imagining movement. The hard part is that EEG is noisy, low-resolution, and different for every person. I built a decoder for the four-class motor imagery task on the BCI Competition IV-2a dataset: given four seconds of scalp EEG, decide which of four movements the person was imagining. The model is EEGNet, deliberately tiny at 3,508 parameters, since EEG datasets are small and large models just memorise. I band-pass filtered to 4-38 Hz (the mu and beta rhythms where motor imagery lives), cut the recordings into labelled trials, and trained on one session while testing on a second session recorded on a different day, so nothing leaks between them. The best subject reaches 83% against a 25% chance baseline. Across all nine subjects the average is 57.7%, ranging from 38% to 84%. That 45-point spread reflects a real, unsolved problem in the field, not a flaw in the pipeline. Training one shared model on all nine subjects instead beats the per-subject average, reaching 61.7%. KEY RESULTS • 83.0% four-class accuracy on the best subject (chance: 25%) • 78.1% mean over five retrains, range 70.5-81.6% (the honest spread) • 57.7% mean across all nine subjects, range 38.5-84.0% • 61.7% for a single model pooled across all subjects, beating nine personal models • 3,508 parameters. Runs on a laptop CPU in minutes • Tested on a separate recording session from a different day Data: BNCI2014_001 (BCI Competition IV-2a), 9 subjects, 26 channels at 250 Hz, two sessions each. Preprocessing: 4-38 Hz band-pass, rescaled to microvolts, epoched into 4-second trials around each cue (288 train / 288 test per subject). Model: EEGNet via braindecode, Adam at lr 0.0625, batch size 32, 100 epochs. Evaluation: session 1 trains, session 2 tests. A cross-day split, never a random shuffle of trials. 83%: Best-subject four-class accuracy (chance 25%) 57.7%: Mean across nine subjects (38% to 84%) 3,508: Parameters. Trains on a laptop CPU 61.7%: Pooled model beats nine personal models Training loss. The model converges cleanly over epochs. Accuracy gain vs. 25% chance on four-class motor imagery Confusion matrix with clear diagonal dominance across four classes Per-subject accuracy from 38% to 84% across nine people SKILLS Technical: Microsoft Office (Outlook, Word, Excel, PowerPoint), Power BI, Jira, Power Query, DAX, AMP scheduling portal, Python Administrative: Calendar and meeting coordination, Scheduling for multiple team members, High-volume phone handling, Front desk and reception coverage, Filing and records, Data entry, Sensitive document handling Research & Data: EEG preprocessing, Motor imagery decoding, Machine learning basics, Dashboard design & KPIs, Experiment documentation Strengths: Attention to detail, Confidentiality and discretion, Problem solving, Dependability, Time management under deadlines Languages: English (fluent), Tibetan (native) KEYWORDS Tenzin Tsering, research assistant NYC, HR product intern, office manager Queens, administrative assistant New York, people operations, business administration CUNY, LaGuardia Community College, Power BI dashboard, HR attrition dashboard, Project Management Google Coursera, Agile with Jira Atlassian, scheduling coordination, stakeholder communication, EEG researcher, brain-computer interface, motor imagery classification, EEGNet, Python machine learning, US citizen no sponsorship, New York City JOB TITLES Research Assistant | HR Product Intern | Office Manager | Administrative Assistant | People Operations | Business Administration Student | Independent EEG Researcher CONTACT Open to roles in research, HR, operations, administration, and related work where clear communication and follow-through matter. Email or LinkedIn works best.