Search the Community

Showing results for tags 'ms'.

More search options

  • Search By Tags

    Type tags separated by commas.
  • Search By Author

Content Type


  • Comment Card
    • Announcements
    • Comments, Questions, Etc.
  • The Cafe
    • City Guide
    • IHOG: International House of Grads
    • The Lobby
  • Applying to Graduate School
    • The April 15th is this week! Freak-out forum.
    • Applications
    • Questions and Answers
    • Waiting it Out
    • Decisions, Decisions
    • The Bank
  • Grad School Life
    • Meet and Greet
    • Officially Grads
    • Coursework, Advising, and Exams
    • Research
    • Teaching
    • Writing, Presenting and Publishing
    • Jobs
  • The Menu
    • Applied Sciences & Mathematics
    • Arts
    • Humanities
    • Interdisciplinary Studies
    • Life Sciences
    • Physical Sciences
    • Professional Programs
    • Social Sciences


  • An Optimist's PhD Blog
  • coyabean's Blog
  • Saved for a Rainy Day
  • To infinity and beyond
  • captiv8ed's Blog
  • Pea-Jay's Educational Journey
  • Procrastinating
  • alexis' Blog
  • grassroots and bamboo shoots.
  • Ridgey's blog
  • ScreamingHairyArmadillo's Blog
  • amyeray's Blog
  • Blemo Girl's Guide to Grad School
  • Psychdork's Blog
  • missesENG's Blog
  • bgk's Blog
  • Tall Chai Latte's blog
  • PhD is for Chumps
  • bloggin'
  • NY or KY
  • Deadlines Blog Ferment
  • Going All In
  • In Itinere ad Eruditus
  • Adventures in Grad School-ing
  • inafuturelife
  • The Alchemist's Path
  • The Rocking Blog
  • And Here We Go!
  • Presbygeek's Blog
  • zennin' it
  • Magical Mystery Tour
  • A Beggar's Blog
  • A Senseless Game
  • Jumping into the Fray
  • Asian Studies Masters
  • Around the Block Again
  • A complicated affair
  • Click My Heels Three Times and Get In
  • dimanche0829's Blog
  • Computer Science Crossed Fingers
  • To the Lighthouse
  • Blog of Abnormally Aberrant
  • MissMoneyJenny's Blog
  • Two Masters, an Archive and Tea
  • 20/20 Hindsight
  • Right Now I'm A-Roaming
  • A Future Historian's Journey to PhD
  • St Andrews Lynx's Blog
  • Amerz's Blog
  • Musings of a Biotech Babe
  • TheFez's Blog
  • PhD, Please!
  • Blooming Ecologist
  • Brittle Ductile Transitions
  • Pleiotropic Notions
  • EdTech Enthusiast
  • The Many Flavors of Rhetoric
  • Expanding Horizons
  • Yes, and...
  • Flailing Upward
  • Traumatized, Exhausted, and Still Going

Found 134 results

  1. Please help me in improving my CMU MS in Language Technologies essay My primary research objective and interest are in the area of natural language processing (NLP). This interest stems from the importance human language, the most expressive means of communication that we have, plays in creating machines that have a revolutionary impact on our society. From Apple’s Siri being able to understand an autistic child to IBM Watson winning Jeopardy, we can witness that when we have a deep understanding of the syntax, semantics, discourse, and speech of human language, we can create transformative technologies that help businesses grow and improve quality of human life. This fact coupled with the interest I have developed in machine learning and natural language processing from my past five years of research is one of the primary motivations that drives me to go through the rigors of a master’s degree. I am especially interested in problems of question answering systems and natural language generation. After I graduate, I intend to contribute toward solving some of the most pressing problems in this field and ultimately transform my ideas into a business of my own. During my undergraduate study, I did extensive work under the supervision of Prof OP Vyas on machine learning and Linked Open Data. During the course of my research, I observed that we were forced to work on a small subset of the DBpedia graph. I solved this challenge by creating a data loading setup that could fit the entire graph on an open-source software called Virtuoso in a single machine. This accomplishment helped many Ph.D. scholars validate their approaches on an expanded DBpedia dataset and was appreciated by everyone in the department. In my sixth semester, I developed a hybrid (collaborative-filtering and content-based) recommender system and used regression analysis to predict the rating a user might give to a song based on their profile. Specifically, I used an ensemble learner called AdaBoost to predict the rating of an unrated item by a user. The approach that I developed can solve the data sparsity problems seen in most data sets such as IMDB and MovieLens and allow them to provide better recommendations to their users. I spent my last semester working on a thesis project on feature extraction with Dr. Bharat Singh. In this project, I used particle swarm optimization with chaotic inertia weight strategy, as the initialization variant for non-negative matrix factorization. My work helped Dr. Bharat publish a paper titled, “A PSO Based Approach for Producing Optimized Latent Factor in Special Reference to Big Data” in the International Journal of Service Science, Management, Engineering, and Technology in 2016. I did not limit myself to only academics. I organized various workshops, held the position of student placement coordinator and member of the college literary club. Even though on a few occasions these made my academic life more difficult and got reflected in my grades a few times, I do not regret them. They have taught me how to manage my time as skillfully as possible. From 2015, I started working in XXX as an application developer. I got the opportunity to work in a department called XXX Research, which publishes thousands of financial text publications for clients, and this enormous amount of data was an excellent resource for solving many classic problems in machine learning. In XXX Research, I observed that my team was using Apache Lucene’s text matching capabilities to perform named entity recognition (NER). While Lucene is a fast text indexing solution, it lacks the capabilities of modern NER systems. I hence developed a conditional-random-field (CRF) based NER system that could recognize 20,000 different organizations. I also worked with Mr. XXX and drew upon his experience as a researcher at Cornell University, to solve a multi-label classification problem on a large text dataset. After a few months, I had the opportunity to work with XXX Innovation Lab based in Tel Aviv, on a global R&D project to develop a question answering system to answer text questions entered by users based on the publications we produce. I contributed toward the development of a knowledge-based question answering system, where I helped in converting thousands of text publications into RDF using semantic ontology. For this project, I evaluated and worked with multiple vendors, including IBM’s Watson, Thomson Reuters Open Calais, Lymba and Amenity’s VIP. I dug deep into their products and came to understand how they solve major problems like NER, entity disambiguation, co-reference resolution, conversion of text to an RDF graph, and many more. I was part of a two-member evaluation team whose aim was to suggest the best vendor-solution to buy. My thorough analysis of the pros and cons of each vendor helped XXX in making this very important financial decision and was appreciated by everyone particularly the Global Director of XXX Research. Presently, I am working on using an information-retrieval-based approach to find answers to Factoid questions. I have also mentored two new joiners in my team and helped them to get on-boarded to their projects. Continuing my current trajectory, some of the topics that are of great interest to me are deep learning for NLP, optimal feature selection and reduction strategy for problems, building scalable machine learning solutions and combining linked open data principles with NLP. Besides these areas, I see a huge potential for natural language generation solutions. I was deeply inspired by Andrej Karpathy’s char-RNN, where he uses a multi-layer recurrent neural network that learns to predict the next character in a sequence. Considering the revolution this will create in the publishing industry, I want to contribute toward an efficient solution to this problem. Word embedding in NLP also fascinates me, and I would like to work with one of its novel extensions, namely Thought Vectors. I am determined to gain a strong background in computer science and plan to use it to make a dent in the vast field of machine learning and natural language processing, by contributing to academic research. I understand Prof Jaime Carbonell has done significant work in the field of NLP. His work history and current pursuits have a strong convergence with my interests, and I would like to request that you consider me as a graduate research assistant under his direction. I also found Prof Taylor Berg’s work, especially in Question Answering systems, highly inspiring. To excel as a graduate student and in my later career, I possess strong problem-solving skills along with the ability to understand new ideas and implement them using the programming skills I have gained working in the industry. I plan to use the experience I gained as a Student Placement Coordinator to forge strong industry partnerships in the form of research projects and placement opportunities. I am confident that the time I spent mentoring others in college and work, would help me make a good teaching assistant. I believe the resonance of my interests and previous work with that of Department of Computer Science at the University of Maryland will help me to enrich my knowledge and make a meaningful contribution to my field. Thank you very much for your time and attention.
  2. Hello, I am apply for mostly phD programs in statistics for fall 2018, and I would love to hear your thoughts on my profile. Undergraduate Institution: Top 40 USnews, public school; no statistics department, but math department is top 20 major: statistics GPA:3.732 in general, major GPA is 3.76 Ethnicity: International Asian Male (what I have heard is that my actual competition will be the applicants from my country, as opposed to applicants that went to undergraduate schools in the US ) GRE: 161 verbal, 166 quant, 4.5 writing Math GRE: did not take it TOEFL: I know this is not required since my undergraduate school is in the US, and I took my TOEFL in 2013, but still, just in case. 112 Programs Applying: phD in statistics, phD in biostatistics, ms in statistics Noticeable Courses Taken: all A's in freshman calculus series (4 courses, including multivariate), A's in linear algebra ( 2 courses, one elementary, the other applied upper division), 2 A-'s in real analysis (took two courses in a supposedly rigorous 3-course series), A in complex analysis, A- in a project-based big-data statistics course, A+ in computational statistic, A and B in a two-course mathematical statistic series, ABA in a 3-course probability/stochastic series, B+ in time series, A in numerical analysis, A in a project-based math programming course, A in financial mathematics. P(pass) in a graduate-level applied statistics course. Recommendation letters: one from the professor I took computational statistic with, also doing a reading course with him this quarter; one from a professor I took multiple statistics courses with; and one from the professor I took numerical analysis and math programming with. There first two professors are quite well-known in the statistics industry, although I doubt that their recommendations would be strong because of the usual high standards they set. The third professor is less well-known and his letter should be better than a generic letter. Research experience: had a big group project in which I build regression models on a Kaggle data-set, on which I developed most of the theory and did all of the coding; a walk-through research on a R-package on isotonic regression, had a 5-page long research report Professional experience: no experience in the industry so far, but I am looking into some data analysis internships after I graduate. Misc: I TA'ed a elementary statistics course for two quarters, and I also have TA'ed some calculus courses; the TA reviews were all quite good. I am graduating in the start of my 4th year as an undergraduate. It is unusual, but I am not too sure how this will things. If I do get into a masters program, I really hope to get enough funding and TA-ship to at least reduce my tuition: I just really want to be financially independent and not have my parents pay tuition anymore. Schools and programs I am applying to: OSU, UIUC, Florida State, UC Davis, Iowa State, North Carolina State, Purdue Pittsburg University, Rutgers UConn UC Irvine Texas A&M Texas Austin Northwestern Rice UCLA North Carolina Chapel Hill I am applying to phD in statistics to all of those schools above, phD in biostatistics if available, masters in statistics/biostatistics if enough funding is provided. The way I list these schools is the by how much I fit, Northwestern, Rice,UCLA, NC Chapel Hill are reaches; UIUC, OSU, Texas A&M, Texas Austin, Florida State, NC State, Iowa State are hopefully matches; and the rest are naively considered safeties for now. Concerns: I have had some B's in statistic courses; no GRE math score; research is not stellar; no professional experience; no strong recommendation letters. Please let me know your thoughts: what other schools should I apply to? what phD programs do I have a decent chance at ? what masters programs should I apply to, given that I really want some funding ? Thank you so much for your time and thoughts.
  3. My undergrad marksheets are of two types: 1. A single sheet with all the final marks for each subect and course throughout my 8 semesters (no papers repeated visible). 2. Separate marksheets for each semester and paper repeated(8 + repeated papers). I have transcripts for both types. Can I upload type 1 with no repercussions?
  4. I urgently need feedback on my SoP draft for my application for a Master's in Computer Science. Anyone willing to help? I'll PM the SoP to you. Thanks in advance
  5. Hey fellow MSE gradcafe residents! Based on @shur42's thread from last year, I thought it would be good to start a thread for MSE students applying for the Fall 2018 cycle, to get to know each other and learn about each other's application profiles. Would also be helpful for those from the Fall 2017 cycle to contribute their profiles and any tips This is the template that they used (from a biology thread) for application profiles: Undergrad Institution: (School or type of school, such as big state, lib arts, ivy, technical, foreign (what country?))Major(s):Minor(s):GPA in Major:Overall GPA:Position in Class: (No numbers needed, but are you top? near top? average? struggling?)Type of Student: (Domestic/International, male/female, minority?)GRE Scores (revised/old version):Q:V:W:TOEFL Total: (if applicable, otherwise delete this)Research Experience: (At your school or elsewhere? What field? How much time? Any publications (Mth author out of N?) or conference talks etc...)Awards/Honors/Recognitions: (Within your school or outside?)Pertinent Activities or Jobs: (Such as tutor, TA, etc...)Any Miscellaneous Accomplishments that Might Help:Special Bonus Points: (Such as connections, grad classes, famous recommenders, female or minority status etc...)Any Other Info That Shows Up On Your App and Might Matter:Applying to Where:School - Department - Research InterestSchool - Department - Research InterestSchool - Department - Research Interest
  6. Undergrad/Graduate Institutions: University of Texas at Austin Major: Mathematics Cumulative GPA: 3.82 cumulative; 3.75 in major Type of Student: Domestic White Female Math/Stats Courses: Multivariable Calculus (A-), Probability (A), Linear Algebra (B+), Mathematical Statistics (A), Stochastic Processes (A), Applied Regression Analysis (A), Biostatistics (A), Real Analysis (A), Differential Equations (B) Got credit for Calculus I and II so hopefully that doesn't matter too much considering I've needed calculus in nearly every class since? Also going to graduate with 18 hours in CS classes (data visualization, basic programming in python, databases, mobile computing and numerical analysis) Quantitative/ Programming Courses: Elements of Computing (A) - this was basic programming in Python, Elements of Software Design (A) - more Python, Elements of Data Visualization (A) - included SQL, R, Tableau Elements of Databases (in progress) - SQL, Python, BeautifulSoup, learning to understand how cloud services such as AWS work 2 more classes next semester: Numerical Analysis in C and Mobile Computing where we will have to create a fully functioning app. I feel very confident in RStudio and pretty good about SQL as well. I think my coding in Python is decent but could definitely use some brushing up. GRE: Just took today and am incredibly upset. Quant: 160 - obviously don't have the percentages yet but this is much lower than I planned. This was my only run and I had a lot of anxiety surrounding it. Just couldn't get in a clear headspace due to anxiety. Verbal: 160. Don't have analytic score yet but I think it'll be > 4.0 at least. My first essay was pretty mediocre but I felt my second one was pretty good. Research Experience: 125 hours over the summer in a Human Development and Family Sciences lab; worked on coding in SAS and SPSS to calculate BMI percentiles for the teenagers in the study; also helped organize a small team to put together data for the professor's grant analysis. Recommendations: 1. The professor I worked with over the summer. She was surprised I completed the BMI percentiles task and said she expected that from a graduate student. 2. Applied Regression Professor - feel like it will be good? He said he'll discuss how I did in the class and my work ethic (i'm hoping the second part is what really stands out) 3. Stochastic processes professor who is very well-known; I can't say it'll be very personal or good but I hope that's offset by others who I feel know me more personally. 4. My Real Analysis professor. I had him for a class over the summer and he's just a really sweet guy. It might not be glowing, but I feel he can attest to my mathematical ability due to my work in the course. Other tidbits: I have worked about 20-30 hours per week consistently at my part-time job as a front desk attendant at a recreation center. This isn't really all that relevant, but I included how my daily encounters with the homeless people there have motivated me to pursue biostatistics in my personal statement. It also explains some Bs during my sophomore year as I was getting used to working. I'm mildly concerned about the B+ in linear algebra; however, I am taking Applied Linear Algebra this semester to brush up on those skills. Currently a TA for Biostatistics where I grade and assist with lecture and lab. Programs I'm applying to based on order of preference: University of Washington (biostatistics) (MY DREAM SCHOOL) but I'm feeling very discouraged atm with getting in due to my low quant and I won't have time to retake . University of Wisconsin-Madison (biostatistics) UT Health Science Center (biostatistics) Rice University (statistics) Oregon State University (statistics) Colorado State University (statistics) University of NC - Chapel Hill (biostatistics) Baylor (statistics) University of Texas Dallas (statistics) * Some of these schools only have a statistics program but with biostatistics electives; I marked whether I'm applying biostatistics or statistics. Thank you so much for reviewing. Please let me know if I should consider any other schools/drop some of these because honestly if it isn't necessary to keep schools like UT Dallas and Baylor I'd be thrilled with dropping them. Lat note: I won't have time to retake the GRE for my top 3 schools (of course >:( ); however I will be retaking it ASAP for other applications.
  7. Hello. I need some advice and help from you guys. I will greatly appreciate them! 1. I have heard from someone that many graduate programs admit students on rolling basis. Is this true? I have been researching schools for some time and I have not seen any mention of "rolling admission" on their websites. I was a bit panicking when I heard this because I just started narrowing down my school list and filling out the applications and I think I will be able to submit it right before the deadlines.... 2. I have B.S. degree in molecular biology. I have read some posts that it is doable but a lot harder to get into engineering programs with B.S. degree in biology. (The reason I am going for MS instead of PhD is also because I am bio major.) I was aiming for very top schools (top 10) because my GPA and GRE scores are competitive, but now I am not sure what kind of schools I should apply for. I do have some industry research experience: a little more than 2 years at a diagnostic device company (company manufacturing things like pregnancy test kits). I did not do anything really engineering-like because the company does not manufacture biosensors. 3. Consider my circumstances (#2) and stats below and recommend me schools that would give me some chance. GRE Q: 165 V: 162 W: ?? overall GPA: 3.74 major GPA: not sure but little higher than overall GPA Publication - 1 publication but it was during high school and I barely knew what's going on (it was computational organic chemistry) although they gave me credit for it.. so... it's negligible.. Research experience - one summer as a lab tech in a pathology lab; 2 years as a R&D research scientist at a diagnostic device company, responsible for developing new products (as mentioned above) Recommendations - 2 okay ones from my undergrad; 1 excellent one from my supervisor at the diagnostic device company
  8. GPA: 3.37 TOEFL: 115 GRE (unofficial): V164 Q168 I want to pursue a Master's degree with a focus on machine learning and / or natural language processing, but I'm having a difficult time determining the range of schools I should be applying to. Ultimately, I would like to get a PhD at a top 20 university, and get involved in CS research at a competitive R&D division or laboratory. Therefore, it's important for me to find a place that is productive in terms of research in ML / NLP. I'm especially interested in grad schools in Canada, but I'm certainly open to other suggestions. To give you an idea about my profile: I graduated with a degree in computer engineering from the most internationally esteemed university in my country with a subpar GPA, which was mostly caused by poor performance over a couple of semesters during which my family was going through a rough time. I did get back on track, and did fairly well in the last three semesters though. I was wondering how those obvious and sudden transitions in my transcript would be interpreted by the admission office? Should I address the reason in my SOP, or just leave it alone? I also ended up graduating in the top 10 of my class anyway, so that might be worth mentioning. (Yes, the highest GPA that year was ~3.7, and the second ~3.5, which I know seems ridiculously low compared to most other schools.) As for experience, I worked at a large defense company as an intern, and later at a robotics laboratory participating in deep reinforcement learning research for a few months. I also spent two years working part-time as an undergraduate TA. Unfortunately, I have no publications or serious research experience to speak of. Not sure if these are at all relevant, but I also played chess in the national junior team, and the violin in my country's first amateur symphony orchestra, whose establishment I took some part in. I have a few decent LORs, but I'm still unclear as to what constitutes a "strong LOR". That's basically it. I feel incredibly incompetent, and afraid that I might not get in at all. Currently, I'm considering U of Montreal, U of Alberta, McGill and Simon Fraser to begin with. Are all of these too ambitious for me? Should I aim somewhat higher - or lower? What other schools should I look into? Many thanks in advance!
  9. Hey New gradcafe user and prospective MS applicant for an MS in Operations Research / Statistics / Management Science for Fall 2018. Please evaluate my profile and whether I'm being too ambitious/do I stand a chance here? Key features of my profile - Low undergrad GPA, high quant GRE score, related work ex Here's my profile followed by an initial university shortlist GRE 324 - Q 166 (91st percentile) V 158 (80th percentile) (Might give again to offset low undergrad GPA) TOEFL - Yet to give Undergrad CGPA - 6.3/10 from the National Institute of Technology Warangal in Mechanical Engg - Top 10 India for engineering Work Experience - 3 years 1.5 years - Data Analyst for a Fortune 200 MNC (1 promotion) + 3 good projects - Quantitative Sales/Marketing analytics 1.5 years - Senior Analyst for a Loyalty Card company (jump in designation from previous org) Fair amount of projects on quantitative modelling work Research papers/Publications: None Certifications: 1. SAS certified base programmer 2. SAS certified statistical business analyst: Regression and Modelling 3. Machine Learning from Coursera Recommendations: 1. HOD from work - ex prof at a premier MBA school in India - Strong 2. Team Leader from previous org - Moderate 3. Professor from college - Moderate SOP structure: Considering that my weakest point is my undergrad GPA, I'll bring in a point about how I messed up the first year but post that my gpa has been increasing plus ever since starting work I've been really driven and talk about my projects as proof. Programs I'm looking to apply for: 1. Statistics (with electives from the CS department) 2. Operations Research Not applying for "Analytics" or "Data Science" masters because I feel such programs have breadth but seriously lack depth. Current university shortlist: 1. Columbia 2. University of Chicago 3. UCLA 4. Georgia Tech 5. University of Michigan 6. John Hopkins 7. University of North Carolina at Chapel Hill 8. University of Illinois at Urbana Champaign 9. Duke 10. Cornell Questions: 1. What is my profile like? 2. How would you categorise the above universities considering my profile as Safe, Moderate, Ambitious and reasons for the same? 3. Thoughts on the SOP structure?
  10. I will apply several graduate schools in US, would it be hard to get admissions from any of schools below? here is my profile Status : International / B.S degree in Industrial engineering in Top 10 univ in my country / Currently working for National Army as an info/signal officer (service in duty) Working/Research exp : worked as a research intern in school laboratory(Quantitative Technology Management) for 8 months (proceeded paper related to the project in the lab but didnt published) CGPA : 3.33 / 4.0 Major GPA : 3.66/4.0 (several schools want major gpa and my major gpa is lot better than horrible cgpa...) TOEFL : 103 GRE : havent taken but expect around V160/Q168/W3.5 Relevant courses : Multivariate Calculus(C+..... I shouldve retaken this) Probability theory(B+) Applied statistics(A) Quantitative analytics(A+) Data mining(A) Operations research(A+) Linear algebra(A+) Discrete Math(B+) .... and other many courses provided in industrial engineering at least better than B+ grade And below is my school list Stanford(data science) , Berkeley(IEOR) , Michigan(Industrial Eng) , Purdue(stat) , UIUC(industrial eng) , USC(data science) , GA tech(analytics) , Northwestern(analytics) UCLA(statistics) , NYU(data science) , CMU(Data science) , Washington at seattle(industrial eng) , Johns hopkins (applied statistics) I do think to get ad from one of them is difficult... should I add some safe school in the list? Im afraid I would get all rejection... I ll be waiting for your comment thank you
  11. Objective: MS in Natural Language Processing related programs More Inclination towards Industry than a PhD GRE: 330 (170 quant + 160 verbal+ 4.5 AWA) TOEFL: 117 (R-29, L-30, S-28, W-30) GPA: 9.36/10.0 College: LNMIIT, Jaipur (Deemed University in India) Papers: 2 at CoNLL-2017 (Top-tier, view) {4th author out of 6} and ICON-2016 (National Level, view) {2nd author out of 2} [Both are in NLP] LORs: Yale Prof+Stanford PostDoc+LNMIIT Associate Prof Internships (all remote): Yale University (Resulting in CoNLL Paper), Nanyang Technological University (Resulting in ICON Paper), NCSR Demokritos and a startup More: Resume, LinkedIn, Github *Universities already finalized*- Stanford,CMU,Columbia,UPenn, UC Berkeley, UWashinton, USC *Only 1 quick point needs to be resolved* After extensive research, I've shortlisted these universities which I need to classify as Ambi/Mod/Safe. I've not received replies from popular profile evaluation platforms and hence need your expertise in the matter. If possible, could you also provide any specific points about why I should/shouldn't apply here- (Eg- Funded MS? Most Job Prospects? Good as a safety school?) 1) Harvard University 2) Georgia Tech 3) UT Austin 4) John Hopkins 5) University of Illinois Urbana Champaign 6) Cornell 7) UCLA 8) Princeton 9) UC San Diago 10) University of Michigan 11) University of Wisconsin Madison 12) Yale University 13) NYU, TAMU, OSU, ASU (Safety) 14) University of Maryland,College Park 15) Purdue University -Thankyou so much!!
  12. I will be sending in early applications within the next 20-22 days, for the Fall '18 in-take. The list is as follows: (NOT IN ANY PARTICULAR ORDER) -Carnegie Mellon (MS Computational Biology ) -IU Purdue Uni of Indianapolis (MS Bioinformatics) -UC San Diego (MS Bioinformatics) -John Hopkins (MS Biomedical-informatics) -Virginia Tech (MS Bioinformatics) -Boston University (MS Bioinformatics) -Georgia Tech (MS Bioinformatics) -University of Michigan Ann Arbor (MS Biomedical-informatics) -Northeastern University (MS Bioinformatics) -Indiana University Bloomington (MS Bioinformatics) -Rutgers (MS Computational and Integrative Biology) I would love to hear inputs on these universities.
  13. I will be sending in early applications within the next 20-22 days, for the Fall '18 in-take. The list is as follows: (NOT IN ANY PARTICULAR ORDER) -Carnegie Mellon (MS Computational Biology ) -IU Purdue Uni of Indianapolis (MS Bioinformatics) -UC San Diego (MS Bioinformatics) -John Hopkins (MS Biomedical-informatics) -Virginia Tech (MS Bioinformatics) -Boston University (MS Bioinformatics) -Georgia Tech (MS Bioinformatics) -University of Michigan Ann Arbor (MS Biomedical-informatics) -Northeastern University (MS Bioinformatics) -Indiana University Bloomington (MS Bioinformatics) -Rutgers (MS Computational and Integrative Biology) I would love to hear inputs on these universities.
  14. Hola! I am having a tough time selecting universities to apply to for MS -CS (AI / ML ). Tough cause I am trying to shortlist universities that will take me and are ranked in the top 50. Profile: GRE: 169Q, 158 V, 4 AWA TOEFL: 117 GPA: 7.2 ( 65% Mumbai university) 3-4 projects 2 years work experience (software developer) Please help me shortlist universities as safe/mod/ambi. Right now I am looking at - ASU, UC Davis, UCI, Virginia Tech, Indiana bloomington.
  15. HI , I am from a third tire college from India. Currently I have 6.8 GPA in 5 semesters. I know its quite low, what should be considered as decent GPA for getting in good Universities for Masters in Computer Science. What should I be doing to increase my chances for getting acceptance in decent Universities despite having low GPA. What Universities should I focus on for specialization in Machine Learning/Data Science. Thanx
  16. MSCS admits

    Hi GradCafe! I recently gave the GRE General Test and scored: (Q:170 (97), V:155 (67), AWA: 4 (60)). As you can see the verbal and AWA scores are not top notch. I wanted to know what are my chances so that I can make an informed decision. I have a great GPA (3.988 / 4), doing B. Tech. CSE at a reputed college of India. I have a research internship and have also done some projects (one long research one) and small internships. I don't have any publications yet. I hope to get good LoRs too. The dilemma here is, I have been placed at a medium-high paying job to be started after my graduation. So my options are: 1. Give TOEFL, and apply for MS for Fall 18 2. Take the job and do for some years, and then apply for a MS/PhD later (this will add more work experience to the profile) 3. Anything else. The colleges I am currently looking at are UIUC, UWash, Cornell, Princeton and GeorgiaTech. Thanks
  17. To have a chance at a career in academia, a phd from a top 20 school/ amazing connections is pretty much required. My interests is in data science/ML, and to my knowledge the best professors are found in the top schools (would love other suggestions though). For an Phd applicant, my stats are pretty average, and wonder if I have a chance at been accepted at schools like Cornell for a Phd. If not, what are some Phd or Ms programs I have a shot at, which could feasible give me a shot at academia long term? Bio: I was pretty undecided as an undergrad when in regards to my path - started out with an unrelated major, ended up double majoring in CS. Did some volunteering and leadership stuff in my 1st 2 year, before doing an REU my junior summer at an unknown school. Did an industry internship this summer at a local company and realized I vastly preferred research vs working for a company. Doing research again my senior year with a new faculty at my school. GPA: 3.87 Gre: ?, but probably pretty high Current school: Ranked 50-100 in CS Recommendations: Average. 1 from a professor I TAed for (Tenured but completely unrelated), 1 from my REU supervisor (new associate professor) who works in a different field, 1 from my current adviser (new associate professor) who works in a mildly related field. Research: REU paper (doesn't really count), mentioned on a conference paper by REU professor. 2 brief stints of research (3 months each)
  18. Hello! I recently decided to go to a graduate school in bioengineering/biomedical engineering field. I've been doing some research past couple weeks on schools and programs but I'm still pretty lost. Not sure how much of things (stats and experiences) are required for PhD or MS in engineering especially someone like me, who only has biology background... Here are some info about me so that you can provide me better advice: Undergrad: top 5 public school in the U.S. Major: Molecular Biology GPA: 3.74 (do not remember but my science gpa is higher) GRE: started studying just today! Research experiences: Pathology lab (summer during undergrad years) 2 years at medical device company R&D lab (related to bioconjugates and nanotechnology, but not really hard-core engineering.. more of biological sciences area) AND here are the questions: 1) Do I need to have a specific research area that I'm interested in before applying to schools? I'm sure of some things that I don't want to pursue such as tissue engineering and regenerative medicine, but I am not entirely sure of what I want yet (currently, I am interested in biosensors (bio-MEMs), medical informatics, or biomechanics but I haven't had enough exposure to decide which to pursue) Will I be at a big disadvantage in application if i don't state my interest specifically? Or is it okay to have a general area? 2) And since I'm not entirely sure of the research area, would it be better to go for MS instead before deciding to go on with PhD? 3) I do have 2 years of experience in working at a medical device company as a researcher, but these are more closely related to areas I am not really interested in anymore... the company I worked at produces biosensors, but I was not part of that division and was not exposed to such technologies. Should I join engineering labs for some experiences? (I am not working anymore and have plenty of time for some experiences now... not sure getting them now is gonna help though) 4) If I choose to apply for PhD but did not get accepted, am I automatically considered for MS admissions? Or does it not work that way? 5) I don't see a big difference between bioengineering and biomedical engineering. Is there a difference in how these majors are perceived in the industry? Or does it not matter? Many of you might be wondering why i want to pursue bioengineering even when I don't really have specific research area that I am interested in. After being in an industry for a couple years, I figured that there are not many opportunities for a B.S. in biology to do. I've always thought that bioengineering was cool and I see a lot of potential in the field as I glimpsed a little bit of the industry through working in the company. And here I am! Any advices, comments, concerns, anything will be greatly appreciated! Thanks!
  19. Applying For MS in CS

    Hi I am from India (IIT) and will be applying to grad school this year. I have a decent gpa of 9.5+ and am interested in Machine learning and natural language understanding. Hopefully, will have one publication in mid-tier conference. I will be giving gre/toefl next month. Just wanted to ask what are my chances of getting top schools? What matters most -- research publications, academic honors, gre score? Any comments are welcome. Thanks.
  20. Switching to a CS masters in Canada

    Hello. I graduated from Stanford University after a MS in Chemical Engineering and failed to find a job. Reason - my major isn't the best when it comes to jobs. I have a good GPA (3.75/4.0) and did my undergraduation from a top univ in India (IIT-KGP) with a good GPA again (8.61/10.) I am considering switching to CS by the following route - doing assistantships with professors in various universities for a couple of years to get recos and then applying to Canada for an MS in CS. I have two questions. 1) Has anyone done something similar? I get the feeling that getting a prof to say yes to someone with no background is tough, but I see no other way to get my career in CS started. How difficult is it to get a research project with a prof in any decent Indian univ? I'm willing to go unpaid as well. 2) Do I stand a chance of getting into good univs? (Waterloo, UBC and the like). I don't want to go to an average university. Kindly help me out.
  21. CMU is my dream school and i have read a lot about its programs online but i was hoping if anyone can share from there own experiences what particular things should i highlight in my SOP for admission into MS Mechanical Engineering at CMU. My Profile is Undergraduate: 3.77 / 4.00 (2nd in Department , Silver Medal) GRE: (166 Q, 154 V, 3.5 AWA) 320 IELTS 8 3 years industry experience in Machinery Diagnostics
  22. My GRE score is 166Q and 154V. My Undergrad CGPA is 3.77/4.00 and have 3 years industry experience. Can anyone please share from their experience what are the things virginia tech looks for in an individual that I should highlight in my SOP. I intend to apply in Fall 18.
  23. Hi, I'm a class of 2017 grad from UIUC's CS program. What type of schools would be a good fit for a MS in CS or Math or Stats? 3.00/4.00 GPA 170 Verbal 170 Quant 5 Writing - Internship at one of [Google, Apple, Facebook] - Full-time job in algorithmic trading starting soon at one of [Citadel, Jump Trading, Optiver] - 3 years of research done within U of I in the CS department, and at the NCSA (National Center for Supercomputing Applications) in HPC. I have one strong letter of rec I can get from a UIUC CS professor, and I could get more from my other research positions at school, but none that I feel were that great. What tier of schools should I be applying to for my MS? Open to and interested in CS, Applied Math, or Stats. May be able to join the professor I was doing research with at UIUC as a MS student in CS. Really quite interested in programming languages and compilers as a research topic - something U of I is very strong in. My strong letter of rec is from a UIUC compilers research professor. Thanks!
  24. NCSU CS vs Rutgers CS

    Which one should i choose for MS CS: Rutgers, New Brunswick, NJ North Carolina State University,NC Which one has better internship and job opportunities??
  25. Hello, I'm interested in the one year Master of Science in Public Policy program at NYU's Wagner School. It seems like a pretty unique program to me, and I'm having trouble finding any information about acceptance rates or thoughts on past applicants. etc. Does anyone have any thoughts about this program or is anyone else applying/does anyone know any admissions statistics? Thank you! - M