columbia university research papers

Research at Columbia

Columbia   has a long and distinguished history in the discovery of knowledge. We work to foster the continuation of those creative endeavors and to promote an environment that sustains the highest standards of scholarship, learning, and safety.

Office of the EVP of Research

New Inventions Annually

Nobel Laureates

In Annual Research

" Research pushes the boundary between what we know and don’t know. It’s the cornerstone of any great university: to seek, create, and disseminate new knowledge . " Jeannette Wing, Executive Vice President of Research

Research Institutes and Initiatives

A selection of Columbia's major cross-disciplinary research centers and initiatives.

uses Columbia research to tackle global challenges and positively affect people’s lives, and is also home to the Obama Foundation Scholars Program.

dedicated to advancing state-of-the-art artificial intelligence, and its application in all fields, and ensuring responsible use of data to benefit society.

partners across Columbia in research coordination with faculty from the social sciences, humanities, and natural sciences. 

deals with litigation, research, and education. It includes Knight First Amendment Institute, Global Freedom of Expression, and other centers.  

includes the Vagelos College of Physicians and Surgeons, the College of Dental Medicine, the School of Nursing, and the Mailman School of Public Health.

is a landmark initiative designed to help prepare NYC for future public health threats, from infectious diseases to climate-related health emergencies.

brings quantum technology out of the lab and makes it available for massive adoption in information science, health, sensing, and metrology. 

brings together researchers from across the University to study the brain, gaining insights that promise to benefit people and societies everywhere.

Schools at Columbia

Research takes place in a range of departments, labs, centers, and institutes at Columbia’s 17 schools and 4 affiliated schools, whose research pages are listed below.

The Power of Research

Recent research highlights from across the university. 

University Research Administration

The Office of the Executive Vice President for Research has overall responsibility for the University's research enterprise.

Submissions

Submission preparation checklist.

• I have read the CUSJ Research Submission Info below and identify as an undergraduate student who has completed an original research or review paper.
• I have read and attached the  Author Agreement .  The author(s) agree to the terms of this Author Agreement, which will apply to this submission if and when it is published by this journal (comments to the editor can be added below).
• For original research submissions to CUSJ, the text follows the Manuscript Template . I understand that failure to do so could result in rejection.
• The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
• The submission file is in OpenOffice, Microsoft Word, RTF, or WordPerfect document file format (not PDF).
• Where available, URLs for the references have been provided.
• The text contains no information about the author's identity.
• The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.
• The text is single-spaced; uses a 12-point font; employs italics rather than underlining (except with URL addresses); and all illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end.

Submission Instructions

We accept original research manuscript submissions for our research journal CUSJ , and other non-original research submissions for our editorial journal Columbia Scientist . For original research manuscript submissions, please prepare your manuscript according to the  manuscript template.  All authors to CUSJ and  Columbia Scientist  must make sure to submit the Author Agreement . Please read the guidelines below before submitting.

The Columbia Undergraduate Science Journal only accepts work from undergraduates. If you're in high school, please visit the Columbia Junior Science Journal or Columbia Scientist.

Please follow ALL GUIDELINES listed in the submission info section below. Failure to do so will result in an automatic rejection. 

For information on how to submit an article for our next edition of Columbia Scientist please check this webpage again in the fall. 

Questions about submitting? Email  [email protected]

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CUSJ Research Submission Info (see below for Columbia Scientist info)

Who can submit? 

Undergraduate students enrolled full-time in a university. The first author must have completed a high school education but not yet graduated from their undergraduate studies at the time of submission in order to be considered for CUSJ 2024 . Mentors are allowed to be listed as secondary authors.

Manuscript Review

Manuscripts submitted to CUSJ  will undergo a double-blind peer-review process. This process is extensive. Competitive submissions for publication will be concise and contain original scientific research. Exceptional review papers critically examining the existing body of scientific research will also be considered.

We expect that authors of manuscripts selected for publication will have worked extensively with their research mentor preparing their manuscript for submission. Papers prepared for submission should have sufficient introduction for those involved in other scientific disciplines to gain a general intuition about the paper’s original subject matter.

Authors published in CUSJ will retain all rights to their work except the right to publish in another undergraduate-level journal; work previously published in such a journal will not be considered for publication in CUSJ . The editors of CUSJ  additionally ask that submissions to CUSJ not be concurrently considered for publication by other undergraduate-level journals. All authors and their mentors must sign off on the author agreement which can be found on the submission portal.

Author Guidelines

• Original research submissions must be at  least 5 pages and no more than 10 pages  in length and constitute a detailed overview of your research. Scholarly review submissions may be up to 15 pages. This page limit includes figures, captions, acknowledgements, and references/citations. 
• All submissions must follow the “CUSJ Template 2019” . Take care to follow all formatting specifications. Papers formatted incorrectly may not be reviewed.
• All submissions must be accompanied by the form(s) in the submission portal. Please provide there all of the requested information in addition to anything else you would like us to know. This information will not be considered during the review process but is required for acceptance & publication.
• All documents submitted must be named and formatted correctly. Please submit your research paper as a .docx or .rtf file with the necessary text, figures, and captions. Please also submit your figures & captions in a separate .ppt file. Follow the naming schema: CUSJ_LastnameFirstname_form/paper/figures.rtf (e.g., you would submit CUSJ_DoeJohn_form.pdf, CUSJ_DoeJohn_paper.docx, and CUSJ_DoeJohn_figures.ppt).
• All papers & supporting documentation must be submitted by March 1st, 2024 at 11:59PM EST to be considered for the 2024 journal. Although in unusual instances papers may be accepted on a rolling basis after this deadline, those submitted before the deadline will be given priority & not all those received afterward may be reviewed.

Submission Deadline

Submissions for the 2023-2024 edition of CUSJ are now CLOSED. We will open submissions for the 2024-2025 in JULY 2024.

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CUSJ Columbia Scientist Submission Info (see above for CUSJ Research Journal submissions)

All members of the scientific community, including high school students, undergraduates, and graduate students at institutions in the United States and abroad. Mentors are allowed to be listed as secondary authors.

Manuscripts submitted to CUSJ  Columbia Scientist will be reviewed by a team of editors, and assessed for their contribution to science literature and our aim to promote scientific literacy. 

Submission should be written at a level that allows for those involved in other scientific disciplines to gain a general intuition about the submission's original subject matter.

Authors published in CUSJ will retain all rights to their work except the right to publish in another undergraduate level journal; work previously published in such a journal will not be considered for publication in  CUSJ . The editors of CUSJ  additionally ask that submissions to CUSJ not be concurrently considered for publication by other undergraduate-level journals. All authors and their mentors must sign off on the author agreement which can be found on the submission portal.

• Written letter commenting on, or responding (support or critique) a recently published science article  
• Up to 100-1000 words.

Question and Answer

• Response to the question prompt in the previous Columbia Scientist publication.
• Responses should be short (at most 200 words) and directly answer the question.
• You may also submit questions to be published and answered in a future publication.

Opinion Editorial

• A timely piece devoted to a specific topic with a strong perspective
• Written to a general audience, avoiding scientific jargon and assuming no priori specialized knowledge
• 500-1500 words
• 1-2 pages (two pages will be consecutive, not front and back) with images and text
• Visually pleasing, and engaging in its attempt to convey information
• Takes a complex science topic, or advanced principle, and communicates it in a way that it could be understood by those with a high school level understanding of science

Mini Lecture

• A short (5-15 minute) interactive video that explains a topic to the audience, explains novel research, communicates new theories, or introduces application exercises.

Science Art Showcase

• Include a title and a 100-word description of the entry
• Photograph, graphic design, comic, illustration, infographic, architecture, painting, sketch, sculpture, music, theater, film, fashion in any style
• PDF, JPG, MP3, .docx, .doc, YouTube Link
• 5-30-minute-high quality audio clip discussing novel science-based content
• Name, title, 100-word description, and cover artwork/image required
• All papers & supporting documentation must be submitted by  March 10, 2023 at 11:59PM EST to be considered for our Spring 2023 edition. Although in unusual instances papers may be accepted on a rolling basis after this deadline, those submitted before the deadline will be given priority & not all those received afterward may be reviewed.

The deadline of March 10th, 2023 to submit to our Spring 2023 edition of Columbia Scientist has passed. Although in unusual instances papers may be accepted on a rolling basis after this deadline, those submitted before it will be given priority & not all those received afterward may be reviewed. Please reach out to us if you have any questions regarding this deadline.

This page will be updated in February with the submission deadline for our Fall 2023 edition.

Letter to Columbia Scientist

A written letter communicating on, or responding to (support or critique) a recently published scientific article. Please include the name of your school and the location (city and country). 

Response to the question prompt in the previous Columbia Scientist Publication, or a question to be posed in future publications. Please include the name of your school and the location (city and country). 

A timely piece devoted to a specific topic with a strong perspective. Written to a general audience, avoiding scientific jargon and assuming to prior specialized knowledge. Please include the name of your school and the location (city and country). 

Science Briefing

From 1 to 2 pages (two pages will be consecutive, not front and back) with images and text. Visually pleasing, engaging in its attempt to convey information. Takes a complex science topic or advanced principle and communicates it in a way that could be understood by those with a high school understanding of science. Please include the name of your school and the location (city and country). 

Include a 100-word description of the entry. Submissions may consist of a photograph, graphic design, comic, illustration, infographic, architecture, painting, sketch, sculpture, music, theater, film, fashion in any style. Please include the name of your school and the location (city and country). 

A 5-15 minute presentation taking the form of an interactive video that explains a topic to the audience, presents novel research communicates new theories or introduces application exercises.  Please include the name of your school and the location (city and country). 

A 5-30 minute podcast with an accompanying 100-word description, and cover artwork/image. High-quality audio clip discussing novel science-based content. Please include the name of your school and the location (city and country). 

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The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

10 Research Papers Accepted to CVPR 2023

Research from the department has been accepted to the 2023 Computer Vision and Pattern Recognition (CVPR) Conference . The annual event explores machine learning, artificial intelligence, and computer vision research and its applications. 

CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation Samir Yitzhak Gadre Columbia University , Mitchell Wortsman University of Washington , Gabriel Ilharco University of Washington , Ludwig Schmidt University of Washington , Shuran Song Columbia University

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 21 CoW baselines across Habitat, RoboTHOR, and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration — and no additional training — matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model.

Towards Fast Adaptation of Pretrained Contrastive Models for Multi-Channel Video-Language Retrieval  Xudong Lin Columbia University , Simran Tiwari Columbia University , Shiyuan Huang Columbia University , Manling Li UIUC , Mike Zheng Shou National University of Singapore , Heng Ji UIUC , Shih-Fu Chang Columbia University

Multi-channel video-language retrieval require models to understand information from different channels (e.g. video+question, video+speech) to correctly link a video with a textual response or query. Fortunately, contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text, e.g., CLIP; text contrastive models are extensively studied recently for their strong ability of producing discriminative sentence embeddings, e.g., SimCSE. However, there is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources. In this paper, we identify a principled model design space with two axes: how to represent videos and how to fuse video and text information. Based on categorization of recent methods, we investigate the options of representing videos using continuous feature vectors or discrete text tokens; for the fusion method, we explore the use of a multimodal transformer or a pretrained contrastive text model. We extensively evaluate the four combinations on five video-language datasets. We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data. Further analysis shows that this is because representing videos as text tokens captures the key visual information and text tokens are naturally aligned with text models that are strong retrievers after the contrastive pretraining process. All the empirical analysis establishes a solid foundation for future research on affordable and upgradable multimodal intelligence.

DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection  Jiawei Ma Columbia University , Yulei Niu Columbia University , Jincheng Xu Columbia University , Shiyuan Huang Columbia University , Guangxing Han Columbia University , Shih-Fu Chang Columbia University

Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with limited improvement in novel-class adaptation. In this paper, we point out the reason is insufficient Discriminative feature learning for all of the classes. As such, we propose a new training framework, DiGeo, to learn Geometry-aware features of inter-class separation and intra-class compactness. To guide the separation of feature clusters, we derive an offline simplex equiangular tight frame (ETF) classifier whose weights serve as class centers and are maximally and equally separated. To tighten the cluster for each class, we include adaptive class-specific margins into the classification loss and encourage the features close to the class centers. Experimental studies on two few-shot benchmark datasets (VOC, COCO) and one long-tail dataset (LVIS) demonstrate that, with a single model, our method can effectively improve generalization on novel classes without hurting the detection of base classes.

Supervised Masked Knowledge Distillation for Few-Shot Transformers Han Lin Columbia University , Guangxing Han Columbia University , Jiawei Ma Columbia University , Shiyuan Huang Columbia University , Xudong Lin Columbia University , Shih-Fu Chang Columbia University

Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However, under few-shot learning (FSL) settings on small datasets with only a few labeled data, ViT tends to overfit and suffers from severe performance degradation due to its absence of CNN-alike inductive bias. Previous works in FSL avoid such problem either through the help of self-supervised auxiliary losses, or through the dextile uses of label information under supervised settings. But the gap between self-supervised and supervised few-shot Transformers is still unfilled. Inspired by recent advances in self-supervised knowledge distillation and masked image modeling (MIM), we propose a novel Supervised Masked Knowledge Distillation model (SMKD) for few-shot Transformers which incorporates label information into self-distillation frameworks. Compared with previous self-supervised methods, we allow intra-class knowledge distillation on both class and patch tokens, and introduce the challenging task of masked patch tokens reconstruction across intra-class images. Experimental results on four few-shot classification benchmark datasets show that our method with simple design outperforms previous methods by a large margin and achieves a new start-of-the-art. Detailed ablation studies confirm the effectiveness of each component of our model. Code for this paper is available here: this https URL .

FLEX: Full-Body Grasping Without Full-Body Grasps Purva Tendulkar Columbia University , Dídac Surís Columbia University , Carl Vondrick Columbia University

Synthesizing 3D human avatars interacting realistically with a scene is an important problem with applications in AR/VR, video games and robotics. Towards this goal, we address the task of generating a virtual human — hands and full body — grasping everyday objects. Existing methods approach this problem by collecting a 3D dataset of humans interacting with objects and training on this data. However, 1) these methods do not generalize to different object positions and orientations, or to the presence of furniture in the scene, and 2) the diversity of their generated full-body poses is very limited. In this work, we address all the above challenges to generate realistic, diverse full-body grasps in everyday scenes without requiring any 3D full-body grasping data. Our key insight is to leverage the existence of both full-body pose and hand grasping priors, composing them using 3D geometrical constraints to obtain full-body grasps. We empirically validate that these constraints can generate a variety of feasible human grasps that are superior to baselines both quantitatively and qualitatively. See our webpage for more details: this https URL .

Humans As Light Bulbs: 3D Human Reconstruction From Thermal Reflection Ruoshi Liu Columbia University , Carl Vondrick Columbia University

The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, many surfaces in typical scenes act as infrared mirrors with strong specular reflections. We exploit the thermal reflections of a person onto objects in order to locate their position and reconstruct their pose, even if they are not visible to a normal camera. We propose an analysis-by-synthesis framework that jointly models the objects, people, and their thermal reflections, which combines generative models with differentiable rendering of reflections. Quantitative and qualitative experiments show our approach works in highly challenging cases, such as with curved mirrors or when the person is completely unseen by a normal camera.

Tracking Through Containers and Occluders in the Wild Basile Van Hoorick Columbia University , Pavel Tokmakov Toyota Research Institute , Simon Stent Woven Planet , Jie Li Toyota Research Institute , Carl Vondrick Columbia University

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce TCOW, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.

Doubly Right Object Recognition: A Why Prompt for Visual Rationales Chengzhi Mao Columbia University , Revant Teotia Columbia University , Amrutha Sundar Columbia University , Sachit Menon Columbia University , Junfeng Yang Columbia University , Xin Wang Microsoft Research , Carl Vondrick Columbia University

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a “doubly right” object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a “why prompt,” which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

What You Can Reconstruct From a Shadow Ruoshi Liu Columbia University , Sachit Menon Columbia University , Chengzhi Mao Columbia University , Dennis Park Toyota Research Institute , Simon Stent Woven Planet , Carl Vondrick Columbia University

3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a method that uses the shadows cast by an unobserved object in order to infer the possible 3D volumes under occlusion. We create a differentiable image formation model that allows us to jointly infer the 3D shape of an object, its pose, and the position of a light source. Since the approach is end-to-end differentiable, we are able to integrate learned priors of object geometry in order to generate realistic 3D shapes of different object categories. Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow. Our approach works even when the position of the light source and object pose are both unknown. Our approach is also robust to real-world images where ground-truth shadow mask is unknown.

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes From Natural Language Aditya Sanghi Autodesk Research , Rao Fu Brown University , Vivian Liu Columbia University , Karl D.D. Willis Autodesk Research , Hooman Shayani Autodesk Research , Amir H. Khasahmadi Autodesk Research , Srinath Sridhar Brown University , Daniel Ritchie Brown University

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP’s image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines.

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In the News

Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Related Collections

Barnard College The Barnard Archives and Special Collections serves as the final repository for the historical records of Barnard College, from its founding in 1889 to the present day. For more information, please contact [email protected] .

Health Sciences Library The Archives and Special Collections at the Augustus C. Long Health Sciences Library of Columbia University can help you find information about the schools of the Medical Center: College of Physicians & Surgeons, School of Nursing, College of Dental Medicine (formerly the School of Dental & Oral Surgery), Mailman School of Public Health, and the College of Pharmaceutical Sciences. For more information, please contact [email protected] .

How to find information about the Manhattan Project

The Department of Energy releases declassified Manhattan Project-related reports and documents on its  OpenNet  website. This searchable database includes bibliographical references to all documents declassified and made publicly available after October 1, 1994. Some documents can be viewed as full text. This website also provides a comprehensive Manhattan District History.

To start your research into Columbia's role in the Manhattan Project, read Laurence Lippsett's article "The race to make the bomb. The Manhattan Project: Columbia's wartime secret." The article appeared in Columbia College Today , Spring/Summer 1995, 18-21, 45. 

• The Manhattan Project: Columbia's wartime secret Lippsett, Laurence. "The race to make the bomb. The Manhattan Project: Columbia's wartime secret." Columbia College Today, Spring/Summer 1995, pp. 18-21, 45.
• Archival Collections
• Additional Sources

The following are the most often consulted resources available at the University Archives. Archival collections are  non-circulating  and  can only be viewed in the Rare Book & Manuscript Library's reading room  (RBML).  In order to use the University Archives collections at the RBML, y ou will be required to register your own Special Collections Research Account before your visit and to validate the account in person with government-issued photo identification or Columbia ID card. Once you have created your Special Collections Research Account , you will be able to request materials directly from the finding aid: click the check box located on the right for the box(es) you need, and then scroll back to the top of the container list document and click “Submit Request” button in the red-rimmed box at top. This should lead you directly to your Special Collections Research Account to complete the request form.

• Annual Reports The Annual Reports of the President and Treasurer to the Trustees offer a yearly "state of the University" from 1891 to 1946. You can find statements about Columbia's role in the 1945 Annual Report presented to the Trustees (starting on page 14).  
• Central Files, 1890-1984 Central Files contain the core administrative records of the University. The records that comprise Central Files originated in the Office of the President starting in 1890 and continue through the present. Central Files chiefly contains correspondence (sent and received) between Columbia University administrators and other University officers, faculty, trustees, and individuals and organizations from outside the University. Box 301, folder 1 contains the August 6 and 14, 1945 telegrams from War Department to President Butler about continuing the secrecy of atomic bomb research. In addition to the War Department, Central Files includes correspondence with Fermi, Dunning, US Atomic Energy Commission, etc.  
• George Braxton Pegram papers, 1903-1958 Nuclear physicist, professor of physics, and Dean of Graduate Faculties at Columbia University, Pegram conducted a great deal of defense-related research and was responsible for the famous meeting between Franklin Delano Roosevelt and American nuclear scientists prior to World War II that eventually led to the establishment of the Manhattan Project. The National Defense Research Committee contracts for work on uranium and the Physics Department, correspondence, 1940-1947 (declassified in 1960), can be found in the "Atomic Energy Commission" folder in Box 41. There is also a folder titled "Atomic Bomb Discussion" in this box. Box 41 is stored offsite and must be requested at least 48 business hours in advance of use in our reading room.  
• Historical Photograph Collection - Series V: Atomic Energy A small series of images related to atomic research conducted by Columbia. Included are images of the Nevis and Pupin Laboratories and a 1948 exhibit about atomic energy; including the Seventh Biennial Award Dinner for The Atomic Bomb Project, sponsored by Chemical & Metallurgical Engineering; Waldorf-Astoria, 1946.  
• "Atomic Energy Research, 1930s-1980s" in Box 6 folder 5
• "Manhattan Project, 1940s-2000s" in Box 41, folder 10
• "SAM Labs--Manhattan Project, 1940s-1990s" in Box 48, folder 3

For more information on how to access our collections, check out our Research & Access website. If you have any questions about how to find materials or how to access materials, please contact [email protected] .

Archival collections are  non-circulating  and  can only be viewed in the Rare Book & Manuscript Library's reading room  (RBML).  In order to use the University Archives collections at the RBML, y ou will be required to register your own Special Collections Research Account before your visit and to validate the account in person with government-issued photo identification or Columbia ID card. Once you have created your Special Collections Research Account , you will be able to request materials directly from the finding aid: click the check box located on the right for the box(es) you need, and then scroll back to the top of the container list document and click “Submit Request” button in the red-rimmed box at top. This should lead you directly to your Special Collections Research Account to complete the request form.

• C. S. (Chien-shiung) Wu Papers The collection consists of speeches, reports, publications, research notes, and correspondence. The bulk of the collection relates to Wu's involvement in the American Physical Society as well as her research activities. The correspondence is chiefly professional, relating to C. S. Wu's physics research, professional commitments, appointments, meetings, conferences, and publications. Correspondence also includes letters from individuals around the world praising Wu for her accomplishments, asking advice, arranging speaking engagements, discussing administrative matters, and trading research notes, as well as information on publications and other topics. In addition, the collection contains information on Wu's involvement in the development of an affirmative action program at Columbia University in the 1970s.  
• Selig Hecht papers, 1914-1937 Professor of biophysics at Columbia University, 1926-1947, and author of Explaining the Atom (1947).  
• Dana Paul Mitchel Papers, [ca. 1925]-1960 Professional and personal correspondence, administrative records, manuscript lecture notes, and some miscellaneous printed materials. The general correspondence file, 1927-1958, contains letters, both personal and professional, with colleagues, with and about his students, about laboratory equipment, about weapons for the Army and Navy, and with industry concerning his research.  
• Department of Physics Historical records, 1862-1997 This collection is made up of an assortment of historical material, consisting of photographs, negatives, faculty and guest lecturer correspondence, biographical materials for some of the faculty, programs from various lecture series given at Columbia, publications, picture postcards, and even a sheet of commemorative postage stamps. These documents were collected in The Columbia Physics Department: a brief history , a booklet of reproductions of some of the archival documents, correspondence, and photographs relating to the history of the Physics department of Columbia. It includes listing and photos of Columbia's Nobel Laureates and discussion of Columbia's involvement in the Manhattan Project. Correspondents include Niels Bohr, Albert Einstein, Enrico Fermi, H. A. Lorentz, R. A. Millikan, and Max Planck.  
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Some results uranium dioxide powder structure investigation

• Processes of Obtaining and Properties of Powders
• Published: 28 June 2009
• Volume 50 , pages 281–285, ( 2009 )

Cite this article

• E. I. Andreev 1 ,
• K. V. Glavin 2 ,
• A. V. Ivanov 3 ,
• V. V. Malovik 3 ,
• V. V. Martynov 3 &
• V. S. Panov 2  

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Features of the macrostructure and microstructure of uranium dioxide powders are considered. Assumptions are made on the mechanisms of the behavior of powders of various natures during pelletizing. Experimental data that reflect the effect of these powders on the quality of fuel pellets, which is evaluated by modern procedures, are presented. To investigate the structure of the powders, modern methods of electron microscopy, helium pycnometry, etc., are used. The presented results indicate the disadvantages of wet methods for obtaining the starting UO 2 powders by the ammonium diuranate (ADU) flow sheet because strong agglomerates and conglomerates, which complicate the process of pelletizing, are formed. The main directions of investigation that can lead to understanding the regularities of formation of the structure of starting UO 2 powders, which will allow one to control the process of their fabrication and stabilize the properties of powders and pellets, are emphasized.

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Elektrostal’ Polytechnical Institute (Branch), Moscow Institute of Steel and Alloys, ul. Pervomaiskaya 7, Elektrostal’, Moscow oblast, 144000, Russia

E. I. Andreev

Moscow Institute of Steel and Alloys (State Technical University), Leninskii pr. 4, Moscow, 119049, Russia

K. V. Glavin & V. S. Panov

JSC “Mashinostroitelny Zavod”, ul. K. Marksa 12, Elektrostal’, Moscow oblast, 144001, Russia

A. V. Ivanov, V. V. Malovik & V. V. Martynov

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Correspondence to K. V. Glavin .

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Original Russian Text © E.I. Andreev, K.V. Glavin, A.V. Ivanov, V.V. Malovik, V.V. Martynov, V.S. Panov, 2009, published in Izvestiya VUZ. Poroshkovaya Metallurgiya i Funktsional’nye Pokrytiya, 2008, No. 4, pp. 19–24.

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Andreev, E.I., Glavin, K.V., Ivanov, A.V. et al. Some results uranium dioxide powder structure investigation. Russ. J. Non-ferrous Metals 50 , 281–285 (2009). https://doi.org/10.3103/S1067821209030183

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Published : 28 June 2009

Issue Date : June 2009

DOI : https://doi.org/10.3103/S1067821209030183

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30 Best universities for Mechanical Engineering in Moscow, Russia

Updated: February 29, 2024

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Below is a list of best universities in Moscow ranked based on their research performance in Mechanical Engineering. A graph of 269K citations received by 45.8K academic papers made by 30 universities in Moscow was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

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1. Moscow State University

For Mechanical Engineering

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