AI in Cancer Treatment - by Aditya Gupta and Sonika Aneja
Chi-Square Distribution and Test -
by Arushi Joshi and Tejal Jadhav
Vijay Kumar Patodi (1945-1976) - by Dropad Saxena
QURIOSITY MARCH 2018 MONTHLY NEWSLETTER OF QUANTINUUM
– The quant & analytic forum
VOLUME 09 EDITION 03 K J SIMSR Vidyavihar, Mumbai
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Editor’s Note Welcome to the latest issue of Quriosity, the monthly newsletter of Quantinuum! Quantinuum, the Quant and Analytics committee of KJ Somaiya Institute of Management Studies and Research aims to empower students and professionals alike to organize and understand numbers and, in turn, to make good and rational decisions as future managers. The newsletter published monthly consists of a gamut of articles for readers ranging from beginners to advanced learners so as to further enrich the young minds understand the contributions made to the field of mathematics along with a couple of brain-racking puzzles. The objective of Quriosity is to publish up-to-date articles on data analytics, alongside relevant and insightful news. This way the magazine aspires to be vibrant, engaging and accessible, and at the same time integrative. The edition includes how artificial intelligence is used in cancer treatment. It also gives us insights on the Chi-Square distribution function and its test. Vijay Kumar Patodi, a great mathematician is discussed as well. Needless to say, any articles that you wish to submit, either individually or collaboratively, are much appreciated and will make a substantial contribution to the development and success of the newsletter. For any further queries and feedback, please contact the following address: KJ Somaiya Institute of Management Studies and Research, Vidyanagar, Vidyavihar, Ghatkopar East, Mumbai 400077 or drop us a mail @ newsletter.quantinuum@gmail.com
Thank you and Happy Reading! Editorial Team Quantinuum Editorial Team: VVNS Anudeep (+919441201685) Kapil Gupta (+917727936906) Khushbu Mehta (+919930158610) Aditya Sharma (+918302525599) Samoshri Mitra (+918697440265) Dropad Saxena (+919582337930) Akshay Nagpal (+918800114925)
Mentor: Prof. N.S.Nilakantan (+919820680741) Email – nilakantan@somaiya.edu Team Leaders: Purav Shah (+917708521382) VVNS Anudeep (+919441201685) Yatharth Jaiswal (+919969698361)
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Contents TOPIC
PAGE-NO
Cover Story AI in cancer treatment
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by Sonika Aneja and Aditya Gupta
Sub-article Chi-Square Distribution and Test
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by Arushi Joshi and Tejal Jadhav
Quant Guru – Vijay Kumar Patodi (1945-1976)
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by Dropad Saxena
Quant Curiosity Update
10 by Khushbu Mehta
News Digest
12 by Akshay Nagpal
Quant Fun
14 by Khushbu Mehta
Quant Connect
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Quant Cover Story AI in Cancer Treatment There have been approximately 14 million new cases of cancer in 2012 throughout the world which is further expected to rise by about 70% over the next two decades. Cancer is the second leading cause of death globally, and was responsible for 8.8 million deaths in 2015. In India alone, estimated number of people living with the disease is around 2.5 million, with every year over 7 lakhs new cancer patients are being registered. The scientists nowadays are more into development of deep learning or artificial intelligence based applications and services in order to increase efficiency and accuracy on a sustainable aspect. A popular research area, both for scientists and medical practitioners around the world, right now is cancer detection Artificial Intelligence (AIs) that take a CT scan and determine whether or not the patient has cancer. They may even diagnose the type and characteristics of cancer. This can also lead to earlier cancer detection, as some systems may catch cancer long before a doctor can. It's not perfect, but can be a huge help. Deep Learning plays a vital role in the early detection of cancer. It drops error rate for breast cancer diagnosis by 85%. In 2010, the total annual cost of cancer was estimated at around $1.6 trillion. But if you detect cancer early, your probability of survival is 10 times higher. Early detection can save not only billions of dollars but also countless lives. Deep learning has also shown capabilities in achieving higher diagnostic accuracy results in comparison to many domain experts. So we build a new system. This one takes as features data about a person’s lifestyle and any symptoms they may be experiencing and any other health/environment/genetic data we have. It aims to predict a person’s risk of getting cancer. Those with high risk are then advised to get semi-regular CT scans. So now when you go for your regular check-ups, you answer a few questions that get fed into a model and it determines if you are at risk for cancer. If so, it recommends a CT scan. Another model then examines that CT scan. It may diagnose you with cancer. Then what? Well, we probably feed the same scan and maybe a second scan through a host of other models just to double-check. If they come back positive as well, we need a treatment plan. What can AI do for you here? Just ask IBM’s Watson. It is already being used to develop treatment plans for cancer patients. Watson reads medical journals and uses that information of the patient to recommend a diagnosis and treatment options. An AI for medical diagnoses can be like a search engine, but instead of typing in “cute cats in big hats” like you usually do, the query is provide all the information it has on a particular patient. It then selects all the relevant research or diagnoses or treatments it is aware of, discarding research that it deems irrelevant.
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Finally, it ranks the relevant information based on relevance, likelihood of success, side-effects, costs, etc. This approach expands on a doctor’s expertise and saves time, allowing for faster, cheaper, more effective healthcare. The actual treatment may not be any cheaper, but patients will pay less for the doctors’ time.
Applications of Deep Learning in Oncology Gene expression is the method by which the “genetic code” - the nucleotide sequence of a gene is used to direct protein synthesis and produce the structures of a particular cell. It plays an important role in Cancer detection. However, Gene expression data is very complex due to its high dimensionality, making it challenging to use such data for cancer detection. Researchers from Oregon State University were able to use deep learning for the extraction of meaningful features from gene expression data, which in turn enabled the classification of breast cancer cells. The technology was used to extract genes considered useful for cancer prediction and for the detection of breast cancer. Cancer Classification with Deep Neural Networks Convolutional neural network are a category of artificial neural networks that is used to analyze visual images. Convolutional neural network (CNN) has achieved performance on par with all experts in classifying skin cancer. A single CNN trained end-to-end can classify skin lesions from images by using pixels and disease labels as inputs. This has kept the diagnosis reach limited not only to clinic but also extended to outside the clinic and into various service-based apps. Tumor Segmentation Deep learning is used for segmenting brain tumors in MR images and has given more stable results in comparison to manually segmentation of the brain tumors done by physicians which can have motion and vision errors. Deep learning can differentiate between benign and malignant breast tumors by using ultrasound shear-wave elastography (SWE). It has given more than 93% accurate results on the elastogram images of around than 200 patients. Histopathologic Cancer Diagnosis Diagnosis and grading of cancer has become increasingly complex due to increase in number of cases of cancer and patient specific diagnosis options. Pathologists go through a large number of slides in order to diagnose. Even there is an increase in the number of quantitative parameters pathologists considered nowadays to extract meaningful results (e.g. lengths, surface areas, mitotic counts). In that case deep 5
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learning can improve the efficiency of histopathologic slide analysis by increasing the objectivity of diagnoses and reduce the workload for pathologists. Therefore it can improve the efficiency of prostate cancer diagnosis and breast cancer staging. Tracking Tumor Development Deep learning can be used to measure the size of tumors and detect new metastases that might be over looked in manual diagnosis process. The Deep learning algorithm is developed by reading more and more CT and MRI scans which will help to improve its accuracy. The algorithm has attained localization score of 89%, in comparison to 73% accuracy rate achieved by pathologists. Prognosis Detection By using Deep learning, prediction model is developed which is used for the prognosis of patients suffering
from gastric cancer and undergoing treatment (i.e. gastrectomy). It showed higher survival predictive powers as compared to other prediction models.
Conclusion: The use of deep learning in oncology increases the chances of survival. Machines are helping researchers to find a coveted cure and prevention methods for the development of cancer as early cancer detection and prognosis can save the patient. It is the important healthcare areas where deep learning can be applied to improve the scenario of cancer treatment. Sources: [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661078/ [2] https://www.sciencedirect.com/science/article/pii/S1361841516300330 [3]https://www.forbes.com/sites/bernardmarr/2017/05/16/how-ai-and-deep-learning-is-now-used-to-diagnosecancer/#486ca786c783
By Sonika Aneja and Aditya Gupta PG-Core (2017-19)
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Quant Sub Article Chi-Square Distribution and Test Chi-square distribution and the Chi-square test also known as test for goodness-of-fit was first invented by Karl Pearson (1857-1936). Pearson's Chi-square distribution and the Chi-square test of independence are considered as his most important contribution to the modern theory of statistics. He elaborated invention of Chi-square distribution and goodness of fit test through his paper in 1900 which was published in philosophical magazine The Chi-square test is a statistical hypothesis test in which the sampling distribution of the test statistic is a chisquare distribution when the null hypothesis is true. Chi-square test is a non-parametric test used for two specific purposes: (a) To test the hypothesis of no association between two or more groups, population or criteria (i.e. to check independence between two variables) (b) To test how likely the observed distribution of data fits with the distribution that is expected (i.e. to test the goodness-of-fit). This test cannot be performed on numeric or continuous data (e.g., height measured in centimeters or weight measured in kg, etc.), data has to be categorical in nature (e.g. gender, smokers and non-smokers) etc. The central tendency of categorical variables is given by its mode, since median and mean can /only be computed on numerical data. Thus, it does not follow a normal bell-curve distribution and cannot be analyzed with tests that are dependent on a normal distribution like the t-test or ANOVA. Therefore, the Pearson's Chi-square distribution was the statistical method that the statisticians could use for the data that did not depend on the normal distribution to interpret the findings. He invented the Chi-square distribution to mainly cater the needs of biologists, economists, and psychologists. Assumptions for Chi-square test The data are randomly drawn from a population The values in the cells are considered adequate when expected counts are not less than 5 and there are no cells with zero count The sample size is sufficiently large. The application of the Chi-square test to a smaller sample could lead to type II error (i.e. accepting the null hypothesis when it is actually false). There is no expected cut-off for the sample size; however, the minimum sample size varies from 20 to 50 The variables under consideration must be mutually exclusive. It means that each variable must only be counted once in a particular category and should not be allowed to appear in other category i.e., no item shall be counted twice.
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The chi-square statistic is defined as
where Oi is the observed frequency, and Ei is the expected frequency. This chi-square statistic is obtained by calculating the difference between the observed frequency and the expected frequency in each category. This difference is squared (to get rid of the negative vales of the difference) and divided by the expected frequency in that category. The square of the difference is divided by the expected count to normalize bigger and smaller values. These values are then added for all the categories, and the total is referred to as the chi squared value. The null hypothesis is a particular claim concerning how the data is distributed. More will be said about the construction of the null hypothesis later. The null and alternative hypothesis for each chi-square test can be stated as H0 : Oi = Ei H1 : Oi ≠ Ei If the claim made in the null hypothesis is true, the observed and the expected values are close to each other and Oi-Ei is small for each category. When the observed data does not conform to what has been expected on the basis of the null hypothesis, the difference between the observed and expected values, O i-Ei, is large. The chi-square statistic is thus small when the null hypothesis is true, and large when the null hypothesis is not true. Calculation of degrees of freedom (df) is required. When we have to compare one sample with another, df equals to (number of columns − 1) × (number of rows − 1) excluding the rows and column containing the total. Also, how large the χ2 value must be in order to be considered large enough to reject the null hypothesis, can be determined from the level of significance and the chi-square table. Scientists and statisticians use large tables of values to calculate the P value (probability) for their experiment. These tables are generally set up with the vertical axis on the left corresponding to df and the horizontal axis on the top corresponding to P value. df value is found out first, then reading that row across from the left to the right until the first value bigger than our Chi-square value, will give the corresponding P value at the top of the column. Chi-square distribution tables are available from a variety of sources online or in science and statistics textbooks. In the end, we can have a conclusion with the application of Chi-square test which only tells the probability of independence of a distribution of data or will only test that whether two variables are associated with each other or not. It will not tell us that how closely they are associated. However, once we got to know that there is a relation between these two variables, we can explore other methods to calculate the amount of association between them.
Sources: [1] http://www.j-pcs.org/article.asp?issn=2395-5414;year=2015;volume=1;issue=1;spage=69;epage=71;aulast=Rana [2] http://uregina.ca/~gingrich/ch10.pdf By Arushi Joshi & Tejal Jadhav
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QuantGuru Vijay Kumar Patodi (1945- 1976) Vijay Kumar Patodi was born on 12th March 1945. He was an Indian mathematician who made significant contributions in the field of differential geometry and topology. Mathematical fame came early in his life with his papers on which he worked as part of his Ph.D. thesis.
Education Patodi studied in Government High School, Guna, Madhya Pradesh and later he received his bachelor's degree from Vikram University, Ujjain. He completed his post-graduation from the Banaras Hindu University in Mathematics, and his Ph.D. from the University of Bombay under the guidance of M. S. Narasimhan and S. Ramanan at the Tata Institute of Fundamental Research. He made important discovery through two of his papers namely, "Curvature and Eigen forms of the Laplace Operator" (Journal of Differential Geometry), and "An Analytical Proof of the Riemann-Roch-Hirzebruch Formula for Kaehler Manifolds" (Journal of Differential Geometry).
Research career He did research work at the Institute for Advanced Study in Princeton in the period 1971–1973, New Jersey, where he collaborated with Michael Atiyah, Isadore Singer, and Raoul Bott. The research led to a paper named "Spectral Asymmetry and Riemannian Geometry" in which the η-invariant was defined. When he returned back to Mumbai in 1973, he was promoted to associate professor and became full professor in 1976 at the Tata Institute of Fundamental Research, Mumbai. On 21st December 1976, Patodi died as a result of complications prior to surgery for a kidney transplant.
Sources: https://en.wikipedia.org/wiki/Vijay_Kumar_Patodi http://www-groups.dcs.st-and.ac.uk/history/Biographies/Patodi.html https://www.revolvy.com/main/index.php?s=Vijay%20Kumar%20Patodi
By Dropad Saxena PGDM (2017-19)
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Quant Curiosity Update Mars Curiosity Celebrates Sol 2,000
NASA's Mars Curiosity rover just hit a new milestone: its two-thousandth Martian day, or sol, on the Red Planet. An image mosaic taken by the rover in January offers a preview of what comes next. Looming over the image is Mount Sharp, the mound Curiosity has been climbing since September 2014. In the center of the image is the rover's next big, scientific target: an area scientists have studied from orbit and have determined contains clay minerals. The formation of clay minerals requires water. Scientists have already determined that the lower layers of Mount Sharp formed within lakes that once spanned Gale Crater’s floor. The area ahead could offer additional insight into the presence of water, how long it may have persisted, and whether the ancient environment may have been suitable for life. Curiosity's science team is eager to analyze rock samples pulled from the clay-bearing rocks seen in the center of the image. The rover recently started testing its drill again on Mars for the first time since December 2016. A new process for drilling rock samples and delivering them to the rover's onboard laboratories is still being refined in preparation for scientific targets like the area with clay minerals. Curiosity landed in August 2012 and has traveled 11.6 miles (18.7 kilometers) in that time. In 2013, the mission found evidence of an ancient freshwater-lake environment that offered all the basic chemical ingredients for microbial life. Since reaching Mount Sharp in 2014, Curiosity has examined environments where both water and wind have left their marks. Having studied more than 600 vertical feet of rock with signs of lakes and groundwater, Curiosity's international science team concluded that habitable conditions lasted for at least millions of years.
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2001: A Mars Odyssey Geology Science Theme Group Leader Prof. Chris House kicked off our planning today by playing a rousing rendition of "Also sprach Zarathustra" over the phone line. Hearing the theme song to the famous movie 2001: A Space Odyssey was the perfect start to sol 2001 planning and inspired us to choose two new target names that were as close to A Space Odyssey as we could get: "Boddam" ((David) Bowman) and "Kirkcudbright" ((Stanley) Kubrick). Curiosity is currently sitting in front of a steep outcrop that shows some interesting geologic relationships between rocks in the Vera Rubin Ridge. We acquired some great images of these rocks in yestersol's plan, so today we were focused on understanding the properties of the rocks at our feet. In the first sol of the plan, sol 2001, we will be collecting MAHLI images of a target named "Apin," and doing DRT, MAHLI, and APXS on a target named "Brora." The second sol, sol 2002, will focus on remote sensing, with ChemCam observations on targets named Boddam, "Sgurr of Eigg," and Kirkcudbright. The ChemCam observations will be accompanied by Mastcam documentation images. We will also be taking a multispectral observation of the DRT targets from tosol (Brora) and yestersol (Sgurr of Eigg), some multispectral images of the landscape in front of us, and some additional color images of the vertical rocks in front of the rover to complement the data we collected yestersol. We'll top off the science block with a dust devil movie and dust devil survey. We'll stay up after dark on sol 2002 to collect additional nighttime MAHLI images of Appin and Brora. On sol 2003 we will have a bunch of dedicated environmental science measurements, including a tau to measure the dust in the atmosphere, a Navcam 360 sky survey, a Navcam zenith and suprahorizon movies, and a crater rim extinction image. We'll squeeze in another ChemCam RMI mosaic of some distant features in Mt. Sharp. Sol 2003 will finish with an ~50 m drive towards an area where we see some of the strongest spectral signatures of hematite on the ridge in orbital data. We'll take a standard set of post-drive images over the weekend to set us up to characterize this location in the sol 2004 plan. It will be very exciting to see the exact rocks that are the source of the orbital signature which helped us realize the importance of Vera Rubin Ridge over five years ago! Source: https://mars.nasa.gov/msl/mission/mars-rover-curiosity-mission-updates/ By Khushbu Mehta MMS B (2017-19) 11
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Quant News Digest Airtel Partners with Nokia to use their hybrid self-organizing networks Bharti Airtel, India’s largest mobile network operator has announced that it is partnering with Nokia to boost its service quality and operational efficiency through the employment of Nokia’s hybrid self-organizing networks (SON) solution. Earlier this week Bharti Airtel had announced that it had entered into a venture called the Seamless Alliance with companies across sectors to provide seamless low-latency and high-speed connectivity inside aircrafts. The alliance has companies from three sections – aircraft manufacturers (US-based airlines Delta and Airbus), in-flight broadband providers (GoGo) and telecom operators (US-based Sprint). The alliance also includes Softbank-backed satellite start-up, OneWeb.
Google’s new online course will teach you AI and Machine Learning concepts for free Machine learning and artificial intelligence are the most trending topic in the tech world today, with both skeptics and advocates dominating the headlines. Not a day passes without advancement and progress in the artificial intelligence sector, which will soon become mainstream. Now, Google wants to widely open up this technology and make it more accessible to anyone who is interested in machine learning, with its free online course.
Hospitality AI platform Trilyo raises $250,000 in latest round of funding Trilyo, a Bengaluru-based artificial intelligence platform for hospitality industry on Wednesday announced that it had raised $250,000 in its latest round of funding. The funding, led primarily by Startup Buddy also has Pulse Venture Capital and others as its investors. The business-to-business hospitality industry software as a service (SaaS) company offers AI-driven voice chat-based solutions for providing next-generation customer experience. The funds will be primarily used to scale the operations in India and south-east Asia. With this funding, Trilyo aims to reposition itself, and focus on bigger picture, that is, the hotel industry. Their smart guest profiling system works on guest satisfaction listing detailed visit histories and customer preferences. Trilyo’s feedback system, taps into the guests’ preferences and expectations to get instant negative-sentiment alerts and generate analysis to understand their behavior.
NetApp Excellerator announces its second cohort of startups in AI and Analytics Shortly after the success of its first cohort, NetApp has announced the second batch of startups for its flagship accelerator program. Under the NetApp Excellerator, six startups from across India have made their way amongst the 450 companies that applied for it. NetApp will mentor these growth stage startups -ArchSaber, SigTuple Technologies, Nanobi Data & Analytics, BlobCityInc, Data Ken Technologies, and Anlyz. The selected startups will now receive access to collaboration and productivity tools, co-working space out of the NetApp’s global
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center of excellence in Bangalore as well as networking opportunities with potential investors, partners, and customers. These companies will also have access to NetApp platforms and technologies, tools, HR, marketing, legal and tech support. NetApp will provide an equity free grant of USD 15,000 to these startups upon completion of the program.
Qubole appoints Namit Jain to head Worldwide Engineering Management Qubole, the cloud-native big-data activation platform and company, announced on Tuesday, the appointment of Namit Jain as the Senior Vice President of the Engineering division. Jain is an Indian Institute of Technology, Kanpur, alumni and has worked with noted organizations such as Oracle, Facebook and Nutanix before. He brings with him a vast experience in databases and big data technologies, and in running and scaling large global engineering teams. The leading cloud big-data-as-a-service company also recently announced its partnership with Snowflake Computing, the only data warehouse built for the cloud. Moving data warehouse infrastructures to the cloud and building data lakes dramatically improve organizations’ performance, concurrency and simplicity. With this partnership, enterprises have the best of both worlds, giving them access to a simple, outof-the-box integration between Qubole and Snowflake for the most performing, cost effective, and proven solution.
AI dominates PM Modi’s ‘Mann Ki Baat’ talk show this month Prime Minister Narendra Modi’s monthly address ‘Mann Ki Baat’ on Sunday held a whole bunch of surprises for the new tech community, because the PM specifically spoke about the use of machines that Opening his address by referring to noted Indian scientist and Nobel Prize winner Sir CV Raman, PM Modi reiterated his earlier remarks about science being “neutral”, that is possessing the power which humans chose to give it. He also added that the machines were getting smarter through self-learning and urged researchers to make use of artificial intelligence to make the lives of the divyangs (differently-abled), farmers and the needy more simple.
Google’s New AI Algorithm can predict heart disease by scanning your eyes Remember that song by Bryan Adams that said “Look into my eyes… And when you find me there, you’ll search no more”? Google’s new AI algorithm can do one better — it can look into your eyes, search and find signs of cardiovascular risks. Developed by researchers from Google and its health-tech subsidiary Verily, the details of the innovation were published in a paper titled Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning in an international science journal.With Google already working on using deep learning and AI for the diagnosis of diseases such a diabetic retinopathy and cancer, the contributions the technology can make to healthcare may see a significant increase in the future. Source: http://analyticsindiamag.com/
By Akshay Nagpal PGDM (2017-19) 13
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Quant Fun Brain Teasers Teaser 1: A lift is on the ground floor. There are four people in the lift including me. When the lift reaches first, floor, one person gets out and three people get in. The lift goes up to the second floor, 2 people get out 6 people get in. It then goes up to the next floor up, no-one gets out but 12 people get in. Halfway up to the next floor up the lift cable snaps, it crashes to the floor. Everyone else dies in the lift. How did I survive? Teaser 2: I have no voice, yet I speak to you. I tell of all things in the world that people do. I have leaves, but I am not a tree. I have pages, but I am not a bride. I have a spine, but I am not a man. I have hinges, but I am not a door. I have told you all. I cannot tell you more. What am I? Teaser 3: I left my campsite and hiked south for 3 miles. Then I turned east and hiked for 3 miles. I then turned north and hiked for 3 miles, at which time I came upon a bear inside my tent eating my food! What color was the bear? Teaser 4: Crime Scene: A large wooden box was built with one door. The door was locked from the inside, and then nailed shut from the inside. The police break into the room. In the middle of the room there is a dead man hanging from the ceiling, with his shoes 3 feet off the ground. The only other thing in the room is a hammer lying in a puddle of water. Can you explain what happened? Teaser 5: A man is the owner of a winery who recently passed away. In his will, he left 21 barrels (seven of which are filled with wine, seven of which are half full, and seven of which are empty) to his three sons. However, the wine and barrels must be split, so that each son has the same number of full barrels, the same number of half-full barrels, and the same number of empty barrels. Note that there are no measuring devices handy. How can the barrels and wine be evenly divided?
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Teaser Solution: Solution 1: I got out on the second floor! Solution 2: A book Solution 3: White. The only place you can hike 3 miles south, then east for 3 miles, then north for 3 miles and end up back at your starting point is the North Pole. There are only polar bears in the North Pole, and they are white! Solution 4: He committed suicide by hanging himself when the ice melted Solution 5: Two half-full barrels are dumped into one of the empty barrels. Two more half-full barrels are dumped into another one of the empty barrels. This results in nine full barrels, three half-full barrels, and nine empty barrels. Each son gets three full barrels, one half-full barrel, and three empty barrels.
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Quantinuum, the Quant and Analytics committee of KJ Somaiya Institute of Management Studies and Research aims to empower students and professionals alike to organize and understand numbers and, in turn, to make good and rational decisions as future managers. The newsletter published monthly consists of a gamut of articles for readers ranging from beginners to advanced learners so as to further enrich the young minds understand the contributions made to the field of mathematics along with a couple of brain- racking sections of Sudoku to tickle the gray cells. For any further queries and feedback, please contact the following address: KJ Somaiya Institute of Management Studies and Research, Vidya Nagar, Vidyavihar, Ghatkopar East, Mumbai -400077 or drop us a mail @ newsletter.quantinuum@gmail.com
Mentor: Prof. N.S.Nilakantan (+919820680741) Email: nilakantan@somaiya.edu
Team Leaders: Purav Shah (+917708521382) VVNS Anudeep (+919441201685) Yatharth Jaiswal (+919969698361)
Editorial Team: VVNS Anudeep (+919441201685) Khushbu Mehta (+919930158610) Kapil Gupta (+917727936906) Aditya Sharma (+918302525599) Samoshri Mitra (+918697440265) Dropad Saxena (+919582337930) Akshay Nagpal (+918800114925)
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