Monthly Coverage Dossier February 2020 IIT Madras is a campus of choice for JEE high ranking students Date: 7th February 2020 Publication: Latest LY Edition: Online Journalist: NA Headline: Top Engineering Colleges in India: IIT Madras, Delhi, Anna University Are Among the Best Institutes Ranked by NIRF 2019 URL: https://www.latestly.com/india/education/top-medical-colleges-in-india-aiims- new-delhi-pgimer-and-bhu-are-among-the-best-institutes-ranked-by-nirf-2019- 1520321.html Top Engineering Colleges in India: IIT Madras, Delhi, Anna University Are Among the Best Institutes Ranked by NIRF 2019 The National Testing Agency (NTA) has re-opened the application window for National Eligibility cum Entrance Test (NEET). Candidates can correct till February 9, 2020. All those who failed to meet the deadline previously now have a chance to fill in the application by visiting the official website; ntaneet.nic.in. The NEET 2020 exam is scheduled to be held on May 3. As the examination nears, here we bring you the top medical colleges in India according to the National Institutional Ranking Framework (NIRF) 2019. The All India Institute of Medical Sciences (AIIMS), New Delhi, Postgraduate Institute of Medical Education and Research (PGIMER) in Chandigarh, Banaras Hindu University and Aligarh Muslim University are among the best medical institutes ranked by NIRF 2019. Top Engineering Colleges in India: IIT Madras, Delhi, Anna University Are Among the Best Institutes Ranked by NIRF 2019. 1. AIIMS, New Delhi The All India Institute of Medical Sciences, New Delhi (AIIMS) New Delhi is a medical college and medical research public health university. AIIMS has vastly been recognised as the best medical college, hospital and medical research university in South Asia. AIIMS New Delhi once again bagged Rank 1 by NIRF. 2. PGIMER, Chandigarh The Postgraduate Institute of Medical Education and Research (PGIMER) is a medical and research institution in Chandigarh. It has educational, medical research and fine training facilities for its students. Like 2018, PGIMER has secured the second position in NIRF rankings 3. Christian Medical College, Vellore The Christian Medical College (CMC), Vellore is a private, minority-run educational research and institute. It is the institute’s quality study program that ranked it No. 3 both in 2018 and 2019. 4. SGPGI, Lucknow The Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGI), Lucknow offers DM, MCh, MD, Ph.D., postdoctoral fellowships and postdoctoral certificate courses, and senior residency. B.Sc. Nursing 4-year course. The institute saw a massive improvement as in 2018; it could not secure in the top 10 NIRF rankings. In 2019, SGPGI secured the fourth spot in all India rankings. Top MBA Colleges in India: IIM Bangalore, Ahmedabad and Delhi Are Among the Best Management Institutes Ranked by NIRF 2019. 5. Amrita Vishwa Vidyapeetham, Coimbatore Amrita Vishwa Vidyapeetham is a private university. It has six campuses across Tamil Nadu, Kerala and Karnataka. This institute too did not make to the list of top 10 medical colleges in India, as per NIRF rankings 2018 and in 2019, it secured the fifth position. 6. BHU, Banaras The Banaras Hindu University (BHU) in Varanasi, Uttar Pradesh is the largest residential university in Asia. For the second consecutive year, BHU secured the sixth position, in NIRF Ranking. 7. Kasturba Medical College, Manipal Kasturba Medical College (KMC) is a medical college based in Manipal, Karnataka, India. It was the first self-financing medical college in India. The ranking for KMC significantly dropped. In 2018, NIRF ranked KMC in No. 4 and 2019; it was dropped to seventh ranking. 8. JIPMER, Puducherry JIPMER also saw a dip in its ranking. In 2018, it was ranked at No.6, and in 2019 the ranking dropped to No.8. 9. Institute of Liver and Biliary Sciences (ILBS), New Delhi ILBS is a teaching hospital and has been given the status of Deemed University by the University Grants Commission (UGC). In 2018, ILBS secured the eighth position, and in 2019, its ranking dipped to ninth. 10. King George’s Medical University (KGMU), Lucknow KGMU’s MBBS course takes four and a half years to complete and has an intake of 250 students each academic year. In 2018, it did not secure a rank among the top 10 medical institutes. But 2019 NIRF ranked KGMU at its 10th position. These are the top ten medical institutes ranked by NIRF 2019. From 2020, there is going to be only entrance examination NEET for medical courses. AIIMS, MBBS and JIPMER exams have been discontinued. IIT Madras is a multicultural institute Date: 16th February 2020 Publication: The Hindu Edition: Chennai Page No: 9 Journalist: John Xavier Professor: Prof. Susy Varughese Headline: Know the birds in your backyard URL: https://www.thehindu.com/news/cities/chennai/know-the-birds-in-your- backyard/article30829250.ece IIT Madras is a research focused institute Date: 1st February 2020 Publication: BW Education Edition: Online Journalist: NA Professor: Dr. Deepak Padmanabhan Student: Ms. Savitha Abraham Headline: Queen’s University Belfast, IIT Madras Develops Technology To Make AI Fairer URL: http://bweducation.businessworld.in/article/Queen-s-University-Belfast-IIT- Madras-Develops-Technology-To-Make-AI-Fairer/31-01-2020-183145/ Queen’s University Belfast, IIT Madras Develops Technology To Make AI Fairer IIT Madras students were part of an international research project led by a Queen’s University Belfast Researcher in the U.K. who has developed an innovative new algorithm to make Artificial Intelligence (AI) fairer and less biased when processing data. Dr. Deepak Padmanabhan, Researcher at Queen’s University Belfast and Adjunct Faculty Member at IIT Madras, has been leading an international project, working with Savitha Abraham and Sowmya Sundaram, Ph.D. Students, Department of Computer Science and Engineering, IIT Madras, to tackle the discrimination problem within clustering algorithms. Elaborating on this research, Dr. Deepak Padmanabhan said, “AI techniques for exploratory data analysis, known as ‘clustering algorithms’, are often criticised as being biased in terms of ‘sensitive attributes’ such as race, gender, age, religion and country of origin. It is important that AI techniques be fair while aiding shortlisting decisions, to ensure that they are not discriminatory on such attributes.” Companies often use AI technologies to sift through huge amounts of data in situations such as an oversubscribed job vacancy or in policing when there is a large volume of CCTV data linked to a crime. AI sorts through the data, grouping it to form a manageable number of clusters, which are groups of data with common characteristics. It is then much easier for an organisation to analyse manually and either shortlist or reject the entire group. However, while AI can save on time, the process is often biased in terms of race, gender, age, religion, and country of origin. It has been reported that white-sounding names received 50 per cent more call- backs than those with black-sounding names. Studies also suggest that call-back rates tend to fall substantially for workers in their 40s and beyond. Another discriminatory trend is the ‘motherhood penalty’, where working mothers are disadvantaged in the job market while working fathers do better, in what is known as the ‘fatherhood bonus’. When a company is faced with a process that involves lots of data, it is impossible to manually sift through this. Clustering is a common process to use in processes such as recruitment where there are thousands of applications submitted. While this may cut back on time in terms of sifting through large numbers of applications, there is a big catch. It is often observed that this clustering process exacerbates workplace discrimination by producing clusters that are highly skewed. Speaking about this research, Savitha Abraham, PhD Student, Department of Computer Science and Engineering, IIT Madras, said, “Fairness in AI techniques is of significance in developing countries such as India. These countries experience drastic social and economic disparities and these are reflected in the data. Employing AI techniques directly on raw data results in biased insights, which influence public policy and this could amplify existing disparities. The uptake of fairer AI methods is critical, especially in the public sector, when it comes to such scenarios.” Over the last few years ‘fair clustering’ techniques have been developed and these prevent bias in a single chosen attribute, such as gender. The research team has now developed a method that, for the first time, can achieve fairness in many attributes. Highlighting the potential impact of this research, Dr. Padmanabhan said, “Our fair clustering algorithm, called ‘FairKM,’ can be invoked with any number of specified sensitive attributes, leading to a much fairer process. In a way, FairKM takes a significant step towards algorithms assuming the role of ensuring fairness in shortlisting, especially in terms of human resources. With a fairer process in place, the selection committees can focus on other core job-related criteria.” “FairKM can be applied across a number of data scenarios where AI is being used to aid decision making, such as pro-active policing for crime prevention and detection of suspicious activities. This, we believe, marks a significant step forward towards building fair machine learning algorithms that can deal with the demands of our modern democratic society,” he added. The research work will be