Schedule
All times are in IST
June 18
2.15 p.m.–2.25 p.m.
Welcome Address
3.00 p.m.–3.30 p.m.
3:30 p.m.– 3:45 p.m.
BREAK
3.45 p.m.– 4:15 p.m.
4.15 p.m.–4.45 p.m.
Bio:Chandrashekar Lakshminarayanan’s areas are reinforcement learning, stochastic control and deep learning. He obtained his PhD from the Department of Computer Science and Automation, Indian Institute of Science (2016), and was a post-doctoral research fellow at the Department of Computing Science (July 2016- June 2017), University of Alberta, and a research scientist at DeepMind, London (August 2017-July 2018). Prior to his PhD, he was an analog design engineer at Cosmic Circuits, Bangalore for a period of 3 years. He joined IITPKD as an assistant professor in July 2018.
4.45 p.m.–5.15 p.m.
Bio:Dr. Harish is currently an assistant professor at the computer science and engineering (CSE) department of IIT Madras. His primary areas of interest are in machine learning, statistical learning theory and optimisation. He was previously a research scientist at IBM research labs and a post-doc at the University of Michigan. He completed his Ph.D. at the Computer Science and Automation (CSA) department of the Indian Institute of Science (IISc), Bangalore advised by Prof. Shivani Agarwal.
5.15 p.m.–5.30 p.m.
BREAK
5.30 p.m.–6.00 p.m.
BioDr. Sriraam Natarajan is an Associate Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award and the IU trustees Teaching Award from Indiana University. He is the program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He is the specialty chief editor of Frontiers in ML and AI journal, an editorial board member of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR.
6.00 p.m.–6.30 p.m.
Srinivasan Parthasarathy
6.30 p.m.–7 p.m.
Bio : Kristian Kersting is a full professor (W3) for AI and ML at TU Darmstadt. After receiving his Ph.D. from U. Freiburg in 2006, he was with MIT, Fraunhofer IAIS, U. Bonn, and TU Dortmund. His main research interests are (deep) probabilistic programming and learning. Kristian has published over 170 peer-reviewed articles. He is an EurAI Fellow, an ELLIS Fellow and received the inaugural German AI Award (Deutscher KI-Preis) 2019, as well as several paper awards (TPM 2019, AIIDE 2015, ECML 2006) and the EurAI Dissertation Award 2006. Kristian has been on the (senior) PC of major AI/ML conferences (e.g. AAAI, ICML, IJCAI, NeurIPS, ICLR, and CVPR) and co-chaired the PC of ECML PKDD.
June 19
2.00 p.m.–2.30 p.m.
The NeuroPace RNS System is a closed-loop brain responsive neurostimulator that is FDA approved for the adjunctive treatment of patients with medically intractable partial onset epilepsy having 1-2 seizure foci. The RNS System also captures snippets of brain activity in electrocorticographic (ECoG) files. Till date, over 5 million ECoG files have been captured from patients implanted with the RNS System. This large dataset of brain activity recordings facilitates the development of machine learning algorithms for assessing patient outcomes and proposing potential stimulation settings.
Bio:Sharanya Desai received B.E in EEE and M.Sc in Biological Sciences from BITS-Pilani, India and PhD in Bioengineering from Georgia Tech, USA. During her graduate studies in Dr. Steve Potter's lab at Georgia Tech and Dr. Robert Gross's lab at Emory University, she worked on a novel multielectrode microstimulation approach for reducing seizures in a rodent model of epilepsy. She currently works as a Principal Research Scientist at NeuroPace - a medical device company that manufactures closed-loop brain responsive neurostimulators for epilepsy. Her research focus is on applying data science techniques for understanding and improving chronic brain diseases.
Sharanya is a recipient of the International Schlumberger Faculty for the Future fellowship and author of publications and patents with a focus on understanding and improving human health.
2.30 p.m.–3.00 p.m.
Helping machine learning to help us in personalized medicine
Julio Saez-Rodriguez
Bio:Julio is Professor of Medical Bioinformatics and Data Analysis, University of Heidelberg, and director of the Institute of computational biomedicine. He is also a group leader of the EMBL-Heidelberg University Molecular Medicine Partnership Unit, and a co-director of the DREAM challenges (http://dreamchallenges.org) to crowdsource computational systems biology. He obtained his M.S. in Chemical Engineering in 2001, and a PhD in 2007 at the University of Magdeburg and the Max-Planck-Institute. He was a postdoctoral fellow at Harvard Medical School and M.I.T., and a Scientific Coordinator of the NIH-NIGMS Cell Decision Process Center from 2007 to 2010. From 2010 until 2015 he was a group leader at EMBL-EBI with a joint appointment in the EMBL Genome Biology Unit in Heidelberg, as well as a senior fellow at Wolfson College (Cambridge). From 2015 to 2018 he was professor of Computational Biomedicine at the RWTH University Medical Hospital in Aachen, Germany.He is interested in developing and applying computational methods to acquire a functional understanding of signaling networks and their deregulation in disease, and to apply this knowledge to develop novel therapeutics. Current emphasis in his group is on use of single-cell technologies, multi-omics integration, and understanding multi-cellular communication.
3.00 p.m.–3.30 p.m.
Predicting disease susceptibility and disease spread in H1N1 influenza
Nagasuma Chandra
Bio:Nagasuma Chandra is a molecular systems biologist and bioinformatician and works on modelling complex biological processes and applying them to study human health and disease. Her research is interdisciplinary involving computational modelling of complex biological processes to address fundamental questions about how genome-wide molecular networks respond to a variety of pathological conditions and how that knowledge can be translated into biomedical applications. She is currently a Professor at the Department of Biochemistry, Indian Institute of Science and additionally affiliated with Bioengineering and Mathematical biology initiatives at the Institute. She received her PhD from the University of Bristol, UK. She is an elected fellow of the Indian Academy of Sciences.
3.30 p.m.–3.45 p.m.
BREAK
3.45 p.m.–4.15 p.m.
From Genomes To Systems-Level Models- Towards leveraging reprogrammed metabolism In Cancer And Infectious Disease
Anu Raghunathan
Bio:Anu Raghunathan is currently a Senior Principal Scientist in the Chemical Engineering Division at CSIR-National Chemical Laboratory in Pune. She has been with NCL for the last 10 years. Before that she was working as a faculty in Infectious Diseases at Mount Sinai School of Medicine in NY, NY. Her research group, the Metabolic Inquiry and Cellular Engineering (MICE Lab) group, uses systems approaches (both experimental and computational) to understand biological cell behavior and function. She works on applying these principles to understand drug/antibiotic resistance in cells and also manipulate cells to make desired products. She is an associate editor for Biosystems an Elsevier Publication and an active board member of the International Study Group for Systems Biology.
4.15 p.m.–4.45 p.m.
Bio:Himanshu Sinha is currently an Associate Professor at the Department of Biotechnology,Bhupat and Jyoti Mehta School of Biosciences, IIT Madras and coordinates Initiative forBiological Systems Engineering (IBSE), an interdisciplinary centre to study biological systemsand develop innovative approaches and algorithms for analysis of clinical and healthcaredata. He received his PhD from University of Cambridge, UK, in 2000; followed by postdocstints at Duke University Medical Center, Durham, NC USA and European Molecular BiologyLaboratory (EMBL), Heidelberg, Germany. At IIT Madras, he leads Systems Genetics Lab,where his group works to uncover complex relationships between genetic and phenotypicvariation and the role of evolution in changing the dynamics of these relationships; andapply computational methods on various omics and clinical datasets to model relationshipsbetween genetic variation and disease susceptibility.
4.45 p.m.– 5.15 p.m.
Bio:Swagatika Sahoo is currently DST-INPIRE Faculty at the Dept. of Chemical Engineering,and core faculty at the Initiative for Biological Systems Engineering, IIT Madras. Shereceived her Ph.D. from the Centre for Systems Biology, University of Iceland in 2014,followed by her post doc from the Luxembourg Centre for Systems Biomedicine, Universityof Luxembourg. Since, 2016 she is with IIT Madras, where in, her research lab focuses onusing systems biology approaches for predicting therapeutic strategies for metabolic diseases,including brain disorders and cancer. She has served reviewer for Bioinformatics (OxfordAcademic) & Journal of Inherited Metabolic Disease (Official Journal of the Society for theStudy of Inborn Errors of Metabolism).
5.15 p.m.– 5.30 p.m.
BREAK
5.30 p.m.–6.00 p.m.
Can cellphone data help fight Covid-19 in India?
Satchit Balsari
Bio:Dr. Satchit Balsari is assistant professor at the medical and public health schools at Harvard, where he directs the India Digital Health Network, at the Lakshmi Mittal South Asia Institute.His interdisciplinary interests in digital technology, disaster response, and population health have resulted in innovative applications of mobile, cloud-based technology to address public health challenges in mass gatherings, disasters, and humanitarian crises. Balsari’s signature initiatives include project EMcounter (a customizable, portable digital surveillance tool, the latest iteration of which was used at the world’s largest mass gathering, the Kumbh Mela in India) and Voices, a crowd-sourced, online disaster response analysis tool. He co-founded the Covid19 Mobility Data Network, in March 2020.
6.00 p.m.–6.30 p.m.
Bio:Debashis Sahoo received B.Tech. degree in Computer Science and Engineering from Indian Institute of Technology, Kharagpur, in 2000, and the M.S. and Ph.D. degrees in Electrical Engineering from the Stanford University, in 2003 and 2008, respectively. He is currently a joint Assistant Professor of Computer Science and Engineering and Pediatrics at University of California, San Diego. His research interests include analysis of biological systems and formal verification of hardware designs. Dr. Sahoo pioneered identifying simple Boolean relationships between gene expression values. Dr. Sahoo’s research has produced impacts in understanding several biological systems including, B cell differentiation, colon cancer, bladder cancer, and prostate cancer. Among the successes was Dr. Sahoo’s previous work where he identified the expression level of CDX2 as a predictive biomarker for favorable response to conventional chemotherapy among stage II colon cancer patients (Dalerba, Sahoo et al. NEJM 2016). Dr. Sahoo has received many grants for his achievements, notable among them are the prestigious NIH Pathway to Independence K99/R00 Award, DOD Prostate Cancer Training Award, and Bladder Cancer Advocacy Network Young Investigator Award. He has continued his status as a highly accomplished researcher, publishing in high-impact journals and receiving grants for his computational biology adventure.
June 20
2.00 p.m.–2.30 p.m.
Integration of Machine Learning with Domain Knowledge for Manufacturing Applications Video recording
Raghunathan Rengasamy
2.30 p.m.–3.00 p.m.
Scientific plots differ from natural images in three crucial ways. First, unlike natural images, they combine both text (e.g., axes, tick labels) and visual elements (e.g., bars, and legends). Second, they exhibit significant variation in scale and aspect ratio of objects (e.g., thin dot-lines and thick, long bars). Lastly, there are underlying structural relationships between objects (e.g., a tick label and corresponding bar in a bar-plot) which can be exploited for better understanding and reasoning. Further, localization accuracy is significantly more critical for plots than for natural images. This leads to the following interesting question, “Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different from the objects found in natural images?” To answer this question, we train and compare the accuracy of nine state-of-the-art object detection networks on the PlotQA dataset with over 220,000 scientific plots. At the standard IOU setting of 0.5, most networks perform well with mAP scores higher than 80% in detecting the relatively simple objects in plots. However, the performance drops drastically when evaluated at a stricter IOU of 0.9 with the best model giving an mAP of 35.70%. Note that such a stricter evaluation is essential when dealing with scientific plots where even minor localization errors can lead to significant fallacies in downstream numerical inferences.
Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks. While this significantly improves the performance, there are still two main issues: (i) performance on textual objects which are essential for reasoning is abysmal, and (ii) inference time is unacceptably large considering the simplicity of plots. Based on these experiments and results, we identify the following considerations for improving object detection on plots: (a) lower inference time, (b) higher precision on textual objects, and (c) more accurate localization with a custom loss function with non-negligible loss values at high IOU (> 0.8). We propose a network, namely, PlotNet which meets all these considerations: It is 16x faster than the best performing competitor and significantly improves upon the accuracy of existing models with an mAP of 93.44% at an IOU of 0.9.
Bio:Mitesh M. Khapra is an Assistant Professor in the Department of Computer Science and Engineering at IIT Madras. While at IIT Madras he plans to pursue his interests in the areas of Deep Learning, Multimodal Multilingual Processing, Dialog systems and Question Answering. Prior to that he worked as a Researcher at IBM Research India. During the four and half years that he spent at IBM he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. This work led to publications in top conferences in the areas of Computational Linguistics and Machine Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively. His PhD thesis dealt with the important problem of reusing resources for multilingual computation. During his PhD he was a recipient of the IBM PhD Fellowship and the Microsoft Rising Star Award. He is also a recipient of the Google Faculty Research Award, 2018.
3.00 p.m.–3.30 p.m.
Bio:Shiv is CTO, Energy & Mobility, Microsoft R&D India for Azure Global. Previously Shiv was Executive General Manager of Growth Offerings at GE Power Conversion responsible for new offerings in e-Mobility & digital/AI. Earlier he was at IBM Research - India, and the Chief Scientist of IBM Research - Australia. Before IBM, he was a tenured Full Professor at Rensselaer Polytechnic Institute in Troy, NY, USA. He has degrees from Indian Institute of Technology, Madras (B.Tech), Ohio State University (MS, PhD) and RPI (MBA). Shiv is a Fellow of the IEEE (2010), Fellow of Indian National Academy of Engineering (2015), ACM Distinguished Scientist (2010), MIT Technology Review TR100 young innovator (1999).
3.30 p.m.–3.45 p.m.
BREAK
3.45 p.m.–4.15 p.m.
4.15 p.m.–4.45 p.m.
4.45 p.m.–5.15 p.m.
Bayesian Matrix and Tensor Completion Methods and its Applications in Environmental Sensing and Transportation Video recording
Pravesh Biyani
Bio:Pravesh is an associate professor at IIIT Delhi. He received his B.Tech from IIT Bombay in 2002 and MS from McMaster University in the year 2004. In late 2012, he was a post- doctoral researcher at the University of Minnesota, Minneapolis. He also won the INSPIRE Faculty award by the Govt. of India in 2012. His research interests lie in the intersection of signal processing and machine learning with applications in urban transportation and speech/audio processing. He is passionate about public transportation and is interested in building systems as well as policy research to improve urban mobility. He has specifically been an active supporter of the open transit data systems.
5:15 p.m.–5.30 p.m.
Break
5.30 p.m.–6.00 p.m.
Bio:Shourya Roy is the Head and Vice President of Amex AI Labs which is a center of excellence developing AI capabilities and solutions for addressing large scale real-life business problems. Prior to joining Amex in 2016, he spent nearly fifteen years in the labs of IBM and Xerox playing several leadership roles in technical research as well as research and strategic management. Shourya’s interest spans generally in machine learning and AI with specific focus on NLP as well as challenges pertaining to real-life development and deployment of AI techniques.
6.00 p.m.–6.30 p.m.
AI for Public Health and Conservation: Learning and Planning in the Data-to-Deployment Pipeline Video recording
Milind Tambe
Bio:Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Director "AI for Social Good" at Google Research India. He is recipient of the IJCAI John McCarthy Award, ACM/SIGAI Autonomous Agents Research Award, AAAI Robert S Engelmore Memorial Lecture award; he is also a fellow of AAAI and ACM. For his research in and pioneering real-world deployment of security games, Prof. Tambe has received the INFORMS Wagner prize, the Rist Prize of the Military Operations Research Society, the Christopher Columbus Fellowship Foundation Homeland security award, as well as Commendations from the US Coast Guard, LA Airport Police, and US Federal Air Marshals Service. Additionally, he and his team have received over a dozen influential paper and best paper awards at premier Artificial Intelligence Conferences. Prof. Tambe received his Ph.D. from the School of Computer Science at Carnegie Mellon University.
6.30 p.m. - 6.45 p.m
Concluding remarks