List of Speakers

Fuad Rahman

1. Fuad Rahman is a Silicon Valley technologist with deep experience in building cutting-edge Big Data analytics solutions in the healthcare and financial sectors. Extensive background in Big Data modeling and analytics, Natural Language Processing (NLP), computational linguistics, and pattern recognition. Adjunct Professor at Department of Biomedical Engineering at the University of Arizona. Early RD life spent working on SBIR and ATP/NIST projects - worked with all three branches of armed forces: US Air Force, Navy and Army. For the last eight years, Fuad has been an entrepreneur with four successful exits under his belt. Widely published with 90+ publications in peer reviewed journals and conferences.

Fuad is the Founder and CEO of Apurba Technologies Inc., a software company headquartered in the Silicon Valley, USA. Apurba has branch offices in Bangladesh and Malaysia. Apurba specializes in building software, specifically in the healthcare and financial sectors. Fuad has published widely in the scientific journals. His published body of work comprises of over 90 technical peer reviewed articles in the scientific literature including 6 book chapters and 24 journal papers. He has worked on the editorial boards of technical journals, acted as referee to many journals, and worked as technical committee member in many international conferences.

Fuad has a Ph.D. in Ubiquitous Computer Vision and Pattern Recognition from University of Kent in England (1998). He is a BUET alumnus â˘A ˇT M.Sc. in Computer Science Engineering (1994) and B.Sc. in Electrical and Electronics Engineering (1992) - both from BUET. Also served as a Lecturer in Department of Computer Science Engineering, BUET (1992-1994).

Fuad has founded and co-founded many companies and had four successful exits in the last seven years:

  1. ▸ Pinscriptive Inc.: An Orange County based healthcare company
  2. ▸ Newsfile Corp.: A Vancouver based financial services company,
  3. ▸ SEC Connect LLC: A San Diego based financial software company, and
  4. ▸ Compliance Xpressware LLP: A Washington State based company that builds software in the areas of regulatory compliance.


Natural Language Processing within the context of Big-Data has opened up enormous opportunities for a variety of industries. Our ability to process and understand large amounts of unstructured data has been instrumental in pushing the boundaries of what can be achieved from our computational models and how we can apply these to solve real life problems. This presentation discusses some of these applications and associated challenges.


2. Mohammad A. Karim is Executive Vice Chancellor, Provost, and Chief Operating Officer of the University of Massachusetts Dartmouth. Previously, he served as vice president for research of Old Dominion University in Virginia (2004-2013), and Dean of Engineering at the City University of New York (2000-2004). Professor Karim is an elected fellow of the Institution of Electrical and Electronics Engineers (IEEE), Optical Society of America (OSA), Society of Photo-Instrumentation Engineers (SPIE), the Institute of Physics (InstP), the Institution of Engineering & Technology (IET), and Bangladesh Academy of Sciences.With expertise in optical computing, electro-optical displays and systems, information processing, and pattern recognition, he has authored 19 books, 8 book chapters, and over 375 research articles, and served as guest editor to 33 journal special issues. Karim received his BS in physics in 1976 from the University of Dacca, Bangladesh, and MS degrees in both physics and electrical engineering, and a Ph.D. in electrical engineering all from the University of Alabama respectively in 1978, 1979 and 1981.


The speaker will be highlighting process, matrix, and ethical context that are integral to (a) high-quality research, (b) impactful dissemination and (c) ranking of worldclass universities and institutions. Data and trends showing progression of global universities and institutions, in general, and those of Bangladesh, India, and Pakistan, in particular, will be presented and discussed.


3. Abhik Roychoudhury is a Professor of Computer Science at National University of Singapore. His research focuses on software testing and analysis, software security and trust-worthy software construction. His research group has built scalable techniques for testing, debugging and repair of programs using systematic semantic analysis. He has been an ACM Distinguished Speaker (2013-19). He is currently leading a large five-year long targeted research effort funded by National Research Foundation in the domain of trust-worthy software. He is the Lead Principal Investigator of the Singapore Cyber-security Consortium, which is a consortium of over 35 companies in the cyber-security space engaging with academia for research and collaboration. He has served as Program Chair of ACM International Symposium on Software Testing and Analysis (ISSTA) 2016 and Editorial Board member of IEEETransactions on Software Engineering (TSE) from 2014 to 2018. Abhik received his Ph.D. in Computer Science from the State University of New York at Stony Brook in 2000.


Software systems, are prone to vulnerabilities which can be exploited. One of the key difficulties in building trustworthy software systems – is the lack of specifications, or intended behavior, or a description of how the software system is supposed to behave. In our work, we have developed semantic analysis techniques to extract or discover specifications from an erroneous or vulnerable program. Such a specification discovery process helps in automatically generating repairs, thereby moving closer to the goal of self-healing software systems. As more and more of our daily functionalities become software controlled, and with the impending arrival of technology like personalized drones, the need for self-healing software has never been greater. There exist exciting possibilities for combining semantics based repair approaches with search-based repair, and this is under investigation in our research team. We envision that automated repair capabilities should be integrated into programming environments in the future. We will also discuss the possibility of using automated repair for grading and teaching of introductory programming to various learner groups, and this is of particular interest to countries of the world which have a large young population.


4. Brian A. Barsky is Professor of the Graduate School at the University of California at Berkeley, USA where he is Professor Emeritus of Computer Science and Vision Science, and Affiliate Professor Emeritus of Optometry. He is also a member of the Joint Graduate Group in Bioengineering, an interdisciplinary and inter-campus program, between UC Berkeley and UC San Francisco and he is affiliated with the the Berkeley Center for New Media, Berkeley Institute of Design, and Arts Research Center. He is a Fellow of the American Academy of Optometry (F.A.A.O.) and a Warren and Marjorie Minner Faculty Fellow in Engineering Ethics and Professional/Social Responsibility. He holds degrees from McGill University, Cornell University, and the University of Utah.


Present research on simulating human vision and on vision correcting displays that compensate for the optical aberrations in the viewer’s eyes will be discussed. The simulation is not an abstract model but incorporates real measurements of a particular individual’s entire optical system. In its simplest form, these measurements can be the individual’s eyeglasses prescription; beyond that, more detailed measurements can be obtained using an instrument that captures the individual’s wavefront aberrations. Using these measurements, synthetics images are generated. This process modifies input images to simulate the appearance of the scene for the individual. Examples will be shown of simulations using data measured from individuals with high myopia (near-sightedness), astigmatism, and keratoconus, as well as simulations based on measurements obtained before and after corneal refractive (LASIK) surgery.

Given the measurements of the optical aberrations of a user’s eye, a vision correcting display will present a transformed image that when viewed by this individual will appear in sharp focus. This could impact computer monitors, laptops, tablets, and mobile phones. Vision correction could be provided in some cases where spectacles are ineffective. One of the potential applications of possible interest is a heads-up display that would enable a driver or pilot to read the instruments and gauges with his or her lens still focused for the far distance. This research was selected by Scientific American as one of its ten annual “World Changing Ideas.”


5. Biplab Sikdar is an Associate Professor in the Department of Electrical and Computer Engineering at the National University of Singapore. He received the B. Tech. degree in electronics and communication engineering from North Eastern Hill University, Shillong, India, in 1996, the M.Tech. degree in electrical engineering from the Indian Institute of Technology, Kanpur, India, in 1998, and the Ph.D. degree in electrical engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 2001. He was an Assistant Professor from 2001-2007 and Associate Professor from 2007-2013 in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute from 2001 to 2013. He is a recipient of the NSF CAREER award, the Tan Chin Tuan fellowship from NTU Singapore, the Japan Society for Promotion of Science fellowship, and the Leiv Eiriksson fellowship from the Research Council of Norway. His research interests include IoT and cyber-physical system security, network security, and network performance evaluation. Dr. Sikdar is a member of Eta Kappa Nu and Tau Beta Pi. He served as an Associate Editor for the IEEE Transactions on Communications from 2007 to 2012 and as an Associate Editor for the IEEE Transactions on Mobile Computing from 2014-2017.


The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices.


6. Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science depart-ment at the University of Texas at Dallas, USA where he has been teaching and conductingresearch since September 2000. He received his Ph.D. degree in Computer Science fromthe University of Southern California (USC) in August of 2000. Dr. Khan obtained hisB.Sc. degree in Computer Science and Engineering from Bangladesh University of Engi-neering and Technology (BUET), Dhaka, Bangladesh in November, 1993 with First classHonors (2nd position). He was a recipient of Chancellor Awards from the President ofBangladesh.Dr. Khan is an ACM Distinguished Scientist and received IEEE Big Data Security SeniorResearch Award, in May 2019, and Fellow of SIRI (Society of Information Reuse andIntegration) award in Aug, 2018. He has received prestigious awards including the IEEETechnical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award and IBMFaculty Award (research) 2016. Dr. Latifur Khan has published over 300 papers in premier journals such as VLDB,Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE TSMC, and AI Research and in prestigious conferencessuch as AAAI, IJCAI, ACM WWW, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACMMultimedia, ICWC, ACM SACMAT, IEEE ICSC, IEEE Cloud and INFOCOM. He has been invited to give keynotesand invited talks at a number of conferences hosted by IEEE and ACM. In addition, he has conducted tutorialsessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011& 2012, DASFAA 2012, ACM WWW 2005, MIS2005, and DASFAA 2007.Currently, Dr. Khan’s research area focuses on big data management and analytics, data mining and its applicationover cyber security, complex data management including geo-spatial data and multimedia data. His research hasbeen supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM andHPE. More details can be found


Deep neural networks (DNN) require a large number of an-notations, which sometimes is very expensive and cumber-some. Over the years, various efforts have been proposed forreducing the annotation cost when training the DNN. Semi-Supervised Learning (SSL) is one of the solutions that has beenprovably handy in leveraging unlabeled instances to mitigatethe efficacy of the model’s performance and has been attract-ing an increasing amount of attention in recent times. In thiswork, our main insight is that semi-supervised learning canbenefit from recently proposed unsupervised contrastive learn-ing approach, which aims to achieve the positive concentratedand negative separated representation in the unlabeled fea-ture space. Herein, we introduce MultiCon, a semi-supervisedlearning paradigm that aims at learning data augmentation in-variant based embedding. In particular, we combine the multi-contrastive learning approach with a consistency regularizationmethod for maximizing the similarity between differently aug-mented views of one sample and pushing the embedding of dif-ferent instances away in the latent space simultaneously. Exper-iments on multiple standard datasets including Covid19 ChestX-ray images and CT Scans demonstrate that MultiCon achievesstate-of-the-art performance across existing SSL benchmarks.This work is funded by NSF and NIH.